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All Journal Jurnal Minds: Manajemen Ide dan Inspirasi Jusikom : Jurnal Sistem Komputer Musirawas Journal of English for Academic and Specific Purposes (JEASP) Jurnal Sinergitas PkM & CSR FaST : Jurnal Sains dan Teknologi SEIKO : Journal of Management & Business Jurnal Teknologi Informasi MURA Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL AKUNIDA International Journal of Public Budgeting, Accounting and Finance Nation State : Journal of International Studies Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) Best Journal (Biology Education, Sains and Technology) Jurnal Abdi Insani Jurnal Ilmiah Manajemen Kesatuan Journal of Industrial Engineering & Management Research (JIEMAR) Jurnal Pengabdian Nasional SUPREMASI Jurnal Hukum Nobel Management Review Jurnal Info Sains : Informatika dan Sains Didaktika Jurnal Pengabdian Kesehatan Masyarakat Prosiding Konferensi Nasional PKM-CSR PAKDEMAS : Jurnal Pengabdian Kepada Masyarakat Journal Of Human And Education (JAHE) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi TRANSFORMATIONAL LANGUAGE, LITERATURE, AND TECHNOLOGY OVERVIEW IN LEARNING (TRANSTOOL) Proceeding Applied Business and Engineering Conference Journal of Artificial Intelligence and Digital Business Jurnal Ekonomi, Teknologi dan Bisnis Entrepreneurship and Small Business Research International Journal of Application on Economics and Business Jurnal Teknologi Informasi Mura The Indonesian Journal of General Medicine Berbakti Journey: Journal of English Language and Pedagogy Economics Monetary Journal
Sylvia, Sylvia
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EVALUASI KINERJA KNN DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN KOMENTAR INSTAGRAM PEJABAT PUBLIK BERBASIS TF-IDF Permata, Rizka; Sylvia, Sylvia; Arpan, Atika
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2861

Abstract

Penelitian ini bertujuan mengevaluasi kinerja algoritma K-Nearest Neighbors (KNN) dan Naïve Bayes untuk klasifikasi sentimen komentar publik pada akun Instagram pejabat publik di Indonesia. Sebanyak 137.141 komentar dikumpulkan melalui web scraping dan diproses menggunakan tahapan text preprocessing, meliputi case folding, pembersihan teks, tokenization, stopword removal, normalisasi, dan stemming. Representasi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF). Evaluasi performa meliputi accuracy, precision, recall, F1-score, dan waktu komputasi. Hasil menunjukkan bahwa Naïve Bayes memiliki akurasi tertinggi sebesar 0,9230, sedikit lebih baik dibandingkan KNN sebesar 0,9209. Kedua model menunjukkan performa sangat tinggi pada kelas netral, namun rendah pada kelas positif dan negatif akibat ketidakseimbangan kelas. Temuan ini menegaskan bahwa kombinasi TF-IDF dan Naïve Bayes masih menjadi baseline yang efektif untuk analisis sentimen berskala besar pada teks pendek media sosial.
EVALUASI KINERJA KNN DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN KOMENTAR INSTAGRAM PEJABAT PUBLIK BERBASIS TF-IDF Permata, Rizka; Sylvia, Sylvia; Arpan, Atika
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2861

Abstract

Penelitian ini bertujuan mengevaluasi kinerja algoritma K-Nearest Neighbors (KNN) dan Naïve Bayes untuk klasifikasi sentimen komentar publik pada akun Instagram pejabat publik di Indonesia. Sebanyak 137.141 komentar dikumpulkan melalui web scraping dan diproses menggunakan tahapan text preprocessing, meliputi case folding, pembersihan teks, tokenization, stopword removal, normalisasi, dan stemming. Representasi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF). Evaluasi performa meliputi accuracy, precision, recall, F1-score, dan waktu komputasi. Hasil menunjukkan bahwa Naïve Bayes memiliki akurasi tertinggi sebesar 0,9230, sedikit lebih baik dibandingkan KNN sebesar 0,9209. Kedua model menunjukkan performa sangat tinggi pada kelas netral, namun rendah pada kelas positif dan negatif akibat ketidakseimbangan kelas. Temuan ini menegaskan bahwa kombinasi TF-IDF dan Naïve Bayes masih menjadi baseline yang efektif untuk analisis sentimen berskala besar pada teks pendek media sosial.
Teacher’s Perception on AI Overuse by Students: Did AI Do Your Task? Wicaksono, Julien Arief; Setiarini, Rimbi Budi; Suharsono, Degita Danur; Surayya, Siti Ayu; Murti, Ghanesya Hari; Sylvia, Sylvia
Journey: Journal of English Language and Pedagogy Vol. 8 No. 2 (2025): Journey: Journal of English Language and Pedagogy
Publisher : UIBU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33503/journey.v8i2.1778

Abstract

Artificial Intelligence (AI) applications such as ChatGPT, Grammarly, and Google Translate are often used by students to help them to find ideas, write, translate, and even organize the ideas. These applications can support learning, yet many teachers have noticed that students are using them excessively. This study aims to look at university English teachers’ perceptions and responses toward students’ overuse of AI tools in asynchronous learning contexts. This study employed a narrative inquiry approach. The source of the data was five narratives from five university teachers reflected on real situations when they suspected students were overusing AI. The narratives were then thematically analysed to identify emerging patterns and emotional responses. The findings of this research highlight some emotional and ethical dilemmas that teachers experienced, the signs they noticed in student work, and the strategies they used to deal with the problems, and he strategies they employed to promote responsible AI use among students. This study also shows that teachers are trying to balance the use of AI as a helpful tool, not a replacement for learning. It also suggests clearer policies from the institutions, better digital ethics education for the students, and stronger support for teachers in today’s AI era.
Co-Authors Abdul Haeba Ramli Adnan Aqsya, Mohammad Albitar Septian Syarifudin Ariawan Gunadi Arifin, Oki Arpan, Atika Atika Arpan Azlan Azhari Bachtiar Bachtiar Cahyani, Mutiara Casta, Ramadhani Eria Cecilia, Cecilia Christian, Natalis Christiana, Petrycia Christiani, Agustina Dara Ayu Nianty Delima Rahayu Istiqomah, Delima Rahayu Derista, Fanny Dharmawan, C.N. Djangkaru, Elliana DM., Rustan Dwi Handoko Dwirgo Sahlinal Dwirgo Sahlinal, Dwirgo Egnes, Egnes Fathurrahman Kurniawan Ikhsan Febrianus, Justin Fitriani, Hema Fitriyah Fitriyah Frederica, Viona Gaora, Putra Ansa Ghanesya Hari Murti Gunawan, Andrew Riccardo Harmiati Harnis, Zola Efa Hendri Purnomo Herman, Erry Irene Irene Iskandar Itan, Iskandar Ismayanti, Galuh Jennifer Jennifer Jony Jony Juhana, Juhana Kyoko, Kyoko Laurence Laurence Lila Maria Kaban Limiyana, Fanny Linda Santioso magfiroh, diana Maryadi Maryadi Mas’ud, Masdar Meiviana, Meiviana Mercyo, Steve Monica , Christiana Gracia Muh. Said, Muh. Mulyawan, Heru Dwi Nurkhotimah, Jihan Susan Ofel Boas , William Pedrason, Rodon Permata, Rizka Pratiwi, Yessi Anissa Puspitasari, Kristanti Ambar Rahayu, Priskila Christine Rifin Khong Rima Maulini Romanda, Novandro Sendi Ramdhani Setiarini, Rimbi Budi Silalahi, Rudy Solly Aryza Sri Dewi Nirmala sri wuryastuti Suhardi, Steven Suhardjo, Iwan Suharsono, Degita Danur Sukmawati, Shintya Alvira Suratno, Tatang Surayya, St. Ayu Topuh, Jelpris Viona viona, Viona Wahyurianto, Ibnu Wibowo, Yusep Windhu Ari Wicaksono, Julien Arief Widya Rizky Pratiwi, Widya Rizky Widyawati, Dewi Kania Zuhaira, Salsabila Zuriati, Zuriati