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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Indonesian Journal of Data and Science
Rosmasari
Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Digital Burnout Risk Classification Based on Doomscrolling and Fear of Missing Out Using the Mamdani Fuzzy Method Matelda Yunanta Ambon; Rosmasari; Fahrul Agus
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.442

Abstract

Introduction: Intensive digital technology use among university students may contribute to Digital Burnout, particularly when accompanied by doomscrolling and Fear of Missing Out (FOMO). This study classifies Digital Burnout risk into low, moderate, and high categories using these two digital-behavior factors. Method: A Mamdani Fuzzy Inference System was implemented in MATLAB using Doomscrolling and FOMO as inputs and Digital Burnout as the output. Data were collected through validated self-report questionnaires from 414 students, resulting in 408 valid records. The system employed nine IF–THEN rules, trapezoidal and triangular membership functions within a normalized [0,100] domain, centroid defuzzification, and twelve parameter-adjustment iterations. Results and Discussion: The ground-truth distribution showed that 47.5% of students were categorized as having high Digital Burnout, 30.1% moderate, and 22.3% low. The optimal fuzzy configuration achieved 67.89% accuracy and a macro F1-score of 0.6725. The High category achieved very high precision of 0.9688 but moderate recall of 0.6392, indicating that some high-risk students remained undetected. Classification accuracy was higher among gamers (77.19%) than general respondents (61.18%). Conclusion: The Mamdani Fuzzy approach demonstrates the feasibility of classifying Digital Burnout risk from Doomscrolling and FOMO; however, its moderate accuracy and reliance on self-reported, non-clinical labels indicate that it should be considered an exploratory prototype requiring independent validation and expert-reviewed rules before practical deployment.
Sentiment Classification of TikTok Comments on The Free Nutritious Meal Program Using Multinomial Naïve Bayes Salom Sefanya Onibala; Rosmasari; Kezia Arum Sary
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.448

Abstract

Introduction: TikTok has become an important platform for public discussion of government programs, including Indonesia’s Free Nutritious Meal (MBG) Program, while the large volume of comments makes manual sentiment analysis inefficient. This study evaluates Multinomial Naïve Bayes for classifying public sentiment toward the MBG Program using TikTok comments. Method: A total of 1,048 comments were collected from the official National Nutrition Agency TikTok account using Apify, with 1,028 comments remaining after preprocessing. Sentiment pseudo-labels were generated using IndoRoBERTa, producing 333 positive and 695 negative comments. Undersampling balanced the dataset to 666 comments, followed by an 80:20 stratified train–test split. TF-IDF feature extraction and Multinomial Naïve Bayes classification were integrated in a Scikit-learn Pipeline, while GridSearchCV with 5-fold cross-validation optimized model parameters. Results and Discussion: The optimized model achieved 80.60% accuracy, 81.04% precision, 80.60% recall, and an 80.53% F1-score on 134 test comments. Negative sentiment dominated the original post-preprocessing dataset at 67.61%. However, these results primarily indicate agreement with IndoRoBERTa-generated pseudo-labels rather than human-verified sentiment labels. Conclusion: The TF-IDF–Multinomial Naïve Bayes pipeline provides a useful baseline for MBG-related TikTok sentiment classification, but manual label validation and comparison with alternative classifiers are required to establish stronger reliability and generalizability.
Co-Authors Fahrul Agus Kezia Arum Sary Matelda Yunanta Ambon Salom Sefanya Onibala