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All Journal REKA KARSA JURNAL FOKUS KONSELING Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL KEPEMIMPINAN DAN PENGURUSAN SEKOLAH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Al-Syakhshiyyah : Jurnal Hukum Keluarga Islam dan Kemanusiaan Jurnal Teknologi Informatika dan Komputer KomunikA JUSTER: Jurnal Sains dan Terapan Jurnal Pengabdian Masyarakat Bhinneka Jurnal Pengabdian Masyarakat Bangsa Bangun Desa : Jurnal Pengabdian Masyarakat PPSDP International Journal of Education Malacca: Journal of Management and Business Development Smatika Jurnal : STIKI Informatika Jurnal
Agung Prasetya
Jurusan Teknik Arsitektur Fakultas Teknik Sipil dan Perencanaan Institut Teknologi Nasional
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ANALISIS SENTIMEN PADA TREN OPINI PUBLIK TERHADAP PROGRAM #MAKANBERGIZIGRATIS DI PLATFORM X MENGGUNAKAN JARINGAN LONG SHORT-TERM MEMORY (LSTM) Wahyuningsih, Veny Dwi; Sari, Yayak Kartika; Prasetya, Agung
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2260

Abstract

The implementation of the Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) as a strategic government initiative has generated diverse responses from the public, widely discussed on social media, particularly on the X (Twitter) platform. Differences in perceptions regarding the objectives, implementation, and impacts of the policy have encouraged intensive public discussions. However, the tendency of public sentiment toward this program has not been widely analyzed systematically using machine learning approaches based on contextual representations. Therefore, this study analyzes public sentiment toward the hashtag #MakanBergiziGratis using the Long Short-Term Memory (LSTM) method. A total of 5,516 Indonesian-language tweets were collected through a web scraping process within the period of January 1 to November 30, 2025. Sentiment labeling employed a lexicon-based approach to classify the data into three categories: positive, neutral, and negative. The analysis stages included text preprocessing, BERT tokenization and embedding, handling imbalanced data using the Synthetic Minority Over-sampling Technique (SMOTE), and sentiment classification using LSTM. The results reveal that neutral sentiment dominates with 60.80%, followed by positive sentiment at 34.34% and negative sentiment at 4.86%. The developed model achieved an accuracy of 82.50% with a weighted F1-score of 82.66%. Furthermore, evaluation using 5-fold cross-validation produced an average accuracy of 82.8%, indicating stable model performance and good generalization capability in identifying public opinion trends toward the MBG policy.
Multi-Sentence Contextual Modeling using Transformer Architecture for Arithmetic Operation Identification in Mathematical Word Problems Dwidyanto, Cikal; Prasetya, Agung; Ansor, Mohamad Khoirul
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3431

Abstract

Mathematical word problems in Indonesian are generally presented as multi-sentence paragraphs, making the identification of arithmetic operations not solely dependent on recognizing numerical values, but also requiring an understanding of events and semantic relationships across sentences. This study formulates the task of arithmetic operation identification as a classification problem of a single primary operation into four classes: addition, subtraction, multiplication, and division. To capture contextual relationships across sentences, an encoder-based Transformer architecture is employed, which is capable of modeling long-range dependencies through a self-attention mechanism. The dataset consists of 900 elementary school-level mathematical word problems constructed in accordance with the Indonesian curriculum. Experimental results show that the model achieves an accuracy of 0.98 and an F1-score of 0.98. Per-class evaluation indicates high and consistent performance, although prediction errors are still observed in cases with ambiguous narrative patterns, particularly where addition is misclassified as multiplication or subtraction, and multiplication is misclassified as division. These findings demonstrate that the Transformer architecture is effective in leveraging multi-sentence context to improve the accuracy of arithmetic operation identification in mathematical word problems.
Co-Authors ANDRIANA, ANITA Ansor, Mohamad Khoirul Anwar, Ndaru Syaiful Arif Fadllullah Awang Pradana, Awang Chastine Fatichah Dahnilsyah, Dahnilsyah Deny Yuliawan Dessy Wardiah, Dessy Desy Arum Sunarta Dewi, Meyda Avita Dwidyanto, Cikal Dwijayanti, Oky Fadliansyah, Fadliansyah Fadly Azhar Fatimatuzzahro, Isti Oktavia Faturrahim, Muhammad Harry Perdana Hartanti, Aan Budi Herjuno, Muhammad Ilham Arvan Junaidi Iskandar, Joko Jamaludin, Hediyani Kuswandari, Vena Mahdum Minanurohman, Muhamad Adib Natun Wulandari, Aning Amin Nur Laela Latifah Nurul Aini Rahmawati Oswald P. M Siahaan Pratama, Andra Jaya Rahmawati, Rahmawati Ramadhaningsih, Dwi Rambe, Armita Novriana Riski Apriliani Rusdaya Basri, Rusdaya Rusdianto Rusdianto, Rusdianto Sari, Ani Murti Sari, Nurul Permata Sari, Yayak Kartika Shofiana, Atika Nur Sunuwati, Sunuwati Taufiq Agung Cahyono Wahyuningsih, Veny Dwi Yuhana, Umi Laili