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Charitas Fibriani (SCOPUS ID=57192643331)
Universitas Kristen Satya Wacana
Computer Science & IT Electrical & Electronics Engineering

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Optimization of Support Vector Machine for the Classification of Nutritional Status in Children Emanuell Deftavalandra Rahmanto; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6192

Abstract

Toddlerhood is a critical developmental period that requires precise nutritional monitoring. However, automated classification systems are often challenged by imbalanced data, which makes minority classes difficult to detect accurately. This study aims to optimize a Support Vector Machine (SVM) using a polynomial kernel to improve detection sensitivity for critical classes. By excluding BMI features to avoid redundancy, the proposed model achieved an accuracy of 98%. The main novelty of this research lies in its achievement of an F1 Macro Score of 0.86, confirming that the model provides balanced and reliable classification performance across all nutritional status categories. These results demonstrate the model’s superiority in identifying the minority classes of Severe Malnutrition and Undernutrition more effectively than previous studies. Therefore, the model is highly recommended as an objective decision support system for the early detection of stunting.
Novel Genre Classification based on Synopsis using the Random Forest Algorithm Prananing Mahanani; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5815

Abstract

Novel genre classification based on synopses presents a significant challenge in text processing, as each genre exhibits distinct lexical characteristics. This study evaluates the performance of the Random Forest algorithm in classifying novel genres under conditions of imbalanced data distribution. The research stages include text preprocessing—comprising case folding, tokenization, stopword removal, and stemming—feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and model training with Random Forest. In addition, manual data balancing was applied by increasing samples in minority classes through simple oversampling. The model was evaluated using accuracy metrics and confusion matrix analysis. The results indicate that Random Forest is able to identify most genres with moderate accuracy, particularly for classes with larger data volumes. The initial model achieved an accuracy of 42.11%, which increased to 46.67% after the application of data balancing. Misclassification primarily occurred in genres with limited samples that share similar vocabulary with dominant genres. These findings demonstrate that Random Forest can still be applied to synopsis-based novel genre classification without fully relying on balancing techniques. However, performance remains uneven across classes, highlighting the need for per-genre analysis to obtain a more comprehensive evaluation.
Classification of Online Game Player Engagement Levels using the Random Forest Algorithm Febrian Joseph Laia; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6514

Abstract

The rapid growth in the number of online game players has generated large volumes of behavioral data that can be leveraged to analyze player engagement levels. However, the primary challenges include class imbalance in engagement data and the limited interpretability of predictive models regarding the factors influencing their decisions. This study aims to develop a classification model for online game player engagement levels using the Random Forest algorithm, while addressing class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) and identifying the most influential features using Feature Importance analysis. The study utilized the Online Gaming Behavior Dataset from Kaggle, comprising 40,034 records. The Random Forest model was optimized using RandomizedSearchCV with five-fold cross-validation to determine the optimal hyperparameter configuration. The experimental results demonstrate that the proposed model achieved an overall accuracy of 92%, with recall values of 90%, 94%, and 89% for the Low, Medium, and High engagement classes, respectively. Feature Importance analysis using both impurity-based and permutation approaches consistently identified SessionsPerWeek (0.4375 and 0.4309) and AvgSessionDurationMinutes (0.3371 and 0.3540) as the two most influential features, jointly accounting for more than 77% of the model's predictive decisions, whereas demographic features contributed only marginally. The novelty of this study lies in the integration of Random Forest, SMOTE, and Feature Importance to develop a classification model that is not only highly accurate but also interpretable. These findings provide valuable insights for game developers in designing evidence-based player retention strategies, such as implementing daily login rewards to increase session frequency and time-limited events to encourage longer gameplay sessions.
Co-Authors Emanuell Deftavalandra Rahmanto Febrian Joseph Laia Prananing Mahanani