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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Imam Cholisoddin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

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Penerapan Algoritme Genetika Untuk Penjadwalan Latihan Reguler Pemain Brass Marching Band (Studi Kasus: Ekalavya Suara Brawijaya) Marina Debora Rindengan; Imam Cholisoddin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Marching band is an extracurricular where the players are required to work together in a team in order to give a good appearance. The rehearsals require a lot of times with many players. A poor schedule of marching band rehearsal or conflict of schedule between players can cause problems in doing the rehearsals. Data schedule of each player is taken from Marching Band Ekalavya Suara Brawijaya, and national holiday from September 2016 until December 2016. After getting the data, process of genetic algorithm that start from chromosome representation to time and practice day, and then do the process of extended intermediate crossover and reciprocal exchange mutations for new offspring that will be selected by elitism selection for next generation. The optimal schedule is obtained through testing, the largest average fitness score is 1 on the population size 130, the number of generation is 140, and combinations of cr and mr is 0,5.
Optimasi Penjadwalan Bimbingan Skripsi Menggunakan Algoritme Genetika (Studi Kasus : Fakultas Ilmu Komputer Universitas Brawijaya) Lilis Damayanti; Imam Cholisoddin; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Thesis consultation is an activity that must be done for students who are taking thesis. Usually students who will conduct guidance will meet their lecturer or contact the lecturer before. Because the lecturer also has time to teach and perform other activities related to the campus. The number of students in Fakultas Ilmu Komputer Universitas Brawijaya (FILKOM UB) which they want to do guidance to make the students queue in front of lecturers room while the lecturer has a hectic schedule. Therefore it is necessary for the system to schedule thesis consultation. This research applies the concept of solution obtained using genetic algorithm. Genetic algorithm is a search algorithm that aims to find the optimal solution. The result of genetic parameters obtained in the optimal solution is population size 70, number of generation of 2500, combination of cr and mr value is 0,4 and 0,6. This built system resulted in an optimal thesis guidance schedule with a fitness value of 1,0305.
Penerapan Metode Extreme Learning Machine (ELM) Untuk Memprediksi Jumlah Produksi Pipa Yang Layak (Studi Kasus Pada PT. KHI Pipe Industries) Nirzha Maulidya Ashar; Imam Cholisoddin; Candra Dewi
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

KHI Pipe Industries is a company that specializes in producing high-quality steel pipes. This company produces its end product based on customer demand, with the measurement specifications which is diameter, thickness and the pipe length. In the production process, the amount of viable pipes do not always match with the number of customers demand since there were always a number of damaged pipes. Therefore, the company has always have to spend additional cost to cover the the damaged pipes. The number of production on each specifications varies so that it becomes a challenge for the company to predict the exact amount of pipes to produce. With the appropriate prediction of the number of pipes to produce can help the company to determine the production target. In this research applied method of Artificial Neural Network (ANN) that is Extreme Learning Machine (ELM) to predict the amount of approved pipe production. The prediction process is normalization, training, testing, and denormalization, and to calculate the error value using Mean Square Error (MSE). Based on evaluation performed, the use of 7 hidden neurons, 5 features, and percentage comparison 80% of training data 20% of testing data resulted in the smallest error average is 0,00372 with difference ± 1% to actual data.
Prediksi Jumlah Kriminalitas Menggunakan Metode Extreme Learning Machine (Studi Kasus Di Kabupaten Probolinggo) Sema Nabillah Dewi; Imam Cholisoddin; Edy 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 crime rate in Indonesia is highly increased. A lot of people want to become wealthy in a wrong way by commiting a crime. Criminality is an act that violates the rules of the law that can disturb the public. Every society has a risk of becoming a victim of crime. The greater the risk that the community has, the more unsafe their area is. However, the number of criminal acts cant't be ensured from time to time due to the uncertain number. This causes the police will having a trouble in resolving the criminal acts. A proper and accurate prediction can help minimizing criminal acts that will be happened. This research is intended to get predicted numbers of criminality using Extreme Learning Machine method (ELM). Based on the implementation and testing done by using crime data of Probolinggo District Police in 2012 until 2017, obtained the maximum network architecture that is the number of features as much as 7, the comparison of data ratio is 80%: 20%, and the number of neurons in the hidden layer as much as 7 and the binary sigmoid activation function. The low error value is calculated using the Mean Square Error (MSE) error rate and the result is 0.037662.
Klasifikasi Risiko Gagal Ginjal Kronis Menggunakan Extreme Learning Machine Dimas Prenky Dicky Irawan; Imam Cholisoddin; Edy 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

Kidney is an organ in humans that have a very important role in the process of managing fluid and electrolyte needs. Chronic renal failure is a disease of kidney that occurs due to kidney infection and the existence of blockage due to kidney stones. To perform the classification of chronic renal failure medical personnel are still not maximally in handling it, to deal with this problem researchers use the Extreme Learning Machine to perform the classification of chronic renal failure. The Extreme Learning Machine is a classification algorithm in which this algorithm is part of a neural network that has a good learning speed and also according to existing research results in a good accuracy value when compared to using other algorithms. This study obtained a comparison of the value of training data as well as the optimal test data with a 70:30 ratio value, many hidden layer neurons of 10 and using the bipolar sigmoid activation function of these parameters resulted in an accuracy of 99.13%. From the results of accuracy obtained, indicating that the method of Extreme Learning Machine is good enough to be used for the process of classification of chronic renal failure.
Penerapan Algoritme Particle Swarm Optimization-Learning Vector Quantization (PSO-LVQ) Pada Klasifikasi Data Iris Ilham Romadhona; Imam Cholisoddin; Marji Marji
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

Currently Iris flowers are easily found in around the world with various species. In Greek Iris mean the goddess of the rainbow because Iris species has reached 260 to 300 various species with colorful and light flowers. Because of the large number of Iris species, it is necessary to classify the Iris species. To solve the problem, used the Learning Vector Quantization (LVQ) algorithm which will be optimization using the Particle Swarm Optimization (PSO) algorithm was used to classify species into Sentosa Iris, Virginica Iris and Versicolor Iris category where the species previously recorded on Iris dataset. Then the result of this study was compared with the classification using LVQ algorithm. The average accuracy obtained with PSO-LVQ algorithm is 93.334%, whereas the average accuracy with LVQ algorithm is 84.268%. The differece in accuracy is 9.066% it is mean PSO-LVQ algorithm give more a good provides result than LVQ algorithm.
Optimasi Penjadwalan Mesin dan Shift Karyawan Menggunakan Algoritme Genetika (Studi Kasus Pada PT. Petro Jordan Abadi) Sarah Aditya Darmawan; Imam Cholisoddin; Tibyani Tibyani
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

Employee shift scheduling and machine scheduling are two things to keep in mind at a factory. Employee shift scheduling is needed to manage employees' working hours so that the work quality of employees is well maintained and has a positive impact on the company. So it is with machine scheduling. Machine scheduling is needed to regulate the order and process of machine work in goods production activities, in order to shorten production time of goods and increase production of goods. The crossover method used is one-cut point crossover, mutation method used for shift scheduling is reciprocal exchange mutation and insertion mutation for machine scheduling, and selection method used is elitism selection. There are 4 scheduling systems used in this study, testing the value of popsize, testing the value of generation, convergence testing, testing the combination of cr and mr values, and testing of global analysis. In testing the value of popsize obtained the highest popsize is 70 with a fitness value of 0.6198. The generation value test got the highest generation in generation 400 with fitness value 0,5624. As for testing cr and mr get the best value at cr of 1 and mr of 0 with a fitness value of 0.5926. Results obtained from global analysis is the fitness value of the system has a higher yield of 0.5162. It can be concluded that the application of genetic algorithms in the optimization of machine scheduling and shift employees is very influential in the process of obtaining the best solution. The greater the fitness value obtained the better the solution obtained, and vice versa. So that the engine optimization system and employee shift scheduling can be used as a reference for the schedule for the company.
Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Menggunakan Algoritme Genetika (Studi Kasus : Sekolah MI Salafiyah Kasim Blitar) Ivarianti Sihaloho; Imam Cholisoddin; Tibyani Tibyani
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

Most aspects in all sectors are now fundamentally dependent on the information technology. One of the aspect is about the quality of school services for students, like school bus service. School bus service is one of the efficient school service to support safety for children while their parents are busy to pick up them at school. This is one of the reason from the imporovement of MI Salafiyah Kasim school Bus service for children by optimizing the school bus routes. The Optimization system is built on with Traveling Salesman Problem (TSP) and genetic algorithm methods. MI Salafiyah Kasim school bus service has morning and afternoon arrival and departure of students. The actual road data used in this research consist of the sample within 3 days. This data will be compared to find out and show what method needs less time in comparison with known methods and so efficient for such problem. Based on the data research, the optimization results in the morning departure is about 5.5 km (accuracy = 19.78%) and in the afternoon arrival is about 17.17 km (accuracy = 36.30%). It can be concluded that this system is running well by producing a good optimal value.
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.
Rekomendasi Pemilihan Burung Menggunakan Metode Simple Additive Weighting (SAW) dan Technique Order Preference by Similarity To Ideal Solution (TOPSIS) Rizal Rudiantoro; Imam Cholisoddin; Ratih Kartika Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The melodious bird is in great demand for a group of animal lovers, for a group of bird lovers the price of birds can reach an expensive price for one type of bird that has a sweet voice. In social media, there are so many singing bird sellers that promote various kinds of singing birds. Many of Indonesian people have been moved from offline birds market to social media to buy birds because it's easier to choose the type and price of birds. This research makes a system which able to find the criteria for singing bird recommendation that appropriate to the bird lovers. To implement a singing bird recommendation system there is a need for a method to find the best, accurate, and precise recommendation. This system uses Simple Addictive Weighting (SAW) and Technique Order Preference by Similarity to Ideal Solution (TOPSIS) method. This system has been tested trough Spearman Correlation get significant results from 3 kinds of birds e.g Green Lovebird: 6,34, Green Cucak 4,92, Kacer: 5, 80 with value α=1,96. The final result from this system recommends the first rank bird based on the highest preference score.
Co-Authors Achmad Choirur Roziqin Arsya Monica Pravina Bayu Rahayudi Budi Darma Setiawan Candra Dewi Diajeng Tania Ananda Paramitha Dian Werdiningsih Dwi Rahmawati Dimas Prenky Dicky Irawan Edy Santoso Ilham Romadhona Ivarianti Sihaloho Lilis Damayanti Marina Debora Rindengan Marji Marji Maya Novita Putri Riyanto Muhammad Faris Mas'ud Muhammad Jibril Alqarni Nirzha Maulidya Ashar Nurudin Santoso Putra Pandu Adikara Rahmat Faizal Ratih Kartika Dewi Rizal Rudiantoro Sarah Aditya Darmawan Sema Nabillah Dewi Tibyani Tibyani Tria Melia Masdiana Safitri Wayan Firdaus Mahmudy Yuwilda Wilantikasari