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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Sarjana Teknik Informatika Rekursif: Jurnal Informatika Komuniti : Jurnal Komunikasi dan Teknologi Informasi Jurnal Pengabdian UntukMu NegeRI EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) ABDIMASTEK
Dewi Soyusiawaty
Universitas Ahmad Dahlan
Religion Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Public Health Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Klasifikasi Jenis Kejahatan berdasarkan Teks Amar Putusan Pengadilan Hukum Pidana KUHP menggunakan IndoBERT Perdana, Tirtanusa Kurnia Adhi; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30326

Abstract

The increasing number of the court’s rulings each year presents a challenge for the judiciary. One strategic solution is the application of Artificial Intelligence (AI). Indonesian-based models such as IndoBERT is potential to ease workloads by automatically classifying legal cases. This study aims to explore the capability of IndoBERT to automatically classifying the verdict of section of Indonesian KUHP rulings to accelerate crime type identification. This is an experimental study using supervised text classification. The dataset consists of 12000 verdicts collected from the Indonesian Supreme Court website, classified using IndoBERT fine-tuned with various hyperparameter configuration. Our findings show that the model with a batch size of 8 and learning rate 5e-5 achieved accuracy of 92.59%, precison of 92.93%, recall of 92.59%, and F1-Score of 92.59% on unseen test data. The high accuracy is supported by the explicit mention of crime types within verdict texts. To date, no study has specifically utilized IndoBERT or other models for automatic classification of KUHP articles. This finding has the potential to be integrated into the Supreme Court’s Directory of Decision as a support tool for automatic classification and legal document archiving.
Analisis Sentimen Program Makan Siang Gratis di Twitter/X menggunakan Metode BI-LSTM Attaulah, Dimas Thaqif; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29725

Abstract

The free lunch program became a widely discussed topic on social media, reflecting public opinion towards the policy. This research aims to analyze public sentiment towards free lunch program to evaluate the policy's effectiveness and understand public perception. Data was collected through web crawling techniques on the Twitter/X platform, resulting in 7,441 data. Processing stages include preprocessing, sentiment labeling using VADER, keyword visualization with wordcloud, and application of word embedding using Word2Vec. The oversampling technique is used to overcome data imbalance. Sentiment classification was developed using Bi-LSTM and evaluated with accuracy, precision, recall, and F1-score. The developed Bi-LSTM model achieved 88.75% accuracy, with 88.9% precision, 88.8% recall, and 88.8% F1-score. Analysis results show that the majority of public responses are positive or neutral, although there were negative sentiments that highlighted potential problems such as corruption and increasing national debt. These results provide insight into public opinion on the free lunch policy and demonstrate the effectiveness of the Bi-LSTM model in social media sentiment classification.
Co-Authors Abdul Fadlil Aldini Fujiarti Aning Setiya Rini Ardian M.N. Aris Rakhmadi Attaulah, Dimas Thaqif Buhari Muslim Catur Prasongko Devi Ayunani Wandasari Endri Rahmawanto Enggar Tyasto Wingwirawan Esty Istiqamah Failasuf Abid Dainuri Fathurrahman Fathurrahman Freddy Alpino Furqon, Ihtiar Nur Hapsari, Intan Herman Yuliansyah, Herman Irfan Nur Aditya Jones, Anna Hendri Soleliza Khoirunnisa, Ninda Kholid Rohman Saputro Linda Mariana Lisna Zahrotun Muhammad Kunta Biddinika Nizar Anggit Pramana Nuri Piyanto Perdana, Tirtanusa Kurnia Adhi Putri Silmina, Esi Ramadhan, Imam Razak, Farhan Radhiansyah Rosary Indah Dewi Rusydi Umar Sari, Hana Jelita Sri Winiarti Sunardi Wintolo, Hero