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All Journal Jurnal Teknologi Informasi MURA Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Teknologi Informasi Mura
Nunumete, Laura S
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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Peningkatan Literasi Digital dan Keamanan Siber Bagi Siswa SMAS BPD Tobelo Selatan Pattiasina, Tiska; Luturmas, Join Rachel; Fredriksz, Grace; Salhuteru, Andrie CH; Matuankotta, Febiola; Nunumete, Laura S
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 5, No 4 (2025): Abdira, Oktober
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v5i4.1093

Abstract

The development of digital technology has had a significant impact on high school students, particularly in their use of the internet and social media. However, the lack of digital literacy and cybersecurity awareness remains a problem that needs to be addressed. This community service activity aims to improve digital literacy and cybersecurity understanding among students at SMAS BPD Tobelo Selatan. The methods used included lectures, discussions, and QA sessions, with material covering digital literacy, social media ethics, cyberbullying, hoaxes, and personal data protection. The activity was held offline on September 7, 2025, with 15 students participating. Evaluation was conducted using a Guttman Scale questionnaire to assess participant responses to the activity. Results showed that all students (100%) expressed satisfaction, indicating that the activity successfully improved students' understanding of digital literacy and cybersecurity. Therefore, this community service activity makes a positive contribution in equipping students with wise, safe, and responsible digital skills to face the challenges of the digital era.
PERBANDINGAN KINERJA ALGORITMA SVM DAN NAIVE BAYES PADA KLASIFIKASI PRESTASI AKADEMIK SISWA: STUDI KASUS SMAS BPD TOBELO SELATAN Pattiasina, Tiska; Fredriksz, Grace; Luturmas, Join Rachel; Salhuteru, Andrie CH; Matuankotta, Febiola; Nunumete, Laura S; Jupriyanto, Jupriyanto
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2915

Abstract

Students’ academic achievement is an important indicator of the success of the educational process; however, its assessment is often subjective and not yet fully data-driven. Therefore, a systematic analytical approach is required to classify students’ academic achievement objectively and accurately. This study aims to compare the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying the academic achievement of grade III students at SMAS BPD Tobelo Selatan. A data mining approach using classification techniques was applied, involving 17 attributes as predictor variables and two target classes of academic achievement, namely Very Good and Good. Data processing and model evaluation were conducted using the WEKA software, with performance measured through accuracy, precision, recall, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the SVM algorithm achieves the best performance in terms of accuracy, precision, and recall, each reaching 97.78%, while the Naive Bayes algorithm obtains the highest AUC-ROC value of 98.08%. These findings demonstrate that SVM is superior in prediction accuracy, whereas Naive Bayes shows excellent capability in class discrimination. This study is expected to support data-driven academic decision-making in school environments.
PERBANDINGAN KINERJA ALGORITMA SVM DAN NAIVE BAYES PADA KLASIFIKASI PRESTASI AKADEMIK SISWA: STUDI KASUS SMAS BPD TOBELO SELATAN Pattiasina, Tiska; Fredriksz, Grace; Luturmas, Join Rachel; Salhuteru, Andrie CH; Matuankotta, Febiola; Nunumete, Laura S; Jupriyanto, Jupriyanto
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2915

Abstract

Students’ academic achievement is an important indicator of the success of the educational process; however, its assessment is often subjective and not yet fully data-driven. Therefore, a systematic analytical approach is required to classify students’ academic achievement objectively and accurately. This study aims to compare the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying the academic achievement of grade III students at SMAS BPD Tobelo Selatan. A data mining approach using classification techniques was applied, involving 17 attributes as predictor variables and two target classes of academic achievement, namely Very Good and Good. Data processing and model evaluation were conducted using the WEKA software, with performance measured through accuracy, precision, recall, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the SVM algorithm achieves the best performance in terms of accuracy, precision, and recall, each reaching 97.78%, while the Naive Bayes algorithm obtains the highest AUC-ROC value of 98.08%. These findings demonstrate that SVM is superior in prediction accuracy, whereas Naive Bayes shows excellent capability in class discrimination. This study is expected to support data-driven academic decision-making in school environments.
Co-Authors Fredriksz, Grace Jupriyanto . Luturmas, Join Rachel Matuankotta, Febiola Pattiasina, Tiska Salhuteru, Andrie CH