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All Journal Jurnal Informatika: Jurnal Pengembangan IT Indonesian Journal of Education (INJOE)
Fathoni Mahardika
Universitas Sebelas April
Computer Science & IT

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ANALYSIS OF SENTIMENS IN ONLINE DISCUSSION FORUMS AS A TOOL TO IMPROVE SCHOOLER COMPATIBILITY IN DISTANCE LEARNING Yulian Purnama; Fathoni Mahardika; Cynthia Petra Haumahu
Indonesian Journal of Education (INJOE) Vol. 3 No. 2 (2024): Indonesian Journal of Education (INJOE)
Publisher : CV. ADIBA AISHA AMIRA

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Abstract

This research aims to analyze the use of sentimental analysis in online discussion forums in the context of distance learning. This research method is literary research, in which we review publications and previous research in this field. The results of the study show that sentimental analysis can be used effectively in analyzing student responses to distance learning through online discussion forums. In some studies, it has been found that sentiment analysis can help in measuring student involvement, identifying student needs, and evaluating the effectiveness of teaching. The use of sentiment analysis has successfully improved the quality of student interaction and provided educators with a better understanding of student needs. Moreover, sentimental analysis can also help in assessing the efficiency of the teaching methods used, so that educators can take necessary corrective action.
Analisis Sentimen pada Implementasi Pembelajaran Berbasis AI : Studi Kasus Persepsi Mahasiswa dan Dosen di Institusi Swasta Fathoni Mahardika
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9069

Abstract

Penelitian ini mengevaluasi persepsi publik terhadap implementasi Kecerdasan Buatan (Artificial Intelligence/AI) dalam lingkungan pembelajaran di institusi pendidikan swasta. Dengan pendekatan Pemrosesan Bahasa Alami (Natural Language Processing/NLP), penelitian ini melakukan analisis sentimen terhadap tanggapan yang dikumpulkan melalui survei daring dari mahasiswa dan dosen. Tahapan pra-pemrosesan data mencakup case folding, tokenisasi, penghapusan stopword, stemming, dan vektorisasi menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Model klasifikasi Logistic Regression digunakan untuk mengelompokkan data ke dalam tiga kategori sentimen: positif, netral, dan negatif. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 88,24% pada data uji. Sebagian besar responden menunjukkan pandangan positif terhadap pembelajaran berbasis AI, meskipun masih terdapat kekhawatiran mengenai kesenjangan akses digital dan efektivitas metode pembelajaran. Temuan ini memberikan masukan strategis bagi institusi pendidikan dalam merancang kebijakan adopsi AI yang inklusif dan efektif di lingkungan akademik.
Co-Authors CYNTHIA PETRA HAUMAHU Purnama, Yulian