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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Penelitian Pendidikan Jurnal Ilmiah KOMPUTASI JISICOM (Journal of Information System, Infomatics and Computing) Journal of Engineering and Scientific Research (JESR) RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) Journal of Dinda : Data Science, Information Technology, and Data Analytics Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Knowbase : International Journal of Knowledge in Database Jurnal Abdi Masyarakat Humanis Hortatori : Jurnal Pendidikan Bahasa dan Sastra Indonesia Nuansa Informatika Nusantara Journal of Multidisciplinary Science Journal of Computer Science Contributions (Jucosco) Journal of Informatics and Information Security RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Journal : Journal of Students‘ Research in Computer Science (JSRCS)

 1 
Analisis Sentimen Masyarakat Terhadap Perkuliahan Daring di Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine Samuel, Federick Dedi; Atika, Prima Dina; Setiawati, Siti
Journal of Students‘ Research in Computer Science Vol. 4 No. 2 (2023): November 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/6691v571

Abstract

The COVID-19 pandemic has changed the education landscape around the world, resulting in the cessation of in-person teaching and learning activities and encouraging the adoption of online learning systems. Many Indonesians express their opinions and thoughts about online courses through the social media Twitter. Therefore, this study aims to analyze people's sentiment towards online lectures on Twitter using Naïve Bayes and Support Vector Machine (SVM) methods. Data for sentiment analysis is taken from Twitter using the keywords "#college", "#daring", and "#kuliahdaring". This study limits data collection to the range of 2021-2022. A total of 1,260 Tweets were analyzed, with 633 Tweets having positive sentiments and 627 Tweets having negative sentiments. This study uses Naïve Bayes and Support Vector Machine algorithms to classify positive and negative sentiments in Tweets. The results showed that Naïve Bayes algorithm achieved the highest accuracy of 72%, while Support Vector Machine achieved 66% accuracy.
Metode Naïve Bayes dan Support Vector Machine untuk Mengolah Sentimen Ulasan dan Komentar di Platform Digital Herlawati; Srisulistiowati, Dwi Budi; Agustin, Syafira Cessa; Syafina, Prilia Hashifah; Rachmatin, Nida; Setiawati, Siti
Journal of Students‘ Research in Computer Science Vol. 5 No. 2 (2024): November 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/dby15h32

Abstract

This study analyzes sentiment from user Reviews of the FLO app, Taman Mini Indonesia Indah (TMII), and public comments on infidelity cases on Instagram, using Naïve Bayes and Support Vector Machine (SVM) algorithms. FLO, an app that helps users track reproductive health, was analyzed based on 1,393 Reviews on Google Play Store. Of these, 796 Reviews expressed positive sentiment, while 597 were negative. Although both Naïve Bayes and SVM achieved an accuracy of 74%, SVM performed better in recall (74%) and precision (71%). For TMII Reviews, the analysis involved 1,616 Google Reviews, with 1,263 showing negative sentiment, indicating complaints about facilities and services, and 353 expressing positive sentiment. SVM outperformed Naïve Bayes, achieving an accuracy of 85% and an f1-score of 87%, compared to Naïve Bayes’ 82% accuracy and 83% f1-score. Additionally, the analysis of 1,200 public comments on Instagram accounts @lambe_turah and @awreceh.id revealed 918 negative comments and 282 positive ones. SVM once again demonstrated superior performance with an accuracy of 91%, precision of 87%, recall of 96%, and an f1-score of 92%, surpassing Naïve Bayes, which achieved an accuracy of 86%. These findings confirm that SVM is more effective for sentiment classification across various digital Platforms, including social issues and service evaluations. The results can be applied to develop public opinion analysis systems that support strategic decision-making and enhance service quality based on user feedback.
Sistem Informasi Navigasi Wisata Kota Jakarta untuk Menentukan Rute Tercepat Menggunakan Algoritma Dijkstra Berbasis Web Syaumi, Muhammad Rizki; Noeman, Achmad; Setiawati, Siti; Kustanto, Prio; Achmad, Noeman
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/c9by2m49

Abstract

This study aims to design a web-based tourism navigation information system using Dijkstra’s algorithm to determine the fastest rout in Jakarta City. The proposed navigation system assists tourist in planning their trips more efficiently by providing real-time information on the fastest routes, travel distances, and estimated traviel times. By implementing Dijkstra’s algorithm, the system calculates the optimal route based on the starting location from the user’s device and destination data stored in the database. This research employs the waterfall system development method, which inludes the stages of analysis, design, implementation, and testing. The testing results demonstrate that the system accurately provides the fastest routes, enhancing convenience and travel efficiency for tourist.
Co-Authors Achmad, Noeman Adi Muhajirin Afifah, Muanda Agustin, Syafira Cessa Agustina, Mogi Aliyah, Andi Nur Anggoro Laksono Aprilyana, Dhea Putri Arafat Bagoes Setyawan Budi Srisulistiowati, Dwi Dani Yusuf Dwi Dwi Budi Srisulistiowati Fedorova, Oxana Fitri, Diah Angraina Herlawati Herlawati Hernando, Yose Insan, Insan Irfan Fadhilah Jasson Arfian Juliani Kartika, Wahyu Khairunnisa Fadhilla Ramdhania Khalida, Rakhmi Kuning, Layung Kustanto , Prio Kustanto, Prio Kustanto Kusumawati, Amora Lisa Adhani Lubis, Hendarman Marta T, Yoga Mayadi Mayadi, Mayadi Muhamad Khaerudin Muhammad Azhar Khairi Muhammad Khaerudin Naufal Akmaludin Setyawan Nawangsih, Ismasari Noeman, Achmad Pandu Eka Wardhana Priatna , Wowon Prima Dina Atika Rachmatin, Nida Rafika Sari Rakhmi Khalida Ramadhan, Risky Ramdhania, Khairunnisa Fadhilla Rasim Rasim, Rasim rizki nurdiansyah Rizky Apriyanto Rizky Ramadhan, Rizky Samuel, Federick Dedi Siti Musabiah Mega Pratiwi Situmorang Sri Rejeki Sri, Retnaning Subarkah Subarkah Syafina, Prilia Hashifah Syaumi, Muhammad Rizki Tb Ai Munandar Wijati, Damar Yudistira, Syah Dimas