Article Per Year (5 Year)
p-Index From 2019 - 2024
0.718
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics JTIK (Jurnal Teknik Informatika Kaputama)
Erianto Ongko
Akademi Teknologi Industri Immanuel
Computer Science & IT Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
HAR-MI method for multi-class imbalanced datasets H. Hartono; Yeni Risyani; Erianto Ongko; Dahlan Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14818

Abstract

Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. This research has been conducted by using multi-class imbalance datasets sourced from the KEEL Repository. The results show that the HAR-MI method can overcome multi-class imbalance with better data diversity, smaller number of classifiers, and better classifier performance compared to a DES-MI method. These results were tested with a Wilcoxon signed-rank statistical test which showed that the superiority of the HAR-MI method with respect to DES-MI method.
Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise Erianto Ongko; Hartono Hartono
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3057

Abstract

Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.
Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem Hartono Hartono; Erianto Ongko; Dahlan Abdullah
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.515

Abstract

Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay attention to the performance classifier, has led to an understanding of the importance of selecting an instance that will be used as a classifier. In the classic Hybrid Approach Redefinition method classifier selection is done randomly using the Random Under Sampling approach, and it is interesting to study how performance is obtained if the sampling process is based on Cluster-Based by selecting existing instances. The purpose of this study is to apply the Hybrid Approach Redefinition method with Cluster-Based Instance Selection (CBIS) approach so that it can obtain a better performance classifier. The results showed that Hybrid Approach Redefinition with cluster-based instance selection gave better results on the number of classifiers, data diversity, and performance classifiers compared to classic Hybrid Approach Redefinition.
ANALISIS PENGARUH MUTASI TERHADAP PERFORMANCE ALGORITMA GENETIKA Erianto Ongko
JTIK (Jurnal Teknik Informatika Kaputama) Vol 1, No 1 (2017)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.797 KB)

Abstract

Algoritma genetika pada umumnya digunakan untuk pencarian solusi dari suatu permasalahan yang menuntutpencarian solusi yang optimal pada suatu permasalahan. Salah satu tahapan di dalam algoritma genetika adalahproses mutasi. Proses mutasi dilakukan setelah proses crossover dilakukan. Proses mutasi melibatkan pertukarangen untuk beberapa gen yang berada pada posisi tertentu. Proses mutasi dimaksudkan untuk mencegah hasilterjebak di dalam kondisi local optima. Penelitian ini dimaksudkan untuk melihat pengaruh dari proses mutasiterhadap performance dari algoritma genetika. Studi kasus di dalam permasalahan ini adalah permasalahanTravelling Salesman Problem (TSP), dimana menggunakan library Berlin52.tsp. Hasil penelitian ini diharapkandapat memberikan gambaran mengenai pengaruh mutasi terhadap performance algoritma genetika denganmemvariasikan nilai probabilitas mutasi.
Co-Authors Dahlan Abdullah H. Hartono Hartono Hartono Yeni Risyani