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All Journal Teika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Flash JurTI (JURNAL TEKNOLOGI INFORMASI) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Applied Data Sciences HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal Evaluation in Education (JEE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Manajemen Informatika & Teknologi Jurnal Publikasi Manajemen Informatika
Petrus Katemba
Sekolah Tinggi Manajemen Informatika Komputer STIKOM Uyelindo Kupang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Classification of Student Responses to the Free Nutritional Meal Program at SMAK Giovanni Kupang UsingDeep Learning Agustina Sartini Sukardi; Franki Yusuf Bisilisin; Petrus Katemba; Meliana Oktavia Meo
TeIKa Vol 16 No 2 (2026): Jurnal TeIKa Volume 16, Nomor 2, Oktober 2026
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/5c7m7452

Abstract

The Free Nutritious Meal Program (MBG) is a government initiative aimed at improving nutritional quality and supporting students’ learning processes in schools. The implementation of this program at SMAK Giovanni Kupang has generated various student responses, making it necessary to develop a system that can automatically classify these perceptions. This study aims to classify students’ responses to the MBG program using a deep learning approach based on Long Short-Term Memory (LSTM). The data used consists of 1,238 student comments collected through questionnaires and processed through several text preprocessing stages, including cleaning, tokenization, stopword removal, and padding. The LSTM model is employed to classify sentiment into three categories: positive, neutral, and negative. Evaluation is conducted using the K-Fold Cross Validation method to measure the model’s performance and consistency. The results show that the developed model achieves an average accuracy of 0.7036 (70.36%), with fold 4 being the best-performing fold, achieving an accuracy of 0.7611 (76.11%). The model performs well in classifying positive sentiment but still faces limitations in distinguishing between neutral and negative sentiments due to data imbalance and the ambiguous nature of the text.
Co-Authors Adhy Syaputra Nge Agustina Sartini Sukardi Amalo, Nehemia Daniel Edwin Ariesto Umbu Malahina Franki Yusuf Bisilisin Gregorius Rinduh Iriane Heni Jimi, Asmara Kornelis Andrian Kabo Magsyar, Ahmad Fajar Marcello Bernado S. Balawala Mardiani Bana Meliana O Meo Muhammad Agus Muljanto Niklinton Nehemia Neolak Raden Mohamad Herdian Bhakti Remerta Noni Naatonis Remerta Noni Na’aTonis Retriani Banobe Revansa Eli Manafe Rosita Koro Djoh S Sumarlin Sumarlin Sumarlin Yohanes Suban Belutowe Yohanis Malelak Yossi Aristo Adu