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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Handala Simetris Harahap
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Computer Science & IT Decision Sciences, Operations Research & Management Other

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IMPLEMENTASI SISTEM ABSENSI FACE RECOGNITION BERBASIS WEB PADA BAGIAN KESEJAHTERAAN RAKYAT KABUPATEN BEKASI suprapto; Isarianto; Alhadi Saputra; Handala Simetris Harahap; Ahmad Fauzi
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.5067

Abstract

At this time, Bagian Kesejahteraan Rakyat Kabupaten Bekasi still uses finger print attendance, the attendance process using finger print is often problematic on machines that do not detect fingers, so the attendance process is done manually by writing on the attendance form. Plus, to carry out the attendance process, employees have to queue, so it is quite a waste of time. During the Covid-19 pandemic, finger print attendance is still dangerous because of physical contact when going to the attendance process. The attendance process needs to be improved again so that its use is more flexible, safe and efficient. By utilizing face recognition technology, face recognition-based attendance is attendance that is carried out using the detection of parts of the human face. Then in the design of the face recognition-based attendance system, the researcher uses a system modeling with Undefined Modeling Language (UML) and developed with the prototype method. This research shows that with the construction of this web-based facial recognition face attendance system, Bagian Kesejahteraan Rakyat Kabupaten Bekasi can be easier and safer from the Covid-19 outbreak in carrying out attendance in every condition, then in the recapitulation of the list of employees who attend Bagian Kesejahteraan Rakyat Kabupaten Bekasi it is easier because it is already stored in the database.
ANALISIS SENTIMEN MASYARAKAT INDONESIA TERHADAP KASUS PERSETERUAN YAI MIM DAN SAHARA DI TIKTOK MENGGUNAKAN ALGORITMA NAIVE BAYES Darryl Yanuar Ar-rafi; Asep Arwan Sulaeman; Handala Simetris Harahap
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 2, Juni 2026 Publish
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.48841

Abstract

This study aims to analyze public sentiment toward the conflict case between Yai Mim and Sahara, which went viral on the TikTok platform. The data used in this study were TikTok user comments collected using Apify Instant Data Scraper, with a total of 10,504 comments. The research stages included data preprocessing (cleaning, normalization, tokenization, stopword removal, and stemming), sentiment labeling using a lexicon-based approach, feature weighting using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and classification using the Naïve Bayes algorithm. As a comparison model, this study also implemented the Neural Network algorithm to compare classification performance. Model testing was conducted using four data split scenarios: 90:10, 80:20, 70:30, and 60:40 for training and testing data. The results showed that the Naïve Bayes model achieved the highest accuracy of 94.85% in the 90:10 scenario. Meanwhile, the Neural Network model demonstrated better performance with the highest accuracy of 96.49% in the 80:20 scenario. Based on these results, the 80:20 scenario was selected as the main reference because it provides a better balance in model evaluation. Overall, the combination of TF-IDF, Naïve Bayes, and Neural Network methods proved effective in classifying Indonesian sentiment comments on TikTok social media, with Neural Network showing more optimal performance compared to Naïve Bayes.
Co-Authors Ahmad Fauzi Alhadi Saputra Asep Arwan Sulaeman Darryl Yanuar Ar-rafi Isarianto SUPRAPTO