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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 1: January 2025" : 5 Documents clear
A Gaussian Naive Bayes and SMOTE-Based Approach for Predicting Breast Cancer Aggressiveness in Imbalanced Datasets Dewi, Deshinta Arrowa; Kurniawan, Tri Basuki
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.250

Abstract

Breast cancer remains one of the leading causes of death among women worldwide, making early and accurate detection essential to improving patient outcomes. This study aims to develop a predictive model for breast cancer aggressiveness using the Gaussian Naive Bayes algorithm on the Breast Cancer Wisconsin Diagnostic Dataset. The dataset contains 569 instances with 30 numerical features representing various cell characteristics. Preprocessing steps included data cleaning, label encoding, and Min-Max normalization. The model was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. Initially, the model achieved an accuracy of 78.88%; however, the recall for malignant cases was relatively low at 45.5%, highlighting a critical limitation in detecting aggressive cancer. To address class imbalance and improve model sensitivity, the Synthetic Minority Oversampling Technique (SMOTE) was applied. While detailed post-SMOTE metrics were not reported in this version, the approach is expected to enhance recall and F1-score for the malignant class. This research demonstrates the potential of Gaussian Naive Bayes, combined with data balancing techniques, as a fast and interpretable tool for early breast cancer diagnosis. Future work will focus on model comparison, cross-validation, and statistical evaluation to improve robustness and reliability.
Identifying Adolescent Behavioral Profiles Through K-Means Clustering Based on Smartphone Usage, Mental Health, and Academic Performance Aristo, Dominic Dinand; Srinivasan, Bhavana
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.231

Abstract

The pervasive integration of digital devices into students’ daily lives has profoundly shaped their learning habits and psychological well-being. As technology becomes increasingly embedded in academic and personal routines, understanding the relationship between digital engagement, mental health, and academic outcomes is vital for developing effective student-support and intervention frameworks in higher education. This study seeks to uncover behavioral patterns among college students by examining the interconnections between smartphone usage, mental health indicators, and academic performance through a data-driven machine learning approach. Utilizing the K-Means clustering algorithm, students were categorized into distinct behavioral profiles derived from eight core features: daily screen time, sleep duration, grade performance, exercise frequency, anxiety level, depression level, self-confidence, and screen exposure before sleep. A dataset comprising 3,000 entries was preprocessed through normalization and analyzed within the Knowledge Discovery in Databases (KDD) framework to ensure structured and reliable data processing. The Elbow Method identified four optimal clusters, each reflecting unique behavioral characteristics. Cluster 1 represented well-balanced students with stable academic and emotional states; Cluster 2 included high-achieving yet anxious individuals; Cluster 3 captured those exhibiting excessive digital engagement and psychological distress; and Cluster 4 comprised moderately engaged students with lower self-confidence. Visual representations, including bar and radar charts, were generated to illustrate inter-cluster variations and enhance interpretability of behavioral distinctions. The findings reveal that digital usage patterns are closely linked to mental health and academic performance, suggesting that excessive or unregulated device use can heighten emotional strain and academic inconsistency. These insights highlight the necessity of personalized mental health initiatives and targeted digital literacy programs grounded in behavioral segmentation. Overall, the study demonstrates the applicability of unsupervised machine learning for behavioral profiling and provides evidence-based recommendations for educators, mental health practitioners, and policymakers seeking to foster balanced and healthy digital habits among students.
Classification and Prediction of Video Game Sales Levels Using the Naive Bayes Algorithm Based on Platform, Genre, and Regional Market Data Putra, Rafi Pratama; Ramadani, Nevita Cahaya; Nanjar, Agi
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.242

Abstract

The exponential expansion of the video game industry has resulted in a vast accumulation of market data that can be leveraged to analyze and predict sales performance. This study aims to construct a classification model for video game sales levels by applying the Naïve Bayes algorithm, recognized for its simplicity, efficiency, and strong baseline performance in supervised learning tasks. The research employs a public dataset containing over 13,000 video game entries, encompassing key attributes such as genre, platform, publisher, release year, user and critic ratings, and global sales figures. The target variable global sales was discretized into three categories: Low (1 million units), Medium (1–5 million units), and High (5 million units) to represent distinct tiers of commercial success. Prior to modeling, the dataset underwent a comprehensive preprocessing pipeline involving duplicate removal, handling of missing data, normalization of numerical attributes, and feature selection to ensure optimal model performance. The Multinomial Naïve Bayes classifier was then implemented and assessed using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results revealed an accuracy of 71.82% and an F1-score of 70.03%, signifying strong predictive capability for a probabilistic model of this simplicity. The classifier effectively identified low and medium sales categories, though slightly underperformed on the high sales group due to class imbalance within the dataset. Further analysis of conditional probabilities indicated that game genre, platform popularity (especially PS2 and Wii), and critic scores were the most influential determinants of higher sales outcomes. These findings affirm that the Naïve Bayes algorithm provides a reliable and interpretable foundation for video game sales prediction, serving as a benchmark model in market analytics. Future studies are encouraged to address data imbalance through oversampling or synthetic data generation, incorporate contextual variables such as marketing strategies and release schedules, and explore ensemble or deep learning approaches to enhance predictive accuracy and robustness.
An Efficient Sampling Approach for Village Elections: Quick Count Using Stratified Systematic Cluster Sampling Nugroho, Khabib Adi; Turino, Turino
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.245

Abstract

Quick counts are widely used to estimate election outcomes before official results are announced. However, their accuracy depends on the quality of the sampling method used. This study applies the Stratified Systematic Cluster Random Sampling method in the 2019 village head election in Panembangan Village, Indonesia, to provide an efficient and statistically sound quick count process suitable for rural contexts with limited resources. The method integrates stratification (dividing voters by hamlet), clustering (grouping by polling stations), and systematic sampling (selecting polling stations at fixed intervals). Out of 10 polling stations, 5 were systematically selected after stratification. All valid votes from these polling stations were used for estimation. The results show that candidate Untung Sanyoto received 59.16% of the vote, while his opponent received 40.84%. The margin of error was ±0.69% at a 95% confidence level, and the estimates closely matched the official election results, demonstrating the method’s reliability. This study shows that combining statistical rigor with practical sampling strategies can produce fast, accurate, and cost-effective results. It provides a scalable model for local elections, especially in regions facing geographic or logistical challenges, and contributes to the development of transparent and trustworthy democratic practices.
A Machine Learning Approach to Indonesian Climate Change Sentiment Analysis Using Naive Bayes Henderi, Henderi; Sofiana, Sofa
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.246

Abstract

Climate change poses a significant global challenge, particularly for archipelagic nations such as Indonesia that are highly vulnerable to rising temperatures and extreme weather events. This study applies machine learning-based sentiment analysis to assess Indonesian public opinion on climate change using Twitter data. A total of 5,120 Indonesian-language tweets were collected through keyword-based scraping related to climate and weather conditions. Following text preprocessing (lowercasing, stopword removal, stemming, and cleaning), TF-IDF vectorization was used to extract the top 400 most significant terms. The dataset was divided into training (80%) and testing (20%) subsets, and a Multinomial Naïve Bayes classifier was trained to categorize sentiments into positive, neutral, and negative classes. The results show a dominance of negative sentiment (62%), primarily associated with extreme heat and storm-related events, while neutral (24%) and positive (14%) sentiments were linked to moderate weather conditions. Model evaluation achieved an F1-score of 0.95 for negative, 0.86 for neutral, and 0.83 for positive sentiment, yielding a macro-average F1-score of 0.88. The analysis also identified “panas (hot),” “hujan (rain),” and “banjir (flood)” as top lexical indicators influencing classification. Overall, the findings highlight that Indonesian public sentiment toward climate change is highly reactive to extreme weather. The study underscores the potential of Naïve Bayes as a baseline model for real-time environmental sentiment monitoring, offering valuable insights for institutions such as BMKG to enhance public communication and climate awareness strategies.

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