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All Journal Jurnal PkM MIFTEK Jurnal Algoritma Indonesian Journal of Computer Science and Engineering
Fauzi, Sandi Muhtar
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Seminar Literasi Digital Bertema : Meningkatkan Ekonomi dan Menjaga Budaya di Era Digital Nashrulloh, Muhammad Rikza; Yanti, Yanti; Riyandi, Fachrry Al-Fachrizzy; Nurodin, Azmi Miftah; Alinurdin, Rendi; Jipar, Muzaki Abdul; Faris, Salman Adam; Dzulkhomzah, Moh. Rival; Kifti, Jalaludin; Masripah, Ulpah; Alawiyah, Dini; Muttaqin, Iswan Nurul; Mubarok, Muhammad Imam Husni; Maulana, Moh. Alvin Apita; Akib, Yasir Al; Sidik, Ghimnastiar Moch; Nurhakim, Sultan Jabar; Rizki, Ikbal; Ramadhan, Tasya Shakila; Ridha, Muhamad Munna Ar; Oktaviany, Sindi; Fauzi, Sandi Muhtar
Jurnal PkM MIFTEK Vol 4 No 1 (2023): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.4-1.1326

Abstract

Globalization and the acceleration of technological advances have had such a significant impact in various fields. The presence of technology provides a new face for economic development and this becomes an opportunity and a big change for the system, process or economic actors themselves. Therefore it really needs counseling to help digitally literate people through digital literacy seminars with the theme «improving the economy and maintaining culture in the digital era» which will help, assist the community, for example in the use of common applications needed daily, the ethics of using technology wisely, maintaining culture in the digital era and so on, everything is explained in counseling on digital literacy.
Analisis Sentimen Pemilu Dalam Text Mining Terhadap Hasil Real Count 2024 Fauzi, Sandi Muhtar; Ramdani, Rizki; Cahyana, Rinda
Indonesian Journal of Computer Science and Engineering Vol. 1 No. 02 (2024): IJCSE Volume 01, Nomor 02, November 2024
Publisher : CV. Cendekiawan Muda Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70656/ijcse.v1i02.117

Abstract

Penelitian ini telah mengeksplorasi berbagai metode dan algoritma untuk melakukan analisis sentimen terhadap opini masyarakat terkait Pemilihan Umum (Pemilu) di Indonesia, khususnya menggunakan data dari media sosial seperti Twitter. Beberapa penelitian sebelumnya telah menggunakan teknik seperti bigram, trigram, dan vektorisasi data, serta algoritma klasifikasi seperti Naïve Bayes, Support Vector Machine (SVM), dan lainnya untuk mengklasifikasikan sentimen masyarakat. Selain itu, ada juga penelitian yang mencoba mengoptimalkan algoritma menggunakan metode seperti Particle Swarm Optimization (PSO) untuk meningkatkan akurasi analisis sentimen. Hasil dari berbagai penelitian tersebut menunjukkan tingkat akurasi dan performa yang bervariasi, namun secara keseluruhan menunjukkan potensi besar dalam menggunakan data media sosial untuk memprediksi dan memahami sentimen masyarakat terkait pemilihan umum. Beberapa penelitian juga menghadapi tantangan dalam representasi data yang terbatas dari platform media sosial tertentu, serta keterbatasan dalam mengklasifikasikan sentimen berdasarkan geolokasi atau atribut tertentu.
Analisis Sentimen Publik Pada Media Sosial Multi-Platform Terhadap Kinerja Presiden Prabowo Subianto Menggunakan Algoritma Naive Bayes Fauzi, Sandi Muhtar; Cahyana, Rinda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2586

Abstract

In the rapidly evolving landscape of digital politics, a deep understanding of public sentiment on social media has become crucial due to its significant influence on public opinion. This study aims to analyze public sentiment toward the performance of President Prabowo Subianto by utilizing data from three popular social media platforms: Twitter, TikTok, and Instagram.The classification method employed is the Naïve Bayes algorithm, implemented within the SEMMA framework, which consists of five stages: Sample, Explore, Modify, Model, and Assess. Data from each platform was collected and processed through text cleaning, TF-IDF transformation, and class balancing using the SMOTE technique. Evaluation was conducted using Stratified K-Fold Cross Validation and the F1-score metric to assess model performance.The results indicate that classification performance varies across platforms. The model achieved the highest F1-score on Twitter data (0.82), followed by Instagram (0.72), and TikTok (0.68). Overall, the model reached an average accuracy of 75.41%. These findings suggest that the linguistic characteristics and text structures of each platform significantly affect sentiment classification effectiveness.This research provides practical implications for the application of AI-based sentiment analysis in the realm of digital politics. It offers actionable insights for policymakers to monitor public opinion in real time and for system developers to design sentiment analysis algorithms that are more adaptive to the unique characteristics of each platform.
Co-Authors Akib, Yasir Al Alawiyah, Dini Alinurdin, Rendi Dzulkhomzah, Moh. Rival Faris, Salman Adam Jipar, Muzaki Abdul Kifti, Jalaludin Masripah, Ulpah Maulana, Moh. Alvin Apita Mubarok, Muhammad Imam Husni Muhammad Rikza Nashrulloh Muttaqin, Iswan Nurul Nurhakim, Sultan Jabar Nurodin, Azmi Miftah Oktaviany, Sindi Ramadhan, Tasya Shakila Ramdani, Rizki Ridha, Muhamad Munna Ar Rinda Cahyana Riyandi, Fachrry Al-Fachrizzy Rizki, Ikbal Sidik, Ghimnastiar Moch Yanti Yanti