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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Ilmiah Kursor Journal of Innovation and Applied Technology International Journal of Local Economic Governance Journal of Environmental Engineering and Sustainable Technology Jurnal Pembangunan dan Alam Lestari Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science Jambura Law Review Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology Indexia Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Bulletin of Culinary Art and Hospitality Bulletin of Social Informatics Theory and Application Jurnal ilmiah teknologi informasi Asia Signal and Image Processing Letters
Wayan Firdaus Mahmudy
Brawijaya University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pelatihan Multi-Layer Neural Network Menggunakan Algoritma Genetika untuk Memprediksi Harga Saham Esok Hari (T+1) Grady Davinsyah; Wayan Firdaus Mahmudy; Rizal Setya Perdana
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

Stock is one of investment instrument which is popular because of the high profit potential and risk. These profit potential and risk are caused by fluctuations of the stock price in the stock market. To minimalize the risk, a system which is able to predict closing price of the next day is required. The architecture which is used in this research is multi-layer neural network. This architecture is trained with 2 different training methods, which is backpropagation and genetic algorithm. Both of the methods aim to gain weights of all network's architecture. Backpropagation's parameters which obtained during the research are 4500 iteration and 0.7 learning rate. For genetic algorithm's parameters which obtained during the research are 2000 generations, population size of 200, crossover rate 0.1 and mutation rate 0.9. By using those parameters, average RMSE value which produced using backpropagation algorithm is 0.048006. Meanwhile when using genetic algorithm as a training method, average RMSE value which produced by the network is 0.065205. So in this research, average error value which is produced by using backpropagation training is smaller than using genetic algorithm training method.
Optimasi Bobot Multi-Layer Perceptron Menggunakan Algoritma Genetika Untuk Klasifikasi Tingkat Resiko Penyakit Stroke Nadya Oktavia Rahardiani; Wayan Firdaus Mahmudy; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stroke is one of a high mortality disease in Indonesia. A various ways can be done to detect stroke, such as blood test. The result is known just after a few hour. Unfortunately, in some case it took a long time to find out whether a patient at risk of stroke or not. The level of risk can be easily done with a system. Multi-layer perceptron (MLP) network is one of artificial neural network (ANN) model which has a random weight from backpropagation (BP) learning. This study is doing optimization to obtain proper weights, using genetic algorithm (GA) as a training method, so that the classification results are more accurate. Implementation, testing, and analysis are done in BP learning algorithm and GA to compare its accuracy on classifying the risk level of stroke. MSE value obtained in testing phase is 0.0122 with number of iteration = 190, number of neuron in hidden layer = 10, and learning rate = 0.9. While in testing phase of GA obtained 0.0549 with population size = 100, generation size = 400, Cr = 0.8, and Mr = 0.2. In final result, average data accuracy of BP is 88.40% with average MSE value is 0.0122 and GA is 60.60% with average MSE value 0.0549 by 10 times trial.
Peramalan Jumlah Pemakaian Air Di PT. Pembangkitan Jawa Bali Unit Pembangkit Gresik Menggunakan Support Vector Regression Novi Nur Putriwijaya; Wayan Firdaus Mahmudy
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

There are several kinds of resources that are often used in daily activities, one of which is water. PT. PJB UP Gresik utilizes water as a catalyst in producing electricity and other operational needs. This leads to a large reduction in the quantity of water, thus allowing a lack of water supply in PT.PJB UP Gresik. To anticipate the problem, it is necessary to forecast the amount of water usage. Support Vector Regression (SVR) is used to forecast the amount of water usage. The data used is taken from history of water usage for 7 months. Testing data used is 49, which is taken randomly from the whole data. To determine the best parameters in forming regression model, testing will be done by calculating Mean Average Percentage Error (MAPE). From the testing results that have been done, the optimal parameters obtained are the amount of training data is 110 with average MAPE value 26.104, number of iterations is 650 with average MAPE value 20.222, value of lambda is 6 with average MAPE value 19.058, value of epsilon is 0.001 with average MAPE value 19.049, value of cLr is 0.00001 with average MAPE value 19.676, value of C is 0.00006 with average MAPE value 20.018, and value of sigma is 0.75 with average MAPE value 19.455. The final result accuracy of forecasting conducted using MAPE with average MAPE value 19.051 that is included in the good category.
Penerapan Algoritme Genetika Untuk Optimasi Penyusunan Barang Dalam Mobil Box Fitria Dwi Nurhayati; Wayan Firdaus Mahmudy; Achmad Arwan
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

As the industry grows massively, the profits sought are also maximized by minimizing the cost of expenditure. One of the company's activities in minimizing the cost of expenditure is by optimizing the goods distribution process. Looking for patterns of arrangement of goods in the optimal goods distribution process is necessary to avoid the cost of such expenses incurred additional vehicle accommodation costs, overload of goods or even require more time if not using the system. Therefore, this research is done to overcome the problem of the preparation of goods in the car box for optimal use of genetic algorithm. Genetic algorithm is chosen because it can solve complex problems with a relatively fast time. In this study begins with initial individualization with random raised. The representation used is an integer representation. Then the process of reproduction of crossover and mutation. The crossover and mutation methods used by Partially Mapped Crossover and Reciprocal Exchange Mutation. Next is the selection process using Elitsm Selection. Based on the results of the tests that have been done, obtained accuracy of 84,6% with the best parameters that is on the population amount of 100, cr 0.6, mr 0.4 and generation 90. The final result obtained is optimization recommendations optimize the preparation of goods.
Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Particle Swarm Optimization Untuk Klasifikasi Jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini Windi Artha Setyowati; Wayan Firdaus Mahmudy
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

Attention Deficit Hyperactivity Disorder (ADHD) is a mental development disorder that has a major characteristic of inability to focus attention and hyperactivity. Behavior that characterizes ADHD often appears in children aged 3-5 years. ADHD consists of 3 types, namely: inattention, hyperactivity, and impulsivity. Not many people are aware of ADHD, then needed a system for the classification type of ADHD. By observing visible symptoms, ADHD can be classified using the Learning Vector Quantiztion (LVQ) algorithm, but the LVQ algorithm produces a fairly low accuracy. To optimize the accuracy level of LVQ algorithm, Particle Swarm Optimization (PSO) algorithm is used. The PSO is used to find the best LVQ weight vector. To know the difference of accuracy result, 2 test is done, that is LVQ-PSO and LVQ test. The test uses the same data. Test results showed that LVQ-PSO algorithm yielded highest accuracy 87,3% in 84,6 seconds, while LVQ algorithm yielded highest average accuracy of 80,6% in 4,8 seconds. The best parameters of PSO yielding the best accuracy are Wmax 0,6, Wmin 0.5, swarm size 100, maximum iteration PSO 100, α 0,1, and dec α 0,1. From the results of the test accuracy it can be concluded that the PSO algoritme can be used to optimize the LVQ algorithm even though it takes longer computation time.
Implementasi Ekstraksi Fitur Jumlah Keypoint Descriptor Pada Pengenalan Tanda Tangan Dengan Algoritme Learning Vector Quantization Imada Nur Afifah; Wayan Firdaus Mahmudy
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

Signature recognition is important for signature verification process. One of the signature recognition method is implementation Learning Vector Quantization (LVQ) for signature recognition with additional method of features extraction using Scale Invariant Features Transform (SIFT). In the train process, this research used some features such as maximum of black pixel in horizontal and vertical histogram, center of mass, normalized area of signature, aspect ratio, tri surface feature, the Six Fold Surface feature, transition feature and additional features called number of keypoints. Number of keypoints are output of Scale Invariant Features Transform (SIFT) method. The dataset used is 100 images for training data and 100 images for testing data from 20 different classes. And 25 images from out of trained class as unknown data. The result of algorithm testing is 71,2% from testing of non-threshold process, 56% from testing process with maximum value of minimum euclidean distance between data and class as threshold value, 45,6% % from testing process with second maximum value of minimum euclidean distance between data and class as threshold value.
Prediksi Curah Hujan Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Muhammad Isradi Azhar; Wayan Firdaus Mahmudy
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

Rainfall is the amount of rain that occurs in an area within a certain period of time. Information of rainfall is useful in various fields such as agriculture. In the field of agriculture, the information of rainfall can affect the annual planting period and also can determine what kind of crops that are suitable to be planted. Malang Regency is one areas in Indonesia which has 36.359 Ha farming area and produce rice equal to 470.285 Ton. Rice crops have a common criteria for determining the beginning of the rice growing season, with the amount of rainfall more than 50 mm in three consecutive dasarians. But the current wet season is uncertain which resulted in the process of rice cultivation is disrupted. Therefore, rainfall prediction is needed to help farmers to reduce the possibility of loss. Adaptive Neuro Fuzzy Inference System (ANFIS) method can be used to predict rainfall by utilizing dasarian data of rainfall, temperature, humidity, and wind speed. Adaptive Neuro Fuzzy Inference System (ANFIS) is a combination of neural network and fuzzy logic. In the learning process of Adaptive Neuro Fuzzy Inference System (ANFIS), there is backpropagation steepest descent and least square Iestimator (LSE) algorithm. Based on the test results using the best parameters, it obtain best RMSE value of 1.88.
Prediksi Curah Hujan Menggunakan Metode Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series Yulia Trianandi; Wayan Firdaus Mahmudy; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Prediction of rainfall is needed in increasing the production of food crops in a region, one of them is Tengger area. Errors in predicting rainfall can cause errors when determining the planting period, and the right type of plant. In order to produce rainfall predictions with a slight error rate, this study uses the Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series method. The method has been proven to predict the closing price on the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAEIX) with fewer error rates than Chen Fuzzy Time Series methods. This study will produce rainfall predictions in the four districts of Tengger area, namely Puspo, Sumber, Tosari, and Tutur. From the results of testing 36 data in 2014, obtained the best Mean Square Error (MSE) value of 28.0470 in Tosari district.
Optimasi Algoritme Genetika Untuk Memaksimalkan Laba Pembangunan Perumahan Muhammad Faris Mas'ud; Imam Cholisoddin; Wayan Firdaus Mahmudy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Residence is a basic need. The main function of the residence is for a place to rest, security and family activities. Residential development highly demand along High population growth in the Malang city. when building homes, developers always prioritize the benefits in every construction without reducing the quality of the building. House construction requires human resources and some limited material, therefore genetic algorithms will be very helpful in terms of profit-seeking search. Based on several other Genetic Algorithm studies, this algorithm produces the expected solutions such as: hijab profit optimization, optimization of efficient distribution of goods and optimization of selection of targeted building workers. In accordance with the tests performed using data from Margobasuki Residence, obtained the optimal amount of benefits.
Penerapan Metode Decision Tree dan Algoritme Genetika Untuk Klasifikasi Risiko Hipertensi Selly Kurnia Sari; Wayan Firdaus Mahmudy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypertension is one of the third most deadly diseases after stroke and tuberculosis which reached 6.7% of the total age in Indonesia. This shows that hypertension in Indonesia needs serious attention as an effort to deal with this problem. Handling is needed as a step in early detection of hypertension. In relation to the classification for detection of hypertension, one method that can be used is the Decision Tree (DT). But in the previous study DT method produced a fairly low accuracy. To optimize the accuracy of the DT method, Genetic Algorithm (GA) is used. GA is used to generate new rules. To find out the difference in the results of accuracy, a comparison test of DT-C4.5 and DT-GA is conducted. The test uses the same test data. The test results show the DT-GA algorithm produces the highest average accuracy of 84%. While the DT-C4.5 algorithm produces the highest average accuracy of 70,5%. The best parameters to produce the best accuracy are population size 60, Cr = 0,3, Mr = 0,7, with the maximum number of generations used is 10. From the results of these tests it can be concluded that GA can be used to generate new rules from DT
Co-Authors A.N. Afandi Abdul Latief Abadi Abdul Latief Abadi Achmad Arwan Achmad Basuki Achmad Ridok Adimoelja, Ariawan Aditama, Gustian Adyan Nur Alfiyatin Agi Putra Kharisma, Agi Putra Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agung Setia Budi Agus Naba Agus Wahyu Widodo Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Aji Prasetya Wibawa Al Khuluqi, Mabafasa Alauddin, Mukhammad Wildan Alfiani Fitri Alfita Rakhmandasari Alfiyatin, Adyan Nur Alqorni, Faiz Amalia Kartika Ariyani Amalia Kartika Ariyani Amalia Kartika Ariyani Anantha Yullian Sukmadewa Andi Kurniawan Andi Maulidinnawati A K Parewe Andi Maulidinnawati A. K. Parewe Andreas Nugroho Sihananto Andreas Pardede Andreas Patuan G. Pardede Andrew Nafalski Angga Vidianto Aprilia Nur Fauziyah Aprilia Nur Fauziyah Arief Andy Soebroto Arinda Hapsari Achnas Armanda, Rifki Setya Arviananda Bahtiar Arya, Putu Bagus Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi, Asyrofa Bagus Priambodo Bayu Rahayudi Binti Robiyatul Musanah Budi Darma Setiawan Burhan, M.Shochibul Cahya, Reiza Adi Cahyo Prayogo, Cahyo Candra Dewi Candra Fajri Ananda Cleoputri Yusainy Darmawan, Abizard Hashfi Dea Widya Hutami Dhaifullah, Afif Naufal Diah Anggraeni Pitaloka Didik Suprayogo Dinda Novitasari Dinda Novitasari, Dinda Diny Melsye Nurul Fajri Dita Sundarningsih Durrotul Fakhiroh Dyan Putri Mahardika Edi Satriyanto Edy Santoso Eko Widaryanto Elta Sonalitha Ervin Yohannes Evi Nur Azizah Fadhli Almu’iini Ahda Fais Al Huda Fajri, Diny Melsye Nurul Fatchurrochman Fatchurrochman Fatwa Ramdani, Fatwa Fauzi, Muhammad Rifqi Fauziatul Munawaroh Febriyana, Ria Fendy Yulianto Fitra Abdurrachman Bachtiar Fitri Anggarsari Fitria Dwi Nurhayati Gayatri Dwi Santika Ghozali Maski Grady Davinsyah Gusti Ahmad Fanshuri Alfarisy Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Gusti Eka Yuliastuti Hafidz Ubaidillah Hamdianah, Andi Hanggara , Buce Trias Herman Tolle Hernando, Deo Heru Nurwarsito Hidayat, Luthfi Hilman Nuril Hadi Ida Wahyuni Imada Nur Afifah Imam Cholisoddin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indriati Indriati Irvi Oktanisa Ishardita Pambudi Tama Ismiarta Aknuranda Jauhari, Farid Khozaimi, Ach. Kukuh Tejomurti, Kukuh Kuncahyo Setyo Nugroho Kuncahyo Setyo Nugroho Kurnianingtyas, Diva Lily Montarcih Limantara M Chandra Cahyo Utomo M Fadli Ridhani M Shochibul Burhan, M Shochibul M. Shochibul Burhan M. Zainal Arifin Mabafasa Al Khuluqi Mar'i, Farhanna Marji Marji Mayang Anglingsari Putri, Mayang Anglingsari Mochamad Anshori Moh. Khusaini Moh. Sholichin Moh. Zoqi Sarwani Mohammad Zoqi Sarwani Mohammad Zoqi Sarwani, Mohammad Zoqi Mu’asyaroh, Fita Lathifatul Muh. Arif Rahman Muhammad Ardhian Megatama Muhammad Faris Mas'ud Muhammad Halim Natsir Muhammad Isradi Azhar Muhammad Khaerul Ardi Muhammad Noor Taufiq Muhammad Rivai Muhammad Rofiq Nadia Roosmalita Sari Nadia Roosmalita Sari Nadia Roosmalita Sari Nadya Oktavia Rahardiani Nashi Widodo Ni Wayan Surya Wardhani Nindynar Rikatsih Novanto Yudistira Novi Nur Putriwijaya Nurizal Dwi Priandani Nurul Hidayat Oakley, Simon Oktanisa, Irvi Philip Faster Eka Adipraja Prayudi Lestantyo Purnomo Budi Santoso Putra, Firnanda Al Islama Achyunda Putri Hasan, Vitara Nindya Putu Indah Ciptayani Qoirul Kotimah Rachmansyah, Ghenniy Rachmawati, Christina Rani Kurnia Rayandra Yala Pratama, Rayandra Yala Retno Dewi Anissa Riani, Garsinia Ely Rifa’i, Muhaimin Rikatsih, Nindynar Rinda Wahyuni Rizal Setya Perdana Rizal Setya Perdana Rizdania, Rizdania Rizka Suhana Rizki Ramadhan Rody, Rafiuddin Ruth Ema Febrita Ryan Iriany S, M Zaki Samaher . Saragih, Triando Hamonangan Sari, Nadia Roosmalita Sari, Nadia Roosmalita Selly Kurnia Sari Setyawan Purnomo Sakti Sudarto Sudarto Sukarmi Sukarmi, Sukarmi Sulistyo, Danang Arbian Sutrisno . Sutrisno Sutrisno Syafrial Syafrial Syafrial Syafrial Syaiful Anam Syandri, Hafrijal Tirana Noor Fatyanosa, Tirana Noor Titiek Yulianti Titiek Yulianti Titiek YULIANTI Tomi Yahya Christyawan Tri Halomoan Simanjuntak Ullump Pratiwi Utaminingrum, Fitri Utomo, M. Chandra Cahyo Vivi Nur Wijayaningrum Wahyuni, Ida Widdia Lesmawati Windi Artha Setyowati Yeni Herawati Yogi Pinanda Yogie Susdyastama Putra Yudha Alif Aulia Yudha Alif Auliya Yudha Alif Auliya, Yudha Alif Yulia Trianandi Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo, Yusuf Priyo