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All Journal WIDYA LAKSANA Indonesian Journal of Data and Science
Ni Ketut Utami Nilawati
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube Ni Putu Eka Swari; Ni Wayan Jeri Kusuma Dewi; Ni Ketut Utami Nilawati; Aniek Suryanti Kusuma; Ni Luh Wiwik Sri Rahayu Ginantra
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.382

Abstract

The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
Pemberdayaan Masyarakat dalam Mitigasi Bencana Tanah Longsor dan Budi Daya Pertanian Hortikultural Berteknologi Iot di Kawasan Geopark I Gede Adnyana; Ni Ketut Utami Nilawati; I Dewa Gede Aristana; Ida Bagus Putu Mardana; Gede Widayana; Nia Erlina; Gede Arya Amerta
JURNAL WIDYA LAKSANA Vol 14 No 2 (2025): Agustus
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jwl.v14i2.103423

Abstract

Desa Terunyan merupakan desa Bali Aga di kawasan Geowisata Gunung Batur yang menghadapi permasalahan kekeringan dan kerentanan longsor. Program Kosabangsa 2024 diterapkan dengan pendekatan Participatory Action Learning System (PALS) melalui dua mitra utama: Kelompok Tani “Taru Menyan” pada bidang pertanian dan Sekaha Teruna-Teruni “Yowana Kerti” pada mitigasi bencana. Metode pendekatan pelaksanaan yang digunakan bagi kelompok mitra pertama yaitu PALS (Participatory Action Learning System) berdasarkan teori Mayoux disajikan. Tahapan kegiatan meliputi sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan penguatan kelembagaan, dengan indikator keberhasilan setiap tahap terukur. Inovasi yang diterapkan meliputi pompa air 25 HP, sistem irigasi IoT, early warning system longsor, dan pengembangan wisata geopark. Hasilnya menunjukkan peningkatan produktivitas pertanian 25%, efisiensi air 35%, serta peningkatan kesiapsiagaan bencana. Program ini membentuk model smart eco-geopark village berbasis teknologi hijau dan kearifan lokal yang dapat direplikasi di desa rawan bencana lainnya. Implikasinya memperkuat arah riset dalam bidang pembangunan desa berbasis teknologi hijau (green technology) dan ketahanan iklim (climate resilience).
Co-Authors Aniek Suryanti Kusuma Gede Arya Amerta Gede Widayana I Dewa Gede Aristana I Gede Adnyana Ida Bagus Putu Mardana Ni Luh Wiwik Sri Rahayu Ginantra Ni Putu Eka Swari Ni Wayan Jeri Kusuma Dewi Nia Erlina