SAICOET

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012055 doi:10.1088/1757-899X/506/1/012055

Ash content has to do with minerals in a material. The purpose of determining total ash is to determine
whether or not a processing process; to find out the type of material used and the determination of total
ash useful as a parameter of the nutritional value of food ingredients.

The ash content test on tuna shredded fish ranged from 3.6290% to 5.5789%. While the results of
analysis of variance (ANOVA) showed that the steaming treatment with different temperatures gave a
very significant effect on the ash content parameters. This can be seen from the F count> F table 5%,
then to find out the differences in each treatment carried out LSD test. The average ash content in floss
from residual meat of cork fish albumin extraction can be seen in table 6.

Table 6. Swelling Properties of Wound Dressing

No. Temperature (°C) Protein Content (%) No.
Average ± St. Dev Notation
1. 55.0
2. 57.5 3.6543 ± 0.3055 a
3. 60.0 4.7839 ± 0.2460 d
4. 62.5 5.3195 ± 0.5054 c
5. 65.0 5.8253 ± 0.0947 a
4.5524 ± 0.5086 b

Based on the data in table 6 above, it can be seen that at a temperature of 62.5°C the highest average
ash content was 5.8253%, and at 55°C the lowest average value of ash was 3.6543%. The highest ash
content is in treatment D with steaming temperature 62.5°C with an average value of ash content of
5.8253%, this is thought to be a long floss frying process so that the water content in the shredded is low
and leaves minerals in high floss so the ash content increases. While the lowest ash content is in treatment
A with a temperature treatment of 55°C with an average value of ash content of 3.6543%. This is
presumably due to the high moisture content in the ingredients and the floss texture that has not been
smooth so that the ash content is low. The effect of processing on materials can affect the availability of
minerals for the body. The use of water in the process of washing, soaking and boiling can reduce the
availability of minerals because minerals will dissolve by the water used.

The requirements for floss quality standards in general the value of ash content is a maximum of 7%,
and the highest ash content in tuna tuna is 5.8253% so that the ash content of tuna shredded meets floss

quality standards.

3.2. Organoleptic Parameters
3.2.1 Aroma. The aroma of food that is in the mouth is captured by the sense of smell through a
channel that connects the mouth and nose. The number of volatile components released by a product is
influenced by the temperature and its natural components. Food that is brought to the mouth is felt by
the senses of taste and smell which are then continued to be accepted and interpreted by the brain [10].
The results of the organoleptic scent on tuna fish floss ranged from 8,5675 to 9,0098. The average
organoletic aroma results in tuna fish shredded can be seen in table 7.

Table 7. Average Aroma Organoleptic Test on Tuna Fish Floss

No. Temperature (°C) Average ± St. Dev

1. 55.0 9.0098 ± 0.1453
2. 57.5 8.6745 ± 0.0694
3. 60.0 8.5675 ± 0.0962
4. 62.5 8.6748 ± 0.1347
5. 65.0 8.6084 ± 0.0385

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012055 doi:10.1088/1757-899X/506/1/012055

Based on the data in table 7 above, it can be seen that at a temperature of 55°C the highest average
organoleptic aroma value is 9,0098, and at a temperature of 60°C the lowest organoleptic aroma value
is 8,5675. The highest organoleptic value of aroma is in treatment A, which is treatment with a steaming
temperature of 55°C which is equal to 9,0098, this is presumably because it is influenced by the maturity
level of steamed meat. So that the distinctive aroma of fish with spices is still felt. While the lowest
aroma organoleptic value is in treatment C, which is treatment with steaming temperature of 60°C. This
is suspected to be fish floss, no longer typical of floss odor.

Based on the calculation of consumer acceptance of the organoleptic aroma of tuna shredded showed
that the best P value is in treatment A with a value of 9,0000 then rounded to 9. Descriptively, at this
value it can be concluded that the aroma of cork fish on treatment A favored panelists.

3.2.2 Taste. Taste is something that is received by the tongue. In sensing the gecko is divided into
four main gases, namely sweet, bitter, sour and salty and there is an additional response if done
modification [11]. Taste is influenced by several components, namely chemical compounds,
temperature, concentration and interaction with other taste components. The increase in temperature will
increase the stimulation of sweet taste but will reduce the stimulation of salty and bitter taste [12].
The organoleptic test results of taste on tuna fish floss ranged from 8,2908 to 8,9654. The average yield
of organoletic taste in tuna fish floss can be seen in table 8.

Table 8. Average Taste Organoleptic Test on Tuna Fish Floss

No. Temperature (°C) Average ± St. Dev

1. 55.0 8.9654 ± 0.1347
2. 57.5 8.6355 ± 0.1678
3. 60.0 8.2908 ± 0.2457
4. 62.5 8.6252 ± 0.0509
5. 65.0 8.6752 ± 0.1018

Based on the data in table 8 above it can be seen that at a temperature of 55°C the highest organoleptic
taste value was 8,9654, and at a temperature of 60°C it had the lowest organoleptic taste value of 8,2908.
The organoleptic value of taste in tuna floss with different steaming does not give a different value. The
resulting shred has almost the same value. This is because the formulation of making floss is used
constantly, so that the taste produced is almost the same.

Based on the calculation of consumer acceptance of organoleptic taste shows that the best P value is
in treatment A and E with a value of 8,9654 then rounded to 9. Descriptively, at this value it can be
concluded that the tuna fish floss A and E are preferred by panelists.

3.2.3 Color. Color is one parameter besides taste, texture and nutritional value that determines
consumer perceptions of a food ingredient. Consumer preferences are often determined based on the
appearance of a food product. Bright food colors provide more appeal to consumers. Color in food
products has several functions, among others, as an indicator of maturity, especially for fresh food
products such as fruits, as an indicator of freshness for example in vegetable and meat products and as
an indicator of perfection of food processing processes such as frying, brown used as the final indicator
of food product maturity [12].

The color organoleptic test results on tuna fish floss ranged from 8,6315 to 8,9853. The average yield
of color organoletics in tuna fish floss can be seen in table 9.

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012055 doi:10.1088/1757-899X/506/1/012055

Table 9. Average Color Organoleptic Test on Tuna Fish Floss

No. Temperature (°C) Average ± St. Dev

1. 55.0 8.8432 ± 0.3533
2. 57.5 8.7949 ± 0.0192
3. 60.0 8.6762 ± 0.3977
4. 62.5 8.6315 ± 0.2589
5. 65.0 8.9853 ± 0.0667

Based on the data in table 9 above, it can be seen that at 65°C the highest color organoleptic value
was 8,9853, and at 62.5°C the lowest color organoleptic value was 8,6315. The color organoleptic values
of the tuna shredded with different broccoli do not give different values. The resulting shred has almost
the same value.

Based on the calculation of consumer acceptance of the organoleptic color shows that the best P value
is in treatment E and A with a value of 9.1000 then rounded to 9. Descriptively, at this value it can be
concluded that the color of tuna fish floss E and A is preferred by panelists.

3.2.4 Texture. Observation of the texture of fish floss is very important. This is because texture is
one of the things that distinguishes fish floss from other fishery products, namely soft fibers. The texture
of meat is very influential on the final product produced and determines the level of consumer preference
for the product [12].

The organoleptic test results of the texture on tuna fish floss ranged from 8,4557 to 8,8765. The
average yield of organoletic textures on tuna fish floss can be seen in table 10.

Table 10. Average Organoleptic Texture Test on Tuna Fish Floss

No. Temperature (°C) Average ± St. Dev

1. 55.0 8.8765 ± 0.1202
2. 57.5 8.5181 ± 0.1732
3. 60.0 8.4557 ± 0.2143
4. 62.5 8.8471 ± 0.0509
5. 65.0 8.6879 ± 0.0770

Based on the data in Table 10 above, it can be seen that at a temperature of 55°C the highest
average organoleptic texture value was 8,8765, and at a temperature of 60°C it had the lowest color
organoleptic value of 8,4557.

Based on the calculation of consumer acceptance of organoleptic colors shows that the best P value
is in treatment A with a value of 8.9000 then rounded to 9. Descriptively, at this value it can be concluded
that the color of tuna fish floss A is favored by panellists.

4. Conclusion
From this study, we can conclude that different steaming temperatures had a significant effect on the
nutritional and organoleptic content of shredded fish. The best treatment was obtained at a steaming
temperature of 55° C (A) with an average protein content value of 8.9812%; fat content of 1.3871%;
moisture content of 4.9876%; ash content 3.6543%; organoleptic value of aroma 9,0098; taste of
organoleptic value 8,9654; color of organoleptic 8,8432 and organoleptic value of texture 8,8765.

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012055 doi:10.1088/1757-899X/506/1/012055

5. References

[1] Almatsier S 2009 Prinsip Dasar Ilmu Gizi PT Gramedia Pustaka Utama Jakarta pp 337
[2] Andarwulan N F Kusnandar D Herawati 2011 Analisis Pangan Dian Rakyat (Jakarta). 328 hlm
[3] Chayati I and Andian A A 2008 Diktat Kimia Pangan Universitas Negeri Yogyakarta

(Yogyakarta) pp 62
[4] De Garmo E P W G Sullivan J R Canada 1984 Engineering Economy Mac Millan Publishing

Company (New York)
[5] De Man J M 1997 Kimia Makanan Alih Bahasa: Kosasih P. Institut Teknologi Bandung

(Bandung) pp 550
[6] Kusnandar F 2010 Kimia Pangan Komponen Makro Dian Rakyat (Jakarta) pp 264
[7] Muchtadi D 2010 Teknik Evaluasi Nilai Gizi Protein Penerbit Alfabeta (Bandung) pp 190
[8] Nazir M 2005 Metode Penelitian. Ghalia Indonesia (Bogor) pp 58-59
[9] Purnomo H 1995 Aktivitas Air dan Peranannya dalam Pengawetan Pangan UI Press (Jakarta)
[10] SNI 01-3707-1995. Floss. http://sisni.bsn.go.id/index.php?/sni_main/sni/detail_sni /4128.
[11] Sudarmadji S B Haryono Suhardi 2007 Analisa Bahan Makanan dan Pertanian Penerbit Liberty

(Yogyakarta)
[12] Sulistiyati T D 2011 Pengaruh Suhu dan Lama Pemanasan dengan Menggunakan Ekstraktor

Vakum terhadap Crude Albumin Ikan Gabus (Ophiocephalus striatus) (Malang, Jawa Timur)
[13] Suprayitno E A Chamidah and Carvallo 1998 Studi Profil Asam Amino Albumin dan Seng pada

Ikan Gabus (Ophiocephalus striatus) Fakultas Perikanan dan Ilmu Kelautan (Universitas
Brawijaya. Malang)
[14] Ulandari A D Kurniawan and A S Putri 2011 Potensi Protein Ikan Gabus dalam Mencegah
Kwashiorkor pada Balita di Provinsi Jambi Universitas Jambi (Jambi) pp 6
[15] Winarno F G 2002 Kimia Pangan dan Gizi PT Gramedia (Jakarta)
[16] Zura C F 2006 Cita Rasa (Flavor) Departemen Kimia FMIPA (Universitas Sumatera Utara,
Medan)
[17] Rihayat Suryani Agusnar H Wirjosentono B and Nurhanifa 2018 Thermal Degradation of
Aceh’s Bentonite Reinforced Poly Lactic Acid (PLA) Based on Renewable Resources for
Packaging Application AIP Conference Proceedings vol 2049
[18] Ridwan Wirjosentono B Tamrin Rihayat T Nurhanifa 2018 Modification of PLA/PCL/Aceh's
Bentonite Nanocomposites as Biomedical Materials AIP Conference Proceedings vol 2049
[19] Zulkifli Z Rihayat T Suryani Zaimahwati Z and Rosalina R 2018 Purification process of
Jelantah Oil using Active Chorcoal Kepok's Banana AIP Conference Proceedings vol 2049
[20] Rihayat T Suryani Satriananda Fitriah and Helmi 2018 Poly Lactic Acid
(PLA)/Chitosan/Bentonite Nanocomposites Based on Cassava Starch for Materials in
Biomedical Applications AIP Conference Proceedings vol 2049
[21] Rihayat T Suryani Satriananda Riskina S Khan N S P and Saifuddin 2018 Influence of Coating
Polyurethane with Mixture of Bentonite and Chitosan Nanocomposites AIP Conference
Proceedings vol 2049
[22] Rihayat T Suryani Fauzi T Nurhanifa Alam P N and Sami M 2018 Synthesis and Innovation
of PLA/clay Nanocomposite Characterization Againts to Mechanical and Thermal Properties
IOP Conf. Series: Materials Science and Engineering vol 334

9

IOP Conference Series: Materials Science and Engineering

PAPER • OPEN ACCESS

Wound Dressing Based on Banana Peels Waste and Chitosan by
Strengthening Lignin as Wound Healing Medicine

To cite this article: Teuku Rihayat et al 2019 IOP Conf. Ser.: Mater. Sci. Eng. 506 012056
View the article online for updates and enhancements.

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

Wound Dressing Based on Banana Peels Waste and Chitosan
by Strengthening Lignin as Wound Healing Medicine

Teuku Rihayat1, Suryani1, Januar Parlaungan Siregar3, Zaimahwati1, Salmyah1,
Helmi1, Sariadi1, Fitria2, Satriananda1, Alfian Putra1, Zahra Fona1, Juanda1,
Raudah1, Mawaddah1, Nurhanifa1, Shafira Riskina1, Wildan Syahputra1,
Jamiluddin Jaafar3

1 Department of Chemical Engineering, Lhokseumawe State Polytechnic, 24301,
Aceh, Indonesia
2 Department of Dermato Venereology, Medical Faculty, University Syiah Kuala,
24311, Aceh, Indonesia
3 Structural Materials and Degradation Focus Group, Faculty of Mechanical
Engineering, Universiti Malasyia Pahang, 26600 Pekan, Pahang, Malaysia

Corresponding author: [email protected]

Abstract. Recent studies in new applications of wound dressing offer and promote the process
of wound healing. The purpose of this study was to develop lignin-based wound dressing on
banana peel and then put it into chitosan film for wound dressing applications. The use of banana
peels helps the formation of skin compounds so that they can be used to treat better wounds.
Banana peel added to chitosan as matrix filler with concentration (0, 1, 3, 5, 7, 10% wt), then the
dressing membrane swelling in 48 hours is at 0% wt 120.5931%, at 1% wt 99. 9981%, 3% wt
79.2916%, 5% wt is 68.1819%, 7% wt is 61.9173% and 10% wt is 45.3981%. Fourier Transform
Infrared (FTIR) test, Scanning Electron Microscopy (SEM), swelling properties were carried out
to characterize the prepared film. Antimicrobial tests were carried out by disc diffusion method
and film growth inhibitory effects including different amounts studied in Escherichia coli and
Staphylococcus aureus. FTIR at a concentration of 7% wt showed that there was an interaction
between banana peel and chitosan on the absorption band 3185,728 cm-1. In SEM the best sample
morphology structure at 5% wt shows a good interface interface. Addition of banana peel as
lignin decreases the level of swelling of water in wound dressing. In addition, Staphylococcus
aureus is the most sensitive strain recorded in wound dressing. Banana skin which is included in
chitosan film seems to be a potential and new biomaterial for wound healing applications.

Keyword : chitosan, banana peels powder, fourier transform infrared scanning electron
microscope

1. Introduction
Wound infections are serious infections that have occurred in all parts of the world [1]. Many solutions have been
studied and found to be able to cure wound infections. Wound healing requires a complex method and certainly
does not restore the skin as usual. Severe wounds can even die from dark scars due to damage to skin tissue [2,3].

At present there are many multifunctional biomaterials for cell scaffolding, one of them is chitosan which is
highly recommended in the engineering of skin tissue because it has structural characteristics similar to glucosamine

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from the natural extracellular matrix [4]. Chitosan has good biocompatibility, low toxicity, anti-infection activity
and biodegradability [5]. Judging from the physical-chemical properties of chitosan, it can be used for various cell
forms such as wound dressing, membranes, coatings, fibers, and sponges. Several studies have shown that higher
molecular weight chitosan has better film-forming properties in skin tissue engineering [6]. This is the reason for
the use of chitosan as a cell scaffold, specifically in making wound dressing.

Up until now, there have been many studies and development of wound dressing as an approach to the field of
tissue engineering to deal with the effects of injuries in a faster period of time [7].
Several studies have shown that chitosan has a weakness if it is used as a wound dressing, this is due to the low
mechanical strength of chitosan. In [8], trying to add lignin to chitosan, the results show that the tensile strength,
storage modulus, thermal degradation temperature and chitosan can be increased by adding lignin.

Lignin is considered a raw material with high recovery potential, widely available in nature, cheap and
environmentally friendly [9]. Lignin is almost present in all types of wood fiber plants [10], such as bananas, sugar
cane, sengon and so on.

Especially bananas, containing about 10% lignin [11]. For making wound dressing, the banana part used is the
skin, this is because besides lignin, the banana skin also helps the formation of skin compounds so that it can be
used to treat bruises, burns, and other wound infections [12]. In addition, the use of banana peel as a wound dressing
is supported by the availability of banana skin as waste [13]. The reuse of banana peel waste into a product can
increase its economic value.

The development of chitosan-based wound dressing and banana peel waste in Indonesia tends to be lacking, due
to limited information and research conducted. This is a good opportunity, especially in the medical field in
Indonesia, which generally relies on healing methods using chemicals. The incorporation of natural polymers
namely chitosan with banana peel is expected to be able to create wound dressing products with characteristics that
are appropriate and provide benefits, both in terms of science, health, and in terms of economics. The purpose of
this study was to make chitosan-based wound dressing and banana peel waste which were characterized using
Fourier Transform Infrared (FTIR), Scanning Electron Microscopy (SEM), Swelling Properties and antibacterial
susceptibility to Escherichia coli and Staphylococcus aureus.

2. Materials and Methods
2.1 Materials
Chitosan banana peel waste, glycerol, acetic acid, Escherichia coli bacteria and Staphylococcus aureus.

2.2 Processing of Banana Peels Powder
Banana Peels that have been collected, washed with water to remove dirt, and cut into small pieces and then dried
in an oven at 120°C for 24 hours. After drying, the banana peel is ground with micro milling. Then the skin is
crushed and sifted so that the particle size remains stable and stored at room temperature in a plastic container until
it is used. Furthermore, banana peel powder was carried out by morphological testing using Scanning Electron
Microscope (SEM) and functional group testing using the Fourier Transform Infrared (FTIR).

2.3 Synthesis of Membrane Wound Dressing Chitosan and Banana Peels Powder
Prepared as much as 1 gram of chitosan powder, dissolved in 2% acetic acid solution and stirred for 4 hours at room
temperature. Prepared 1 ml of glycerol and added as a crosslinker and plasticizer. Chitosan solution was put into
Teflon mold and mixed with banana peel powder with different concentrations (0, 1, 3, 5, 7 and 10% wt), dried at
40°C for 24 hours to evaporate the solvent and form a membrane. The prepared membrane is gently peeled, and
further dried by keeping the oven 40°C for 4 hours. To give a better sight, the membrane wound dressing
sample were depicted in Figure 1.

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

Figure 1. Membrane Wound dressing sample

2.4 Measuring Techniques

2.4.1 Fourier Transform Infrared (FTIR)

The testing of synthesized chitosan functional groups was characterized using Fourier Transform Infrared (FTIR)
on wave absorption of 4000-500 cm-1. For the wound dressing sample a thin and clear sized layer is formed. After

that the sample is inserted into the FTIR device tube to get the functional group contained in the sample. The
spectrum will be seen in the range 4000-500 cm-1.

2.4.2 Scanning Electron Microscopy (SEM)
The homogeneity of the morphological structure of banana skin powder and wound dressing membrane as
distribution of chitosan matrix fillers was seen by Scanning Electron Microscope (SEM).

2.4.3 Swelling Properties
Weight specimens were immersed in distilled water at 37°C. After 24 hours of immersion, the specimen is removed
from the water, dried with a paper filter and weighed. The sample was soaked again for another 24 hours and then
removed from the water, dried with filter paper and weighed.

% Swelling = (ெ௪ିௌ) (1)
ௌ×ଵ଴଴

where, Mw is the wet weight and Md is the dry weight of the sample.

2.4.4 Antimicrobial Susceptibilty
The antibacterial activity of membrane chitosan-banana powder wound dressing was investigated using the disc
diffusion method on a petri dish [14]. Bacterial cultures that grow in the mid-logarithmic phase are placed in agar
media. Escherichia coli and Staphylococcus aureus are injected into agar media. After compaction to coat the agar,
the chitosan-banana powder (15 mm diameter) membrane with different concentrations (0, 1, 3, 5, 7 and 10% wt)
was placed on the agar surface. The layers were incubated at 37°C for 24 hours and at 28°C for the next 72 hours.

3. Result and Discussion
3.1 Fourier Transform Infrared (FTIR)

The spectra of FTIR membrane chitosan wound dressing-waste banana peel with different concentrations (0, 1, 3,
5, 7 and 10% wt) has been recorded in wave spectrum of 4000-500 cm-1. The chitosan spectrum and banana peel
powder are shown in Figures 2 and 3, respectively.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

35

30

%Transmittance 25

20

15

10

5 3500 3000 2500 2000 1500 1000 500
4000

Wavenumbers (cm-1)

Figure 2. FTIR Spectrum of Chitosan

From Figure 2 it is shown that chitosan contains many functional groups (hydroxyl, carbonyl, carboxyl, amine,
and amide). Please note that adding glycerol to chitosan does not provide a new band in the chitosan spectrum.
Previous studies on banana peel powder revealed that banana peel powder contains polymers (pectin, hemicellulose
and lignin) and they are rich in organic functional groups such as -COOH, -NH2 and O-H [15]. Characteristics of
absorption of chitosan and banana peel bands are similar to those previously studied [16]. FT-IR on banana skin
chitosan (10% by weight), as shown in the picture there is a clear composite difference between the ingredients
compared to the pure chitosan spectrum. Its peak at 3520 cm-1 corresponds to the expanding O-H stretch wave and
shifts to 3628 cm-1. Increasing the intensity of the band 2898 cm-1 which corresponds to the C-H wave. The new
band appears at 1760 cm-1 which corresponds to the carboxyl group.

FTIR results also show the role of acid in chitosan. With FTIR profiles that show absorbance similar to each

other, this shows the role of acids in the reaction of chitosan nanoparticles preparation. Acids are not intra- and
intermolecular-bound with di- and tri- carboxylic acids [17]. The FTIR spectrum also does not show the formation

of new polymers. This shows that chitosan does not polymerize with acid. It can be concluded that acids only act
as proton donors which can dissolve chitosan [18].

57.5

52.5

%Transmittance 47.5

42.5

37.5

32.5

27.5

22.5 3500 3000 2500 2000 1500 1000 500
4000

Wavenumbers (cm-1)

Figure 3. FTIR Spectrum of Banana Peels Powder

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

Figure 3 shows the membrane membrane of wound dressing containing banana peel powder with different

concentrations (0, 1, 3, 5, 7 and 10% wt). From the picture it was noted that there was an increase in amide intensity
which reached its peak at 1638 cm-1 with the addition of fillers. Bands on the absorption band of 2890 cm-1 and
2,900 cm-1 have increased as indicated by the characteristics for C-H due to the addition of fillers. This band appears
on the absorption band 1760 cm-1 which corresponds to the carboxyl group in addition to increasing the intensity of
the absorption band 1440 cm-1 which occurs an interaction between the positive charge of the amine chitosan group

and the negatively charged carboxyl group on banana skin which makes good interactions filler and matrix.

3

2.5

%Transmittance 2

1.5

1

0.5

0 3500 3000 2500 2000 1500 1000 500
4000

Wavenumbers (cm-1)

Figure 4. The FTIR of the membrane wound dressing of 7% wt

3.2 Scanning Electron Microscope (SEM)
In Figure 5 shows the SEM morphological structure of banana skin powder particles. The particle size is in the
range of 20-80 μm in length and 20-30 μm in width.

Figure 5. Morphological structure of banana peels powder
The morphological structure of SEM with concentrations (0, 1, 3, 5, 7 and 10% wt) of chitosan and banana peel
powder is shown in Figure 5. Morphological analysis of SEM showed that uniform wound dressing membrane
fillers were distributed in a 5% wt concentration chitosan matrix, with the increase in filler concentration in irregular
distribution with several filler aggregates.

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

(a) (b) (c)

Figure 6. Membrane morphological structure of wound dressing at concentrations (a) 0% wt; (b) 5% wt; and (c)
10% wt

3.3 Swelling Properties
It is important to determine the swelling properties of water for biomaterials if they are used as wound cover
material. This is a measure of the film's capacity to absorb wound exudates. The ideal environment for good wound
healing is to keep the wound moist [19].

Sample Table 1. Swelling Properties of Wound Dressing 48 h
Swelling (%)
0% wt 120.59
1% wt 0 h 24 h 100
3% wt 79.29
5% wt 57.94 93.14 68.18
7% wt 33.06 60.17 61.92
10% wt 19.22 50.94 45.4
10.18 40.94
59.72 33.73
29.92 24.18

Water absorption from chitosan is related to the hydrophilic group (hydroxyl and amino groups) of the
polysaccharide. Membrane swelling of chitosan-banana powder wound dressing after 24 hours and 48 hours of
immersion in distilled water were shown in Figure 7.

Swelling (%) 140 Sample 0 % wt
120 Sample 1 % wt
100 Sample 3 % wt
Sample 5 % wt
80 Sample 7 % wt
60 Sample 10 % wt
40
20 10 20 30 40 50 60
Time (h)
0
0

Figure 7. Swelling properties on membranes wound dressing chitosan-banana peels powder

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

It is clear from the figure that the 0% wt sample shows a higher swelling rate compared to the added filler,
adding banana skin powder to chitosan results in a decrease in swelling and hence increases water resistance This
behavior is caused by a kind of interaction between negatively charged compounds in banana peel powder such as
carboxylic acids and positively charged groups on the chitosan backbone chain. This interaction limits the mobility
of the chitosan chain which reduces water uptake. To better understand the effect of banana peel powder on chitosan
swelling behavior, the plot of water uptake versus time is illustrated in Figure 7.

3.4 Antimicrobial Activity
The point of this study shows that the combination of chitosan with banana peel fillers has a synergistic

interaction with a broad antimicrobial spectrum against gram negative, gram positive bacteria and even against
strains of yeast culture that show the ability of biofilm formation. The results in Figure 8 show that the chitosan-
sanitary membrane as a banana peel has a synergistic action with the highest activity of 10% wt. In addition,
Staphylococcus aureus is the most sensitive strain recorded for this membrane.

Chitosan-banana peel powder dressing membrane also showed higher activity against Escherichia coli strains
(gram negative bacteria) than Staphylococcus aureus strains (gram positive bacteria). On that basis, several studies
note that chitosan affects gram negative and positive but rather like argumentative effects, some findings indicate
more effectiveness against gram negative compared to gram positive [20].

However, this finding regarding the surface polarity of bacteria, the outer membrane of gram-negative bacteria
has a lipopolysaccharide, very negatively charged, which allows attachment to polycational chitosan relative to
gram-positive, which consists of peptidoglycan associated with polysaccharides and teicoic acid [21]. which in turn
supports glucose [22]. Effects similar to fungal and yeast cells that can interfere with growth [23].

(a) (b)
Figure 8. Effect of antibacterial activity to membrane wound dressing chitosan and banana peel powder on (a)

Escherichia coli and (b) Staphylococcus aureus

Scientists support another mechanism, the antimicrobial activity of chitosan which is associated with metal
chelation, where chitosan has excellent metal binding ability because the amine group takes cationic metals with
chelation [24]. Here, we hypothesize that such a relationship is one of the new approaches to improve the properties
of these composites [25] such as increasing biodegradability and antimicrobial activity, moreover banana peels
contain polymers such as lignin, hemicellulose and pectin [26] which can help improve composite properties newly
formed. Therefore, we can use it for medical purposes as wound dressing. In the food industry, banana flour/chitosan
is applied to preserve new vegetables, showing antimicrobial properties against bacteria when used [27].

Although in this study banana peels were used as they are, the results of antibacterial studies were comparable
to those studied by P.B. Franco and his co-authors studied the antibacterial activity of chitosan membranes
associated with active compounds extracted from banana peels [28].

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012056 doi:10.1088/1757-899X/506/1/012056

4. Conclusion
The use of banana skin helps the formation of skin compounds so that it can be used to treat better wounds.
Banana skin is added to chitosan as a matrix filler with concentrations (0, 1, 3, 5, 7, 10% wt), then the swelling
of the swelling membrane in 48 hours is at 0% wt 120.5931%, at 1% wt 99. 9981 %, 3% with 79.2916%, 5%
with 68.1819%, 7% with 61.9173% and 10% with 45.3981%. FTIR at a concentration of 7% wt showed that
there was an interaction between banana peel and chitosan on the absorption band 3185,728 cm-1. In SEM
the best morphological sample structure at 5% wt shows a good interface. Addition of banana skin because
lignin reduces the level of swelling of water in wound dressing. In addition, Staphylococcus aureus is the
most sensitive type recorded in wound dressing. Banana peels included in chitosan films seem to be potential
and new biomaterials for wound healing applications.

References
[1] Kamel N A Salwa L A E Neveen M.S. 2017. Chitosan/banana peel powder nanocomposites for

wound dressing application: Preparation and characterization Materials Science and
Engineering vol 72 pp 543-550
[2] Guarino V C Altobelli T R Ambrosio L 2015 Degradation Properties and Metabolic Activity of
Alginate and Chitosan Polyelectrolytes for Drug Delivery and Tissue Engineering Applications
AIMS Materials Science vol 2 (4) pp 497-502.
[3] Güneş S Funda Tıhmınlıoğlu 2017 Hypericum perforatum Incorporated Chitosan Films as
Potential Bioactive Wound Dressing Material International Journal of Biological
Macromolecules
[4] Kumar M.N.V Ravi 2000 A review of chitin and chitosan applications. Reactive & Functional
Polymers vol 46 pp 1-27.
[5] Ma Y Lian X H T Ming F Jianliang L Yang J Zhonghua L. Yong C Xiaohong H 2017. Chitosan
membrane dressings toughened by glycerol to load antibacterial drugs for wound healing.
Materials Science & Engineering vol 81 pp 522-531.
[6] Tan H Constance R C Karin A P Kacey G M 2009 Injectable in Situ Forming Biodegradable
Chitosan-Hyaluronic Acid Based Hydrogels for Cartilage Tissue Engineering. Biomaterials vol
30 pp 2499-2506.
[7] Ishihara M Kuniaki N Katsuaki O Masato S Makoto K Yoshio S Hirofumi Y Takemi M Hidemi
H Maki U Akira K 2002 Photocrosslinkable chitosan as a dressing for wound occlusion and
accelerator in healing process. Biomaterials vol 23 pp 833-840.
[8] Chen L Chang Y T Nan Y N Chao Y W Qiang F Qin Z 2009 Preparation and Properties of
Chitosan/Lignin Composite Films Chinese Journal of Polymer Science vol 27 (5) pp 739-746.
[9] Popa V I Adina M C Silvia G Teodor M 2011 Nanoparticles Based on Modified Lignins With
Biocide Properties Cellulose Chemistry and Technology vol 45 (3-4) pp 221-226.
[10] Bahri S 2015 Pembuatan Pulp dari Batang Pisang. Jurnal Teknologi Kimia Unimal vol 4 (2) pp
36-50.
[11] Wina E 2001 Tanaman Pisang Sebagai Pakan Ternak Ruminansia Balai Penelitian Ternak.
Wartazoa vol 11 1.
[12] Rangan A Manjula V.M Rajendran M.T Satyanarayana G.K Reghu M 2017 Novel method for
the preparation of lignin-rich nanoparticles from lignocellulosic fibers. Industrial Crops and
Products vol 103 pp 152-160.
[13] Vu H T Scarlett C & Vuong Q V 2018 Phenolic Compounds Within Banana Peel and Their
Potential Uses: A Review. Journal of Functional Foods vol 40 pp 238-248.
[14] C Deng L He M Zhao D Yang Y Liu 2007 Biological properties of the chitosan-gelatin sponge
wound dressing. Carbohydr. Polym vol 69 pp 83-589.
[15] H Zheng L Wang 2013 Banana peel carbon that containing functional groups applied to the
selective adsorption of Au (III) from waste printed circuit boards Soft Nanosci Lett vol 3 (2) pp
29-36.
[16] Castro R S D Cetano L Ferreira G Padilha P M Saeki M J Zara L F Martines M A U and Castro

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G R 2011. Banana peel applied to the solid phase extraction of copper and lead from river water:
preconcentration of metal ions with a fruit waste Ind. Eng. Chem. Res. Vol 50 (6) pp 3446-3451.
[17] Bodnar M Hartmann J F Borbely J 2005 Preparation and Characterization of Chitosan-Based
Nanoparticles Biomacromolecules vol 6 pp 2521-2527.
[18] Moura M R & Aouada F A 2008 Preparation of Chitosan Nanoparticles Using Methacrylic Acid
Journal of Colloid and Interface Science vol 321(2) pp 477-483.
[19] W H Eaglstein et al Optimal use of an occlusive dressing to enhance healing: effect of delayed
application and early removal on wound healing Arch Dermatol vol 124 (3) pp 392–395.
[20] Papineau A M Hoover D G Knorr D & Farkas D F 1991 Antimicrobial Effect of Water-Soluble
Chitosans With High Hydrostatic Pressure Food Biotechnol. Vol 5 (1) pp 45-57.
[21] Huang J C 2002 Carbon Black Filled Conducting Polymers and Polymer Blends. Adv. Polym.
Technol vol 21 (4) pp 299-313.
[22] T H Emaga Robert C Ronkart S N Wathelet B Paquot M 2008 Dietary fibre components and
pectin chemical features of peels during ripening in banana and plantain varieties Bioresour.
Technol. Vol 99 (10) pp 4346–4354.
[23] N. Pitak S.K. Rakshit 2011 Physical and antimicrobial properties of banana flour/chitosan
biodegradable and self-sealing films used for preserving Fresh-cut vegetables LWT-Food Sci.
Technol. Vol 44 (10) pp 2310-2315.
[24] P Battaglini Franco L A de Almeida R F C Marques G Brucha M G N Campos 2016 Evaluation
of antibacterial activity of chitosan membranes associated to unripe banana peel Mater. Sci.
Forum vol 869 pp 859-863.
[25] Zaimahwati Harry A Rihayat, T Deni R Saharman G 2015. The Manufacture of Palm Oil-Based
Polyurethane Nanocomposite with Organic Montmorillonite Nanoparticle as Paint Coatings
International Journal of Chem Tech Research vol 7 pp 2537-2544.
[26] Rihayat, T Suryani Fauzi T Agusnar H Syafruddin Helmi Zulkifli Alam P N Sami M 2017
Mechanical Properties Evaluation of Single and Hybrid Composite Polyester Reinforced
Bamboo PALF and Coir Fiber vol 334 pp 1-8.
[27] Rihayat, T Suryani Arlina A Fonna Z Jalal R Alam P N Zaimahwati Sami M Syarif J Juhan N
2017 Determination of CEC Value (Cation Exchange Capacity) of Bentonite from North Aceh
and Bener Meriah, Aceh Province, Indonesia Using Three Methods vol 334 pp 1-7
[28] Suryani Agusnar H Wirjosentono B Rihayat T Nurhanifa 2017 Improving the Quality of
Biopolymer (Poly Lactid Acid) With the Addition of Bentonite as Filler vol 222 pp 1-7
[29] Rihayat T Suryani Zimahwati 2014 Preparation and Properties and Application of Renewable
Source (Palm Oil Polyol) Based Polyurethanes Coatings and its Thermal Stability Improvement
by Clay Nanocomposite Advanced Materials Research vol 887-888 pp 566-569.
[30] Rihayat T Suryani Zaimahwati 2014 Effect of Heat Treatment on The Properties of
Polyurethane/Clay Nanocomposite Paint Advanced Materials Research vol 525 pp 97-100.
[31] Rihayat T Saari M Hilmi M M Wan Yunus W M Z Suraya A R Dahlan K Z H M Sapuan S M
2007 Mechanical Characterisation of Polyurethane/Clay Nanocomposite Polymer and Polymer
Composites vol 15 pp 647-652
[32] Rihayat T Suryani 2010 Synthesis and Properties of Biobased Polyurethane/Montmorillonite
Nanocomposites World Academy of Science Engineering and Technology vol 4 pp 714-718

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The Benefit of Infrastructure Development: An Analysis

To cite this article: Khairuman Khairuman et al 2019 IOP Conf. Ser.: Mater. Sci. Eng. 506 012057
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The Benefit of Infrastructure Development: An Analysis

Khairuman Khairuman1, E S Barus2,3, Niskarto Zendrato3, Diana Alemin Barus4,
Jijon Rapitha Sagala5, Andi Elhanafi 6, Jenni Veronika Ginting2

1 Department of Teknik Komputer, Politeknik Aceh Selatan, Indonesia
2Department of Sistem Informasi, STMIK Kristen Neumann Indonesia, Jalan Jamin
Ginting Km 10.5, Medan, Indonesia
3 Faculty of Science Computer and Information Technology, Universitas Sumatera
Utara, Medan, Indonesia
4 Faculty of MIPA Universitas Sumatera Utara, Medan, Indonesia
5 Department of Teknik Informatika, STMIK Pelita Nusantara, Medan, Indonesia
6 Department of Teknik Informatika, Universitas Harapan Medan, Medan, Indonesia

*Corresponding author: [email protected]

Abstract. In analyzing the benefits of infrastructure development, several rules of economics
and feasibility studies for infrastructure development are used, namely aspects of benefits,
effectiveness and efficiency. These rules are applied to the results of the benefits data when
infrastructure development is carried out in the first year and the results of the benefits data are
processed using mamdani fuzzy logic reasoning which consists of 2 inference processes. In
processing fuzzy input data produces output from the inference process which is then classified
in 5 feasibility conditions, namely, low, normal, high, very high and not feasible which is used
as a support facility in making infrastructure development decisions.

1. Introduction
The increasing rapidly development of the era triggered the government's performance in terms of
equitable development in each region in Indonesia. Infrastructure development in each region is one of
the government's efforts to improve the welfare of the Indonesian people [1]. So that to see an
infrastructure development in an area that is really beneficial to improve the welfare of the people
around, it is necessary to design a computer application that can analyze the benefits of infrastructure
development that has been implemented in area that is in accordance with government objectives and
shows a percentage value that represents the level of prosperity of the local people after infrastructure
development was carried out.

This system is an analysis of the benefits of infrastructure development based on fuzzy logic that
shows the level of feasibility of infrastructure development in an area, so that it is expected to facilitate
officers working to evaluate infrastructure development in an area in determining which areas can be
used as development priorities and which areas need to be evaluated re-building the project. Thus, the
initial goal of the government to improve people's welfare and equitable development through
infrastructure development in the regions can be achieved.

The purpose of this research is to design an analysis of the benefits of infrastructure development
based on fuzzy logic in an area that can analyze the increase in the level of people's welfare in an area
after infrastructure development is carried out.

To avoid extensive discussion, the author will limit the discussion of this Final Project with riset
Analysis of people's prosperity in terms of economics and infrastructure development feasibility
studies. Rules are determined based on the experience of the PNPM PISEW economic expert team in

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution

of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

analyzing the benefits of infrastructure development. Analyzing the prosperity of the people only in
Sitinjo sub-district, Dairi Regency, where infrastructure development has been carried out by PNPM
PISEW. Analysis of the system model mamdani method fuzzy inference system

2. Methodology
2.1 System Application Thinking Framework

The first step is to develop the benefits of infrastructure development variables according to figure
1, where the benefit variable consists of 3 aspects, namely aspects of efficiency, aspects of
effectiveness and aspects of benefits. These three aspects are measured from the following parameters,
namely: the benefits of rolling out funds, saving benefits, benefits of increasing production, BC Ratio,
increasing access to economic business productivity, facilitating social relations of citizens, increasing
accessibility of the poor and opening isolation among citizens. The following is a system application
framework. The benefits of developing fuzzy logic base on infrastructure development.

Analysis of the Benefits of Infrastructure
Development

Development benefit variable

Aspect Aspect of effectiveness: Aspects of
Efficiency: • Increase access to Benefits:
x BC Ratio economic productivity • Benefits of
• Facilitate social savings
relations of citizens • Benefits of
• Increase community increasing
accessibility production
• Opening the isolation • Benefits of
of relations between rolling out funds
citizens

Performance Performance Performance
system system system

The results of the analysis of the benefits of infrastructure development benefits

Not feasible Low Normal High Very High

Figure 1. Framework system thinking.

For sensitivity analysis calculations to see how many percent increase and decrease the factors that
cause changes in the benefits of infrastructure development in each aspect, namely from proper,
normal or improper so that it needs to be re-evaluated against the process of infrastructure
development. Figure 1 is the framework of the application system that will be built.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

2.2 Flowchart Research
Flowchart is a scheme that describes the sequence of activities from beginning to end. Flowcharts are
used to describe a program algorithm in an easier and simpler way. The process that occurs in this
system can be described in the flowchart as shown in figure 2 below:

Start

Input data:
Variable input

Define Fuzzy
membership function,

rule and predicate

Composition MAX-MIN

Deffuzification

Result NO
YES

End

Figure 2. Flowchart of the FIS problem solving process using Mamdani Method.

Based on the figure 2 can be explained that the data training is the first data must be there and
stored in the computer. Then the training data must first be normalized using the formula contained in
equation (1). The normalized data will then be processed using backpropagation algorithm parameter
using the bipolar sigmoid activation function. The next stage of the network will train the data training
based on the parameters that have been determined. After all the steps are done, it will get the best
testing results that will be used to predict.

2.3 Determine fuzzy sets and inputs
Based on figure 1 can be determined there are 10 fuzzy variables that can be model are namely:

i. BC Ratio (BcR) consists of 4 fuzzy sets, namely: low, normal, high and very high.
ii. Increasing access to economic productivity (Pr) consists of 6 fuzzy sets, namely: very low, low,

normal, very normal, high and very high.
iii. Facilitating citizens' social relations (SR) consists of 3 fuzzy sets, namely: Low, Medium and

High.
iv. Increasing community accessibility (Acc) consists of 6 fuzzy sets, namely: very low, low,

normal, very normal, high and very high.
v. Opening isolation between citizens (Is); consists of 3 fuzzy sets, namely: low, medium and high.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

vi. Effectiveness aspects (Ef) consist of 4 fuzzy sets, namely: very low, low, normal and high
vii. The saving benefit (Sa) consists of 4 fuzzy sets, namely: low, normal, high and very high.
viii. The benefits of increasing income (Inc) consist of 4 fuzzy sets, namely: low, normal, high and

very high.
ix. The benefits of rolling out funds (Fu) consist of 4 fuzzy sets, namely: low, normal, high and

very high.
x. Benefits (B) aspects consist of 4 fuzzy sets, namely: low, normal, high and very high.

2.4 Membership function
Facilitating citizens' social relations (Sr) is shown in figure 3 below.

Membership function:

1; h ≤ 30

μHuS Low [h] = (50-h)/20; 30 ≤ h ≤ 50 (1)

0; h ≥ 50

μHuS Normal [h] = 0; h ≤ 40 or h ≥ 60 (2)
(h-40)/10; 40 ≤ h ≤ 50
(60-h)/10; 50 ≤ h ≤ 60

μHuS High [h] = 0; h ≤ 50 (3)
(h-50)/30; 50 ≤ h ≤ 80

1; h ≥ 80

Lo Nor Hi

Member
ship function

μ [h]

0 30 40 50 60 Sosial
85
80 Relationship

Figure 3. Fuzzy set of levels of citizens Social Relations (Sr).

3. Results And Discussion

3.1 Display of effectiveness aspect input

The following is a membership function of the productivity aspect of the productivity variable input
which consists of 5 conditions, namely low, medium, very medium, ordinary, very ordinary and high
according to formula (1), (2) dan (3).

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

Figure 4. Display productivity membership functions.
Next is the display of the aspects of effectiveness of the input variables of social relations where
this input has the appearance of a membership function consisting of 3 conditions namely low,
medium and high. Figure 4 shows a display of the effectiveness aspects of the input accessibility
variable of the community where this input has the appearance of a membership function consisting of
6 conditions namely low, medium, very medium, ordinary, very ordinary and high. The following is a
display of the accessibility input membership function.
Figure 5 shows is a display of the effectiveness aspects of the community isolation input variable
where this input has the appearance of a membership function consisting of 3 conditions, namely low,
medium and high following is a display of the community isolation input membership function. It can
be seen in the image display that the low and high conditions use the tramp curve type while the
conditions using the trim curve type (triangle), the parameter values. These can be seen in Figure.

Figure 5. Display of isolation membership functions.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

Figure 6 is a display of the effectiveness aspects of the community isolation input variable where
this input has the appearance of a membership function consisting of 3 conditions, namely low,
medium and high according to equations 3.20, 3.21 and 3.22. The following is a display of the
community isolation input membership function. It can be seen in the image display that the low and
high conditions use the tramp curve type while the conditions using the trim curve type (triangle), the
parameter values shown are adjusted to equations 3.20, 3.21 and 3.22. These can be seen in Figure 7.

Figure 6 Display accessibility membership functions.

Figure 7. Display of isolation membership functions.

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IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

3.2. Display of Benefit Inference Results

After all membership functions of each input are filled. Then the rule button contains a rule that is
used for the benefit aspects in accordance with the rule in figure 8.

Then the view rules button will display the results of the centroid benefit analysis as seen in figure
7. Similarly, the results of the analysis of effectiveness in the display of results can also be changed in
the input values so as to produce a variable output value. To change the input value can be done by
changing the value in the text input in the lower left panel or it can also be done by sliding the red line
on each input curve to the left or right. The shape of the blue curve is the output curve and the output
value on this aspect of benefit, which is what appears in the description of benefits, while the yellow
curve is the input curves whose values can be changed so as to produce varying output values.

Figure 8. Display of functionality function.
In Figure 8, the input value can be changed in the input text on the lower left, by changing the input
values, we can obtain a varied output value. Furthermore, the changes made in the input column can
be seen the results in the testing table 1 Every changing made to the input value will result in a
significant change in the output, then the output is then classified into 5 conditions of the feasibility
analysis system of benefits.

3.3 Testing
From the tests performed, where the user enters fuzzy input as follows:

x BC Ratio = 2.8
x Productivity access = 53
x Social Citizens = 49
x Community Accessibility = 61.4
x Inter-citizenship isolation = 41.2
x Savings = 2.47
x Income Increase = 1.9
x Revolving of Funds = 3.22

Fuzzy input will go through the inference process 1, then the inference 2 process in the form of
classification of feasibility in this case consists of 5, namely feasible, very feasible, normal, very

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

normal and not feasible. Following are the details of the process. For the calculation of inference 1
aspect of effectiveness begins with the calculation of the implications of the implications.

3.4 Application function implications
Based on the rules in figure 8, the rules of the predicate α-pred can be determined as following:

Inference 1 effectiveness
Pers 3.5; Pers 3.10; Pers 3.10; Pers 3.18;

R1 = α-pred = min (μAkP Normal, HuS Low1, AkM Low2, IsW normal3)
= min (0,0,0,0)
=0

Pers 3.6; Pers 3.11; Pers 3.14; Pers 3.19;
R2 = α-pred = min (μAkP very normal, HuS normal1, AkM very normal2, IsW normal3)

= min (0,0,0,0)
=0
Pers 3.7; Pers 3.12; Pers 3.15; Pers 3.20;
R3 = α-pred = min (μAkP normal, HuS high1, AkM normal2, IsW high3)
= min (0,0.67,0.8,0.33)
= 0.33
Pers 3.8; Pers 3.12; Pers 3.16; Pers 3.20;
R4 = α-pred = min (μAkP very normal, HuS normal1, AkM very normal2, IsW normal3)
= min (0.8,0.67,0,0.33)
= 0.33
Pers 3.9; Pers 3.10; Pers 3.17; Pers 3.18;
R5 = α-pred = min (μAkP low, HuS low1, AkM low2, IsW normal3)
= min (0,0,0,0)

3.5 Rule composition
Inference 1 effectiveness from the rules of the existing predicate, the fuzzy boundary region is
generated as follows:

(e1-65)/ 20 = 0.33
e1 = 71.67

(85-e)/20 = 0.33
e2 = 78.33

Inference 1 effectiveness from the rules of the existing predicate, the fuzzy boundary region is
generated as follows:

μ[x] = (x -65)/ 20; x ≤ 71.67
0.33; 71.67 ≤ x≤78.33
(85-x)/20; x ≥ 78.33

3.6 Defuzzy
Inference 1 effectiveness can be calculated using the centroid method, the calculation of the moment is
as follows:

71.67

M1 = ∫ (0.05x2 – 3.25x) dx

65

= 0.06167 x3 – 1.625x2
= -2199.0303 + 2279.3875
= 80.3572

73.33

M2 = ∫ (0.33x) dx

71.67

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

= 0.165x2
= 887.25267-847.53717
= 39.7255

85

M23= ∫ (4.25x- 0.05x2) dx

73.33

= 2.125x2 – 0.0167x3
= 5097.237-4841.6518
= 255.5852

Then calculate the area of each region:
A1 = ((71.67-65) *0.33)/2= 1.10055
A2 = (73.33-71.67) *0.33= 0.5478
A3 = (85-73.33) *0.33= 3.8511

Result

= 67.85

The value of 67.85 is the value of the aspect of effectiveness obtained from the input of
productivity access inputs, social relations, community accessibility and the isolation of citizens.
Value 67, 85 then classified where the value is included in the category of infrastructure development
in the Normal category. Thus, the calculation of the effectiveness inference process manually, as well
as the calculation of the manual inference benefit calculation process. After calculation, the results will
be classified into 5 conditions. The input values entered in the program, the output of the analysis
results is obtained as 1 shows 10 conditions for changes in input values that are different so that it can
be seen the form of output variations generated from the simulation program that is adjusted to the
input.

4. Conclusions
Analysis of the benefits of infrastructure development based on fuzzy logic can be used as one of

the references in decision-making on infrastructure development in an area, supported by fuzzy logic
reasoning is expected to produce accurate data. The more rules used in the inference process will
produce better output. The fuzzy inference process in this application is used to determine the value of
the benefit aspects and aspects of effectiveness then classify them into the boundary values of the 5
standard feasibility conditions, namely low, normal, high, very high and not feasible.

References
[1] Sri Hartati, Imas S Sitanggang, A Fuzzy Based Decision Support System for Evaluating Land

Suitability and Selecting Crops Journal of computer science 6(4):417-424 2010
[2] Panduan Pelaksana PNPM PISEW TAHUN 2010
[3] Kecerdasan Buatan http://idhaclassroom.com/2007/09/15/./ kecerdasan buatan.html.
[4] Suparman 2007 Komputer Masa Depan Pengenalan Artificial Intelligence
[5] Kusumadewi S 2002 Analisis Desain Sistem Fuzzy Menggunakan Tool Box Matlab, Penerbit

Graha Ilmu, Yogyakarta
[6] Kusumadewi S, dan Purnomo H 2004 Aplikasi Logika Fuzzy untuk Pendukung Keputusan,

Penerbit Graha Ilmu, Yogyakarta
[7] IGunaidi Abdia Away 2010 MATLAB Programming, Informatika, Bandung
[8] Kusumadewi S 2002 Analisis Desain Sistem Fuzzy Menggunakan Tool Box Matlab, Penerbit

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1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012057 doi:10.1088/1757-899X/506/1/012057

Graha Ilmu, Yogyakarta
[9] Efraim Turban, Jay E Aronson, Ting Peng Liang 2005 Dicision Support System and Intelligent

Systems (Sistem Pendukung Keputusan dan Sistem Cerdas) ANDI, Yogyakarta
[10] Agus Naba 2009 Belajar Cepat Fuzzy Logic menggunakan MATLAB, ANDI, Yogyakarta

10

IOP Conference Series: Materials Science and Engineering

PAPER • OPEN ACCESS

The Shelf-life Prediction of Sweet Orange Based on Its Total Soluble
Solid by Using Arrhenius and Q 10 Approach

To cite this article: Rita Khathir et al 2019 IOP Conf. Ser.: Mater. Sci. Eng. 506 012058
View the article online for updates and enhancements.

This content was downloaded from IP address 180.241.46.109 on 29/10/2019 at 04:07

1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012058 doi:10.1088/1757-899X/506/1/012058

The Shelf-life Prediction of Sweet Orange Based on Its Total
Soluble Solid by Using Arrhenius and Q 10 Approach

Rita Khathir1*, Ria Yuliana1, Raida Agustina1 and Bambang Sukarno Putra1
1 Department of Agricultural Engineering, Faculty of Agriculture, University of Syiah
Kuala

*Corresponding author: [email protected]

Abstract. The information about shelf-life of sweet orange is necessary for its post-harvest
handling to reduce the loss facing by farmers and sellers. The study aimed to observe if the
Arrhenius and Q 10 models can be used to predict the shelf-life of sweet orange based on its total
soluble solid (TSS). The fresh sweet oranges obtained from the market were stored at three
extreme temperatures i.e. 55, 65, and 70˚C. During the storage, the TSS was analyzed every
hour, until the sweet oranges quality had decreased. The TSS was observed by using a hand
digital refractometer. The TSS change in linear model was chosen to perform the Arrhenius
model since its R-square value was higher than that of exponential model. The study had proved
that there was a possibility of using the Arrhenius and Q 10 models to predict the shelf-life of
sweet oranges based on its TSS contents. The Arrhenius model of TSS change was and the Q 10
value was 1.92. The real shelf-life of sweet orange at storage temperature 10°C was 18 days. The
shelf-life of sweet orange at any storage temperatures can be predicted by, and as the results of
drawing this prediction, the shelf-life of sweet orange can be simply calculated by, for more
comfortable and easy purposes. However, the shelf-life prediction model of sweet orange should
be improved by further study since it contains a relative error above 2%. For further study it is
suggested to assure the TSS change at temperature 55°C as well as the ascorbic acid change in
order to improve the shelf-life prediction of sweet oranges.

Keywords: Sweet orange, Shelf-life, Total soluble solid, Arrhenius, Q10

1. Introduction
The sweet orange (Citrus sinensis) is one of potential fruits produced in Aceh Province, Indonesia. The
central production of sweet orange is Bireuen Distric with share about 77% of whole production in Aceh
Province (Zikria, 2015). Therefore, it is necessary to predict the shelf-life of this product with the
objective of to reduce the loss facing by farmers and sellers. The information of shelf-life of sweet
orange can be used to organize its post-harvest handling method, and maintain its quality. The farmers
can use this information to maintain the production while the sellers can use this information for
controlling their stock in the market. method (simulation). One of the simulation methods that can be
used to predict the shelf-life of the product is the Arrhenius Model [3-6]. The Arrhenius Model is based
on some assumptions i.e. the quality change is caused by a specific reaction during constant temperature
without the influence of previous process [7]. The further study of Arrhenius Model is the Q10 model
provided the more convenient results of the prediction [8]. Therefore, the combination use of Arrhenius
and Q10 Models is applied to predict the shelf-life of sweet orange.

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Published under licence by IOP Publishing Ltd 1

1st South Aceh International Conference on Engineering and Technology IOP Publishing

IOP Conf. Series: Materials Science and Engineering 506 (2019) 012058 doi:10.1088/1757-899X/506/1/012058

Since the change of total soluble solid (TSS) values were indicated to the decreased quality of sweet
orange [9-11], the study aimed to predict the shelf-life of sweet orange by using Arrhenius and Q10
models based on its TSS contents.

2. Methodology
The study was conducted at Laboratory of Postharvest Technology, Department of Agricultural
Engineering, Faculty of Agriculture, University of Syiah Kuala, Banda Aceh, Indonesia from Mei to
October, 2018. The instruments used were refractometer DBR 85 and oven. About 10 kg of sweet
oranges were observed.

The fruits were selected based on its size and colour to have the same maturity index. The selected
sweet oranges were stored at three extreme temperatures i.e. 55, 65, and 70 °C. During the storage, the
total soluble solid (TSS) was observed hourly until the quality of the fruit had decreased indicated by
the colour and texture changes. The TSS was measured by using a hand digital refractometer. After
calibrated to zero by using distilled water at 20°C, the juice of sweet oranges was placed a few drops on
the prism window. Then, by a single press on “meas” button, the instrument starts to test in a few second.

The TSS changes were analysed by using linear model in equation 1 and exponential model using
equation 2. The model with the higher R-square was chosen for the next calculation. The rate constant
of TSS changes was determined as the slope of the model (k). Furthermore, the slope was calculated as
the natural logarithm (ln k) and the temperature in degree Centigrade was transformed to Kelvin as 1/T.
The trend line of this curve was used to determine the Arrhenius model. The Arrhenius model is written
as equation 3 and the Q10 is written as equation 4. Finally, the shelf-life of sweet orange was predicted
by using equation 5. Previously, another separated experiment by storing the fruits at temperatures 10°C
was applied to find out the real shelf-life of this fruits as tsT2.

‫ܣ = ܣ‬଴ − ݇‫ݐ‬ (1)
‫ܣ = ܣ‬଴ × ݁ି௞௧ (2)
݇ = ݇଴ × ݁ିா/ோ் (3)
୩౐శభబ ୲ୱ౐ (4)
Qஔଵ୘଴/ଵ଴ = ୩ = ୲ୱ౐శభబ
(5)
ts୘ଵ = ts୘ଶ × Qଵ଴ஔ୘/ଵ଴

Where; A is the TSS content of sweet oranges (% brix), A0 is the initial TSS content of sweet oranges
(% brix), t is Storage time (hour), k is rate constant of quality change, ko is rate constant without

temperature influence, E is the activation energy (cal/mol), T is temperature (K), R is Gas constant
(1.986 kal/mol K), tsT1 is the shelf-life of sweet oranges at estimated temperature (day), tsT2 is the shelf-
life of sweet oranges at temperature basis (day) and T+10 is the storage temperature at 10oC higher.

3. Results and Discussion

3.1. The TSS change of sweet oranges at extreme temperature
Results of TSS changes of sweet oranges during storage at three extreme temperatures can be seen in
figure 1-3. The initial TSS content was about 9 to 10 %brix. During the storage, the TSS content had
fluctuated irregulary. The final TSS content was between 3 to 5%brix at temperature 65 and 70°C, which
cut off more than 50% of its initial values. However, the final TSS content at 55°C was approximately
7% brix. The linear and exponential models of TSS changes are shown in table 1.

2