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Taqwa Hariguna
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IJIIS: International Journal of Informatics and Information Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : https://doi.org/10.47738/ijiis
Core Subject : Science,
Computer Science & IT
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 8, No 2: March 2025" : 5 Documents clear
Examining Risk and Trust in Student Mobile Banking Adoption: An Extended Technology Acceptance Model Perspective Rahma, Felinda Aprilia; Ulfah, Siti Zayyana
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.261

Abstract

This study examines the influence of trust, compatibility, satisfaction, perceived risk, and risk acceptance on students’ intention to use mobile banking applications. As mobile banking becomes increasingly popular for its convenience and efficiency, especially among tech-savvy youth, understanding the behavioral factors influencing adoption is crucial. Utilizing an extended Technology Acceptance Model (TAM), this study employs a quantitative approach with multiple linear regression analysis. The dataset, comprising responses from 219 Indonesian university students, was drawn from the "cogent_adoption_m_banking_pls" survey. The results reveal that satisfaction, compatibility, and trust significantly influence students’ intention to use mobile banking, while perceived risk and risk acceptance do not show a significant effect. The model explains approximately 57% of the variance in adoption intention (R² = 0.570), indicating substantial explanatory power. These findings offer valuable insights for mobile banking developers and financial institutions to enhance user satisfaction, align services with user lifestyles, and strengthen trust—key factors for increasing adoption among student users.
Comparative Sentiment Analysis of Digital Wallet Applications in Indonesia Using Naïve Bayes Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.251

Abstract

The rapid growth of financial technology in Indonesia has led to widespread use of digital wallet applications such as OVO, DANA, GoPay, and ShopeePay. User-generated reviews on platforms like the Google Play Store offer valuable insights into customer satisfaction and application performance. This study conducts a comparative sentiment analysis of user reviews for four major Indonesian e-wallets using the Multinomial Naïve Bayes algorithm. A total of 401 Indonesian-language reviews were collected and labeled based on user ratings, with sentiment classified as positive or negative. The TF-IDF method was applied for feature extraction, and the model was evaluated using accuracy, precision, and recall metrics. Results show that ShopeePay achieved the highest classification accuracy (89%), followed by DANA and GoPay (80%), while OVO recorded lower performance due to more informal and ambiguous language. The model demonstrated strong precision for positive sentiment but low recall for negative sentiment (28%), indicating challenges in detecting minority-class feedback. Word cloud visualizations were used to highlight common keywords in each sentiment category. This study confirms that Naïve Bayes is an effective approach for classifying user sentiment in Indonesian-language app reviews, while also emphasizing the need for improved handling of class imbalance in future research. The findings provide practical insights for developers to enhance user experience based on data-driven sentiment patterns.
Interpretable Product Recommendation through Association Rule Mining: An Apriori-Based Analysis on Retail Transaction Data Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.252

Abstract

The rapid growth of e-commerce has generated vast amounts of transactional data, creating opportunities for data-driven decision-making in retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and 45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in modern recommendation systems.
Enhancing Household Energy Consumption Forecasting Using the XGBoost Algorithm with Cross-Validation and Residual-Based Evaluation Sugianto, Dwi; Yulianto, Koko Edy
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.253

Abstract

Accurate forecasting of household energy consumption plays a crucial role in optimizing energy efficiency, supporting sustainable policy decisions, and improving operational management in smart grid systems. This study enhances conventional XGBoost-based forecasting by integrating cross-validation and residual-based evaluation to ensure model robustness and interpretability. Using a dataset of over 90,000 daily household energy records that include temperature, humidity, and appliance-level usage, a systematic preprocessing pipeline was applied—comprising data cleaning, normalization, temporal feature transformation, and partitioning into training and testing subsets. The proposed model was trained using 10-fold cross-validation to minimize overfitting and validated through residual error analysis to assess stability and bias. Evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), demonstrate superior predictive accuracy, achieving MAE = 0.48, RMSE = 0.64, and R² = 0.9864. Visualization of actual versus predicted consumption and symmetric residual distribution further confirm the model’s reliability. The findings highlight that the enhanced XGBoost model not only achieves high precision but also provides a robust foundation for real-time energy monitoring, anomaly detection, and sustainable household energy management. Future work will integrate SHAP-based interpretability and comparative benchmarking with deep learning approaches.
Analyzing Key Factors Influencing Employee Resignation Through Decision Tree Modeling and Class Balancing Techniques Saputra, Jeffri Prayitno Bangkit; Hidayat, Muhammad Taufik
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.259

Abstract

Employee resignation poses a significant challenge to organizational stability and workforce planning. This study aims to analyze the key factors influencing employee resignation by developing an interpretable predictive model using the Decision Tree algorithm. The analysis is conducted on the IBM HR Analytics dataset, which includes 1,470 employee records with diverse demographic, behavioral, and job-related attributes. To address the issue of class imbalance—where resignation cases are underrepresented—the Synthetic Minority Over-sampling Technique (SMOTE) is applied to enhance model sensitivity and balance. After a comprehensive data preprocessing phase, including feature selection and label encoding, the Decision Tree model is trained with a limited depth to reduce overfitting and maintain interpretability. The model achieves an accuracy of 77%, with a recall of 0.80 and an F1-score of 0.77 for the resignation class. Feature importance analysis identifies stock option level, job satisfaction, monthly income, relationship satisfaction, and job involvement as the most influential predictors. These findings provide actionable insights for human resource practitioners seeking to implement targeted and data-driven employee retention strategies. The study highlights the practical value of interpretable machine learning models in human capital analytics.

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