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Journal : Building of Informatics, Technology and Science

Enhancing Student Sentiment Classification on AI in Education using SMOTE and Naive Bayes Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6469

Abstract

This study investigates student sentiment regarding the use of artificial intelligence (AI) in education, employing the Naive Bayes model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues. Class imbalance, a common challenge in sentiment classification, often skews model performance toward majority classes, reducing its effectiveness in recognizing minority classes. To mitigate this, SMOTE was applied to generate synthetic samples for minority classes, achieving a more balanced class distribution. The results demonstrate that incorporating SMOTE improved the Naive Bayes model's accuracy from 65% to 78.87% and significantly increased sensitivity to minority classes. Evaluation metrics, including precision, recall, and F1-score, showed satisfactory performance for certain classes, notably classes 2 and 4. However, challenges remained with class 1, where classification accuracy was lower, indicating inherent complexities in its data patterns. While SMOTE successfully enhanced model performance, it also introduced a potential risk of overfitting, particularly with limited original datasets, highlighting the importance of data quality and size. This research offers actionable insights for educators, developers, and policymakers, emphasizing the need for AI systems in education that are adaptive and responsive to student perceptions. The study concludes that Naive Bayes combined with SMOTE is an effective approach for sentiment analysis in imbalanced datasets. Future research should explore more sophisticated models and larger datasets to achieve more comprehensive and representative outcomes.
Co-Authors Adam Prayogo Kuncoro Aditya Pratama Afrig Aminuddin Agus Pramono Al Haura, Adzkiyatun Nisa Alamsyah, Rizki Albana, Ilham Amalina, Siti Nahla Amin, M. Syaiful Ammar Fauzan, Ammar Andik Wijanarko, Andik Andina, Anisa Nur Anditya Putri, Shifa Anisa, Kholifatun ANNISA HANDAYANI Apitiadi, Satyo Dwi Aprilia, Kharisma Arief Adhy Kurniawan Arsi, Primandani Baetisalamah, Nadiva Amelia Berlilana Berlilana Dewi Cantika, Nourma Islam Diningrum, Dwi Fatma Efendi, Alvin Junio Ilham Eldas Puspita Rini, Eldas Puspita Ely Purnawati, Ely Fadly Yashari Soumena Fariha, Zulfia Nur Ferdianto, Dwi Angga Hafshah, Luqyana Nida Hellik Hermawan Hendra Sudarso Hidayat, Muhammad Taufik Nur Hiiyatin, Dewi LaeIa I Putu Dody Suarnatha Ilham, Fatah Imam Tahyudin Indarto, Debi Iriane, Rara Irma Darmayanti Junianto, Haris Khoirudin, Muhamad Affan Kuat Indartono Kusuma, Bagus Adhi Kusuma, Velizha S Kusuma, Velizha Sandy Maghfira, Rahajeng Sasi Mahardika, Fajar Mahendra, Duta Aditya Marhalatun, Viva Miftahus Surur, Miftahus Muhammad Afif Muliasari Pinilih, Muliasari Muratno, Muratno Murjiatiningsih, Lilis Mustofa, Dinar Najibulloh, Imam Kharits Nandang Hermanto Nanjar, Agi Nugroho, Bagus Aji Nur Hasanah Nuraini, Eka Nurul Hidayati Pandega, Dimas Marsus Prayoga, Agung Priangga, Melaya Puji Hastuti Pujianto , Dimas Eko Purwadi Purwadi Puspitaningrum, Indar Rahayu, Dania Gusmi Rahman Rosyidi Ramadhan, Muhammad Bintang Ranggi Praharaningtyas Aji Riesna, Deby Mega Rizkia Riny, Riny Riyanto Riyanto Riyanto Rujianto Eko Saputro Saekhu, Ahmad Saputra, Alfin Nur Aziz Saputri, Febryka Wulan Saputri, Inka Setiawan, Endri Sitaresmi Wahyu Handani, Sitaresmi Wahyu Sri Widiastuti, Sri Subarkah, Pungkas Taqwa Hariguna Udianti, Asih Utomo, Anwar Tri Winanto, Deden Wirasto, Anggit Wiwik Handayani Yusmedi Nurfaizal Zhafira, Alya