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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 157 Documents
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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
A Text Classification Approach for Detecting Cyberbullying Risk on Twitter Using Support Vector Machine with Naive Bayes and Random Forest Comparison Yarsasi, Sri; Iskoko, Angga
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.290

Abstract

The rapid development of social media as a means of digital interaction also presents serious challenges in the form of the spread of negative content, including cyberbullying. Cyberbullying is a form of verbal violence committed online and has a significant impact on mental health, especially in adolescents. This research aims to develop a text classification model to detect the risk of cyberbullying using the Support Vector Machine (SVM) algorithm. The data used comes from a collection of cyberbullying-themed tweets. The research stages include text preprocessing (normalization, cleaning, tokenization, stopword removal, and stemming), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), data division into training and testing sets, and model training using linear kernel of SVM. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that this approach is able to identify risky comments quite accurately, with optimal performance on the linear kernel. This research contributes to the development of automated detection systems to create a safer and healthier digital ecosystem, and supports preventive efforts in mitigating cyberbullying online.
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.
A Comparative Analysis of Machine Learning Classifier of Anemia Diagnosis Based on Complete Blood Count (CBC) Data Putri, Nadia Awalia; Mukti, Bayu Priya
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.286

Abstract

Anemia is a prevalent hematological condition that requires accurate and timely diagnosis to ensure effective treatment. This study aims to compare the performance of several machine learning algorithms Random Forest, Support Vector Machine (SVM), Naive Bayes, and XGBoost in classifying different types of anemia based on Complete Blood Count (CBC) data. The dataset includes three diagnostic categories: Healthy, Normocytic hypochromic anemia, and Normocytic normochromic anemia. After preprocessing and normalization, each model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that XGBoost achieved the highest overall performance with 99% accuracy and a perfect AUC of 1.00, followed closely by SVM and Naive Bayes. Naive Bayes showed lower performance, particularly in identifying normocytic normochromic anemia. These findings suggest that machine learning, especially ensemble-based models, holds strong potential in supporting clinical diagnosis of anemia using CBC data.
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.
An Analysis of the Relationship Between Social Media Usage Intensity and Anxiety Levels Among University Students Using a Quantitative Approach Guballo, Jayvie Ochona
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.287

Abstract

The rapid development of social media has significantly influenced students' communication patterns and daily habits. While it offers ease in accessing information and interacting with others, excessive use of social media can negatively affect mental health, particularly anxiety. This study aims to analyze the relationship between social media usage intensity and anxiety levels among university students. A descriptive-correlational quantitative approach was applied using secondary data. The analysis was conducted using the Python programming language through several stages, including data cleaning, descriptive statistics, data visualization, and Pearson correlation testing. The results show a significant positive relationship between the duration of social media usage and students' anxiety levels, with a correlation coefficient of 0.52 and a p-value of 0.003. These findings indicate that the more time students spend on social media, the higher their reported anxiety levels. This study is expected to serve as a basis for promoting digital literacy and raising awareness of the importance of mental health among university students.
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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