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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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Optimasi Jumlah Pinjaman Koperasi Menggunakan Fuzzy Tsukamoto Dengan Algoritme Genetika Shelly Puspa Ardina; Imam Cholissodin; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nowadays, almost the majority of cooperatives are still performing calculations lending manually and very rarely utilize the use of computer technology so that in decision making is done less efficient. From the problems encountered, it takes a system that has a relationship with the computer so that it can accelerate and assist the process of making the decision of lending efficiently. The required system is an artificial intelligence system that helps to get the most precisely seen value of the greatest fitness as each of its calculations. The criteria that become the basis for determining the loan amount to the members using the optimized Tsukamoto Fuzzy method using Genetic Algorithm are job status, age, salary and loan duration. The results will be able to show the fitness in each of the calculations that have been optimized by using Genetic Algorithm, so it will get the most appropriate value. The result of system evaluation using Mean Absolute Percentage Error calculation with the example of a case has an error value of 0.661468035 or 1.65% with the resulting fitness value of 0.601877363.
Peramalan Jumlah Pengunjung Wisata Menggunakan Fuzzy Logical Relationship dan Algoritme Genetika (Studi Kasus Wisatawan Kabupaten Banyuwangi) Irma Lailatul Khoiriyah; Imam Cholissodin; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tourism is one of the important sectors in Banyuwangi Regency. An unexpected increase in the number of tourists makes it difficult for tourism department to give their best service. On the contrary, if there is a reduction, it will cause the decrease of the occupancy rate and the tourism sector that already exist. Forecasting the number of tourists is needed to determine the number of visitors in the future, so the solution can be anticipated as early as possible when number of tourists is more or less than the targeted. Forecasting that conducted in this study was using Fuzzy Logical Relationship and Genetic Algorithm. Fuzzy Logical Relationship is used to forecast the number of tourist based on tourist data history, then Genetic Algorithm is used to perform optimization interval distribution that will be used on Fuzzy Logical Relationship. Data that were used as many as 144 historical data from January 2005 to December 2016, number of tourist data was achieved from the Department of Culture and Tourism of Banyuwangi Regency. The results of the tests that was conducted on forecasting the number of visitors using the FLR and GA equations produce 280x10-9 in fitness which means the difference between the average of actual data and the result of forecasting is 3572978344 in MSE.
Implementasi Algoritme Support Vector Regression Pada Prediksi Jumlah Pengunjung Pariwisata Mimin Putri Raharyani; Rekyan Regasari Mardi Putri; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tourism has an important role for the economic growth of a region. One of the factors affecting the tourism revenue sector is the number of visitors. The more number of visitors can increase revenue, if the number of visitors decreased it will have an impact on the development of tourist attractions that can harm the manager of tourism. The prediction system of the number of visitors is needed as an illustration of the level of the number of tourism visitors for the period to come and can provide information to the managers of tourism to prepare better facilities and infrastructure and able to manage income and expenses to minimize losses. The prediction of the number of visitors to tourism can be done by applying the Support vector regression algorithm. Support vector regression algorithm is a method that can solve regression problems and produce good performance in the solution. In this study data used 72 data on the number of visitors monthly on tourism from 2010 to 2015. Test results show that the average value of MAPE minimum generated is 9,16% and the best MAPE value obtained is 6,98% which means The average difference between the predicted result and the actual data is 115 visitor number with sigma parameter = 925,8409 lambda = 0,3868, cLR = 0,0802, epsilon = 1,27E-10, complexity = 3234,539, maximal iteration 5000.Keywords: prediction, tourism, visitor number, support vector regression
Optimasi Fuzzy Time Series Untuk Peramalan Kebutuhan Hidup Layak Kota Kediri Dengan Menggunakan Algoritme Genetika Tahajuda Mandariansah; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Proper living needs (KHL) is a standard requirement for a worker or single person physically can live well for the needs of one month, the value of KHL is one of the five minimum wage determinataion factors. The value of KHL is determined based on survey value from january to september, while in determining regional minimum wage (UMR) shall be done no later than 60 days or two months before january 1st of the following year. Therefore it's necessary to forecast the value of KHL. This forecasting is helpful for the goverment in the process of determining UMR. In forecasting using fuzzy time series method optimized with genetic algorithm. The optimization is done on the interval value in the fuzzy time series method to get good accuracy in forecasting. Based on the results of tests on value of KHL Kediri from 2009 to 2015, using the parameter of the interval number 7, using the combination of Cr 0.9 and Mr 0.5, the population number 1050, and the number of generations 100 using the average forecasting error rate (AFER) has obtained an error value of 4.7211%
Optimasi Penentuan Rute Terpendek Pengambilan Sampah Menggunakan Multi Travelling Salesman Problem Ryan Mahaputra Krishnanda; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Garbage is an unending environmental problem and this issue needs to be considered and handled together. According to data of 2015 from the Satuan Kerja Perangkat Daerah (SKPD) or Regional Device Work Unit of Denpasar, the annual garbage production in Denpasar is 1,335,819.48 m3. In the same year, the volume of garbage transport from the Department of Hygiene and Gardening or also known as Dinas Kebersihan dan Pertamanan (DKP) reached 1,065,016 m3 or realized 79.73% and shows the DKP transport fleet Denpasar can not touch the 80% target. This study will determine the optimal route for some garbage transport vehicles from the DKP office to the dump points and end up in the landfill. This happens because of the problem from Multi Traveling Salesman Problem (m-TSP) and one of the algorithms to solve m-TSP problems is with genetic algorithm. The process of this genetic algorithm uses permutation representation, crossover reproduction process with one-cut point, mutation process with exchange mutation, and selection process with elitism selection. After conducting the experiment, the most optimal parameter is obtained in population with the amount of 100, with the number of garbage transport vehicles as much as 4, the value of cr = 0.3, mr = 0.7 and the generation of 900. The results of the program with the parameters will yield 0.569 as maximum average of fitness value.
Implementasi Algoritma K-Means untuk Klasterisasi Kinerja Akademik Mahasiswa Fajar Nur Rohmat Fauzan Jaya Aziz; Budi Darma Setiawan; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Selection of student acceptance in a college produces abundant data and can be utilized to obtain useful information for the college. In this study, student data taken by the authors are Student ID Number, University Entrance Path, Parent Revenue and Student Achievement Index. Excavation of information on a large data could not be done easily and this can be done with data mining technology. Data mining also known as Knowledge Discovery in Database is an automated process of searching data in a very large memory of data to know patterns by using tools such as association or clustering. By using k-means clustering method, the researcher tries to extract the knowledge which can depict the performance of student achievement at the end of semester and the result of the research indicates that of all cluster quantities inserted, for clusters amounting to 3 (three) has the value of silhouette coefficient closest to the value of = 1, that is with the value of 0.108690751. In addition, parental income does not affect the level of academic performance of students and the academic value of students who enter through the regular path & achievement paths have the value of the highest average GPA. Thus, the faculty can consider to prioritize the acceptance of new students through regular channels & achievement contract.
Optimasi Fuzzy Time Series Menggunakan Algoritme Particle Swarm Optimization untuk Peramalan Nilai Pembayaran Penjaminan Kredit Macet Ratna Candra Ika; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Any problems related to bad credits or problem loans in Indonesia are not constant, there can be any decrease or increase in each month. So, it can cause on uncertain provision of fund budget for underwriting payment of credit claims by credit underwriting institutions. Therefore, it is necessary for a system that can predict on value of underwriting payment on bad credit claims as a consideration to determine nominal value to be provided in the following months by the credit underwriting institutions. In this research, the prediction is conducted using Fuzzy Time Series method, because the data used are prepared in a consecutive time from month to month. To create better prediction, it is optimized using Particle Swarm Optimization (PSO) algorithm, because the PSO algorithm has high decentralization with simple implementation so that it can solve any optimization problems in an efficient manner. The error level is calculated using Root Mean Squared Error (RMSE). Based on the testing, the best solution has an average cost value by Rp. 159215 with its program operation time by 13,2 second. The solution is created with maximum iteration by 250, the population by 100, length of particle dimension by 250, value of cognitive coefficient variable (c1) is equal with 1 and the social coefficient variable (c2) is equal with 1.5, as well as inertia weight value (w) is equal with 0,6. So that it can be concluded that this research can be applied for prediction on value of underwriting payment on bad credit.
Implementasi Metode Fuzzy K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Kedelai Pada Citra Daun Yerry Anggoro; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Protein is one of essential thing to the human body, there are many source of protein and one of it is a soy which is nabati protein source. besides corn and rice, soy is the main food commodities in Indonesia. However, domestic production of soybean has not been enough to fulfil the necessity. Soybean production at the level of actual farmers could still be enhanced through technological innovation, one of that is to detect plant disease of soybeans on the leaves by the method of Fuzzy K-Nearest Neighbor and the segmentation using the method of Otsu. The image processed with the method of Otsu to separate parts that are diseased with parts that are not diseased and then do a classification by the method of Fuzzy K-Nearest Neighbor to determine leaf rust disease, Downy Mildew, and bacterial pustule. There are four tests such as test comparison data training and test data with the highest accuracy in comparison with a total of 90:10 54 training data and test data of 6 100%, testing against the values of Threshold with T = 10 generates 83,33% accuracy, testing against the values of k = 5 generates 83,33%, accuracy and testing against the values m = 2 with accuracy of 83,33%.
Penerapan Algoritma C4.5 Untuk Memprediksi Nilai Kelulusan Siswa Sekolah Menengah Berdasarkan Faktor Eksternal Rizky Haqmanullah Pambudi; Budi Darma Setiawan; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Education in the life of a country plays a very important role to ensure the survival of the state and nation. Statistics show that Portugal's education level is at the bottom of the list due to many students dropping out of school. External factors affect the failure of students in completing the field of study, especially the field of study of mathematics. Algorithm C4.5 is one method of data mining to predict students' ability in completing the field of study seen from the external factors of students. The C4.5 algorithm is used to find out the accuracy of the prediction ability of high school students. The feature selection parameters are the factors that affect the ability of high school students in the field of mathematics studies. Testing and analysis results show that the Decision Tree C4.5 algorithm is accurately applied to predict the final grade of high school students with a 60% accuracy rate.
Optimasi Interval Fuzzy Time Series Menggunakan Particle Swarm Optimization pada Peramalan Permintaan Darah : Studi Kasus Unit Transfusi Darah Cabang - PMI Kota Malang Angga Dwi Apria Rifandi; Budi Darma Setiawan; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Blood is an important fluid that naturally produced in the human body. When a human lost a lot of blood, a blood transfusion is needed . Blood for the transfusion is provided by a blood storage center in charge of estimating blood demand to minimize the excessive amount of blood in storage or wasted blood. Lack of blood supply can affect to the increased death of the patient, while an oversupply of blood until passes it shelf life (35 days) should also be avoided. In order to minimize the loss, a method to forecast the blood demand is needed, that is fuzzy time series. To increase the accuracy, the method is optimized with particle swarm optimization to determine the best interval in fuzzy time series. Based on the results of a series of tests, the optimum solution with average of cost value (MSE) of 60435,685 is obtained on 40 particles, 30 dimensions, 1.5 and 1.5 for the combination of and value respectively, the weight of inertia of 0.3, and the maximum number of iterations of 950. By using 12 testing data, the error rate generated by this system (MAPE) is 7.50330%.
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 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 Issa Arwani Jobel, Roenrico Karina Widyawati Khairunnisa, Alifah Kholifa'ul Khoirin Koko Pradityo Lailil Muflikhah 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'mah Firsta Cahya Susilo Nihru Nafi' Dzikrulloh Noval Dini Maulana Novanto Yudistira Nur Intan Savitri Bromastuty 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 Randy Cahya Wihandika Ratna Candra Ika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rekyan Regasari MP 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 Sutrisno Sutrisno Tahajuda Mandariansah Talitha Raissa Tibyani Tibyani Tri Afirianto Tria Melia Masdiana Safitri Ulfah Mutmainnah Vina Meilia Wayan Firdaus Mahmudy Yerry Anggoro Yosendra Evriyantino Yuita Arum Sari Yuita Arum Sari Yulfa Hadi Wicaksono Zubaidah Al Ubaidah Sakti