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

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Optimasi Penjadwalan Mata Pelajaran pada Kurikulum 2013 dengan menggunakan Hibridisasi Algoritme Genetika dan Simulated Annealing (Studi Kasus: SMA Negeri 6 Surabaya) Priscillia Vinda Gunawan; Imam Cholissodin; Bayu Rahayudi
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

Scheduling is one of the computational problems that is not easily solved. For solving it must be prepared systematically, by maximizing resources and time available effectively and efficiently. Scheduling problems can occur in various fields, including education. SMA Negeri 6 Surabaya is one of the high schools in Surabaya that has problems finding the right time slot with a limited number of teachers and there are some constraints that must be met in the scheduling of subjects. One method that can be used in the scheduling of subjects is to use genetic algorithm hybridization and simulated annealing (GA-SA) because GA has the weakness of early convergence and possibly stuck in local optimum, then SA is given as a solution to cover the weakness of GA and able to survive on a local optimum. The algorithm hybridization process is done with the first step in GA using chromosome representation of integer numbers, one-cut point crossover, reciprocal exchange mutation, and elitism selection. In the second step, a simulated annealing process is done using neighborhood move. The results given are scheduling the subjects by meeting the existing constraints. Based on the research, the optimum parameters are the number of generation 270, the population 90, the combination of cr and mr are 0.3 and 0.7 with the average fitness value of 0.999000.
Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Dengan Algoritme Particle Swarm Optimization M. Khusnul Azhari; Imam Cholissodin; Fitra Abdurrachman Bachtiar
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

Currently, The implementation of school transport has been done a lot of school, private and even the government. One of them is MI Salafiyah Kasim school. Although the school transport system has been implemented for years, there are obstacles such as students who are delivered not always the same every day, the driver delays in delivering to the destination, the school driver who always prioritizes personal experience and fund of transportation operations that are still unstable . To overcome these problems, the authors use the Particle Swarm Optimization Algorithm in the optimization to get the order of delivery of students with the shortest route that can be passed by the school driver. The results of this study compared actual sample data one day delivery with the system that has been designed. Of the five experiments applied to each kloter, three of them are able to produce a better route recommendation than the usual driver. Once reviewed overall, the system is considered to work well and produce a fairly optimal solution.
Prediksi Jumlah Produksi Kelapa Sawit Dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi kasus: PT. Sandabi Indah Lestari Kota Bengkulu) Ema Agasta; Imam Cholissodin; Dian Eka Ratnawati
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

Palm oil is a plantation that became the number one sector in Indonesia. This plant has a cost and a better production than other plantation crops such as sugar cane and rubber. In a company, palm oil production becomes the driving force of the economy, as well as what happened to PT. Sandabi Indah Lestari. In every week the company plans to predict the production. Planning done sometimes still give less than optimal results. This is because the calculation process is still using manual analysis. In this research will use four prediction features that are plant age, number of trees, land, and production. The prediction technique used is the learning method of Extreme Learning Machine (ELM). This method has advantages in learning speed and accuracy in predicted results. The calculation process starts from the process of data normalization, training a number of training data and test data, calculation of the prediction error value and produce the final value. The data used is production data in the period 2015 - 2017 with a total of 297 data. From a number of data will be divided into two data with percentage of 80% training data and 20% test data. The result of the research was obtained the optimal parameter value that is 13 hidden neuron in testing the number of neurons with Mean Absolute Perscentage (MAPE) value of 21.25%, 20.42% on the data feature test with the best 2 technical features and 20,19% on testing the pattern with the final result of the data pattern 1.
Implementasi Algoritme Improved Particle Swarm Optimization Untuk Optimasi Komposisi Bahan Makanan Untuk Memenuhi Kebutuhan Gizi Penderita Penyakit Diabetes Melitus Gregorius Dhanasatya Pudyakinarya; Imam Cholissodin; Fitra Abdurrachman Bachtiar
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

Diabetes Mellitus is one of the diseases with the highest number of casualties in Indonesia. The high diabetics in Indonesia are due to the lack of public knowledge about healthy food controls that result in poor diet. As a result, many people have not met the balance of nutritional intake that is the most important part in managing a good and healthy diet. Information on the right diet is needed for diabetics to improve their health condition. The Particle Swarm Optimization (PSO) algorithm is often used in performing optimization cases with good and optimal results, in particular, there is development to Improved Particle Swarm Optimization (IPSO) which further improves PSO performance. Therefore, this study designs an optimization system for the composition of food ingredients for the nutritional needs of people with Diabetes Mellitus using Improved-PSO algorithm. The results obtained from this study are optimized Improved-PSO parameters that are population number = 150, acceleration coefficient value = 2, 1, and convergent system on iteration to 550. In addition, from the results of global analysis shows that the nutrient calculation of the system can meet the nutritional needs of patients with a difference of tolerance ± 10% of expert calculations.
Peramalan Persediaan Spare Part Sepeda Motor Menggunakan Algoritme Backpropagation Danastri Ramya Mehaninda; Imam Cholissodin; Sutrisno Sutrisno
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

Motorcycle are the most used roudways transportation because they are more affordable and more efficient. Motorcycle require good maintenance to keep comfortable uses and maintain motorcycle performance so as to minimize accidents. Motorcycle maintenance can be done by replacing spare parts regularly in the workshop. To support the maintenance of motorcycle, the workshop should provide the best care services including having spare part inventory to suffice customer who maintance of motorcycle. If the workshop has sufficient spare part, the workshop can minimize the cost of ordering and can minimize the damage caused by storage for too long. There are many workshops that provide spare part replacement service such as Yamaha Motor. At Yamaha Motor is having difficulty in determining the spare part inventory for the next month. Inventory forecasting can help to determine the supply of spare part on Yamaha Motor. This research uses backpropagation algorithm for forecasting spare part inventory. The best backpropagation architecture is 9-7-1, which mean 9 input nodes, 7 hidden nodes and 1 output node. The input used is the history of spare part sales the previous month. The average MSE (error value) obtained from the test result is 0.0094506 and the smallest MSE obtained is 0.0085305 with the average difference of the actual value with the forecasting result is 6. At the smallest MSE value, the forecasting result approaches the actual value and has a pattern that almost the same.
Optimasi Penjadwalan Shift Jaga Dokter di IGD Menggunakan Algoritme Genetika (Studi Kasus Rumah Sakit di Malang) Annisaa Amalia Safitri; Imam Cholissodin; Lailil Muflikhah
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

Emergency room (ER) is one of the units in a hospital who the first of receiving patients in case of an emergency. In ER, there are doctors who should be available for 24 hours to deal with patient who come everytime when an emergency form happen. To keep the performance of doctors who working 24 hours in ER, then we make a schedule that use with shift system. For 1 month scheduling, 11 doctors will split into 3 shift work in a day. In order to optimize and make the best combination in doctor's schedule at ER, then made the doctor's scheduling system in a ER using genetic algorithm. Reproductive process using 2 ways, first the process of crossover by using extended intermediate crossover and second the mutation process by using a reciprocal exchange mutation, and then will use the last process of algorithm and the name is elitism selection process. Testing that is used for doctor's scheduling system in a ER is there are 3 types of testing. The first test is testing the number of popSize, with the highest fitness at a value of 40 with an average of 1,766, the second test is testing the value of generation with the highest fitness at generation value 40 with an average value of fitness 1,608, and the last test is combination of cr mr with the highest fitness value on a combination of 0,7 and 0,3 with average fitness 2,064. From those results, do more testing to compare the fitness value of fitness values of the system with real data provided by the hospital. And the results show that the value of fitness of the system = 11,111 is greater than the value of the data on real fitness given hospital = 7,692.
Optimasi Rute Multiple Travelling Salesman Problem Pada Distribusi Es Batu Dengan Algoritme Artificial Bee Colony (ABC) Muhammad Aghni Nur Lazuardy; Imam Cholissodin; Muhammad Tanzil Furqon
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

The distribution is done to improving the productivity of the company. A strategy in the process of distribution is required primarily in determining the distribution route. An optimal route is essential in product distribution especially ice cubes. A company needs to send its products to multiple addresses, because the numbers of shipping addresses and varying distances creates a problem such as needing a long time to reach the destination. In solving these problem need a system that has a purpose to help the distribution process with the number of sales more than one, the problem is named Multiple Travelling Salesman Problem (M-TSP). One of the methods that can solve the problem of M-TSP is Artificial Bee Colony (ABC) algorithm which compared to other algorithm based on swarm intelligence. The initial process of ABC algorithm looks for random ice cubes distribution routes based on customer's ordering data. Furthermore swapping and insertion route is done then taken the route with optimal fitness. The last is comparison with the initial route whether the result is better or not. The test result show the numbers of optimal parameters are 23 size problems, 80 pop sizes, 10 limits, and 600 iterations. From these parameters obtained average fitness value based on system optimization of 0,078163 and manual selection of routes the sales goes through obtain average fitness value of 0,043472, with the result that path selection can be optimized by system.
Klasifikasi Penderita Penyakit Ginjal Kronis Menggunakan Algoritme Support Vector Machine (SVM) Ega Ajie Kurnianto; Imam Cholissodin; Edy Santoso
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

Data mining is one of the processes that can be used in the healthcare industry currently. With the large amount of data collected, it can be used to get some information or an interesting pattern. Later on, the information can be used to provide assistance, diagnose, or decision making of a patient with the certain disease, such as chronic kidney disease, which is one form of disorder in the kidney. It is a deadly disease, but with proper precautions, this disease can also be avoided. Usually, most patients with a chronic kidney disease don't know the suffered disease and patients tend to underestimate when they find early symptoms of chronic kidney disease. Therefore, it needs a system that can facilitate the early detection of the chronic kidney disease. One technique that can be used is the classification using Support Vector Machine (SVM) algorithm. This algorithm aims to create an optimal hyperplane or dividing line. This research used data from 158 patients with 24 features and 2 classes. Based on test results, obtained best accuracy 100% with the details of parameter value is augmenting factor value (λ) = 0,001, learning rate value (γ) = 0,001, complexity value (C) = 0,001, sigma value (σ) = 1, and number of iteration = 1000.
Sistem Pendukung Keputusan Penentuan Kelayakan Pembangunan Crane Menggunakan Metode Analytic Hierarchy Process - Technique For Order Preference By Similarity To Ideal Solution (AHP-TOPSIS) [Studi Kasus PT. MHE DEMAG, Surabaya] Fendra Gunawan; Imam Cholissodin; Ratih Kartika 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

In the construction of the project in the transfer of objects that can not be moved by humans, a transporter is needed, one of which is a crane. Problems with PT. MHE-Demag, Surabaya in determining the initial feasibility of crane construction is the effectiveness and efficiency of time. The solution to the problem requires an application that can process data into a decision support system that is useful to provide the right decision in determining the feasibility of crane construction. The AHP-TOPSIS method was chosen because the method was able to select the best alternative from the alternative determination of the existing crane. The test used is by performing a pairwise comparison matrix to obtain priority weight for each criterion, the right hierarchy, and the ideal amount of data. The results obtained with the combination of values ​​in pairwise comparison matrix and application of Level 1 hierarchy match obtained the value of λmax = 7.53058 CI = 0.08843, CR = 0.067 resulted in a matching level of 92%. The ideal amount of data in this study is to obtain high accuracy values ​​using 10 to 20 data.
Optimasi Komposisi Makanan Bagi Penderita Obesitas Pada Orang Dewasa Menggunakan Algoritme Particle Swarm Optimization (PSO) Shinta Anggun Larasati; Imam Cholissodin; 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

Obesity occurs due to buildup of fat in the body is very high. Thus causing weight gain be not ideal. Obesity can also cause disease complications, some of which can endanger lives. To get the ideal body weight and the minimum cost incurred, the patient needs to control the amount of food intake is by regulating the composition of food that enters the body. The research was done by optimizing food composition for obese people in adults using Particle Swarm Optimization Algorithm (PSO). In this study, the initial particle formation of the particle random based on the amount of food so there is no need to convert that into food index. The results displayed by the program is actual body weight, ideal weight, nutritional status, energy needs, the needs of protein, fat and carbohydrate needs needs. While the test results obtained the optimal parameters such as the number of particles = 80, the number of iterations based on testing convergence of 703, = 0,4, = 0,7 c1i = 1,5 and c1f = 0,3, c2i = 0,3 and c2f = 1,5. The results of the program with the first patient parameters produce an average difference between the actual data with the data from the program registration -2,08% and it can reduce the cost of expenditure up to 6,85%. While the second patient the average of the actual data difference with data from the program amounted to -1,06% and it can reduce the cost of expenditure up to 5,93%.
Co-Authors Achmad Arwan Adam Syarif Hidayatullah Adhipramana Raihan Yuthadi Adhitya Wira Castrena Adinugroho, Sigit Ageng Wibowo Agus Wahyu Widodo Aldino Caturrahmanto Alfen Hasiholan Alif Fachrony Ana Holifatun Nisa Anandita Azharunisa Sasmito Andika Eka Putra Andriko Hedi Prasetyo Anggi Novita Sari Anim Rofi'ah Annisa Alifia Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiansyah Setiajati Arief Andy Soebroto Arina Indana Fahma Arsti Syadzwina Fauziah Atika Anggraeni Aulia Dinia Aulia Herdhyanti Aulia Jasmin Safira Azmi Makarima Yattaqillah Bahruddin El Hayat Bana Falakhi Bayu Andika Paripih Bayu Rahayudi Benita Salsabila Bisma Anassuka Bondan Sapta Prakoso Brendy Oscar Munthe Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Budi Santoso Candra Dewi Cindy Cynthia Nurkholis Citra Nadya Dwi Irianti Daisy Kurniawaty Danastri Ramya Mehaninda Daneswara Jauhari Daniel Agara Siregar Dellia Airyn Diah Priharsari Dian Eka Ratnawati Dieni Anindyasarathi Dinda Adilfi Wirahmi Diva Kurnianingtyas Dyah Ayu Wahyuning Dewi Edy Santoso Ega Ajie Kurnianto Elisa Julie Irianti Siahaan Ellita Nuryandhani Ananti Elmira Faustina Achmal Ema Agasta Ema Rosalina Eriq Muh. Adams Jonemaro Ersya Nadia Candra Fahri Ariseno Faizatul Amalia Faturrahman Muhammad Suryana Fayza Sakina Maghfira Darmawan Febriyani Riyanda Felicia Marvela Evanita Fendra Gunawan Ficry Agam Fathurrachman Fikhi Nugroho Fildzah Amalia Firda Priatmayanti Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Ariwanda George Alexander Suwito Ghulam Mahmudi Al Azis Gregorius Dhanasatya Pudyakinarya Guruh Adi Purnomo Gusti Reza Maulana Heny Dwi Jayanti Heru Nurwarsito Himawat Aryadita Holiyanda Husada Husin Muhamad I Gusti Ayu Putri Diani Ibnu Rasyid Wijayanto Ichwanda Hamdhani Ika Oktaviandita Indriati Indriati Irma Lailatul Khoiriyah Ishak Panangian Sinaga Istiana Rachmi Izzatul Azizah Jeffrey Junior Tedjasulaksana Khairinnisa Rifna Khairiyyah Nur Aisyah Komang Anggada Sugiarta Kresentia Verena Septiana Toy Kukuh Wicaksono Wahyuditomo Laila Restu Setiya Wati Lailil Muflikhah Leni Istikomah Liwenki Jus'ma Olivia M. Ali Fauzi M. Khusnul Azhari Mahendro Agni Giri Pawoko Marji Marji Maulana Ahmad Maliki Maulana Putra Pambudi Mauldy Putra Pratama Mentari Adiza Putri Nasution Michael David Moch Bima Prakoso Moh. Ibnu Assayyis Mohammad Aditya Noviansyah Mohammad Angga Prasetya Askin Mohammad Toriq Muhammad Aghni Nur Lazuardy Muhammad Dio Reyhans Muhammad Fahmi Hidayatullah Muhammad Fuad Efendi Muhammad Halim Natsir Muhammad Hasbi Wa Kafa Muhammad Hidayat Muhammad Maulana Solihin Hidayatullah Muhammad Nadzir Muhammad Rizal Ma'rufi Muhammad Rois Al Haqq Muhammad Shafaat Muhammad Syafiq Muhammad Tanzil Furqon Muhammad Taufan Mukh. Mart Hans Luber Nabila Lubna Irbakanisa Nabilla Putri Sakinah Nadia Natasa Tresia Sitorus Nadia Siburian Nadiah Nur Fadillah Ramadhani Nining Nahdiah Satriani Noerhayati Djumaah Manis Novanto Yudistira Novirra Dwi Asri Nur Afifah Sugianto Nur Firra Hasjidla Nurul Hidayat Nurul Inayah Obed Manuel Silalahi Panji Husni Padhila Priscillia Vinda Gunawan Putra Pandu Adikara Putri Ratna Sari Radita Noer Pratiwi Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Renata Rizki Rafi` Athallah Restu Fitriawanti Reyvaldo Aditya Pradana Reza Aprilliana Fauzi Rien Difitria Rinindya Nurtiara Puteri Rio Cahyo Anggono Riski Ida Agustiyan Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Ramadhan Rosintan Fatwa Rowan Rowan Sabrina Nurfadilla Salsabila Multazam Sandya Ratna Maruti Sari Narulita Hantari Satria Habiburrahman Fathul Hakim Sayyidah Karimah Shafira Eka Aulia Putri Shelly Puspa Ardina Shibron Arby Azizy Shinta Anggun Larasati Siti Mutdilah Sofi Hidyah Anggraini Stefanus Bayu Waskito Supraptoa Supraptoa Sutrisno Sutrisno Tara Dewanti Sukma Tibyani Tibyani Timothy Bastian Sianturi Tobing Setyawan Tony Faqih Prayogi Tusiarti Handayani Tusty Nadia Maghfira Uke Rahma Hidayah Uswatun Hasanah Utaminingrum, Fitri Vergy Ayu Kusumadewi Veronica Kristina Br Simamora Vinesia Yolanda Vivilia Putri Agustin Vivin Vidia Nurdiansyah Wahyu Bimantara Wanda Athira Luqyana Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Yessica Inggir Febiola Yoseansi Mantharora Siahaan Yudha Ananda Kresna Yudo Juni Hardiko Yuita Arum Sari Yunico Ardian Pradana Yusuf Afandi Zanna Annisa Nur Azizah Fareza