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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Electronics, Electromedical Engineering, and Medical Informatics Scientific Journal of Informatics
Wahyu Saputro, Setyo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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A Comparative Analysis of Polynomial-fit-SMOTE Variations with Tree-Based Classifiers on Software Defect Prediction Nur Hidayatullah, Wildan; Herteno, Rudy; Reza Faisal, Mohammad; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Akhtar, Zarif Bin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.455

Abstract

Software defects present a significant challenge to the reliability of software systems, often resulting in substantial economic losses. This study examines the efficacy of polynomial-fit SMOTE (pf-SMOTE) variants in combination with tree-based classifiers for software defect prediction, utilising the NASA Metrics Data Program (MDP) dataset. The research methodology involves partitioning the dataset into training and test subsets, applying pf-SMOTE oversampling, and evaluating classification performance using Decision Trees, Random Forests, and Extra Trees. Findings indicate that the combination of pf-SMOTE-star oversampling with Extra Tree classification achieves the highest average accuracy (90.91%) and AUC (95.67%) across 12 NASA MDP datasets. This demonstrates the potential of pf-SMOTE variants to enhance classification effectiveness. However, it is important to note that caution is warranted regarding potential biases introduced by synthetic data. These findings represent a significant advancement over previous research endeavors, underscoring the critical role of meticulous algorithm selection and dataset characteristics in optimizing classification outcomes. Noteworthy implications include advancements in software reliability and decision support for software project management. Future research may delve into synergies between pf-SMOTE variants and alternative classification methods, as well as explore the integration of hyperparameter tuning to further refine classification performance.
Evaluation of User Experience in the Banjarbaru Disdukcapil Public Service Application Using User Experience Questionnaire and System Usability Scale Martalisa, Asri; Wahyu Saputro, Setyo; Turianto Nugrahadi, Dodon; Abadi, Friska; Budiman, Irwan
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13780

Abstract

Purpose: Dukcapil Banjarbaru is an online-based government agency application used for various public services. According to the complaint report from Disdukcapil Banjarbaru, several users have reported similar problems and difficulties. The application has received a rating of 3.3 stars from approximately 24.000 users on the Google Play Store. Therefore, researchers conducted a user experience analysis using the UEQ methods and a usability evaluation using the SUS methods. Methods: This research analyzes user experience in applications using the UEQ to identify issues faced by users and evaluate usability through the System Usability Scale. The UEQ method is chosen for its efficiency and simplicity in assessing user experience within an application. The SUS method is employed because it is an effective approach for obtaining reliable statistical data and generating clear and accurate scores. Result: The UEQ benchmark results show that the scales for Attractiveness (1.59), Efficiency (1.68), Accuracy (1.66), and Stimulation (1.54) are categorized as "Good." The scales for clarity (1.37) and novelty (0.80) are classified as "Above Average." Meanwhile, the SUS score of 65 positions the application within the "acceptable" category for the acceptability range, the "D" category on the grade scale, and the "OK" category for adjective ratings. This indicates that while the Banjarbaru Dukcapil application has good usability, it requires improvements based on the total SUS score, which reveals several critical areas with scores below the average (258.4). Novelty: In this research, solutions for improvements are provided to Disdukcapil based on each aspect to improve the quality of the application, thereby offering better services to users.
Multi-Criteria Decision Making dalam Seleksi Fitur Ensemble untuk Prediksi Cacat Perangkat Lunak Fikri, Muhammad; Herteno, Rudy; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Abadi, Friska
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Prediksi cacat perangkat lunak merupakan upaya strategis dalam meningkatkan kualitas produk melalui identifikasi dini modul yang berpotensi cacat. Kinerja prediksi dipengaruhi oleh pemilihan fitur, karena informasi yang berlebihan dan tidak relevan dapat mempengaruhi kualitas pembelajaran model. Seleksi fitur ensemble dinilai efektif dalam menyeleksi fitur yang relevan dengan menggabungkan beberapa metode seleksi fitur berbasis filter. Diperlukan mekanisme integrasi untuk menyatukan hasil dari empat teknik filter—Mutual Information, Fisher Score, Uncertainty dan Relief. Penelitian ini membandingkan empat metode Multi‑Criteria Decision Making—TOPSIS, VIKOR, EDAS, dan WASPAS—yang bekerja dengan merangking nilai relevansi fitur hasil seleksi filter tersebut. Sepuluh fitur teratas dari tiap metode kemudian dievaluasi menggunakan model Random Forest dengan metrik AUC melalui K‑Fold cross‑validation. Dari 12 dataset NASA MDP yang diuji, TOPSIS menunjukkan kinerja paling konsisten dan terbaik dengan nilai rata-rata AUC sebesar 0,8038. Temuan ini menegaskan pentingnya pemilihan metode integrasi yang tepat dalam meningkatkan akurasi prediksi cacat perangkat lunak dan memberikan panduan bagi pengembangan model yang lebih efektif.   Abstract Software defect prediction is a strategic effort to improve product quality through early identification of potentially defective modules. Prediction performance is influenced by feature selection, because redundant and irrelevant information can affect the quality of model learning. Ensemble feature selection is considered effective in selecting relevant features by combining several filter-based feature selection methods. An integration mechanism is needed to unify the results of four filter techniques—Mutual Information, Fisher Score, Uncertainty and Relief. This study compares four Multi-Criteria Decision Making methods—TOPSIS, VIKOR, EDAS, and WASPAS—which work by ranking the relevance values ​​of the filter-selected features. The top ten features from each method are then evaluated using the Random Forest model with the AUC metric through K-Fold cross-validation. Of the 12 NASA MDP datasets tested, TOPSIS showed the most consistent and best performance with an average AUC value of 0.8038. These findings emphasize the importance of choosing the right integration method in improving the accuracy of software defect prediction and provide guidance for the development of more effective models.
Co-Authors Akhtar, Zarif Bin Dodon Turianto Nugrahadi Friska Abadi Herteno, Rudy Irwan Budiman Martalisa, Asri Muhammad Fikri Nur Hidayatullah, Wildan Radityo Adi Nugroho Reza Faisal, Mohammad Rudy Herteno