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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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Implementasi Metode Jaringan Saraf Tiruan Backpropagation Pada Prediksi Payload 4G di Telkomsel Jember M. Rikzal Humam Al Kholili; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PT. Telkomsel is one of the largest telecommunication providers in Indonesia which also has the most customers spread throughout Indonesia. Customers of PT. Telkomsel from year to year has experienced an increase and this will result in the use of an increasing number of payloads because the payload is all packages that are received and sent by mobile to a receiver (signal receiver) and if the amount of payload usage is smaller than the number of users it will occur over lagging and users will feel uncomfortable. With these problems, it requires an implementation of several methods to predict the amount of 4G payload usage so that PT. Telkomsel can find out the number of 4G payload usage in the next day or month so that it can anticipate losses or complaints from customers. Of the many prediction methods available, the authors use the backpropagation neural network method to perform a prediction process using artificial neural network architecture 4 input node neurons, 6 hidden node neurons and 1 node output neuron. By using the MAPE calculation (Mean Absolute Precentage Error) the most optimal value is 6.0154830745999%.
Implementasi Metode Bayesian Network untuk Diagnosis Penyakit Telinga Hidung dan Tenggorokan (THT) Mustofa Robbani; Nurul Hidayat; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Ear nose and throat disease one of the harmful disease, lack of hygiene and the difficulty in keeping the communities to diagnose due to the similarity of symptoms and diseases of the ear nose and throat, in addition to the conditions in the village It is located far from hospitals - major hospitals lead to sluggish handling of the ear nose and throat. Allow the occurrence of the disease is worse so detrimental to society or to patients themselves. Then it takes a software which can help the community or patient so expect that software can help communities to diagnose diseases of the ear nose and throat. Then the software was made using Bayesian Network methods, and to facilitate operation then made software with Android-based operating system. This software has the input based on the symptoms - symptoms of the disease that is selected by the user and is processed by methods of Bayesian Network so as to generate output in the form of disease and suffered from the results of testing the accuracy of data 42 It brings the accuracy of 93.75%.
Prediksi Nilai Cryptocurrency Bitcoin menggunakan Algoritme Extreme Learning Machine (ELM) Rahmat Faizal; Budi Darma Setiawan; Imam Cholisoddin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bitcoin is one of cryptocurrency which is popular among people due to decentralized management, well-maintained confidentiality, and easy process. But, this type of cryptocurrency is extremely volatile which makes the owner feel aggrieved. Lots of actions have been taken to overcome this by seeing the statistic movement over and over, Taking actions without considering the future prospect, or make the the asset being untouched until the considered time. Those are inneficient regarding the goal is to get the profit.Therefore, the need of system which can predict the value of Bitcoin accurately and efficiently so it can help decreasing the risk of losing and could give another consideration on trading cryptocurrency Bitcoin. This research has a purpose to obtain the value of cryptocurrency Bitcoin using Extreme Learning Machine (ELM) algorithm. Based on the implementation and analysis conducted using Bitcoin Data from May 1th, 2018 until August 1th, 2018, it can be obtained that the smallest error value using Mean Average Percentage Error (MAPE) is 2,657% with the number of features is 2, the number of hudden neuron is 4, and the percentage of training data is 90%, also the range of with range [-1.8, 1.8].
Optimasi Fuzzy Time Series Untuk Prediksi Jumlah Produksi SAGA Leather Fashion Menggunakan Metode Algoritme Genetika Aditya Chandra Nurhakim; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Leather wallets are an item that has a lot of interest nowadays, but because the process of makingproducts handmade and the price of raw materials is increasingly expensive, so making wallets andother leather-based goods has a higher price value, which makes the producers have decreasedorders. Therefore, an effort is needed to optimize the remaining raw materials used by predicting thenumber of items to be produced in the following month. The prediction method used to solve theproblem in predicting the amount of production is fuzzy time series, which is then optimized using thegenetic algorithm method. From the results of these studies, it is expected that the predicted resultscan help producers in optimize the remaining raw materials in the production process. In this study,the best individual from genetic algorithm on wallet products produce a RMSE (Root Mean SquareError) value of 5.24611658 with an accuracy rate of 96.44%, where the RMSE value is smaller thanthe test without optimization which results in a RMSE value of 7.20507068 with an accuracy of95.06%.
Optimasi Penempatan Ruang Sidang Skripsi Menggunakan Algoritme Genetika Nelli Nur Rahma; Budi Darma Setiawan; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The main problem that often happen in room placement final presentation for thesis in Computer Science Faculty is where lecturer examine more than one sessions continuously with different room which far away. Does not rarely in room placement, lecturer will placement in different building with far away in continuous sessions so raises final presentation for skripsi process will be late from initial scheduling because moving process. With the development of science and technology management, scheduling process should be done better. One of algoritm can use for recommendation of optimal final presentation for thesis is genetic algoritm where this algorithm can use for finished complex problems with many variable and result set of optimal solutions. In the formation of chromosome that used is permutation representation, the crossover process that used is one cut point crossover method, the mutation process that used is one random mutation method, the evaluation process with get fitness value each individual, the selection method that used is elitism. In the test result of room placement final presentation for thesis in one day obtained highest average fitness value is 1,000 in combination of cr 0,5 and mr 0,5, population size is 90, and generation size is 90. Room placement solution that use system can offer optimal scheduling with does not break the constraint at all.
Prediksi Volume Impor Beras Nasional menggunakan Metode Support Vector Regression (SVR) Cindy Inka Sari; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In Indonesia domestic rice production and rice imports are needed in order to attain national rice consumption. As the number of people increases and imported rice are consumed continuously, therefore Indonesia depends on rice imports from other countries. Prediction is needed to control the volume of national rice imports because excessive imports will cause negative losses and impacts. Support Vector Regression (SVR) is used to predict the volume of national rice imports. The data used in the prediction are data on consumption, production, volume of rice imports 1 year earlier as bebas variables and data on the volume of national rice imports in the period 1971 - 2016 as terikat variables. Tests carried out using 9 test data obtained the best parameters Sigma (σ) = 0.07, Lambda (λ) = 0.4, Constanta Learning Rate (cLr) = 0.01, Kompleksitas (C) = 10, Epsilon (ε) = 0.0004, the number of Iterations = 2000 and the number of training data = 37. The evaluation results were measured using Mean Absolute Presentage Error (MAPE). The best MAPE value produced is included in the sufficient category of 32.2748
Identifikasi Jenis Penyakit Mental Ansietas Menggunakan Metode Modified K-Nearest Neighbor Zubaidah Al Ubaidah Sakti; Budi Darma Setiawan; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

On every levels of society and age must have experienced anxiety, from early state to disorder state. Not everyone knows how to deal with it, if it not treated it would become dangerous mental illness for mental and physical condition. There are six kind of anxiety , that is General Anxiety Disorder, Panic Disorder, Social Anxiety Disorder, Specific Phobia, Obsessive Compulsive Disorder, and Post Traumatic Stress Disorder. In this research will be conducted the identification for kind of anxiety based on Hamilton Rating Scale of Anxiety (HIRS) questionnaire with Modified K-nearest Neighbor (MKNN) for the research method. Unlike K-Nearest Neighbor (KNN), MKNN is another version of that where on MKNN training data must be validated first and for the class voting would be weighted. This research indicates that MKNN could identify anxiety better on unbalanced data used 96 training data and 24 test data with value of h=1 and optimum value of K=3 with best average result 95%, while on balanced data with optimum value of K=2 best average result is 93,333%. This research also indicates as comparison with KNN that in this case resulted on KNN has better result processing balanced and unbalanced data because of noisy data on weighted process, and the result from K-fold Cross Validation that conclude the system is capable enough.
Perbandingan Peramalan Jumlah Penumpang Keberangkatan Kereta Api di DKI Jakarta Menggunakan Metode Double Exponential Smoothing dan Triple Exponential Smoothing Irma Nurvianti; Budi Darma Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Every year the population of DKI Jakarta Province always increases. It requires the government to improve the quality of public transportation. Train is one of public transportation in DKI Jakarta. Train is a transportation that is used as an alternative mode of transportation when going to travel far to avoid congestion in the city. Therefore, it needs to plan the train capacity for custumer satisfaction. The excessive or less passengers will have an impact on the performance of PT KAI, resulting in the need forecasting the number of passengers departure for trains which the result can be used by the local government and PT KAI to improve services. This research uses and compares the accuracy of Double Exponential Smoothing and Triple Exponential Smoothing. This research uses 156 records of number of passengers, from Januari 2005 to December 2017 obtained from the data published by Statistic Indonesia. From the study testing, the smallest MAPE is found in Triple Exponential Smoothing, it is 3,213% with the most optimal parameters are =0,4, =0,4 and =0,1. The MAPE value is under 10%, it means the method can predict very well.
Klasifikasi Penempatan Siswa di Sekolah Menengah Atas menggunakan Metode Extreme Learning Machine Akmal Subakti Wicaksana; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Placement of majors is one of the actions that can support the ability and motivation of student learning. Placement of the department itself has several factors that are used as considerations to determine the majors according to the abilities and interests of students. Factors used in determining majors between student psychological test results, report card grades for junior high school, BK evaluation, student interest, and parents' interests. Efficient it is not efficient. In the process of validation, the results of department placement are needed for 1 semester by looking at student learning outcomes data. In the placement of majors a system is needed that can help in classifying students' majors quickly and accurately, can be adjusted to the completion time of majors and errors in the arrangement of majors that are not in accordance with the interests and abilities of students. In this study using the Extreme Learning Machine method in grouping majors in high school students. In applying the ELM method the classification results obtained with the best average value of 98% using 14 features. To transfer data, practice and test the data used is 80:20, and the weight value is changed [-1,1], the value is biased with a range of [0,1], and use the activate sigmoid function.
Peramalan Harga Cabai Merah Besar Wilayah Jawa Timur Menggunakan Metode Extreme Learning Machine Pindo Bagus Adiatmaja; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In fulfilling economic needs in Indonesia the agricultural commodity sector has a very important role. Due to agricultural commodities are the livelihoods and basic consumption of the people in Indonesia. Daily people's needs cannot be separated from agricultural commodities, one of which is large red chili. This is due to the level of consumption of large red chili used for kitchen spices and the ingredients are quite high. Therefore large red chili which is included in agricultural commodities can be categorized as the primary food needs in people's lives. The prices of large red chili which are erratic and tend to rise can cause losses to the state and society. To overcome this problem, one solution is to forecast prices that can be used to predict the possibility of chili price increases quickly and accurately. This study aims to forecast the price of large red chili using the ELM method. Based on the results of the implementation and analysis that has been carried out using the data of large red chili prices from July 18, 2016 to December 28, 2018 the smallest error was obtained using Mean Absolute Error (MAPE) of 3 % using 2 feature, the number of neurons as many as 3 and the range of weight values ​​[-1,8, 1,8].
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'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'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