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All Journal Perfecting a Video Game with Game Metrics Journal of Enviromental Engineering and Sustainable Technology Journal of Information Technology and Computer Science

Imam Cholissodin

Brawijaya University

Author-ID : 2452022

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science

Published : 7 Documents Claim Missing Document

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Articles

Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm Yusuf Priyo Anggodo; Imam Cholissodin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.6668

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results.

Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation Dinda Novitasari; Imam Cholissodin; Wayan Firdaus Mahmudy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.2812

This study investigates Particle Swarm Optimization (PSO) hybridization with Simulated Annealing (SA) to optimize Support Vector Machine (SVR). The optimized SVR is used for software effort estimation. The optimization of SVR consists of two sub-problems that must be solved simultaneously; the first is input feature selection that influences method accuracy and computing time. The next sub-problem is finding optimal SVR parameter that each parameter gives significant impact to method performance. To deal with a huge number of candidate solutions of the problems, a powerful approach is required. The proposed approach takes advantages of good solution quality from PSO and SA. We introduce SA based acceptance rule to accept new position in PSO. The SA parameter selection is introduced to improve the quality as stochastic algorithm is sensitive to its parameter. The comparative works have been between PSO in quality of solution and computing time. According to the results, the proposed model outperforms PSO SVR in quality of solution

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS M Gilvy Langgawan Putra; Whenty Ariyanti; Imam Cholissodin
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Gerakan Nasional Orang Tua Asuh Scholarship offers a number of scholarship packages. As there are a number of applicants, a system for selection and recommendation is required. we used 3 methods to solve the problem, the methods are AHP for feature selection, SVM for classification from 3 classes to 2 classes, and then TOPSIS give a rank recommendation who is entitled to receive a scholarship from 2 classes. In testing threshold for AHP method the best accuracy 0.01, AHP selected 33 from 50 subcriteria. SVM has highest accuracy in this research is 89.94% with Sequential Training parameter are Î» =0.5, constant of Î³ =0.01 , Îµ = 0.0001, and C = 1. Keywords: Selection, Recommendation, Scholarships, AHP-SVM-TOPSIS

Review: A State-of-the-Art of Time Complexity (Non-Recursive and Recursive Fibonacci Algorithm) Imam Cholissodin; Efi Riyandani
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Solving strategies in the computation the time complexity of an algorithm is very essentials. Some existing methods have inoptimal in the explanations of solutions, because it takes a long step and for the final result is not exact, or only limited utilize in solving by the approach. Actually there have been several studies that develop the final model equation Fibonacci time complexity of recursive algorithms, but the steps are still needed a complex operation. In this research has been done several major studies related to recursive algorithms Fibonacci analysis, which involves the general formula series, begin with determining the next term directly with the equation and find the sum of series also with an equation too. The method used in this study utilizing decomposition technique with backward substitution based on a single side outlining. The final results show of the single side outlining was found that this technique is able to produce exact solutions, efficient, easy to operate and more understand steps. Keywords: Time Complexity, Non-Recursive, Recursive, Fibonacci Algorithm

Optimizing SVR using Local Best PSO for Software Effort Estimation Dinda Novitasari; Imam Cholissodin; Wayan Firdaus Mahmudy
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. In the software industry world, itâ€™s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy. Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation

IDENTIFICATION OF PATCHOULI LEAVES QUALITY USING SELF ORGANIZING MAPS (SOM) ARTIFICIAL NEURAL NETWORK Kartika Purwandari; Candra Dewi; Imam Cholissodin
Journal of Environmental Engineering and Sustainable Technology Vol 3, No 1 (2016)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

One of the essential oil export commodities from Indonesia is patchouli oil. However, the price of patchouli todays is unstable caused by the low quality of the oils, which has high levels of acid and lower alcohol content. One part of patchouli that is widely used to obtain essential oils is the leaf. The better quality of leaves will produce oil with grade quality. The quality of the leaves can be identified by its physical characteristics. Leaves that have a good quality are small leaves, thick and slightly yellowish red color. This identification process can be done visually, but, it will be easier if it can be done automatically using computer applications. Therefore, this paper performs automatic identification of leaves utilizing image of patchouli leaves and artificial neural network algorithm Self Organizing Maps (SOM). Identification was done to distinguish the leaves with good quality and poor. From the test results using the initial learning rate 0.1, 0.3 deduction learning rate, the minimum rate learning 0.0001, 40 training data and testing the data 60 obtained an average accuracy of 82.82%.

Cancer Classification Based on the Features of Itemset Sequence Pattern of TP53 Protein Code Using Deep Miden - KNN Marji Marji; Imam Cholissodin; Dian Eka Ratnawati; Edy Santoso; Nurul Hidayat
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271401

Cancer is a disease that is still difficult to identify up to today. One of the causes of cancer is genetic modification that because of mutations in p53 gene. Healthy cells have a p53 wild type protein (normal) that is able to manage DNA separation. If DNA mutates, it will be difficult to detect cancer because the composition of the protein has changed. Bioinformatics is a combination of biology and information engineering (TI) that is utilized to manage data. One of the applications of data mining in bioinformatics is the development of pharmaceutical and medical industries. Data mining classification can use variety of methods including K-Nearest Neighbor (KNN), C45, ID3, and several other methods. One of the most reliable data classification methods is KNN. In this study, the development used two algorithms. The first was with the modification of the k-fold method, which divided two data into training data and test data, in which test-1 data and test-2 data were made into slices. The second was by a method for selecting an itemset sequence pattern that had the largest Gain Information, either 2 itemsets, 3 itemsets, and so on (Deep Miden). The best accuracy result of 96.00% was obtained through the process of computation testing in the server based on variations in terms of the number of patterns of Deep Miden itemset sequences and several k values on KNN classification method.

Co-Authors Candra Dewi Dian Eka Ratnawati Dinda Novitasari Edy Santoso Efi Riyandani Kartika Purwandari M Gilvy Langgawan Putra Marji Marji Nurul Hidayat Wayan Firdaus Mahmudy Whenty Ariyanti, Whenty Yusuf Priyo Anggodo

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