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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Indonesian Journal of Business Analytics (IJBA)
Asep Arwan Sulaeman
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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Implementation of a New Web-Based Student Admission Information System at Smp Pelita Nusantara Using the Rapid Application Development (Rad) Method Dian Aditya; Edy Widodo; Asep Arwan Sulaeman
Indonesian Journal of Business Analytics Vol. 4 No. 3 (2024): June 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijba.v4i3.9460

Abstract

The process of accepting new learners in schools often faces challenges in terms of effectiveness and efficiency. This study aims to overcome this problem by implementing a new student admission information system using the Rapid Application Development (RAD) method. The RAD method is used because of its flexibility in rapid and iterative system development. In this study, a web-based information system was developed to facilitate prospective students and schools in the registration process. This system allows prospective students to register online from anywhere. This study also aims to minimize data input errors and optimize the registration process time. The results showed that the implementation of a new student admission information system using the RAD method can increase the effectiveness and efficiency of the admission process in schools.
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 Darryl Yanuar Ar-rafi Dian Aditya Edy Widodo Handala Simetris Harahap