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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Sistemasi: Jurnal Sistem Informasi EDUMATIC: Jurnal Pendidikan Informatika JSAI (Journal Scientific and Applied Informatics) IECON: International Economics and Business Conference Media Elektrik
Mursyid Ardiansyah
Institut Teknologi Sains dan Bisnis Muhammadiyah Selayar
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Journal : edumatic jurnal pendidikan informatika

 1 
Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb Wahyu Hidayat; Mursyid Ardiansyah; Arief Setyanto
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 1 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i1.3125

Abstract

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm.  Data analyse used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.
Analisis Perbandingan Akurasi Algoritma Naïve Bayes dan C4.5 untuk Klasifikasi Diabtes Mursyid Ardiansyah; Andi Sunyoto; Emha Taufiq Luthfi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 2 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i2.3424

Abstract

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.
Revisiting Resampling Strategies under Extreme Class Imbalance: Evidence from Large-Scale Online Payment Fraud Detection Ardiansyah, Mursyid; Abidin, Ali Asgar Zainal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33272

Abstract

Extreme class imbalance in online payment fraud detection creates an accuracy paradox and an operational risk in which improving fraud capture can generate costly false alarms. This study uses a quantitative, experiment-based design to evaluate the operational impact of common resampling strategies under extreme skew using interpretable linear decision rules. The Online Payments Fraud dataset (6.36 million transactions) from Kaggle is analysed using six monetary balance/amount variables (amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest) plus the rule-based isFlaggedFraud indicator to predict the isFraud label. Five training variants (no resampling, ROS, RUS, SMOTE, ADASYN) are compared with two linear decision rules: an ordinary least squares linear scoring model (thresholded at 0.5) and a linear SVM, using a leakage-free protocol in which resampling is applied only to the 80% training split and performance is assessed on an untouched, highly imbalanced 20% test set. The findings indicate that LinReg–RUS achieves the most balanced operating point (Precision 65.938%, Recall 47.718%, F1 55.367%, ROC-AUC 98.720%), whereas ADASYN increases recall but collapses precision (~2.1%), yielding F1 ≈4.17%. These results contribute controlled, large-scale evidence that under extreme imbalance, simpler resampling–model combinations can provide more deployable precision–recall trade-offs than aggressive synthetic sampling, supporting interpretable baselines for capacity-constrained payment screening.
Co-Authors A. Astri Surya Ramadani Abdul Ma'arief Al Imran Abdul Ma’arief Al Imran Al Imran, Abdul Ma’arief Ali Asgar Zainal Abidin Andi Sunyoto Ari Utomo Saputra Arief Setyanto Edi Suhardi Rahman Ema Utami Emha Taufiq Luthfi M Salim Muhammad Ihsan Maro Muhammad Ihsan Maro Ramadani, A. Astri Surya Sulistiawati Rahayu Ahmad Suwanto Raharjo Wahyu Hidayat Wahyu Hidayat Zahra, Aqilah