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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 2: Regular Issue: March 2026" : 5 Documents clear
Classifying Driver Behavior Using Machine Learning: A Simple Approach to Detect Distracted and Aggressive Driving Purba, Ika Christine; Safhadi, Aulia Al-JIhad
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.299

Abstract

This study explores the use of ML models to classify driver behavior as either Distracted or Aggressive, using data derived from real-world driving scenarios. Two ML algorithms, Random Forest (RF) and Support Vector Machine (SVM), were applied to classify driver behavior based on key features such as brake_pressure, lane_deviation, and headway_distance. The RF model outperformed the SVM model, achieving an accuracy of 95% compared to 94% for SVM. The study demonstrates that brake_pressure and headway_distance are the most important features for detecting Aggressive driving, while lane_deviation is crucial for identifying Distracted driving. The findings suggest that RF is particularly effective in handling complex, high-dimensional data, providing accurate and reliable predictions. The results contribute to the advancement of road safety technologies by enhancing the detection of unsafe driving behaviors, which can be integrated into Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Future work should focus on expanding the dataset, integrating more diverse sensor data, and exploring more complex ML models, such as deep learning, to further improve classification accuracy and real-time performance in real-world applications.
Predicting Customer Churn in E-Commerce Using Machine Learning: A Comprehensive Approach Kumar, Priya Sakthi; P, Vinoth Kumar
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.300

Abstract

Customer churn prediction is a critical challenge in e-commerce, as retaining existing customers is often more cost-effective than acquiring new ones. This study evaluates the effectiveness of three machine learning algorithms Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for predicting customer churn using a structured e-commerce dataset that integrates demographic, behavioral, engagement, and transactional features. A systematic preprocessing pipeline was implemented, including missing value imputation, categorical encoding, and feature scaling where appropriate. Model performance was assessed using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix analysis. The results reveal a clear performance gap between linear and ensemble-based approaches. Logistic Regression achieved 77% accuracy but demonstrated limited recall for churned customers, indicating reduced sensitivity in identifying at-risk users. In contrast, Random Forest and XGBoost both achieved 92% accuracy and substantially improved recall and F1-Score for the churn class. Among the evaluated models, XGBoost showed the strongest overall performance, achieving the highest recall and lowest false negatives, making it particularly suitable for retention-focused applications. The findings confirm that ensemble and boosting-based models are more effective in capturing nonlinear interactions among multidimensional churn determinants in e-commerce environments. This study contributes empirical evidence supporting the adoption of advanced machine learning approaches for customer retention optimization and provides practical guidance for integrating churn prediction models into e-commerce platforms.
Optimizing Time Series Forecasting for AI Job Market Dynamics: A Comparative Study of SARIMA and Prophet with Cross-Validation Widodo, Slamet; Santoso, Budi
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.296

Abstract

The rapid growth of Artificial Intelligence (AI) has led to significant shifts in the job market, making accurate forecasting essential for workforce planning and policy-making. This study aims to compare the effectiveness of two time series forecasting models, SARIMA and Prophet, in predicting AI job market trends. The primary focus was to evaluate their performance using RMSE and Time Series Cross-Validation (TSCV). The SARIMA model outperformed Prophet, particularly when capturing seasonal patterns, achieving an RMSE of 83.42, compared to Prophet's 136.01. The results demonstrate that SARIMA’s parameter tuning and seasonality handling contribute to more reliable forecasts, while Prophet’s flexibility offers advantages in non-linear data scenarios. The study provides valuable insights for stakeholders in adapting workforce strategies, particularly in AI-driven sectors, by recommending SARIMA for seasonal trends and Prophet for volatile, non-linear data.
Personalized Hydration Prediction: Leveraging Machine Learning to Model Daily Water Intake Based on Physical Activity and Environmental Factors Yuliati, Emi; Nurdiyanti, Oktavia Mulyo
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.297

Abstract

Adequate hydration is essential for maintaining optimal health and performance, yet individual hydration needs vary due to factors such as physical activity, environmental conditions, and demographic characteristics. Traditional methods of hydration assessment often overlook these dynamic factors, making it difficult to provide personalized recommendations. This study aims to develop a Random Forest Regression model to predict daily water intake based on physical activity levels, weather conditions, and demographic information. The model was trained and evaluated using a dataset that included these variables, and performance was assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that the Random Forest Regression model achieved an R² value of 0.8527, indicating that it explained over 85% of the variance in daily water intake. The MSE (0.1010) and MAE (0.2630) values confirmed that the model made accurate predictions. This study contributes to the field by offering a personalized approach to hydration prediction, which could be integrated into health applications and fitness tracking systems. By incorporating real-time physical activity data and environmental factors, the model provides dynamic hydration recommendations that can optimize health outcomes, particularly for high-risk groups such as athletes and the elderly. This research demonstrates the potential of Random Forest Regression for improving hydration management and advancing personalized health recommendations.
Leveraging Machine Learning Algorithms for Early Detection of Breast Cancer: A Comparative Study Using Diagnostic Features Abikhair, Aulia; Guanghui, Huang
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.298

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, making early and accurate diagnosis essential for improving survival rates and treatment outcomes. To address limitations associated with conventional diagnostic methods, Machine Learning (ML) techniques have been increasingly adopted to enhance classification accuracy and reduce diagnostic variability. This study presents a comparative evaluation of four widely used ML algorithms Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression applied to a structured breast cancer diagnostic dataset. The dataset comprises morphological and texture-based features extracted from digitized tumor samples, enabling binary classification of benign and malignant cases. The models were trained using an 80:20 train–test split and validated through k-fold cross-validation. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix analysis to ensure comprehensive assessment of classification behavior. Experimental results indicate strong predictive performance across all models, with overall accuracy values ranging from 0.95 to 0.96. Among the evaluated approaches, Random Forest demonstrated the most balanced performance, particularly achieving the highest recall for malignant tumors and the lowest false-negative rate, which is critical in clinical diagnostics. Feature importance analysis further revealed that tumor area, concave points, radius, and perimeter were the most influential predictors in classification decisions, consistent with established clinical indicators of malignancy. These findings confirm that classical and interpretable machine learning algorithms, especially ensemble-based methods, remain highly effective for structured breast cancer classification tasks. The study contributes to the advancement of reliable and transparent ML-based decision-support systems, supporting improved early detection and diagnostic accuracy in breast cancer care.

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