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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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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.
Analisis Perbandingan Algoritme K-Means dan Isodata untuk Klasterisasi Data Kejadian Titik Api di Wilayah Sumatera pada Tahun 2001 hingga 2014 Edo Fadila Sirat; Budi Darma Setiawan; Fatwa Ramdani
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 phenomenon is a familiar phenomenon in Indonesia. The high number of fire incidents in Indonesia requires special attention from the government, so that any natural disasters such as forest fires can be overcomed. Satellite monitoring results are recorded on a data file of fire points with a large enough data numbers so that the data is difficult to be processed to become information that is easily received by the user. Based on data obtained from the EOSDIS site recorded as many as 289,256 fire spots occurrence in the region of Sumatra in the timeframe between 2001 to 2014. It takes an algorithm to segment the data or cluster the data, so that large data can be processed into a good information for the user. In this study a comparative study of clustering algorithms between K-Means and Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced. The algorithm used in measuring the quality of cluster in this research is Silhouette Coefficient (SC). The final result value of Shilhouette Coefficient K-Means method is 0.999997187 and Isodata method is 0.999957161, so in this case, K-Means algorithm has a higher SC value compared to the Isodata algorithm in clustering the data of fire spots with a small SC value difference.
Penentuan Portofolio Saham Optimal Menggunakan Algoritme Genetika Terdistribusi Talitha Raissa; Agus Wahyu Widodo; Budi Darma Setiawan
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

In conducting stock investment, it is necessary to verify or spread the investment in order to form a stock portfolio with the proportion or optimal weight of every stock, good profit, and risk that can be borne by investors. Therefore, a system that can determine the optimal stock portfolio must be made by implementing distributed genetic algorithm. Distributed genetic algorithm generate chromosomes randomly at the certain interval as the representation of the stocks proportion. Then reproduction, evaluation and selection can be done based on the largest fitness derived from the calculation with single index model to find the return and risk. Distributed genetic algorithm has migration mechanism that able to maintain a diversity of individual variation. It is necessary to find out a broader solution which can produce a diverse and optimal stock portfolio. From the test result, distributed genetic algorithms can be applied properly and produce an optimal stock portfolio. The best parameter of the popsize test result is 80, the number of generations 150, 0.8 crossover rate (cr), and 0.2 mutation rate (mr) and the number of sub-optimal population of 10 to produce an optimal stock portfolio.
Pemilihan Aturan Fuzzy Inference System Mamdani Menggunakan Algoritme Particle Swarm Optimization Dalam Sistem Penyiraman Otomatis Pada Tanaman Tomat Indah Larasati; Budi Darma Setiawan; Mahendra Data
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

Laboratorium Tanah Benih Balai Pengkajian Teknologi Pertanian (BPTP) East Java is one of the institutes to do some research on plant nursery. One of soil testings which have been done in Laboratorium Tanah BPTP East Java is the parameter testing soil humadity. The research laboratory, right now, is developing an automatic watering based on soil humadity sensory to predict the amount of water needed to keep the humadity of the growing seed media. This research will figure out how much water plant volume with two input variables which are the initial humadity and flushing duration using Fuzzy Inference System Mamdani method. The two variables can cause often repeated rule based fuzzy or the same rule used more than once. Thus, to prevent these things to be happened, it needs fuzzy rule base fuzzy choice by using Particle Swarm Optimization algorithm. The testing was done in ten times trials by applying the amount of particles which is 250, value of inertia quality (w), which is 0,7, combination value of C1 and C2, which are 1,4 and 1,3, and the maximum iteration, which is 2500, and then it obtained the highest fitness value is on the seventh trial, which is 0,39949 with Root Mean Square Error (RMSE) value, which is 1,50319. The result from the highest fitness value is able to change the system from 27 arrangements to eight arrangements.
Prediksi Indeks Harga Konsumen (IHK) Kelompok Perumahan, Air, Listrik, Gas, dan Bahan Bakar Menggunakan Metode Extreme Learning Machine Irma Ramadanti Fitriyani; Budi Darma Setiawan; Rizal Setya Perdana
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 Consumer Price Index is one of the indicators to measure the inflation rate in Indonesia. In 2017 inflation in Indonesia by expenditure group in general is 3,61%. The group of housing, water, electricity, gas, and fuel become the biggest contributor of inflation compared to six other groups with 5,14%. Therefore, the prediction needs to be done to anticipate and reduce domestic inflation rate. Prediction done in this research using method of Extreme Learning Machine (ELM) with initialization of weight using Nguyen-Widrow initialization. The data used in this research are 84 Consumer Price Index data of housing, water, electricity, gas, and fuel from January 2011 until December 2017. The data obtained from the official website of Bank Indonesia (www.bi.go.id). The result of this research is the minimum RMSE value of 0,72 with the number of features = 7, the amount of training data is 30 and the testing data is 11, the number of hidden neurons = 7, and the activation function is sigmoid binary function.
Peramalan Tingkat Produksi Gula Menggunakan Multi Factor Fuzzy Time Series yang Dioptimasi dengan Algoritme Genetika Kholifa'ul Khoirin; 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

Sugar is one of the strategic commodities that affect the Indonesia's economy. This is because sugar is one of a essencial staple for Indonesian society. But on the other hand, the large demand of sugar consumption in Indonesian cannot meet with the low production of sugar. One of sugar factories is PG Candi Baru Sidoarjo. Besides production processing's factors, the factory is experiencing difficulties in its planning. Which in the production planning, the sugar factory will set targets that must to be achieved in future production. In an effort to overcome these problems, this study is expected to provide forecasting to see the possibility of achieving sugar production targets. Multi Factor Fuzzy Time Series method optimized with Genetic Algorithm by considering several factors influencing sugar production process such as number of milling days in one month, percentage of rendeman, and number of milled sugarcane. The genetic algorithm is used to perform subinterval optimization. Forecasting results of sugar production using a combination of these two methods get RMSE of 424,70. These results are smaller than the Multi Factor Fuzzy Time Series method without optimizing the subintervals that yield RMSE 6168,7437. Thus, it can be concluded that the proposed method is capable of forecasting better results than the unoptimized Multi Factor Fuzzy Time Series method.
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 Irawati Nurmala Sari Irfan Aprison Irma Lailatul Khoiriyah Irma Nurvianti Irma Ramadanti Fitriyani Ismiarta Aknuranda Issa Arwani Issa Arwani Jobel, Roenrico Karina Widyawati Keintjem, Arthurito Khairunnisa, Alifah Kholifa'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 Harish Rahmatullah 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'mah Firsta Cahya Susilo Nihru Nafi' 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