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All Journal TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal TIMES International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BIOLINK (Jurnal Biologi Lingkungan, Industri, Kesehatan) JURNAL MEDIA INFORMATIKA BUDIDARMA JTAM (Jurnal Teori dan Aplikasi Matematika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Pelita Masyarakat Journal of Applied Data Sciences JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Science in Information Technology Letters Journal of Applied Computer Science and Technology (JACOST) International Journal of Engineering, Science and Information Technology EXPLORER Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Jurnal Pengabdian Masyarakat (Jubdimas)
Hartono Hartono
Department Of Magister Program In Informatics, Postgraduate School, Universitas Medan Area
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Journal : joiv international journal on informatics visualization

 1 
Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping Hartono Hartono; Erianto Ongko
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.420

Abstract

The class imbalance problem in the multi-class dataset is more challenging to manage than the problem in the two classes and this problem is more complicated if accompanied by overlapping. One method that has proven reliable in dealing with this problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method which is classified as a hybrid approach that combines sampling and classifier ensembles. However, in terms of diversity among classifiers, a hybrid approach that combines sampling and classifier ensembles will give better results. HAR-MI provides excellent results in handling multi-class imbalances. The HAR-MI method uses SMOTE to increase the number of samples in the minority class. However, this SMOTE also has a weakness where an extremely imbalanced dataset and a large number of attributes will be over-fitting. To overcome the problem of over-fitting, the Hybrid Sampling method was proposed. HAR-MI combination with Hybrid Sampling is done to increase the number of samples in the minority class and at the same time reduce the number of noise samples in the majority class. The preprocessing stages at HAR-MI will use the Minimizing Overlapping Selection under Hybrid Sampling (MOSHS) method, and the processing stages will use Different Contribution Sampling. The results obtained will be compared with the results using Neighbourhood-based under-sampling. Overlapping and Classifier Performance will be measured using Augmented R-Value, the Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. The results showed that HAR-MI with Hybrid Sampling gave better results in terms of Augmented R-Value, Precision, Recall, and F-Value
Recipient Feasibility Decision Support System Micro Small Medium Business Assistance Use Method Analytic Hierarchy Process and Simple Additives Weighting Abdullah, Dahlan; Erliana, Cut Ita; Bintoro, Andik; Hartono, Hartono; Ikhwani, Muhammad; Nazaruddin, Nazaruddin
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2321

Abstract

Study This aims To determine the eligibility of MSME assistance recipients with the method AHP (Analytic Hierarchy Process) And SAW (Simple Additives weighting). The AHP method is used to determine the weight of each criterion. Meanwhile, SAW is used To determine the rank selection of beneficiaries. This is very important for Indonesia's economy during the crisis, Where MSME's own Power stands to face a crisis economy. Criteria used in a way This uses six measures: type of business, Amount of power Work, turnover per month, amount of assets, sector MSME, And sector business. Decision support systems are designed to support someone who must make certain decisions. That is, interactive, Flexible, Data quality, and Expert Procedure. Study System Supporters Decision Appropriateness Recipient Help Business Micro Community Use Analytic Hierarchy Process (AHP) and Simple Additive Methods weighting (SAW), Study This done in Subdistrict Intersection Three Regency Pidie Aceh Province to facilitate the Selection of Eligibility of Government Assistance Recipients For Build a business Micro Society. Testing is done in this study, namely black box testing. Results Testing black box shows that the system can walk with Good by function, with results calculation method AHP and results calculation method SAW in determining eligibility selection MSME aid recipients. The results of the level of accuracy testing on the AHP and SAW methods with six criteria and alternatives the requirements is 75%.
Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets Hartono, Hartono; Ongko, Erianto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1292

Abstract

The multi-class imbalance problem has a higher level of complexity when compared to the binary class problem. The difficulty is due to the large number of classes that will present challenges related to overlapping between classes. Many approaches have been proposed to deal with these multi-class problems. One is a hybrid approach combining a data-level approach and an algorithm-level approach. This approach is done by the ensemble on the classifier and also oversampling on the minority class. SMOTE is an oversampling method that provides good performance, but this method is necessary to determine the best sample used in the interpolation process to generate new samples. The need for determining the best sample is related to the overlap between classes that always accompanies the multi-class imbalance problem. The existence of overlap requires efforts to determine the safe region to synthesize the sample in the oversampling process in SMOTE. The safe region is considered the best for synthesizing samples due to the lower tendency of overlapping. It can be done by constructing distance features to determine the safe region. The sample with the best distance and the lowest imbalance ratio will be selected as a sample in the over-sampling process with SMOTE. The main contribution of this research is the proposed method of Hybrid Approach with Distance Feature so that it can determine safe samples, with the main advantage being in addition to handling multi-class imbalances, it is also better for handling overlapping. The results of this study will be compared with Multiple Random Balance (MultiRandBal) which performs a random oversampling process. The results showed that the Augmented R-Value, Class Average Accuracy, Class Balance Accuracy, and Hamming Loss obtained in this method was better than the random oversampling process. These results also show that the Hybrid Approach with Distance Feature provides better results in handling multi-class imbalances when compared to MultiRandBal.
Avoiding Overfitting dan Overlapping in Handling Class Imbalanced Using Hybrid Approach with Smoothed Bootstrap Resampling and Feature Selection Hartono, Hartono; Ongko, Erianto
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.985

Abstract

The dataset tends to have the possibility to experience imbalance as indicated by the presence of a class with a much larger number (majority) compared to other classes(minority). This condition results in the possibility of failing to obtain a minority class even though the accuracy obtained is high. In handling class imbalance, the problems of diversity and classifier performance must be considered. Hence, the Hybrid Approach method that combines the sampling method and classifier ensembles presents satisfactory results. The Hybrid Approach generally uses the oversampling method, which is prone to overfitting problems. The overfitting condition is indicated by high accuracy in the training data, but the testing data can show differences in accuracy. Therefore, in this study, Smoothed Bootstrap Resampling is the oversampling method used in the Hybrid Approach, which can prevent overfitting. However, it is not only the class imbalance that contributes to the decline in classifier performance. There are also overlapping issues that need to be considered. The approach that can be used to overcome overlapping is Feature Selection. Feature selection can reduce overlap by minimizing the overlap degree. This research combined the application of Feature Selection with Hybrid Approach Redefinition, which modifies the use of Smoothed Bootstrap Resampling in handling class imbalance in medical datasets. The preprocessing stage in the proposed method was carried out using Smoothed Bootstrap Resampling and Feature Selection. The Feature Selection method used is Feature Assessment by Sliding Thresholds (FAST). While the processing is done using Random Under Sampling and SMOTE. The overlapping measurement parameters use Augmented R-Value, and Classifier Performance uses the Balanced Error Rate, Precision, Recall, and F-Value parameters. The Balanced Error Rate states the combined error of the majority and minority classes in the 10-Fold Validation test, allowing each subset to become training data. The results showed that the proposed method provides better performance when compared to the comparison method
Co-Authors Aditya Pratama, Bayu Ammar Yasir Nasution Andi Rahmadsyah Andik Bintoro Andre Hasudungan Lubis Andriasan Sudarso Arnes Sembiring Asmah Indrawati B. Herawan Hayadi B. Herawan Hayadi Brilliant Handyman Manalu Citra Rahmadhani Cut Ita Erliana Dadan Ramdan Dahlan Abdullah Dedi Sahputra Desniarti Dian Kurnia Dian Maya Sari Elvie Maria Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erna Budhiarti Nababan Faadhil, Faadhil Finta Aramita Firman Syahputra Firman Syahputra Gio, Prana Ugiana Habib Satria Imelda Maelani Iqbal Giffari Ritonga Irwan Daniel Jaka Kusuma Jaka Kusuma Kelvin Leonardi Kohsasih Limas, Agus Fahmi M. Khahfi Zuhanda Manahan Hutagalung Manan Ginting Margolang, Khairul Fadhli Maricha Elveny Marischa Elveny, Marischa Martini, Dewi Meli Handayani Mendarissan Aritonang Muhammad Ikhwani Muhammad Khahfi Zuhanda Muhammad Sadikin Muhammad Zarlis Muhammad Zulkarnain Lubis Mulkan Andika Situmorang N. Nazaruddin Nadapdap, Kristanty M. N. Nadra Ideyani Vita Nasution, Mahyuddin K.M Necia Mita Novalina Pasaribu Nos Sutrisno Nur Anzelina Nur Azelina Harahap Nurliana Tarigan Nursie, Aly Opim Salim Sitompul Peniel Sam Putra Sitorus Pieter Octaviandy Prana Ugi Rachmat Aulia, Rachmat Rahmad B.Y Syah Rahman, Sayuti Rana Fathinah Ananda Retna Astuti Kuswardani Rezzy Eko Caraka Ria Wuri Andary Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Rohima Rohima Rubianto Sabina Krisdayanti Samsul A Rahman Sidik Hasibuan Sembiring, Arnes Silvia Lestari Silvia Lestari Siti Aisyah Sugeng Riyadi suswati suswati Suswati Suswati Syah, Rahmad B.Y Tulus Tulus Wanayumini Wulan Dari Yeni Risyani Yudi Gebri Foenna Zakarias Situmorang