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All Journal Perfecting a Video Game with Game Metrics Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JAST : Jurnal Aplikasi Sains dan Teknologi

Imam Cholissodin

Universitas Brawijaya

Author-ID : 2453327

Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Other

Published : 22 Documents Claim Missing Document

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Integration Method of Local-global SVR and Parallel Time Variant PSO in Water Level Forecasting for Flood Early Warning System Arief Andy Soebroto; Imam Cholissodin; Maria Tenika Frestantiya; Ziya El Arief
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.6772

Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather.

Invigilator Examination Scheduling using Partial Random Injection and Adaptive Time Variant Genetic Algorithm Maulidya Larasaty Seisarrina; Imam Cholissodin; Heru Nurwarsito
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Examination for every semester is a routine activity for faculties to do. Academic division of faculty responsible to make the schedule for every subject that is going to be tested, and prepare rooms for the test. Meanwhile, coordinators of invigilator committee responsible to make the schedule in FILKOM UB. This research focuses on scheduling the invigilatorâ€™s schedule in FILKOM UB. Scheduling with conventional method or manual takes much time because it needs to consider many rules on scheduling it. That is the reason why we need a system to schedule it. The purpose of making this system is to help the committee to schedule their invigilatorâ€™s time line. This research offers a concept of solution from using genetic algorithm. Genetic algorithm is an algorithm to find the optimum solution. The system of scheduling that use this genetic algorithm method can produce invigilatorâ€™s schedule that is having the least troubles on the arrangement. The data that is used in this research is the final testâ€™s schedule of the odd semester in 2015/2016, lecturer and the employeeâ€™s data of FILKOM UB. The optimal genetic parameter that is obtained from the test consists of 900 population, 3000 generations, and a combination of crossover rate and mutation rate value which are 0,4 and 0,6. The system that is built in making this invigilatorâ€™s schedule is close to the optimum point with 0,877 fitness value.Keywords: scheduling, invigilator, partial random injection, adaptive time variant genetic algorithm.

Optimization of Healthy Diet Menu Variation using PSO-SA Imam Cholissodin; Ratih Kartika Dewi
Journal of Information Technology and Computer Science Vol. 2 No. 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100.Keywords: PSO, SA, optimization, variation, healthy diet menu.

Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance Imam Cholissodin; Arief Andy Soebroto; Sutrisno Sutrisno
Journal of Information Technology and Computer Science Vol. 4 No. 1: June 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning

Prediction of rainfall using improved deep learning with particle swarm optimization Imam Cholissodin; Sutrisno Sutrisno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.14665

Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include Deep Learning (DL) and Particle Swarm Optimization (PSO). The use of the Deep Learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.

Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa (PE) Menggunakan ELM-PSO Di UPT Pembibitan Ternak Dan Hijauan Makanan Ternak Singosari-Malang Imam Cholissodin; Sutrisno Sutrisno; Arief Andy Soebroto; Latifah Hanum; Canny Amerilyse Caesar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4, No 1: Maret 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakSusu merupakan salah satu sumber protein hewani yang mengandung semua zat yang dibutuhkan tubuh. Ternak penghasil susu utama di Indonesia yaitu sapi perah, namun produksi susunya belum dapat mencukupi kebutuhan masyarakat. Alternatifnya adalah kambing peranakan etawa (PE). Tingginya kualitas kandungan gizi susu sangat dipengaruhi oleh beberapa faktor salah satunya, yaitu faktor pakan. Bagian peternakan kambing PE di UPT Pembibitan Ternak dan Hijauan Makanan Ternak Singosari-Malang masih menghadapi permasalahan, yaitu rendahnya kemampuan dalam memberikan komposisi pakan terhadap kambing PE. Kekurangan tersebut berpengaruh terhadap kualitas susu yang dihasilkan. Diperlukan pengetahuan rekayasa kandungan gizi susu untuk menentukan komposisi pakan dalam menghasilkan susu premium dengan kandungan gizi optimal. Penulis menggunakan metode Extreme Learning Machine (ELM)dan Particle Swarm Optimization (PSO) untuk membuat pemodelan pakan kambing dalam mengoptimasi kandungan gizi susu kambing. Dalam analisa pengujian konvergensi menggunakan metode ELM-PSO yang dilakukan dengan kasus untuk berat badan kambing 32 kg, serta jenis pakan yang digunakan yaitu rumput Odot 70% dan rumput Raja 30% menghasilkan sistem mencapai kestabilan dalam konvergensi pada iterasi ke-20 dengan fitness terbaik yaitu 16.2712.Kata Kunci: Susu Kambing, Optimasi, Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Kandungan Nutrisi Pakan.AbstractMilk is one of the animal protein sources which it contains all of the substances needed by human body. The main milk producer cattle in Indonesia is dairy cow, however its milk production has not fulfilled the society needs. The alternative is the goat, the Etawa crossbreed (PE). The high quality of milk nutrients content is greatly influenced by some factors one of them, is the food factor. The PE goat livestock division of the UPT Cattle Breeding and the Cattle Food Greenery in Singosari-Malang still faces the problem, it is the low ability in giving the food composition for PE goat. This flaw affects the quality of the produced milk. It needs the artificial science of the milk nutrients contains in order to determine the food composition to produce premium milk with the optimum nutrients contain. The writer uses the method of the Extreme Learning Machine (ELM) and the Particle Swarm Optimization (PSO) to make the modeling of goat food in optimizing the content of goat milk nutrients. In the analysis of the convergence that is done with the case of 32 kg goat weight, also the food type used is the 70 % Odot grass and 30% Raja grass that system get a stability on the 20th iteration with a fitness value is 16.2712.Keywords: Goat Milk, Optimization, Extreme Learning Machine (ELM), Particle Swarm Optimization (PSO), The Food Nutrients Contain.

Sistem Monitoring Aliran Sungai dan Lingkungan Berbasis Smart Environment di RW 03 Kelurahan Kauman Kota Malang Sutrisno Sutrisno; Imam Cholissodin; Arief Andy Soebroto; Muh Arif Rahman
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 5, No 1 (2021): EDISI JUNI 2021
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v5i1.2259

Monitoring of the rivers state and the environment of roads in the city center is often still inadequate. For example, garbage is often found in the river, while on the roads, there is still not yet a sound security system. Kauman RT 03 RW III Klojen Malang is one of the densely populated regions and is located in the center (point of zero) of Malang city at the time ago still does not have a security system or security guard and there is a river flow which is often found garbage piling up and often causes flooding when it rains heavy. Based on field conditions in Kauman and meetings with residents represented by several RT heads in RW 03 Kauman, Klojen Malang requires the use of a smart environment and CCTV technology integration. Therefore the result of dedication to society to apply CCTV's technology, so it has been used at Kauman for environmental and security monitoring. Considering the high level of the busyness of the urban at Kauman, with providing it, they can be monitoring the environment by automatically systems continuously 24 hours every day. Therefore, the system has been being able to facilitate and help people to monitor the environment and river flow to be more effective, efficient, and modern. ABSTRAKMonitoring keadaan sungai dan lingkungan ruas jalan pada masyarakat tengah kota seringkali masih belum memadai. Di aliran sungai misalnya, masih sering dijumpai sampah yang menumpuk, sedangkan di ruas jalan masih belum dijumpai sistem keamanan yang baik. Kampung Kauman RT 03 RW III kecamatan Klojen Kota Malang merupakan salah satu kampung yang padat penduduk dan berada di pusat (titik nol) kota saat ini belum memiliki sistem keamanan ataupun satpam dan terdapat aliran sungai yang seringkali dijumpai sampah menumpuk bahkan sering menyebabkan banjir bila hujan deras. Berdasarkan kondisi lapangan di kampung Kauman dan pertemuan dengan warga yang diwakili oleh beberapa ketua RT di wilayah RW 03 Kauman yang membutuhkan pemanfaatkan integrasi teknologi smart environment dan teknologi CCTV. Hasil kegiatan pengabdian masyarakat telah dapat secara optimal dimanfaatkan untuk memenuhi kebutuhan pengawasan ataupun monitoring lingkungan tersebut. Mengingat tingkat kesibukan masyarakat perkotaan yang tinggi, dengan adanya sistem monitoring mereka dapat mengambil manfaat besar dengan dikembangkannya sistem pengawasan aliran sungai dan lingkungan yang bisa bekerja secara otomatis dan kontinyu selama 24 jam. Sistem yang dibuat telah mampu memudahkan sekaligus membantu masyarakat untuk monitoring lingkungan dan aliran sungai secara lebih efektif, efisien, dan modern.

Prediction of Rainfall using Simplified Deep Learning based Extreme Learning Machines Imam Cholissodin; Sutrisno Sutrisno
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (such as sea-level temperatures and tropical cyclones) in Indonesia are uncertain and there are weaknesses in each method used by BMKG. Another popular method used for rainfall prediction is the Deep Learning (DL) and Extreme Learning Machine (ELM) included in the Neural Network (NN). ELM has a simpler structure, and non-linear approach capability and better convergence speed from Back Propagation (BP). Unfortunately, Deep Learning method is very complex, if not using the process of simplification, and can be said more complex than the BP. In this study, the prediction system was made using ELM-based Simplified Deep Learning to determine the exact regression equation model according to the number of layers in the hidden node. It is expected that the results of this study will be able to form optimal prediction model.Keywords: prediction, rainfall, ELM, simplified deep learning

Development of Big Data App for Classification based on Map Reduce of Naive Bayes with or without Web and Mobile Interface by RESTful API Using Hadoop and Spark Imam Cholissodin; Diajeng Sekar Seruni; Junda Alfiah Zulqornain; Audi Nuermey Hanafi; Afwan Ghofur; Mikhael Alexander; Muhammad Ismail Hasan
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Big Data App is a developed framework that we made based on our previous project research and we have uploaded it on github, which is developing lightweight serverless both on Windows and Linux OS with the term of EdUBig as Open Source Hadoop Distribution. In this study, the focus is on solving problems related to difficulties in building a frontend and backend model of a Big Data application which by default only runs scripts through consoles in the terminal. This will be quite a tribulation for the end users when the Big Data application has been released and mass produced to general users (end users) and at the same time how the end users test the performance of the Map Reduce Naive Bayes algorithm used in several datasets. In accordance to these problems, we created the Big Data App framework to make the end users, especially developers, feel easier to build a Big Data application by integrating the frontend using the Web App from Django framework and Mobile App Native, while for the backend, we use Django framework that is able to communicate directly with the script either hadoop batch, streaming processing or spark streaming very easily and also to use the script for pig, hive, web hdfs, sqoop, oozie, etc. the making of which is extremely fast with reliable results. Based on the test results, a very significant result in the ease of data computation processing by the end users and the final results showing the highest classification accuracy of 88.3576% was obtained.Keywords: big data, map reduce of naive bayes, serverless, web and mobile app, restful api, django framework

Optimasi Penjadwalan Praktikum Menggunakan Modified Real Code Particle Swarm Optimization (Studi Kasus Fakultas Imu Komputer Universitas Brawijaya) Brigitta Ayu Kusuma Wardhany; Istiana Rachmi; Nur Firra Hasjidla; Zulianur Khaqiqiyah; Idham Triatmaja; Imam Cholissodin
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 3, No 4: Desember 2016
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakPenjadwalan adalah salah satu proses dalam manajemen waktu yang di atur sedemikian rupa agar kegiatan dapat berjalan dengan lancar. Banyak algoritma yang dapat digunakan untuk menyelesaikan permasalahan penjadwalan. Pada kasus ini penulis menggunakan algoritma Modified Real Code PSO (M-RCPSO). Data yang digunakan terdiri dari data dosen, asisten, mahasiswa, ruangan dan waktu praktikum. Dari hasil pengujian popsize, pengujian iterasi, pengujian parameter kognitif dan sosial, dan pengujian parameter terbaik, didapatkan nilai rata-rata fitness adalah 1. Hal ini menunjukkan bahwa solusi yang didapatkan sudah mendekati optimum.Kata kunci: modified real code particle swarm optimization, penjadwalanAbstractScheduling is one of the time management process that well regulated so that the activities can run fluently. Many algorithms can be used to solve scheduling problems. In this case, the author uses a Modified Real Code PSO (M-RCPSO) algorithm. The data used consisted of lecturer, assistant, student, room and lab time. From the results of popsize testing, iterative testing, cognitive parameter testing, and the best parameters testing obtained the average fitness value is 1. This matter shows that the solution obtained is already approaching optimal.Keywords: modified real code particle swarm optimization, scheduling

Co-Authors Achmad Jafar Al Kadafi Aditya Yudha A. N. Afwan Ghofur Agnes Rossi Trisna Lestari Anang Hanafi Ardisa Tamara Putri Arief Andy Soebroto Arrofi Reza Satria Aryeswara Sunaryo Audi Nuermey Hanafi Bariq Najmi R Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Canny Amerilyse Caesar Daisy Kurniawaty Daneswara Jauhari Destyana Ellingga Pratiwi Dhimas Anjar Prabowo Dhimas Anjar Prabowo Diajeng Sekar Seruni Fathony Teguh Irawan Firadi Surya Pramana Handika Agus Fauzi Heru Nurwarsito Hilmi Ilyas Rizaldi Idham Triatmaja Istiana Rachmi Junda Alfiah Zulqornain Lailil Muflikhah Latifah Hanum Listiya Surtiningsih Luqman Hakim H M Fahrul Alam Yuniarsa Maria Tenika Frestantiya Maryamah . Maulidya Larasaty Seisarrina Mikhael Alexander Mochammad Ainun Najib Moh. Fadel Asikin Muh Arif Rahman Muhammad Fhadli Muhammad Ismail Hasan Muhammad Rizal Ma’rufi Muhammad Syafiq Nanda Agung Putra Nur Firra Hasjidla Nurudin Santoso Nurul Hidayat Raissa Arniantya Ratih Kartika Dewi Rekyan Regasari Mardi Putri Rizka Husnun Zakiyyah Selly Kurnia Sari Siti Robbana Sutrisno Sutrisno Ulfa Lina Wulandari Umi Rofiqoh Vivien Fathuroya Wanda Athira Luqyana Winda Estu Nurjanah Ziya El Arief Zulianur Khaqiqiyah

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