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All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Rifki Ramdani
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Implementasi Literasi Digital Sebagai Strategi Peningkatan Computational Thinking Pada Anak Binaan Perlindungan Sosial Desa Balewangi Pamella M Sri Rezeki; Tabina Athifa Rahmaniya; Karina Hoirun Nisa; Girah Ismi Nugraha; Hanif Luqman Muttaqin; Euis Novianti; Muthia Sandi Firamid; Abizar Algifari; Alfarabi Kurniawan; Alfian Arsyad Wijaya; Ali Izzudin; Anyelir Kuntum Sari; Gaga Gunawan Ginanjar; Hildan Albar Islami; Mochamad Solahudin; Muhamad Faisal Fadilah; Muhammad Dzikri Abdi Fathir; Muhammad Lutfi Nurrizal; Rifki Ramdani; Riski Alwi Al-Idrus; Salman Haddad Baihaqi
Jurnal PkM MIFTEK Vol 7 No 1 (2026): Jurnal PkM Miftek
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.7-1.2899

Abstract

Pengabdian kepada masyarakat ini diarahkan untuk meningkatkan pemahaman Computational Thinking pada anak binaan Perlindungan Sosial Anak (PSA) Desa Balewangi. Computational Thinking merupakan kemampuan berpikir logis, sistematis, dan terstruktur yang relevan dalam pemecahan masalah sehari-hari maupun berbasis teknologi. Strategi pembelajaran yang digunakan adalah edukasi interaktif melalui penyajian materi visual, penayangan video, serta diskusi kelompok. Desain penelitian menerapkan pre-test dan post-test guna menilai efektivitas program. Kegiatan dilaksanakan pada 9 Agustus 2025 dengan partisipasi 30 anak. Hasil evaluasi menunjukkan peningkatan nilai pada post-test sebesar 12,5%. Pencapaian ini menegaskan bahwa pendekatan edukasi interaktif mampu memperkuat pemahaman Computational Thinking pada anak binaan PSA, sekaligus berkontribusi pada pengembangan literasi digital di tingkat komunitas pedesaan.
Fake News Detection in Indonesian Language Using IndoBERT with LIME-Based Keyword Interpretation Rifki Ramdani; Muhammad Nadhief Rahmat Firdaus
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.198

Abstract

Fake news dissemination in the digital era has become a serious issue, particularly in political and public information domains. This study proposes an Indonesian fake news detection system that uses the IndoBERT transformer model, combined with LIME (Local Interpretable Model-Agnostic Explanations), for keyword-based interpretation. The primary objective of this study is not only to achieve high classification performance but also to enhance model transparency by identifying the most influential words contributing to prediction results. This study follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, starting with dataset collection, exploratory data analysis, text preprocessing, model fine-tuning, and evaluation, and concluding with interpretability analysis using LIME. The dataset consists of 31,310 Indonesian political news articles categorized into hoax and factual classes. IndoBERT is fine-tuned using the Hugging Face framework with optimized hyperparameters and class weighting to address class imbalance. Experimental results show that the proposed model achieves an accuracy of 99.78%, precision of 99.81%, recall of 99.52%, and F1-score of 99.66%, demonstrating strong performance in distinguishing hoax and factual news. Furthermore, LIME-based analysis provides interpretable insights by highlighting keywords that influence model predictions, thereby improving transparency and user trust. Words associated with conspiracy and unverified claims contribute strongly to the hoax class, while terms related to official institutions and statistical information support factual classification. The results indicate that integrating IndoBERT with LIME not only improves classification performance but also enhances explainability in Indonesian fake news detection systems.
Co-Authors Abizar Algifari Alfarabi Kurniawan Alfian Arsyad Wijaya Ali Izzudin Anyelir Kuntum Sari Euis Novianti Gaga Gunawan Ginanjar Girah Ismi Nugraha Hanif Luqman Muttaqin Hildan Albar Islami Karina Hoirun Nisa Mochamad Solahudin Muhamad Faisal Fadilah Muhammad Dzikri Abdi Fathir Muhammad Lutfi Nurrizal Muhammad Nadhief Rahmat Firdaus Muthia Sandi Firamid Pamella M Sri Rezeki Riski Alwi Al-Idrus Salman Haddad Baihaqi Tabina Athifa Rahmaniya