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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Disability Studies Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Budi Darma Setiawan
Fakultas Ilmu Komputer , Universitas Brawijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Social Sciences

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Journal : Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

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Support Vector Regression Untuk Peramalan Permintaan Darah: Studi Kasus Unit Transfusi Darah Cabang - PMI Kota Malang M. Raabith Rifqi; Budi Darma Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PMI is responsible for meeting blood demand from hospitals. The management of the blood storage center has a very important task, to predict the requirement of blood components to minimize the ex less and the lack of blood supply. Blood has only a life span of 35 days since donated. If it is past the time then it can not be used anymore. Excess or lack of blood supply at the site should not occur, because it can affect the number of patients death. In order to reduce the losses that if it occurs, it is necessary to do research that uses the prediction method of blood predict that is implemented in a system. One of them with Support Vector Regression method that is suitable for blood demand forecasting. Implement SVR using normalized min - max data and use RBF kernel function. Based on the test results for the SVR method that has been done, the result of the minimum MAPE value is 3.899% with the parameter value lambda = 10, sigma = 0.5, cLR = 0.01, C = 0.1, epsilon = 0.01, number of data features = 4 and number of iterations of 5000, of the 12 test data used. The resulting MAPE value is <10% and can be categorized as good for predicting the amount of blood demand.
Algoritme Genetika Untuk Optimasi K-Means Clustering Dalam Pengelompokan Data Tsunami Dwi Anggraeni Kuntjoro; Budi Darma Setiawan; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tsunami is one of the most deadly disaster causing damage and loss of life and wealth. It happens in a sudden and unpredictable. Lack of awareness often leads to a great damage and worsening the impact of tsunami itself. This research implements genetic algorithm optimization into K-Means method for classify tsunami data. By optimazing the initial cluster center it will used as an input on K-Means method. The method result more optimal preference than the conventional K-Means method since the central point is optimized by genetic algorithm. It was proved on this research where fitness value resulted from Silhouette Coefficient to observe how suitable data with cluster. Chromosome representation used here is real code to initialize centroid value. Extended intermediate crossover applied for crossover method. For mutation method, random mutation is run here. Also for selection method it uses elitism selection. Based on testing result, the most optimum parameter accomplished are 50 population, 70 generation, and Cr =0.9 and Mr =0.1 combination with fitness value around 0.995934
Optimasi Fuzzy Inference System Tsukamoto Menggunakan Algoritme Genetika Untuk Mengetahui Lama Waktu Siram Pada Tanaman Strawberry Muhammad Khaerul Ardi; Budi Darma Setiawan; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soil is a crusial component for plant growth. There are many parameters that used for soil examination, and one of its parameter is soil's dampness. Laboratorium Benih Balai Pengkajian Teknologi Pertanian Jawa Timur is one of the work units that has a duty to examine the soil for plant nursery purpose. However, due to the conventional tools that they used sometimes the examination result is not as accurate as they expected. Because of that problem the author did some research to make a smart computing system that can be implemented on a tool that can maintain the soil's dampness automatically. Fuzzy Inference System Tsukamoto is used to calculate how long does it take to water the plants by using two variable inputs; initial dampness and water volume. Genetic algorithm is used to get an optimal membership function by optimizing the boundaries of each membership function. The output of this research will display the optimal time to water the plants. From the examination result we got an error value for about 4,9570, but after optimization the number is reduced to 0,3790. With that result we can conclude that using Fuzzy Inference System Tsukamoto and optimized with genetic algorithm is able to calculate how much time that it takes to water the plants and still able to get a good outcome.
Identifikasi Awal Pengguna Narkoba Menggunakan Metode Learning Vector Quantization (LVQ) Yulfa Hadi Wicaksono; Budi Darma Setiawan; Tri Afirianto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Drug abuse is one of the major problems for Indonesian country. This is due to drug users can cause psychiatric disorders, health and even death. From various surveys conducted, the number of cases of drug users increase every time. In this study, the research will try to do initial identification for users with Learning Vector Quantizationmethod. This study uses the data of drug users from Badan Narkotika Nasional (BNN) in Malang Regency. From 119 data, there will be divided into 3 parts. There are 4 data for initial weight vectors, 103 for training data and 12 for test data. Then, in this data have 16 parameters and 4 classes. In this study, 6 tests were performed, resulting in 0.1 for the learning level with an average score of 74.8%. Then, 0.9 for a learning rate multiplier with an average rating of 79.8%. Then, 0.01 for the minimum level of learning with an average grade. The amount of training data is 60% with average value. The maximum of 14 lawer iterations with average values. Then, for LVQ training stops on which best condition is at maximum iteration 14. Therefore, the minimum of learning rate condition can be ignored. From those results, the average of final accuracy after testing with K-Fold Cross Validation is 78.4%.
Optimasi Penjadwalan Mata Pelajaran Pondok Pesantren Mahasiswa Menggunakan Algoritme Genetika (Studi Kasus: Yayasan Bina Insani Sukses Malang) Rudy Usman Azzakky; Budi Darma Setiawan; Satrio Hadi Wijoyo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The process of drafting schedules manually felt less efficient because it takes a long time. The problem of drafting the schedule will be complex if the number of components is larger amount of data from each component. The expected schedule is not just a schedule that does not clash, but a schedule that can adapt to some constraints that must be met within the schedule. Genetic Algorithms are algorithms that are iterative, self-adjusting and probabilistic algorithms in search for global optimization. The process of chromosome initialization generated from teacher assignment data by integer representation of each gene containing randomly generated assignment codes. Each chromosome with the highest fitness value is a representation of the subject schedule solution. From the testing process that has been done, has obtained the parameters of Genetic Algorithm is the best population number is 100, the value of the combination of Cr and Mr is 0.5 and 0.5, and the number of generations as much as 1000. The process of finding solutions using these parameters obtained the value of fitness that is 0.9977.
Peramalan Produksi Kelapa Sawit Menggunakan Jaringan Syaraf Tiruan Dengan Metode Backpropagation (Studi Kasus PT.Sandabi Indah Lestari) Retiana Fadma Pertiwi Sinaga; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the Big Private Plantation companies in Indonesia is PT. Sandabi Indah Lestari located in Bengkulu Province. PT.Sandabi Indah Lestari designs a budget every year to spend on production process conducted once every week. Each production process of course requires a separate cost, if the production can not change production costs, the company will incur losses. Therefore, it is necessary to forecast the output of palm oil production to be a reference for the production results remain stable or even increased. Forecasting results can later be used by the company to improve production and do not lose from budget planning targets that have been made. This research uses backpropagation method combined with nguyen widrow algorithm. From the test results with the number of 260 data train, the amount of test data 12 test data, the value of learning rate 0.4, the number of hidden layer 5 neurons, the error limit of 0.001, and the maximum iteration of 900 yields MAPE (Mean Absolute Percentage Error) value of 10,0047 %.
Implementasi Fuzzy Time Series untuk Memprediksi Jumlah Kemunculan Titik Api Rizki Agung Pambudi; Budi Darma Setiawan; Satrio Hadi Wijoyo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fire occurrence rates in Indonesia increases every year. Fire occurence which increases every year proves that people doesn't really care about the said disaster. Hotspot can be used to identify the event of fire. Fire can be observed through satellite by detecting hotspot on Earth surface. That's why, there is a need for research to predict the number of hotspot which identify fire disaster in a certain time. This research proposes and creates program to predict the number of hotspot occured in Java island using Fuzzy Time Series. The data used is hotspot data in Java island from January 2012 to December 2016. Testing is done to know the accuracy of the number of hotspot prediction in monthly and 10 days period. The best monthly hotspot prediction has MAPE value of 37,128% with the parameter of training data = 80%, testing data = 100%, and the number of interval = 22. The best 10 days period hotspot prediction has MAPE value of 64,4429% with the parameter of training data = 100%, testing data = 20%, and the number of interval = 6. Further research can be done to repair the MAPE from the prediction result.
Optimasi Menu Makanan Atlet Berdasarkan Jadwal Latihan Menggunakan Algoritme Genetika Muhammad Dimas Setiawan Sanapiah; Budi Darma Setiawan; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

This research aims to solve the problem in doing food menu optimization in athletes. Where this is based on the statement of Ministry of Health that to improve the performance of Indonesian athletes in the future, it is necessary to improve and perfect the system of training and development of sports, especially in approaching and applying Science and Technology including the fulfillment of nutrition athletes. One form of development of science and technology is the genetic algorithm, where this algorithm can solve a problem related to optimization with a large search space. In the preparation of chromosomes to be used genetic algorithm using the representation of integer numbers, with crossover method used is one cut point crossover, and mutation method used is random mutation and selection process used elitism selection process. The recommendation results is the food menu for athletes for five days. While the genetic algorithm parameters in this research obtained optimal generation size of 450, the optimal population size of 70, and the combination of cr and mr optimal value is 0,5 and 0,5.
Optimasi Bobot pada Extreme Learning Machine untuk Prediksi Beban Listrik menggunakan Algoritme Genetika (Studi Kasus: PT. PLN (Persero) APD Kalsel dan Kalteng) Vina Meilia; Budi Darma Setiawan; Nurudin Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity. As an anticipation, beside to add more electrical capacities which will need a lot of costs. PLN also do operations management systems, which is electrical load forecasting. In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range -1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight. Mean Absolute Percentage Error (MAPE) is being used to calculate the accuration of prediction results. According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine method with weight optimization using genetic algorithm can be used in electrical load forecasting problem and give better prediction result.
Particle Swarm Optimization Untuk Optimasi Bobot Extreme Learning Machine Dalam Memprediksi Produksi Gula Kristal Putih Pabrik Gula Candi Baru-Sidoarjo Eka Yuni Darmayanti; Budi Darma Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sugar demand will increase in line with the increase in population, income, and growth in food and beverage processing industry. Therefore, in order for the sugar production process is always increasing in accordance with needs of the sugar itself, hence need for production planning. Accurate forecasting can help companies in taking decisions to determine the amount of sugar to be produced, the materials needed and determine the price of the goods. One method that can be used to do the prediction algorithm is Extreme Learning Machine. But that method in a selection of input and weight bias is chosen randomly, this can lead to the results obtained in the calculation less maximum. This need for a combination of Particle Swarm Optimization algorithms that can perform optimization the input value weight and bias optimally. This research uses data 45 milled sugar production with 5 features. Based on the research that has been performed, the obtained optimal parameters, namely the number of population size 50, 80% training data comparison (36), the number of hidden neurons 10, weighs of inertia 0.5, and a maximum of iterations 250. The parameter value is obtained from the average MAPE of 0.59%. From the average MAPE results obtained, shows that the addition of the PSO algorithm on ELM can determine the value of the input of weight and optimal bias.
Co-Authors Abdul Fatih Achmad Basuki Achmad Fahlevi Addin Sahirah, Rafifa Adinugroho, Sigit Aditya Chandra Nurhakim Aditya Kresna Bayu Arda Putra Agung Nurjaya Megantara Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Eriq Ghozali Akmal Subakti Wicaksana Alfi Nur Rusydi Almira Syawli, Almira Amaliah Gusfadilah Andhi Surya Wicaksana Andika Harlan Angga Dwi Apria Rifandi Anjasari, Ni Luh Made Beathris Aria Bayu Elfajar Asghany, Yusrian Ashidiq, Muhammad Fihan Azmi Makarima Yattaqillah Baihaqi, Galih Restu Barlian Henryranu Prasetio Bayu Rahayudi Bintang, Tulistyana Irfany Budi Santoso Cahyo Adi Prasojo Candra Dewi Candra Dewi Chelsa Farah Virkhansa Cindy Inka Sari Cinthia Vairra Hudiyanti Civica Moehaimin Dhewanty Deby Chintya Dellia Airyn Delpiero, Rangga Raditya Dewi, Buana Dhan Adhillah Mardhika Dian Eka Ratnawati Diva, Zahra Dwi Anggraeni Kuntjoro Dwi Ari Suryaningrum Dwi Damara Kartikasari Edo Fadila Sirat Eka Novita Shandra Eka Yuni Darmayanti Eti Setiawati Fadhlillah Ikhsan Fajar Nur Rohmat Fauzan Jaya Aziz Fajar Pradana Fanny Aulia Dewi Fattah, Rafi Indra Fatwa Ramdani, Fatwa Febri Ramadhani Fikri Hilman Fitra Abdurrachman Bachtiar Fitria, Tharessa Fitrotuzzakiyah, Shafira Puspa Gandhi Ramadhona Gembong Edhi Setiawan Gilang Ramadhan Hendra Pratama Budianto Husin Muhamad Imam Cholisoddin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indah Larasati Indriati Indriati Indriati Irfan Aprison Irma Lailatul Khoiriyah Irma Nurvianti Irma Ramadanti Fitriyani Ismiarta Aknuranda Issa Arwani Issa Arwani Jobel, Roenrico Karina Widyawati Khairunnisa, Alifah Kholifa&#039;ul Khoirin Koko Pradityo Lailil Muflikhah Lathania, Laela Salma M Kevin Pahlevi M. Ali Fauzi M. Raabith Rifqi M. Rikzal Humam Al Kholili M. Tanzil Furqon Mahar Beta Adi Sucipto, Ekmaldzaki Royhan Mahendra Data Mahendra Data Marji Marji Masayu Vidya Rosyidah Maulana, M. Aziz Mayang Arinda Yudantiar Meilia, Vina Mimin Putri Raharyani Mindiasari, Irtiyah Izzaty Miracle Fachrunnisa Almas Moch. Khabibul Karim Mochamad Chandra Saputra Mohamad Alfi Fauzan Muhammad Arif Hermawan Muhammad Dimas Setiawan Sanapiah Muhammad Khaerul Ardi Muhammad Rizkan Arif Muhammad Syaifuddin Zuhri Muhammad Tanzil Furqon Mustofa Robbani Muthia Azzahra Nadia Natasa Tresia Sitorus Nainggolan, Cesilia Natasya Nanda Agung Putra Nashrullah, Nashrullah Nelli Nur Rahma Ni&#039;mah Firsta Cahya Susilo Nihru Nafi&#039; Dzikrulloh Noval Dini Maulana Novanto Yudistira Nur Intan Savitri Bromastuty Nurfansepta, Amira Ghina Nurhana Rahmadani Nurudin Santoso Nurul Hidayat Oky Krisdiantoro Olive Khoirul L.M.A. Panjaitan, Mutiharis Dauber Pindo Bagus Adiatmaja priharsari, diah Purnomo, Welly Putra Pandu Adikara Putra, Octo Perdana Putri, Rania Aprilia Dwi Setya Rachmatika, Isnayni Sugma Radifah Radifah Rafely Chandra Rizkilillah Rahmadi, Anang Bagus Rahmat Faizal Raissa Arniantya Ramadhianti, Fatiha Randy Cahya Wihandika Ratna Candra Ika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rekyan Regasari MP, Rekyan Regasari Rendi Cahya Wihandika Retiana Fadma Pertiwi Sinaga Revanza, Muhammad Nugraha Delta Revinda Bertananda Reza Wahyu Wardani Rhobith, Muhammad Ridho Agung Gumelar Rima Diah Wardhani Rinda Wahyuni Rizal Setya Perdana Rizal Setya Perdana Rizki Agung Pambudi Rizky Haqmanullah Pambudi Robih Dini Rosi Afiqo Rudito Pujiarso Nugroho Rudy Usman Azzakky Ryan Mahaputra Krishnanda Sabriansyah Rizkiqa Akbar Santoso, Nurudin Satrio Hadi Wijoyo Shelly Puspa Ardina Sigit Adinugroho Silfiatul Ulumiyah Sintiya, Karena Siti Fatimah Al Uswah Siti Utami Fhylayli Sri Wahyuni Suryani Agustin Sutrisna, Naufal Putra Sutrisno Sutrisno Tahajuda Mandariansah Talitha Raissa Tibyani Tibyani Tri Afirianto Tria Melia Masdiana Safitri Ulfah Mutmainnah Vina Meilia Wayan Firdaus Mahmudy Wildannantha, Jawadi Ahmad Yerry Anggoro Yosendra Evriyantino Yuhand Pramudita, Rezzy Yuita Arum Sari Yuita Arum Sari Yulfa Hadi Wicaksono Zubaidah Al Ubaidah Sakti