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All Journal Jurnal AGROTEKNOLOGI Jurnal Keteknikan Pertanian Industria: Jurnal Teknologi dan Manajemen Agroindustri Jurnal Teknologi Pertanian Andalas Jurnal Agritechno Food ScienTech Journal Jurnal Agribisnis Teknika

Dedy Wirawan Soedibyo

Universitas Jember

Author-ID : 383577

Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Other

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Model Jaringan Syaraf Tiruan untuk Prakiraan Harga Komponen Bahan Baku Pakan Unggas di PT XYZ Ahmad Haris Hasanuddin Slamet; Bambang Herry Purnomo; Dedy Wirawan Soedibyo
Industria: Jurnal Teknologi dan Manajemen Agroindustri Vol 9, No 2 (2020)
Publisher : Department of Agro-industrial Technology, University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.industria.2020.009.02.9

AbstrakPT XYZ adalah salah satu produsen pakan unggas di Kabupaten Banyuwangi, Jawa Timur. Permasalahan dalam pengembangan pakan unggas di PT XYZ adalah harga pakan unggas yang berfluktuasi. Komponen terbesar bahan baku pembuatan pakan unggas adalah jagung dan bungkil kacang kedelai (BKK). Permasalahan fluktuasi harga pakan unggas dapat diatasi dengan prakiraan harga jagung dan BKK. Prakiraan yang tepat dapat membantu PT XYZ untuk optimalisasi alokasi sumber daya perusahaan. Optimalisasi sumber daya bertujuan untuk meningkatkan keuntungan yang diperoleh perusahaan. Tujuan dari penelitian ini adalah pengembangan model jaringan syaraf tiruan (JST) backpropagation untuk prakiraan harga jagung dan BKK. Model JST dikembangkan dengan perlakuan jumlah lapisan tersembunyi (node hidden layer), fungsi aktivasi, dan laju pembelajaran (learning rate). Data penelitian yang digunakan adalah harga jagung dan BKK pada periode Januari 2016-Oktober 2018. Hasil penelitian menunjukkan bahwa model JST terbaik untuk prakiraan harga jagung adalah 12 node input, 5 node hidden layer, dan 1 node output dengan kombinasi fungsi aktivasi sigmoid biner (logsig)-sigmoid biner (logsig) dan learning rate 0,005. Model JST terbaik untuk prakiraan harga BKK adalah 12 node input, 10 node hidden layer, dan 1 node output dengan kombinasi fungsi aktivasi sigmoid bipolar (tansig)-pure linier (purelin) dan tingkat learning rate 0,006.Kata kunci: harga jagung dan bungkil kacang kedelai, Jaringan Syaraf Tiruan, pakan unggas AbstractPT XYZ is one of the poultry feed producers in Banyuwangi Regency. The problem in developing poultry feed at PT XYZ was related to the fluctuative price of poultry feed itself. The biggest component of raw material for producing poultry feed that affect prices were maize and soybean meal. The problem of poultry feed price fluctuations can be overcome by forecasting the price of maize and soybean meal. The accurate forecast can be used as a reference for PT XYZ in optimizing the allocation of resources so as to increase the profits of the company. The aim of this study was developing a backpropagation neural network (ANN) model. The ANN model was developed by number of hidden layers, activation function, and learning rate. The price of maize and soybean meal in the period January 2016-October 2018 was used as data in this study. The best model for forecasting maize price was 12 input nodes, 5 hidden layer nodes, and 1 output node with a combination of the sigmoid binary (logsig)-sigmoid binary (logsig) activation function and 0.005 learning rate. The best model for forecasting soybean meal was 12 input nodes, 10 hidden layer nodes, and 1 output node with a combination of sigmoid bipolar (tansig)-pure linear activation function (purelin) and 0.006 learning rate.Keywords: Artificial Neural Network, maize and soybean meal prize, poultry feed

Identifikasi Varietas Benih Jagung (Zea Mays L.) Menggunakan Pengolahan Citra Digital Berbasis Jaringan Syaraf Tiruan Mohamad Ihya Ulum Muddin; Dedy Wirawan Soedibyo; Sri Wahyuningsih
Teknika Vol 8 No 2 (2019): November 2019
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v8i2.173

Identifikasi varietas perlu dilakukan untuk membedakan galur yang dihasilkan dengan varietas yang telah ada sehingga sangat penting bila dikaitkan dengan perlindungan varietas tanaman dan hak kekayaan intelektual. Salah satu metode yang umum diterapkan untuk identifikasi varietas jagung adalah dengan cara mendeskripsikan morfologi benih. Namun, hal tersebut membutuhkan waktu lama dan sulit jika dilakukan pengukuran secara manual. Pengolahan citra (image processing) dan jaringan syaraf tiruan (JST) dapat dijadikan sebagai salah satu metode identifikasi varietas yang dapat membantu mengidentifikasi varietas benih jagung. Penelitian ini bertujuan untuk mengetahui variabel citra yang dapat digunakan untuk identifikasi varietas benih jagung sehingga dapat disusun algoritma jaringan syaraf tiruan terbaik dan mengetahui tingkat akurasinya dalam menduga varietas benih jagung. Sampel yang digunakan dalam penelitian ini adalah benih jagung hibrida BISI 18, Pioneer P21, Pioneer P27 dan PERTIWI. Pada masing-masing varietas diambil 600 sampel untuk data training dan 200 sampel untuk data testing, keseluruhan sampel adalah 3200 benih jagung. Penelitian ini menggunakan pengolahan citra digital dengan menggunakan analisis statistik untuk menentukan variabel yang dapat dipergunakan dalam penerapan jaringan syaraf tiruan sebagai metode identifikasi. Hasil penelitian menunjukkan variasi JST terbaik untuk menyusun program identifikasi benih jagung adalah variasi A3 dengan 20 node hidden layer. Hasil validasi menunjukkan, program identifikasi benih jagung memiliki tingkat akurasi dalam menduga varietas sebesar 59,1 %.

Rancang Bangun Sistem Sortasi Cerdas Berbasis Pengolahan Citra untuk Kopi Beras Dedy W. Soedibyo; Usman Ahmad; Kudang Boro Seminar; I Dewa Made Subrata
Jurnal Keteknikan Pertanian Vol. 24 No. 2 (2010): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Abstrack Coffee has good prospects as a motor of development in Indonesia agribusiness and agroindustry, therefore needs to be handled properly and professionally. Grading process in commercial green coffee asgrain commodity is still done manually. This process has the disadvantage of low efficiency, objectivity and the level of consistency. Therefore weneed a machine that can workautomatically to classify the quality of the green coffee by visual inspection. Theobjective of this study was to design the green coffee sorting machine controlled by a computer based on image processing program consisted of conveyor belt, the image capture station used twodigital cameras, and the parallelsimulator divider. The design of sorting machine was used for the development of the green coffee sorting system that will categorizeinto four quality classes based on the qualifications according to the standard of SCAA ( Specialty Coffee Association of America). Keyword: Sorting Machine, green coffee, image procesing, computer programDiterima: 14 Juli 2010; Disetujui: 11 Oktober 2010

PEMUTUAN EDAMAME MENGGUNAKAN PENGOLAHAN CITRA DAN JARINGAN SYARAF TIRUAN Dedy Wirawan Soedibyo; I Dewa Made Subrata; Suroso .; Usman Ahmad
Jurnal Keteknikan Pertanian Vol. 20 No. 3 (2006): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.020.3.%p

ABSTRACT The objective of this research was to develop a computer program of image processing and articial neural netwok to the quality of fresh soybean (edamame) into four classes namely SQ (standar quality), SG (scond grade), TG ( third grade), and RJ (reject) using image processing and artifical neural network. The total samples were 2500 fresh soybean produced by PT. Mitra Tani Dua Tujuh Jember. Soybean image was analyzed to get six quality parameters whose match with Soybean quality criteria namely pod length, pod area, perimeter, defect area, index of red color, and of the artifical neural network (AAN). Six variations of ANN weredeveloped for ANN training purposes (2000 data). The weights of the selected ANN architecture was used to identify the quality class of testing data (500 data). Thenintegrated with image processing program so the program could identify Soybean quality class automatically. The quality parameter used in this research has relevancy with Soybean quality criteria. The selected architecture of the AAN was the one with 20 nodes hidden layer in which normalization onput data representation with zero mean and standard deviation equals one. The accuracy of image processing program observed 81,4 percent based on the 500 testing data. Keyword: Grading, Edamame, image procssing, Artificial neural network Diterima: 17 Mei 2006; Disetujui: 13 Juni 2006

KAJIAN SIFAT FISIK DAN KIMIA BUAH STROBERI BERDASARKAN MASA SIMPAN DENGAN PENGOLAHAN CITRA Rizky Ramadani Dwi Utari; Dedy Wirawan Soedibyo; Dian Purbasari
JURNAL AGROTEKNOLOGI Vol 12 No 02 (2018)
Publisher : Faculty of Agricultural Technology, University of Jember

In the storage processes strawberries experiencesdetrimental changes so that they can affect the quality of fruit. This change can be detected by testing the physical and chemical properties in particular shelf life period. In generally the measurement of physical and chemical properties are done manually andcausing damage to the object being observed (destructive method). Based on description above, it is necessary to measure non-destructive method using digital image processing. This study aimed to identify the relationship between physical and chemical propertiesvariables and image quality variables (area, height, widht, perimeter red index andblue index) based on 0, 1, and 2 days shelf life using an image processing program. The sample used in this study were 155 pieces strawberry from A quality. The strawberry image was taken by using a CCD camerathen extracted using SharpDevelop 4.2 software. Physical and chemical properties of the strawberry were measured using digital O'hauss pioneer scales, penetrometer, refractometer and pH meter to obtain data on weight, hardness, total dissolved solids and acidity (pH). Correlation test results indicated from strong to very strong relationship between physical and chemical properties variablesand image quality variables with a range of correlation coefficient values from 0,725 to 1,000. Image quality variables that could be used as input for estimating shelf life was blue index, with validation test resulted 87,7%total accuracy. Keywords: characteristics, chemical, image processing, physical, shelf life, strawberry

SORTING MANALAGI APPLE (Malus sylvetris Mill) USING IMAGE PROCESSING APPLICATION Ahmad Haris Hasanuddin Slamet; Dedy Wirawan Soedibyo
Food ScienTech Journal Vol 1, No 2 (2019)
Publisher : University of Sultan Ageng Tirtayasa

Manalagi apple (Malus sylvestris Mill) is one type of apple that has been widely known by the public in Indonesia. Fruit sorting in Indonesia, particularly Manalagi apple, is carried out manually so it is still at a disadvantage. This study aimed to arrange image processing applications with criteria for size, width, height, color, and defect area of fruits. The samples of Manalagi apple used in this study were 130 with 50 each in the non reject and reject quality classes and 30 for the validation test. Manalagi apple samples were obtained from one of the collectors located in Batu City. The image of Manalagi apple processed gained several variables, including perimeter, area, height, width, r, g, b, and defect area. Based on statistical analysis, image quality variables that could be used as input for making applications in the form of logical sentences were perimeter, area, and defect area. Based on the application, the validation test obtained an accuracy of 86.65%.

PRAKIRAAN HARGA DAGING AYAM BROILER DAN DAY OLD CHICK (DOC) DI KABUPATEN BANYUWANGI MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION Ahmad Haris Hasanuddin Slamet; Bambang Herry Purnomo; Dedy Wirawan Soedibyo
Jurnal Teknologi Pertanian Andalas Vol 23, No 2 (2019)
Publisher : Universitas Andalas

Harga daging ayam broiler dan day old chick (DOC) adalah dua hal yang menentukan tingkat keuntungan peternak ayam broiler. Harga daging ayam broiler dan day old chick (DOC) di Kabupaten Banyuwangi dalam lima tahun terakhir cenderung berfluktuasi. Prakriaan merupakan salah satu cara yang penting dalam mengatasi permasalah fluktuasi harga daging ayam broiler dan DOC. Penelitian ini bertujuan untuk memprakirakan harga ayam broiler dan DOC di Kabupaten Banyuwangi. Hasil prakiraan tersebut dapat digunakan sebagai pengambilan keputusan oleh pihak-pihak terkait. Penelitian ini menggunakan metode jaringan saraf tiruan (JST) backpropagation. Data yang digunakan dalam penelitian ini adalah harga daging ayam broiler dan DOC di Kabupaten Banyuwangi pada periode 2014-2018. Berdasarkan hasil penelitian, pelatihan jaringan terbaik adalah 12 node input, 5 node hidden, 1 node output untuk prakiraan harga daging ayam broiler dan harga DOC. Nilai mean absolute error (MAPE) yang diperoleh adalah 4,6% untuk harga perkiraan ayam broiler dan 18,99% untuk harga perkiraan DOC. Harga ayam broiler pada tahun 2019 diperkirakan meningkat pada bulan Januari, April dan Desember, sedangkan harga terendah pada bulan Juni. Prakiraan untuk harga DOC ayam pada tahun 2019 cenderung menurun dari awal tahun dan stagnan dari pertengahan bulan hingga akhir bulan.

KAJIAN SIFAT FISIK DAN KIMIA JERUK SIAM (Citrus nobilis var. microcarpa) SEMBORO BERDASARKAN UMUR SIMPAN MENGGUNAKAN PENGOLAHAN CITRA DIGITAL Herman Setiawan; Dedy Wirawan Soedibyo; Dian Purbasari
Jurnal Teknologi Pertanian Andalas Vol 23, No 1 (2019)
Publisher : Universitas Andalas

Since hundreds of years ago, orange have grown in Indonesia either naturally or cultivated. One of the most popular places for producing orange varieties is Semboro, Jember Regency. The quality of Semboro oranges is influenced by the level of harvest age and shelf life. In the storage process, Orange expiriences physically and chemical changes at each shelf life which is detrimental. At present the tests performed on Semboro orange are destructive. Based on this, non-destructive measurements are needed by using other methods such as digital image processing. The purpose of this study was to identify the relationship between the physical and chemical properties of Semboro orange based on shelf life. The Semboro Orange used was 150 fruits of super quality with code size 1 and the same picking age of 28 MSB (weeks after flowering). Semboro oranges are stored for 15 days and measured with variations in shelf life of 1, 8 and 15 days. This research was conducted in two stages, namely taking pictures and measuring physical and chemical characteristics. Orange samples were then measured interm of physical and chemical properties using the O'hauss pioneer digital scales, penetrometer, refractometer and pH meter to obtain data on fruit weight, fruit hardness, total dissolved solids and acidity (pH) of the fruit. The value of the image quality variable and the physical and chemical properties were analyzed using one way anova test, correlation, regression, boxplot and validation test.

PRAKIRAAN HARGA MEAT BONE MEAL (MBM) MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION Ahmad Haris Hasanuddin Slamet; Bambang Herry Purnomo; Dedy Wirawan Soedibyo
JURNAL AGRIBISNIS Vol. 10 No. 1 (2021): Mei 2021
Publisher : Program Studi Agribisnis, Fakultas Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/agribisnis.v10i1.926

XYZ is a poultry feed producer in Banyuwangi Regency, East Java. The problem in developing poultry feed at PT XYZ was the fluctuating price of poultry feed. Meat bone meal (MBM) or what is called meat flour is one of the raw materials for poultry feed that affects the final price of poultry feed products. The price of MBM was greatly influenced by the exchange rate of the rupiah against the dollar. Forecasting is one way that needs to be done in dealing with MBM price fluctuations. The aim of this study was to estimate the price of MBM using backpropagation neural networks (BNN). The data used in this study was the price of MBM in the period January 2016-October 2018. Based on the results of the study, the best BNN architecture for the estimated MBM price was12-10-1 (12 input nodes, 10 hidden nodes, and 1 output node). This architecture has reached the training target of 0.002 with a MAPE test value of 13.93%. Based on forecasts with the BNN the highest MBM price in May 2019 and the lowest MBM price in January 2019.

Pemutuan Belimbing Manis (averrhoa carambola l.) Menggunakan Pengolahan Citra Digital Berbasis Jaringan Syaraf Tiruan Moh Ruky Nur Firmansyah; Dedy Wirawan Soedibyo; Sri Wahyuningsih
Jurnal Agritechno Jurnal Agritechno Vol. 12, Nomor 2, Oktober 2019
Publisher : Depertemen Teknologi Pertanian Universitas Hasanuddin

Penanganan pasca panen belimbing perlu dilakukan agar kualitas buah tetap terjaga, salah satu contohnya adalah melakukan pemutuan sebelum proses penjualan. Pemutuan buah belimbing di Indonesia umumnya masih dilakukan dengan cara manual yang memiliki keterbatasan dari segi waktu, tenaga dan penilaian (subjektif). Pengolahan citra (image processing) dan jaringan syaraf tiruan (JST) dapat dijadikan sebagai salah satu metode penanganan pasca panen yang dapat membantu memutukan buah belimbing lebih seragam dan efektif. Penelitian ini bertujuan untuk menyusun dan menghasilkan sebuah program pengolahan citra pemutuan buah belimbing serta mengetahui tingkat keakuratan program tersebut dalam pendugaan mutu. Sampel bahan yang digunakan yaitu belimbing dengan kelas mutu A, B, C dan Reject yang dimutukan secara manual terlebih dahulu. Total sampel 200 buah, terdiri atas 160 buah (40 buah per kelas mutu) untuk training dan 40 buah (10 per kelas mutu) untuk testing. Tahapan penelitian meliputi image aquisition, pengambilan gambar citra, penentuan variabel mutu citra, pembuatan program pengolahan citra, ektraksi citra, analisis statistik dan pembuatan grafik boxplot, penentuan input JST, penentuan variasi arsitektur JST, training semua variasi JST, simulasi data testing dengan propagasi maju, penentuan variasi terbaik, integrasi model JST dengan program pengolahan citra, serta validasi program. Hasil penelitian menunjukkan variasi JST terbaik untuk menyusun program pemutuan belimbing adalah variasi dengan 10 node hidden layer dan 7 input variabel (area, tinggi, perimeter, area cacat, indeks warna R, G dan B). Hasil validasi menunjukkan, program pemutuan belimbing memiliki tingkat akurasi dalam menduga mutu sebesar 85 %.

Co-Authors Ahmad Haris Hasanuddin Slamet Dian Purbasari Herman Setiawan Herry Purnomo, Bambang I Dewa Made Subrata I Dewa Made Subrata Kudang Boro Seminar Moh Ruky Nur Firmansyah Mohamad Ihya Ulum Muddin Rizky Ramadani Dwi Utari Sri Wahyuningsih Suroso . Usman Ahmad Wahyuningsih, Sri

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