Articles

A Comparative Machine Learning Approach for Usability Evaluation of Localized Software in Afghanistan

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Usability is one of the most important software quality attributes in determining user satisfaction, productivity and system acceptance. Afghanistan is a multilingual country, and usability problems with localized software applications may occur due to linguistic diversity, cultural differences, and scarce resources for evaluating the usability of software applications. These traditional usability assessment techniques, such as heuristic evaluation and laboratory testing, are difficult to implement in a capacity-constrained environment, as they require significant time, expertise and funds. The study suggests an automatic usability evaluation framework by machine learning techniques for localized software systems in Afghanistan. Data were gathered from ISO 9241-11 usability framework-based structured questionnaires from users of localized software applications. After collecting the responses, they were subjected to a pre-processing procedure involving cleaning, encoding and normalizing. Three supervised machine learning algorithms Decision Tree (DT), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) were trained and tested with 80:20 ratio of training and test data. Model performance was evaluated using the accuracy, precision, recall, F1-score and confusion matrix analysis. Experimental results indicate that the highest accuracy of 91.4% is obtained by DT followed by SVM with 90.0% accuracy and KNN with 88.7%. The results show that the Decision Tree is a good model for balancing the prediction accuracy and interpretation of local software environments. The proposed framework has been designed to be both scalable and cost-effective, and can be used to complement existing usability evaluation methods, and can also be used to aid in software quality improvement efforts in multilingual and developing country settings.

Development of a Character Evaluation Model in Risk Management for Microfinance in Individuals of Small Medium Enterprise

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This study develops a character evaluation model for PT.XYZ’s customers in microfinance credit risk management. Integrating psychological and industrial engineering approaches, this research assesses customer personality using the International Personality Item Pool Big-Five Factor Marker-25 (IPIP BFM-25). The five personality dimensions, which are Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, are assessed to classify customers according to their credit risk level. Decision Tree is employed for the classification of customers into risk groups, and the latter are represented graphically with Traffic Light Analysis (TLA) color codes green (low risk), yellow (medium risk), and red (high risk). Research reveals that the predictors of the classification of credit risk are most powerful for conscientiousness and neuroticism, with more conscientiousness equating to less risk and more neuroticism equating to more risk. Most of the customers are medium-risk, and more assessment is necessary prior to granting credit. The study reveals advantages of applying tests of psychology for making financial judgments, giving a better method to financial institutions than traditional financial standards for assessing creditworthiness. The approach enhances risk forecasting quality, assists with the minimization of non-performing.