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All Journal Bestuur Jurnal Cita Hukum JUITA : Jurnal Informatika Jurnal Hukum Novelty Sriwijaya Law Review JOIV : International Journal on Informatics Visualization Jurnal Ilmiah Hukum LEGALITY AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat DE LEGA LATA: Jurnal Ilmu Hukum Jurnal Pengabdian Hukum Indonesia (Indonesian Journal of Legal Community Engagement) JPHI Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Yudisial JURNAL HUKUM dan KENOTARIATAN Indonesian Comparative Law Review JHCLS Audito Comparative Law Journal (ACLJ) Jurnal Dedikasi Hukum Jurnal Medika Mengabdi Jurnal Pengabdian Hukum Indonesia
Nur Putri Hidayah
Universitas Muhammadiyah Malang
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : joiv international journal on informatics visualization

 1 
Implementation of Virtual Reality Moot Court for Simulation and Procedural Law Learning of the Constitutional Court Hidayah, Nur Putri; Wicaksono, Galih Wasis; Perdana, Muhammad Ilham; Faiz, Ahmad; Cholidah, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3125

Abstract

The limited space for moot court simulations in law learning is one of the main obstacles. In the Constitutional Court's judicial practice, no faculty has a Moot courtroom identical to the actual courtroom. Every law student must be able to practice trial to improve their argumentation, advocacy, legal reasoning, and other problem-solving skills. This research aims to build and develop a Virtual Reality (VR) Moot Court that can be used as a Moot Court in the trial of the Constitutional Court. VR Moot Court is a means of practicum in the constitutional procedure law course. This research was carried out through scenario preparation and system design stages, followed by 3D asset optimization, user interaction design, multi-user design, and testing. This research utilizes Unity to build 3D assets and Spatial.io as a VR platform. For more immersive use, users can use VR headsets such as Oculus. However, VR Moot Court can also be accessed via smartphone or PC for broader use. The development of VR Moot Court is quite complex, requiring the optimization of assets used across various devices. This study optimizes poly, texture, material, and lighting. The results of VR Moot Court development in this study tested the system's functionality and measured the optimization results. The results of system optimization tests have shown a decrease in GPU and CPU usage. Meanwhile, the results of the functionality and user satisfaction tests also show that VR Moot Court, in addition to taking course learning outcomes in the constitutional court's procedural law course, this system is also relevant to the actual Constitutional Court courtroom. This research in the future requires the development of a type of moot courtroom for other kinds of courts.
Classification of Industrial Relations Dispute Court Verdict Document with XGBoost and Bidirectional LSTM Wicaksono, Galih Wasis; Nur Oktaviana, Ulfah; Noor Prasetyo, Said; Intana Sari, Tiara; Hidayah, Nur Putri; Yunus, Nur Rohim; Al-Fatih, Solahudin
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2373

Abstract

Industrial relations disputes (Perselisihan Hubungan Industrial (PHI)) are essential to examine because these disputes represent unbalanced bargaining positions between workers and corporations. On the other hand, there are many PHI documents, so they need to be classified and distinguished from other types of other decisions for other types of civil cases. PHI decisions document can be accessed openly from a special directory of civil courts. This ruling has similarities with other decisions regarding consumer protection or bankruptcy. This study used 450 documents consisting of 255 PHI court decisions and 255 non-PHI court decisions. This study takes the case as a classified part. We use several feature extractions and three methods: Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Long Short-Term Memory (Bi-LSTM). For SVM and XGBoost classifier, we utilize Frequency-inverse document frequency (TF-IDF). Another classifier needs word embedding Glove Wikipedia Indonesian with a dimension size of 50. Various experiments conducted found that the best classification results used Bi-LSTM with Gloves. This classification has 100% accuracy without overfitting. We found the second result using XGBoost with parameters optimized using random search, while the lowest accuracy results were obtained using the SVM method. The accuracy of the classification results in this study can impact the availability and quality of open legal knowledge that can be utilized by society and for future research.
Automatic Summarization of Court Decision Documents over Narcotic Cases Using BERT Wicaksono, Galih Wasis; Al asqalani, Sheila Fitria; Azhar, Yufis; Hidayah, Nur Putri; Andreawana, Andreawana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1811

Abstract

Reviewing court decision documents for references in handling similar cases can be time-consuming. From this perspective, we need a system that can allow the summarization of court decision documents to enable adequate information extraction. This study used 50 court decision documents taken from the official website of the Supreme Court of the Republic of Indonesia, with the cases raised being Narcotics and Psychotropics. The court decision document dataset was divided into two types, court decision documents with the identity of the defendant and court decision documents without the defendant's identity. We used BERT specific to the IndoBERT model to summarize the court decision documents. This study uses four types of IndoBert models: IndoBERT-Base-Phase 1, IndoBERT-Lite-Bas-Phase 1, IndoBERT-Large-Phase 1, and IndoBERT-Lite-Large-Phase 1. This study also uses three types of ratios and ROUGE-N in summarizing court decision documents consisting of ratios of 20%, 30%, and 40% ratios, as well as ROUGE1, ROUGE2, and ROUGE3. The results have found that IndoBERT pre-trained model had a better performance in summarizing court decision documents with or without the defendant's identity with a 40% summarizing ratio. The highest ROUGE score produced by IndoBERT was found in the INDOBERT-LITE-BASE PHASE 1 model with a ROUGE value of 1.00 for documents with the defendant's identity and 0.970 for documents without the defendant's identity at a ratio of 40% in R-1. For future research, it is expected to be able to use other types of Bert models such as IndoBERT Phase-2, LegalBert, etc.
Text Summarization on Verdicts of Industrial Relations Disputes Using the Cross-Latent Semantic Analysis and Long Short-Term Memory Wicaksono, Galih Wasis; Hakim, Muhammad Nafi Maula; Hayatin, Nur; Hidayah, Nur Putri; Sari, Tiara Intana
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2052

Abstract

The information presented in the documents regarding industrial relations disputes constitutes four legal disputes. However, too much information leads to difficulty for readers to find essential points highlighted in industrial relations dispute documents. This research aims to summarize automated documents of court decisions over industrial relations disputes with permanent legal force. This research involved 35 documents of court decisions obtained from Indonesia’s official Supreme Court website and employed an extractive summarization approach to summarize the documents by utilizing Cross Latent Semantic Analysis (CLSA) and Long Short-Term Memory (LSTM) methods. The two methods are compared to obtain the best results CLSA was employed to analyze the connection between phrases, requiring the ordering of related words before they were converted into a complete summary. Then, the use of LSTM is combined with the Attention module to decoder and encoder the information entered so that it becomes a form that can be understood by the system and provides a variety of splitting of documents to be trained and tested to see the highest performance that the system can generate. The research has found out that the CLSA method gave a precision of 79.1%, recall score of 39.7%, and ROUGE-1 score of 50.9%, and the use of LSTM was able to improve the performance of the CLSA method with the results obtained 93.6%, recall score of 94.5 %, and ROUGE-1 score of 93.9% on the variation of splitting 95% training and 5% testing.
Co-Authors Abdul Kadir Abdul Kadir Jaelani Ahmad Dzaki Abidin Ahmad Faiz, Ahmad Ahmad Siboy Al asqalani, Sheila Fitria Al Sakinata, Annisa' Al-Fatih, Solahudin Aldi Malik Farhad AlFawwaz, Nauval Amanda Shifa Burhanuddinnisa Amiludin, Amiludin Andesti, Kiki Andreawana, Andreawana Anggraini, Putri Cahyaning Arcelia, Allysa Sonia Arifpadilah, Pajri Arsyi Manggali Arya Putra Arsyi Manggali Arya Putra Bayu Dwi Widdy Jatmiko Cholidah, - Christian Sri kusuma Aditya, Christian Sri kusuma Dandi Sanjaya David Pradhan Deni Alamsah Faisal Firjatullah, Muhamad Sulthan Fitria Esfandiari Hakim, Muhammad Nafi Maula HARIYANTO HARIYANTO Inda Rahadiyan, Inda Intana Sari, Tiara Isdian Anggraeny Karina Ghea Silvana Komariah Komariah Komariah Komariah Komariah Komariah Komariah Komariah Koto, Ismail Maharani Zalianty Mohammad Isrok Muafika, Siti Nurmala Lailatul Muhammad Bahrul Ulum Muhammad Hariz Faizul Anwar Nizam Avif Anhari Noor Prasetyo, Said Novilia Safitri Dewik Nur Fauzia Asmi Nur Hayatin Nur Islamiyah Puspasari Nur Oktaviana, Ulfah Nurpratama, Widya Lestari Ordelia Julayta Syahidah Perdana, Muhammad Ilham Putra, Arsyi Manggali Arya Quincy R. Cloet Ratri Novita Erdianti Saleha, Meisya Maulidina Samira Echaib Sari, Tiara Intana Sholahuddin Al-Fatih Sinta Amipatul Aisiyah Siregar, Santy Anjelyna Br. Siti Kunarti Torri Justiansyah Utami Putri Kinayungan Virga Dwi Efendi Wasis Wasis Wicaksono, Galih Wasis Yuda Munarko Yufis Azhar Yunus, Nur Rohim