Early Warning System for the Impact of E-sports on Academics Based on Hybrid Naïve Bayes and Particle Swarm

Mohammad Syamsul Azis

doi:10.59934/jaiea.v5i2.2208

Authors

Mohammad Syamsul Azis Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.2208

Keywords:

dynamic classification, early warning system, e-sport, Naïve Bayes, particle swarm optimization

Abstract

The integration of e-sports into the lifestyle of high school students in the era of industrial technology 4.0 brings a double dilemma between the development of digital skills and the risk of declining academic performance due to addiction. Previous research has successfully classified the impact of e-sports using the Naïve Bayes algorithm, but the model is static and only provides post-mortem analysis. This study proposes an Early Warning System (EWS) based on a hybrid of Naïve Bayes and Particle Swarm Optimization (PSO) designed to work dynamically and comprehensively. PSO is implemented to heuristically optimize attribute weights to overcome the weakness of the feature independence assumption in the pure Naïve Bayes algorithm. The test was carried out using the 10-fold cross-validation method on 178 student data of Madrasah Aliyah Negeri Rengasdengklok. The algorithm implementation resulted in an accuracy rate of 75.95% and an Area Under Curve (AUC) of 0.792. The main contribution of this research is the transformation from a traditional classification model to a proactive early warning system, where the system real-time monitors playing and studying duration, and then uses a probability threshold (Ƭ≥0.6) to trigger mitigation notifications to teachers and parents. In-depth analysis results show that behavioral variables such as playing duration have a much more massive level of significance (weight 1.0) compared to cognitive intelligence level or IQ (weight 0.001) in predicting academic failure. These findings provide a new paradigm for educational institutions in designing intervention strategies focused on time management and student digital literacy.

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References

R. Riskuna and L. Uce, “ANALYSIS STUDY OF THE EFFECT OF ONLINE GAMES ON STUDENTS’ LEARNING INTEREST,” Jurnal Pendidikan Nusantara , vol. 10, no. 1, Dec. 2024.

HF Ramadhan, P. Wijayanti, and L. Ronggowulan , “ The Influence of E-Sports on Spatial Thinking of Social Studies High School Students in Surakarta City in 2023,” Geadidaktika , vol. 5, no. 2, pp. 171–177, 2025, [Online]. Available: https://dx.doi.org/10.209

R. Ramadhan and S. Merlinda, “THE INFLUENCE OF E-SPORTS INFRASTRUCTURE ON ECONOMIC GROWTH IN SOUTHEAST ASIAN COUNTRIES,” Bina Ekonomi , vol. 28, no. 2, p. 111, 2024.

ED Larasati, M. Kanzunnudin , and IA Pratiwi, “ Impact Intensity Playing Online Games Against Motivation Learning and Children's Social Behavior ,” Indonesian Journal of Education and Social Sciences , vol. 2, no. 2, pp. 112–123, Sep. 2023, doi : 10.56916/ ijess.v 2i2.495.

H. Basri, M. Syamsul Azis, Y. Malau, EW Fridayanthie , K. Rizal, and H. Rianto, " Application of Particle Swarm Optimization in the Naïve Bayes Algorithm for Classification of Learning Outcomes ,” INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS , vol. 6, no. 2, p. 28534471, 2022.

A. Pebdika , R. Herdiana , and D. Solihudin , “CLASSIFICATION USING THE NAIVE BAYES METHOD TO DETERMINE PROSPECTIVE PIP RECIPIENTS,” JATI ( Journal Informatics Engineering Students ) , vol. 7, no. 1, pp. 452–458, Feb. 2023.

A. Arpan and Mohammad Yusup, “ System Early Warning of Declining Student Achievement and Interest in Learning School Middle School in Medan: An Explainable Machine Learning Approach ,” Journal Computer Technology Information System Information (JUKTISI) , vol. 4, no. 2, pp. 1423–1431, Nov. 2025, doi : 10.62712/ juktisi.v 4i2.689.

Sarbudin , Mei Indra Jayanti, Syamfia , and Umar, "Development of a Diagnostic Assessment Model based on the EWS (Early Warning System) Approach to Support Student Academic Performance in Elementary School," JIP Jurnal PGMI Science , vol. 11, no. 2, pp. 174–186, Dec. 2025, doi : 10.19109/ jip.v 11i2.31282.

U. Abshor , W. Russianzi, and Wiraguna Edi, " Application of Early Warning System for Increase Knowledge Employee in Pest Control in Oil Palm Plantations,” Flora: Journal of Scientific Studies Agriculture and Plantation , vol. 2, no. 2, pp. 21–32, Jun. 2025, doi : 10.62951/ flora.v 2i2.403.

PB Widagdo and Khasanah Rohmatul, “ Simple EWS (Early Warning System) As Early Detection of Landslides in the Kenalan Village Area ,” Jurnal Bina Desa , vol. 5, no. 1, pp. 1–8, 2023.

E. Apriliyani and Y. Salim, “ Analysis performance method Naïve Bayes Classifier classification on Unbalanced Dataset," Indonesian Journal of Data and Science (IJODAS) , vol. 3, no. 2, pp. 47–54, Jul. 2022.

SD Anggita and FF Abdulloh, “ Optimization PSO -Based Support Vector Machine Algorithm and Information Gain Feature Selection in Data Analysis Sentiment ," JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY (JACOST) , vol. 4, no. 1, pp. 2723–1453, 2023, doi : 10.52158/jacost.524.

R. Livamianti , HK Saputra, E. Tasrif, and L. Mursyida , "SIMPONIS: A Web-Based Student Violation Point Information System for Enhanced Efficiency and Transparency with an Early Warning Feature," Journal of Hypermedia & Technology-Enhanced Learning , vol. 2, no. 3, pp. 285–303, Oct. 2024, doi : 10.58536/j- hytel.v 2i3.147.

K. Primandani and MI Rumasoreng , “ Analysis The Influence of Learning Strategies Heuristics To Ability Think Creative Junior High School Students,” 2023. [Online]. Available: http://jurnal.minartis.com/index.php/jpst/

A. Nofiyanti and Mohammad Zaky Tatsar, " Application of the Learning Model (LAPS) - Heuristic Assisted by Worksheets for Improving Learning Outcomes Cognitive Students of SMA Negeri 3 Pasuruan ,” Athena: Journal of Social, Culture and Society , vol. 1, no. 1, pp. 1–5, Jan. 2023, doi : 10.58905/ athena.v 1i1.1.

AE Wardana and MN Al Azam, “REAL-TIME MONITORING AND EARLY WARNING SYSTEM OF IOT-BASED SEA WATER LEVEL (CASE STUDY: TANJUNG EMAS SEMARANG PORT),” JATI ( Journal Informatics Engineering Students ) , vol. 8, no. 2, pp. 1921–1931, Apr. 2024.

S. Bumbungan , Kusrini , and Kusnawi , “ Application of Particle Swarm Optimization (PSO) in Parameter Selection Automatic on Support Vector Machine (SVM) for Prediction Graduation Student Polytechnic Amamapare Timika," AMATA Engineering Journal , vol. 4, no. 1, pp. 81–93, 2023.

A. Gunawan, A. Hidayatullah, and A. Hidayat, “ Development of E-Sports and the Gaming Industry Using “Swot Analysis ,” Jurnal Syntax Transformation , vol. 2, no. 4, pp. 409–420, Apr. 2021, doi : 10.46799/ jurnalsyntaxtransformation.v 2i4.266.

E. Tenda, E. Alfonsius, M. Lumembang , and E. Ketaren , “EARLY WARNING SYSTEM FOR POTENTIAL LANDSLIDE DISASTERS IN MANADO CITY BASED ON THE INTERNET OF THINGS,” TIMES Journal , vol. XII, no. 2, pp. 64–70, Dec. 2023, [Online]. Available: http://ejournal.stmik-time.ac.id

A. Damuri, U. Riyanto, H. Rusdianto , and M. Aminudin, “ Implementation of Data Mining with Naïve Bayes Algorithm For Classification Eligibility Recipient Basic Food Assistance ," JURIKOM ( Computer Research Journal ) , vol. 8, no. 6, pp. 219–225, Dec. 2021, doi : 10.30865/ jurikom.v 8i6.3655.

ET Handayani and A. Sulistiyawati , “SENTIMENT ANALYSIS OF PUBLIC RESPONSE TOWARDS DAILY COVID-19 NEWS ON THE MINISTRY OF HEALTH'S TWITTER USING THE NAIVE BAYES CLASSIFICATION METHOD,” Journal Technology and Systems Information (JTSI) , vol. 2, no. 3, pp. 32–37, Sep. 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI

H. Susana, N. Suarna , Fathurrohman , and Kaslani , "APPLICATION OF THE NAIVE BAYES METHOD CLASSIFICATION MODEL TO THE USE OF INTERNET ACCESS," JURSISTEKNI ( Journal System Information and Technology Information ) , vol. 4, no. 1, pp. 1–8, Jan. 2022.

U. Nini et al. , “E-FOOTBALL GAME AS AUDIOVISUAL MEDIA ON LEARNING OUTCOMES OF FUNDAMENTAL FOOTBALL SKILLS E-FOOTBALL GAME AS AUDIOVISUAL MEDIA ON LEARNING OUTCOMES OF FUNDAMENTAL FOOTBALL SKILLS,” Jambura Journal of Sports Coaching , vol. 5, no. 2, pp. 162–174, Jul. 2023.

IL Putra, “IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION(PSO) AND K-NEAREST NEIGHBOR(K-NN) ALGORITHMS IN PREDICTING THE SUCCESS OF VOCATIONAL SCHOOL STUDENTS IN GETTING JOBS,” Technologia , vol. 13, no. 4, pp. 339–350, Oct. 2022.

WA Firmansyach , U. Hayati, and YA Wijaya, “ANALYSIS OF OVERFITTING AND UNDERFITTING IN NAIVE BAYES AND DECISION TREE ALGORITHMS USING CROSS VALIDATION TECHNIQUE,” 2023.