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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Computer Science Research
Dimas Febriawan
Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Sentimen Perbedaan Pendapat Netizen Indonesia Terhadap Penutupan Tiktok Shop Menggunakan Algoritma Naïve Bayes Eko Kurnianto; Dimas Febriawan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7170

Abstract

This research uses the Naïve Bayes algorithm to analyze the sentiments of Indonesian netizens regarding the closure of the TikTok Shop. This research focuses on analyzing differences of opinion spread on social media platforms. Data obtained from social media such as Youtube, Tiktok, and Threads. There is data that will later be used in this research with a total of 1366 data. Then, there were 987 positive data and 379 negative data. After conducting research, results will be obtained with an accuracy of 86.97% in the first experiment which does not use the Split Data operator, and an accuracy of 89.23% in the second experiment which uses the Split Data operator. Then the results of this analysis reveal significant variations in sentiment among Indonesian netizens regarding the closure of the TikTok Shop. Some groups of netizens may express disappointment or disapproval while others may show support for the decision. The analysis also identified key factors influencing dissent, such as user experience, expectations of the platform and economic impact. Due to this, this research contributes to the field of sentiment analysis and natural language processing which applies splitting procedures so that netizen comment data on the platform can be classified.
Perancangan Sistem Informasi Pengelolaan Akun Pada Aplikasi SAP dengan Metode Waterfall Hamdan Zulfa Rais; Dimas Febriawan
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.673

Abstract

The need for an account management application in the SAP system is crucial for maintaining the reliability of annual audit trails, which are an essential part of corporate data governance. This application is designed to facilitate access rights management and record every user activity within the SAP system, aiming to ensure the integrity and security of company data. Without a robust system for managing access rights and monitoring user activity, audit trails become vulnerable to risks such as data misuse, shifting of responsibilities, and invalid user credentials. This can lead to financial and reputational losses for the company. This research aims to design and build an effective SAP system account management application to enhance audit trail reliability and minimize risks that can occur in the SAP system's account management process. A good information system will enable companies to manage and secure data more effectively, support license audits, and meet applicable compliance standards. The Waterfall method is used to achieve these objectives, encompassing requirements analysis, system design, implementation, testing, and maintenance phases. Through this approach, the expected outcome is an SAP account management application that is effective, secure, and supports audit standards as well as corporate data security governance.
Analisis Sentimen Terkait Hilirisasi Industri Pada Opini Masyarakat X dengan Menggunakan Naive Bayes Aditya Budi Pratama; Dimas Febriawan
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6795

Abstract

This research examines public sentiment toward Indonesia's industrial downstreaming policy using data sourced from X. The study employs the Naive Bayes algorithm to categorize public opinions into three sentiment types: positive, negative, and neutral. Data collection was conducted via a crawling process utilizing the X API and tools like Tweepy, followed by preprocessing steps such as data cleansing, tokenization, case normalization, stopword removal, and either stemming or lemmatization. Subsequently, the data was manually annotated using a lexicon-based sentiment method to ensure accurate classification. The findings reveal that the Naive Bayes algorithm achieved an accuracy rate of 81.75% in sentiment classification, with the highest performance observed in identifying positive sentiments. This research offers valuable insights into public perspectives on the industrial downstreaming policy and suggests recommendations for policymakers to develop strategies that better resonate with public sentiment. Leveraging X as a data source allows for real-time analysis that adapts to shifts in public opinion.
Co-Authors Aditya Budi Pratama Eko Kurnianto Hamdan Zulfa Rais