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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Sinergi Aksi Nyata Cendekia
Burhanudin, Muhamad Aris
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Agriculture, Biological Sciences & Forestry Computer Science & IT Social Sciences Other

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Sentiment Analysis of User Reviews for the LinkedIn Application Using Support Vector Machine and Naïve Bayes Algorithm Ulinnuha, Nurissaidah; Pertiwi, Aisyah; Basuki, Athiyah Fitriyani; Kristanti, Beni Tiyas; Haniefardy, Addien; Burhanudin, Muhamad Aris; Satria, Vinza Hedi
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.159

Abstract

Social Networking Sites (SNS) have become integral communication platforms for knowledge sharing and professional connections. LinkedIn, a leading professional network, is widely utilized in today's digital era, primarily by professionals and the business community. This research focuses on analyzing user sentiment on LinkedIn through the application of the Support Vector Machine (SVM) and Naive Bayes methods. Understanding user opinions and satisfaction is important, and sentiment analysis serves as a key tool for this purpose. This study is a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithm for classifying user reviews of the LinkedIn application. Drawing on data from Google Play reviews, this research explores a range of user sentiment towards the LinkedIn platform, including positive, negative and neutral reviews. The application of SVM and Naive Bayes algorithms successfully classifies reviews into relevant sentiment categories. Analyzing 2000 review datasets with an 80% training and 20% testing data split, Support Vector Machines demonstrate an 80% accuracy rate, while Naïve Bayes achieves a 70% accuracy rate. The Support Vector Machines (SVM) algorithm has better accuracy than the Naïve Bayes algorithm based on the test scenarios that have been carried out.
Peningkatan Literasi Digital Siswa Sekolah Menengah melalui Program Code for Kids di SMP Negeri 1 Sukaputra Muhammad Septama Prasetya; Yoga Ari Tofan; Muhamad Liswansyah Pratama; Sischa Wahyuning Tyas; Mohammad Al Hafidz; Muhamad Aris Burhanudin; Yerezqy Bagus
Sinergi Aksi Nyata Cendekia Vol 1, No 2 (2025): November
Publisher : Lembaga Penelitian, Pengembangan, Pemberdayaan Potensi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6131/sancaka.v1i2.198

Abstract

Program pengenalan pemrograman bagi siswa usia dini masih terbatas di wilayah non perkotaan, termasuk di lingkungan sekolah sekitar kawasan pegunungan Sukapura. Kondisi tersebut menyebabkan siswa memiliki pemahaman yang rendah terhadap literasi digital dasar serta belum terpapar pada konsep pemrograman. Kegiatan ini bertujuan memperkenalkan logika komputasi dan pemrograman dasar kepada siswa kelas VII di SMP Negeri 1 Sukapura yang berjumlah 120 peserta melalui program Code for Kids. Metode pelaksanaan meliputi observasi awal, penyusunan materi pembelajaran, pelatihan interaktif menggunakan perangkat lunak pemrograman visual, dan evaluasi sederhana terhadap pemahaman peserta. Hasil kegiatan menunjukkan bahwa siswa mampu memahami konsep dasar pemrograman melalui praktik langsung dan bimbingan terstruktur. Peserta juga menunjukkan peningkatan minat dan keterlibatan selama proses pembelajaran. Kesimpulan dari kegiatan ini adalah bahwa pengenalan pemrograman dengan pendekatan interaktif dapat meningkatkan pemahaman awal dan minat siswa terhadap teknologi, serta memberikan kontribusi positif bagi penguatan literasi digital di daerah non perkotaan.
Co-Authors Basuki, Athiyah Fitriyani Haniefardy, Addien kristanti, beni tiyas Mohammad Al Hafidz Muhamad Liswansyah Pratama Muhammad Septama Prasetya Nurissaidah Ulinnuha Pertiwi, Aisyah Satria, Vinza Hedi Sischa Wahyuning Tyas Yerezqy Bagus Yoga Ari Tofan