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All Journal JOIV : International Journal on Informatics Visualization Journal of Soft Computing Exploration Scientific Journal of Informatics
Amirullah, Afif
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Classification of potential customers using C4.5 and k-means algorithms to determine customer service priorities to maintain loyalty Syani, Nur Hazimah; Amirullah, Afif; Saputro, Meidika Bagus; Tamaroh, Ilham Alzahdi
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.89

Abstract

The increasing competition among Middle-Class Micro Enterprises (MSMEs) is a problem because business actors must improve techniques and strategies to maintain customer satisfaction, and the number of customers continues to increase. Customers are an essential asset for the company. To maintain customer loyalty with promising prospects for the company, a strategy is needed to support this. Strategies such as service prioritization can be used to maintain customer loyalty. This research was conducted to classify customers who are estimated to have good prospects for the company so that service priorities are not mistargeted by utilizing 1683 data from store By.SIRR, a fashion store in Semarang, Indonesia contains five attributes, and customers are classified and are estimated to have promising prospects for the company. Data mining methods use the C4.5 and K-Means algorithms to classify the classification process. The research resulted in the grouping of customers into four categories: potential lover, flirting, faithful lover, and spiritual friend. From the validation test conducted using the Confusion Matrix Validation method, the classification results get an Accuracy of 97.70%.
Improve Software Defect Prediction using Particle Swarm Optimization and Synthetic Minority Over-sampling Technique Amirullah, Afif; Umi Laili Yuhana; Muhammad Alfian
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.v11i4.16808

Abstract

Purpose: Early detection of software defects is essential to prevent problems with software maintenance. Although much machine learning research has been used to predict software defects, most have not paid attention to the problems of data imbalance and feature correlation. This research focuses on overcoming the problems of imbalance dataset. It provides new insights into the impact of these two feature extraction techniques in improving the accuracy of software defect prediction. Methods: This research compares three algorithms: Random Forest, Logistic Regression, and XGBoost, with the application of PSO for feature selection and SMOTE to overcome the problem of imbalanced data. Comparison of algorithm performance is measured using F1-Score, Precision, Recall, and Accuracy metrics to evaluate the effectiveness of each approach. Result: This research demonstrates the potential of SMOTE and PSO techniques in enhancing the performance of software defect detection models, particularly in ensemble algorithms like Random Forest (RF) and XGBoost (XGB). The application of SMOTE and PSO resulted in a significant increase in RF accuracy to 87.63%, XGB to 85.40%, but a decrease in Logistic Regression (LR) accuracy to 72.98%. The F1-Score, Precision, and Recall metrics showed substantial improvements in RF and XGB, but not in LR due to the decrease in accuracy, highlighting the impact of the research findings. Novelty: Based on the comparison results, it is proven that the SMOTE and PSO algorithms can improve the Random Forest and XGB models for predicting software defect.
Enhancing User Experience through UI Redesign Using the UEQ+ Method Setiawan, Wahyu Fajar; Amirullah, Afif; Rochimah, Siti
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3596

Abstract

This research redesigned the User Interface (UI) of the XYZ e-wallet application, applying the User Experience Questionnaire Plus (UEQ+) testing method within the Design Thinking framework. This research contributes to the field by addressing the absence of comprehensive UI/UX evaluation in financial technology applications through an iterative design methodology. Initial UEQ+ assessment utilizing nine user experience questionnaire scales revealed significant usability issues, with intuitive use scoring 2.32 and clarity scoring 2.60, indicating substantial potential for improvement. The five stages of Design Thinking (Empathize, Define, Ideate, Prototype, Test) were systematically applied to solve the identified problems. Interactive prototyping in Figma facilitated real user testing of critical features, including the homepage, QRIS Payment, and History & Transfer notify. Post-redesign, there were significant increases in intuitive use (from 2.34 to 3.91; 67.1%), clarity (from 2.90 to 4.33; 49.3%), efficiency (from 3.25 to 4.44; 36.6%), trust metrics (from 3.41 to 4.51; 32.3%), and content quality (from 3.07 to 4.34; 41.4%). The statistical validation yielded a Cronbach’s Alpha of 0.965, indicating excellent reliability of the measurement. The high relationship among the factors (0.313-0.960) reflects a broad improvement. This study introduces the first empirically validated model that combines UEQ+ evaluation with Design Thinking for e-wallet applications, offering evidence-based UI/UX design guidelines for fintech, particularly valuable for Indonesian and similar developing markets where trust critically affects adoption.
Co-Authors Budiman, Kholiq Haryanti, Unik Slamet Muhammad Alfian Nurdin, Alya Aulia Pramesti, Pricilia Intan Saputro, Meidika Bagus Setiawan, Wahyu Fajar Siti Rochimah Syani, Nur Hazimah Tamaroh, Ilham Alzahdi Yuhana, Umi Laili