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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 9, No 1: Regular Issue: January 2026" : 5 Documents clear
An Empirical Study on the Impact of Feature Scaling and Encoding Strategies on Machine Learning Regression Pipelines Toer, Guevara Ananta; Kim, Gwanpil
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.293

Abstract

Data preprocessing is a critical yet often underestimated component of Machine Learning (ML) regression pipelines. While prior studies have largely focused on algorithm selection and model architecture, the combined impact of feature scaling and categorical encoding strategies within end-to-end regression pipelines remains insufficiently explored. This study presents an empirical evaluation of how different preprocessing configurations influence regression model performance. Three regression algorithms, Linear Regression, Random Forest Regression, and Gradient Boosting Regression are evaluated in combination with multiple feature scaling methods (Min–Max, Standard, and Robust scaling) and categorical encoding techniques (One-Hot and Ordinal encoding). Experiments are conducted on a real-world car sales dataset comprising 50,000 records, using a k-fold cross-validation framework to ensure robust performance estimation. Model performance is assessed primarily using mean R², supported by RMSE and MAE as error-based metrics. The results demonstrate that ensemble-based models, particularly Gradient Boosting and Random Forest, consistently outperform Linear Regression across all preprocessing configurations. Feature scaling shows limited influence on ensemble model performance, whereas categorical encoding plays a more significant role, with One-Hot Encoding yielding higher predictive accuracy and lower error dispersion than Ordinal Encoding. Overall, the findings highlight that model choice is the dominant determinant of regression performance, followed by encoding strategy, while scaling has a comparatively minor effect. This study provides empirical guidance for designing robust and effective ML regression pipelines and underscores the importance of evaluating preprocessing techniques in conjunction with model selection.
Forecasting Coffee Sales Using Time-Based Features and Machine Learning Models Wijaya, Yoana Sonia; Wawolangi, Ariel Christopher
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.294

Abstract

Sales forecasting is a critical component of operational and strategic decision-making in retail and coffee businesses, where demand exhibits strong temporal variability and product-level heterogeneity. Accurate hourly-level forecasts enable effective inventory management, workforce scheduling, and data-driven promotional strategies. However, existing studies predominantly rely on aggregated sales data and provide limited comparative analyses between traditional statistical models and machine learning approaches using real transaction-level data. This study addresses this gap by conducting an empirical comparison between a traditional ARIMA model and ensemble machine learning models, namely Random Forest and XGBoost, for hourly coffee sales forecasting. The analysis is based on a real-world dataset comprising 3,547 transaction records enriched with temporal and product-related features. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results demonstrate that machine learning models significantly outperform the ARIMA baseline, with XGBoost achieving the best performance and explaining approximately 83% of the variance in sales data, while ARIMA shows limited explanatory power due to its inability to capture non-linear and highly volatile demand patterns. Feature importance analysis further reveals that product-specific attributes are the dominant drivers of sales predictions, complemented by seasonal and intra-day temporal effects. These findings provide both scientific and practical contributions by offering empirical evidence on the superiority of machine learning models for granular sales forecasting and supporting data-driven decision-making in coffee retail analytics
Machine Learning-Based Fraud Detection in E-Commerce Transactions Evelyn, Evelyn; Paramita, Adi Suryaputra
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.295

Abstract

The rapid growth of e-commerce has heightened fraud risks, demanding advanced fraud detection solutions. This study evaluates five machine learning models Logistic Regression, SVM, KNN, Random Forest, and Gradient Boosting for detecting fraudulent transactions in e-commerce environments. The models were assessed based on accuracy, precision, recall, F1-score, ROC-AUC, and error-related indicators. Results indicate that ensemble-based models, particularly Gradient Boosting and Random Forest, consistently outperform linear models like Logistic Regression, achieving superior balance between precision and recall. Gradient Boosting emerged as the top performer, with the highest accuracy (0.9763), F1-score (0.9765), and ROC-AUC (0.9880), while maintaining a low false negative rate (4.38%). These findings suggest that machine learning models, particularly ensemble methods, provide robust and efficient fraud detection frameworks. The study emphasizes the importance of using recall and F1-score as primary metrics to balance fraud detection sensitivity and operational efficiency.
Identifying Key Psychological, Academic, and Environmental Determinants of Student Stress Using Regression-Based Machine Learning Saekhu, Ahmad; Priyanto, Eko
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.291

Abstract

Student stress in higher education is a multifaceted phenomenon influenced by psychological, academic, and environmental factors, with significant implications for students’ mental health and academic performance. While previous studies have examined stress determinants using traditional statistical approaches, such methods often fail to capture complex, non-linear interactions among multiple stressors and provide limited insight into their relative importance. This study aims to identify and rank the key determinants of student stress using regression-based machine learning models. A structured dataset comprising 1,100 student observations and 21 predictor variables was analyzed. Four regression models Linear Regression, Ridge Regression, Gradient Boosting Regressor, and Random Forest Regressor were evaluated using 5-fold cross-validation and a holdout test set. Model performance was assessed using R², RMSE, and MAE metrics. The Random Forest Regressor demonstrated the best performance, achieving a test R² of 0.812, indicating strong predictive accuracy and generalization capability. Feature importance analysis using permutation importance and model-specific measures revealed that bullying was the most influential determinant of student stress, followed by extracurricular activities, self-esteem, and sleep quality. Environmental factors such as safety and basic needs also showed notable contributions. The consistency between feature importance methods confirms the robustness of the findings. This study contributes to the literature by providing an integrated and interpretable machine learning framework for identifying dominant stress determinants, offering valuable insights to support data-driven mental health interventions and policy development in higher education.
Implementation of Artificial Intelligence to Improve Customer Service Efficiency at PT Jaya Harita Lestari Seno, Axel Sandi; Falah, Najmal
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.292

Abstract

In the era of rapid digitalization, businesses are increasingly adopting Artificial Intelligence (AI) to enhance their operational efficiency and customer service. PT Jaya Harita Lestari, a procurement services company, has integrated AI technology through the PaDi UMKM platform to streamline its procurement processes and improve service delivery. This research explores how the adoption of AI-powered features such as the AI Agent and Tender Kilat has enhanced customer service efficiency. Using a case study approach, the study includes observations, interviews, and a survey to analyze the impact of AI implementation. The findings reveal that AI adoption has significantly reduced manual workloads, improved response times to customer requests, and increased overall customer satisfaction. The AI system’s automation of inquiries and procurement processes has led to faster, more transparent, and efficient services, positioning the company for better competitiveness in the digital marketplace. This study demonstrates the potential of AI to revolutionize service industries by improving operational performance and customer experience, offering a model for other businesses in the digital transformation journey.

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