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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Perdana, Muhammad Ilham
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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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.
Data-driven support vector regression-genetic algorithm model for predicting the diphtheria distribution Anggraeni, Wiwik; Sudiarti, Yeyen; Perdana, Muhammad Ilham; Riksakomara, Edwin; Sooai, Adri Gabriel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2909-2921

Abstract

Indonesia is one of the countries with the largest number of diphtheria sufferers in the world. Diphtheria is a case of re-emerging disease, especially in Indonesia. Diphtheria can be prevented by immunization. Diphtheria immunization has drastically reduced mortality and susceptibility to diphtheria, but it is still a significant childhood health problem. This study predicted the number of diphtheria patients in several regions using support vector regression (SVR) combined with the genetic algorithm (GA) for parameter optimization. The area is grouped into 3 clusters based on the number of cases. The proposed method is proven to overcome overfitting and avoid local optima. Model robustness tests were carried out in several other regions in each cluster. Based on the experiments in three scenarios and 12 areas, the hybrid model shows good forecasting results with an average mean squared error (MSE) of 0.036 and a symmetric mean absolute percentage error (SMAPE) of 41.2% with a standard deviation of 0.075 and 0.442, respectively. Based on experiments in various scenarios, the SVR-GA model shows better performance than others. Compares two- means tests on MSE and SMAPE were given to prove that SVR-GA models have better performance. The results of this forecasting can be used as a basis for policy-making to minimize the spread of diphtheria cases.
Automatic Detection of Wrecked Airplanes from UAV Images Risnumawan, Anhar; Perdana, Muhammad Ilham; Alif Habib Hidayatulloh; A. Khoirul Rizal; Indra Adji Sulistijono; Achmad Basuki; Rokhmat Febrianto
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.424

Abstract

Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.
Integrating Tabular Data and Textual Representations for Clinical Risk Prediction Using Machine Learning and Large Language Models Rahman, M.Rafly; Basuki, Setio; Perdana, Muhammad Ilham; Cynthia, La Febry Andira Rose
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2570

Abstract

Global health is currently facing serious challenges due to the increasing number of chronic disease patients, such as those with heart failure, diabetes, and cancer. This issue arises from the limitations of electronic health record (EHR) systems, which are not yet fully capable of ensuring accurate clinical diagnoses because of potential data input errors and delays in symptom identification by medical personnel. In response to this issue, this paper focuses on the integration of medical tabular data with a classification approach based on classical machine learning (ML) and large language models (LLM) to improve the accuracy of patient diagnosis predictions. This paper aims to develop and compare the performance of various ML models, such as XGBoost, SVM, and logistic regression, as well as LLM models like Gemini, LLaMA, and Qwen in fine-tuning, few-shot, and zero-shot scenarios. The paper results show that the combination of Gemini and the few-shot approach (250 shots) achieved the highest accuracy of up to 99.8% in predicting heart failure risk. The main finding of this study is that the narrative text representation of tabular data processed with LLM significantly enhances contextual understanding and classification accuracy, making this approach highly potent for application in AI-based clinical decision-making.
Co-Authors A. Khoirul Rizal Achmad Basuki Adri Gabriel Sooai Ahmad Faiz, Ahmad Alif Habib Hidayatulloh Basuki, Setio Cholidah, - Indra Adji Sulistijono La Febry Andira Rose Cynthia Nur Putri Hidayah Rahman, M.Rafly Riksakomara, Edwin Risnumawan, Anhar Rokhmat Febrianto Sudiarti, Yeyen Wicaksono, Galih Wasis Wiwik Anggraeni