Article Per Year (5 Year)
p-Index From 2020 - 2025
4.364
P-Index
This Author published in this journals
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

Published : 144 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 144 Documents
Search

 1   2   3   4   5 
Optimasi K-Means untuk Clustering Kinerja Akademik Dosen Menggunakan Algoritme Genetika Budi Santoso; Imam Cholissodin; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (997.399 KB)

Abstract

Lecturers are teacher for students, besides teaching, lecturers also have many other activities by utilizing the expertise they have to develop the potential of the lecturer. Some of the characters that each lecturer are so different, such as education, research, dedication, administration, and support. The difficulties faced by the campus, one of them is related to the grouping of assignments to lecturers. The assignment is related to further studies, recommendations, structural related positions, filling an event, commission, etc.So that required a system that can classify the academic performance of lecturers optimally. In this study to classify the academic performance of lecturers using K-Means method is optimized with genetic algorithm. Genetic algorithm acts to optimize the cluster's initial center on K-Means.Data algorithm used in this research is the data of lecturers in UB's Computer Science faculty in 2016. The data obtained from GJM faculty of computer science of Universitas Brawijaya. The result of clustering test of academic performance of lecturer using GA-Kmeans algorithm has higher cluster quality that is 2,74% compared to K-Means algorithm without genetic algorithm, where the cluster quality obtained using Silhouette Coefficient method.
Optimasi Menu Makanan Untuk Pemenuhan Gizi Penderita Kanker Dengan Algoritme Genetika Dellia Airyn; Imam Cholissodin; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1002.429 KB)

Abstract

One of the most feared disease in the world today is cancer. For cancer patients, various ways have been done, one of them is chemotherapy. But in chemoteraphy, patients will experience digestive and absorption of nutrients disorder, thus affecting the nutritional status of patients. So, the menu orders for cancer patients become the most important thing to reduce the side effect of chemoteraphy, especially in term to fulfill the needs of energy and protein. In this study, there are 111 food menu, consist of 31 foods source of carbohydrate, 34 foods source of animal protein, and 46 foods source of plant protein.The method in this study using genetic algorithm, which is an optimization algorithm that similar to evolution theory in case determining the chromosomes or individual.The representation used is a permutation representation, with One-Cut Point Crossover and Reciprocal Exchange Mutation methods. The results and analysis of crossover rate and mutation rate combination against the average fitness value showed 0,6;0,4 has the largest average value, which is 762,19. In population test, the highest average fitness score was 631,16 in the 300th population. While in generation test, the highest average fitness value was 666,22 in the 200th generation.
Peramalan Harga Saham Menggunakan Support Vector Regression Dengan Algoritme Genetika Nanda Agung Putra; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (987.201 KB)

Abstract

Stock is a proof of investing in a corporation and stock holders have the right to claim part of corporation's earning and assets. Stock holders can gain a lot ot of benefit such receiving dividens and selling their stocks with higher value (capital gain). Stock holders need to be careful to manage their assets because stock prices keep changing over time. Stock holders usually monitor stock prices change and analyze them by forecasting. Support Vector Regression (SVR) is one of forecasting methods that performs well in both linear and non linear data. SVR can obtained a fitted model that is neither overfit nor underfit. However SVR has one drawback. The performance of SVR is greatly affected by its parameter. So finding the right parameter value on SVR is needed to gain a good forecasting result. One of optimization algorithms is Genetic Algorithm. Genetic Algorithm is used in order to get the right value of SVR parameter. SVR that is optimized by Genetic Algorithm is capable of getting a good result in forecasting. The test shows error rate/MAPE of forecasting is 0.165% which is smaller than using SVR which is 1.612% with best parameters such as population size 50, generation 200, crossover rate 0.4, mutation rate 0.6, range of sigma 0.5-1, range of epsilon 10-7-10-3, range of C 0.001-5, and range of gamma 10-5-10-3.
Optimasi Vektor Bobot Learning Vector Quantization Menggunakan Algoritme Genetika untuk Penentuan Kualitas Susu Sapi Karina Widyawati; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (895.737 KB)

Abstract

Milk has a complete nutrition that important for body so every people can consume milk with high quality. Determination of milk quality can by tools called Milkoscope Julie c2 or Lactoscan to test the chemical contents. That tools can identified the chemical content which includes 7 parameters. From 7 parameters, 3 parameters are provisions of SNI and 4 parameters are not listed in porvisions of SNI. If we determine milk quality only from 3 parameter in SNI, the result is not the best. Based on that problems, we need a system that can help us to determine quality of milk considering 7 parameters. Method that can be used for this problem is Learning Vector Quantization (LVQ) but LVQ need an optimazion method to produce the best weight vector and increase accuracy using Genethic Algorithm (GA). Best weight vector of GA will be used for LVQ training and the latest wight vector of training used for testing. The result of this research obtained the highest accuracy average is 88% with best parameters such as population size 30, crossover rate 0,5, mutation rate 0,5, generation 75, alpha 0,6, and alpha decrement 0,3.
Optimasi Parameter Support Vector Regression Dengan Algoritme Genetika Untuk Prediksi Harga Emas Muthia Azzahra; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1068.621 KB)

Abstract

Gold is one of the precious metals that many people interested as commodity to invest because of its resistance to inflation. Fluctuations can occur so extreme that affect the value of gold. Therefore, prospect of gold value in the future is quite important for the investors. One of prediction methods is Support Vector Regression (SVR), but the sensitivity of SVR parameters could influence the prediction result, therefore Genetics Algorithm (GA) can be applied, this method is flexible enough to be hybridized. This study discuss about the optimization of SVR parameters using GA to predict gold prices. Based on the testing result, the best mean absolute percentage error (MAPE) is 0.2407% with SVR loop 50, GA's generation 95, population size 70, crossover rate 0.01, mutation rate 0.99, elitism percentange 80%, range of 1x10-7-1x10-4, range of 0.01-5, range of 1x10-7-1x10-4, range of 1x10-5-1x10-4, and range of 1x10-3-0.1.
Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Algoritme Genetika Untuk Identifikasi Jenis Attention Deficit Hyperactivity Disorder Pada Anak Raissa Arniantya; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1235.914 KB)

Abstract

One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.One of mental disorder which common happened on children under 7 years old. Child with ADHD characterized by lack of ability to concentrate, excessive behavior, and behavior that spontaneously out of control. Type of ADHD are inattention, hyperactive and impulsive. If child with ADHD unidentified early, it will causes psychosocial problem but not many people are aware about ADHD so they need a system for identify the type of ADHD. System uses classification methods Learning Vector Quantization. Some cases classification, LVQ has weak accuracy so it needs optimization methods Genetic Algorithm (GA) for improve the accuracy. LVQ's weight vector will be optimized by GA through genetic process until generated optimum weight vector which LVQ uses for training and testing process. Testing against LVQ and LVQ-GA generate LVQ's accuracy 77% and LVQ-GA's accuracy 92% with best parameters are population size is 75, crossover rate is 0.6, mutation rate is 0.4, number of generation is 80, learning rate is 0.001, learning rate decrement is 0.1, maximum epoch is 1000 and learning rate minimum is 10-16.
Implementasi Algoritme Genetika Dalam Optimasi Knapsack Problem Penentuan Objek Wisata Wilayah Malang Raya Abdul Fatih; Budi Darma Setiawan; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1739.484 KB)

Abstract

Tourism has become commodity that can't be separated from human's life. There are some areas in Indonesia make tourism into the specific characteristics of their region which one is Malang Raya. The form of attention in the tourism sector is activated the building of new tourism objects. There was more tourism object than before will be more coddling for the tourists and also give a new problem. The tourist have knapsack problem which the tourist must decided all of tourism objects list that visited with the limited time. The optimization of knapsack problem can be resolved by using genetic algorithm. The genetic algorithm will make a formation of chromosome as representation of solution. The structures of genetic algorithm consist of initialization, reproduction, evaluation, and selection. The process of genetic algorithm did in the 50 generations with 100 populations whereas the pc value is 0, 7 and the value of pm is 0, 8. Result of processing genetic algorithm towards case study that has been tested gave the solution resemble to nearby tourism areas list and grouping in the certain areas.
Optimasi Fungsi Keanggotaan Fuzzy Mamdani menggunakan Algoritme Genetika untuk Penentuan Kesesuaian Lahan Tanam Tembakau Fikri Hilman; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1263.979 KB)

Abstract

One of the requirements for good quality tobacco is a good land use. Improved quality of the land will result in increased quality and quantity of tobacco plants produced. The main constraints experienced by tobacco farmers in determining land suitability are the limited knowledge and difficulty of obtaining correct data on the quality of land suitable for tobacco plants. Therefore, a computerized system is needed to assist farmers in making decisions on prospective land to be used. This system is implemented using Fuzzy Inference System (FIS) Mamdani and optimized using Genetic Algorithm. Some of the factors used in this system include the percentage of land affected by the disease, the openness of the region, the degree of weight of the soil, the thickness of the layer, the ease of irrigation, terrain conditions, and soil pH. The reproduction method used is extended intermediate crossover and random mutation, while the selection method used is elitism. Based on the results of the tests that have been done, the most optimum solution obtained on the total number for 90 population , the combination of cr and mr value of 0.2 and 0.8 respectively and the number of generations of 500, with the average of fitness value generated of 0,917. The Accuracy generated by this system is 80 % using 10 test data.
Optimasi Penjadwalan Asisten Praktikum pada Laboratorium Pembelajaran Menggunakan Algoritme Genetika (Studi Kasus : Fakultas Ilmu Komputer Universitas Brawijaya) Nadia Natasa Tresia Sitorus; 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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1417.959 KB)

Abstract

Scheduling is an important thing to be prepared carefully for the sake of an activity doing well.Good and effective scheduling will make the activities work and be well organized. Practicumis a routine activity for the application of materials that have been accepted by students.Practicum can work well if the teaching schedule of the practicum assistant does not collidewith the lecture schedule or their other activities. Genetic algorithm is one of algorithm thatcan complete the teaching schedule of practicum assistant through computation process. Thedata in conducting the research is the data from practicum schedule, practicum assistant andthe schedule of the assistant's willingness. The code of the practicum assistant is representedon the chromosome using the permutation method. The sequence of genes on thechromosomes represents the code of the practicum schedule. The crossover method appliedis one-cut-point, with mutation method using reciprocal exchange, and elitsm selectionmethod. The test result obtained optimal genetic algorithm parameter with total population5000, generation 500, with cr and mr value is 0,9 and 0,1. The output of the system is theteaching schedule of the laboratory teaching assistant.
Algoritma Genetika Untuk Optimasi Fuzzy Time Series Dalam Memprediksi Kepadatan Lalu Lintas di Jalan Tol Andhi Surya Wicaksana; Budi Darma Setiawan; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1043.698 KB)

Abstract

Fixed and improved traffic facilities continue to be done continuously, but the timing of uncompleted fixes that really only add to the existing traffic congestion will have an impact on the convenience and security of traffic users. Research has been done a lot to predict traffic density but not much focused on traffic on the highway. With fuzzy time series method optimized with Genetic Algorithm method, the writer wants to help solve the problem to predict traffic density on the highway, hopefully the result of research can help as a reference for improvement and improvement of facility in traffic do not increase congestion. Based on the results of top-level testing of predicted results using the Average Forecasting Error Rate (AFER) method, the result of the error rate of 16.66% is included in both qualification and successful.
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