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All Journal Nuansa Informatika Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Informatika Jurnal ICT : Information and Communication Technologies Nuansa Informatika Jurnal Sistem Informasi Galuh Jurnal Pengabdian Masyarakat Nauli Jurnal Ilmu Komputer Ruru
Eryan Ahmad Firdaus
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Sistem Manajemen Laboratorium Komputer Berbasis Website Naufal Rizki, Muhammad; Christanto, Febrian Wahyu; Saufik Suasana, Iman; Ahmad Firdaus, Eryan; Saragih, Hondor; Maulani, Shanti
Jurnal Sistem Informasi Galuh Vol 3 No 2 (2025): Journal of Galuh Information Systems
Publisher : Fakultas Teknik Jurusan Sistem Informasi Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jsig.v3i2.4959

Abstract

Computer laboratory is an important aspect of supporting the learning process at the Politeknik Kesehatan Kemenkes Semarang. However, manual laboratory management is currently inefficient and prone to errors that can reduce the effectiveness of laboratory use by up to 30% and have a direct impact on the quality of learning. Primary data collected includes laboratory specifications such as the availability of 165 computer units, adequate printers for printing needs, internet networks, and additional tools to support practicums. The laboratory is also supported by technical and admin staff who are responsible for the operation and maintenance of equipment and are equipped with a data management system to help smooth activities. To overcome these management problems, this study implemented a website-based laboratory management system using the Prototype method which is designed to simplify the administration process with centralized management, increase management efficiency, and reduce administrative errors. The system implementation test using the Black Box method obtained 100% functional system results running well. The results of the User Acceptance Testing (UAT) analysis obtained a value of 90.6% which indicates that the developed system can overcome existing problems. Further development is expected to improve the quality of laboratory services and expand the use of information technology in education management at the Politeknik Kesehatan Kemenkes Semarang.
Optimization of Weapon Management in the Warehouse of the Indonesian Defense University Using Web-Based QR Code Technology Fadhil, Muhammad Fadhil; Putra, I Made Aditya Pradhana; Setyawan, Muhammad Iqbal; Herris, Fhatur Robby Tanzil; Zhafirah, Findi; Firdaus, Eryan Ahmad
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 1 (2025): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i1.202

Abstract

This study aims to develop a weapon management system in the warehouse of the Indonesian Defense University using QR code-based technology integrated into a web application. This system is designed to address various challenges of conventional management, such as slow recording processes, human error, and data discrepancies, which frequently occur in weapon inventory management. QR code technology enables automated data entry, tracking, and reporting processes, enhancing efficiency, accuracy, and data security. The Agile methodology is applied in the development of this system, covering several stages, including planning, sprint development, iterative testing, and refinement. This system provides key features such as weapon recording using QR codes, student data management, weapon borrowing and returning, and inventory report generation. Testing results show that this system successfully minimizes recording errors and accelerates operational processes. This research significantly contributes to creating a professional, transparent, and accountable weapon warehouse management model, which can serve as a reference for the development of similar systems in other defense environments.
Optimizing the performance of the K-Nearest Neighbors algorithm using grid search and feature scaling to improve data classification accuracy Manurung, Jonson; Saragih, Hondor; Prabukusumo, Muhammad Azhar; Firdaus, Eryan Ahmad
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.466

Abstract

The performance of distance-based classification algorithms such as K-Nearest Neighbors (KNN) is highly dependent on proper feature scaling and optimal parameter selection. Without systematic optimization, KNN may experience decreased accuracy due to feature scale disparities and suboptimal k-values. This study aims to enhance the performance of the KNN algorithm through the integration of Feature Scaling and Grid Search Cross-Validation as a parameter optimization strategy. The research employs the Breast Cancer Wisconsin Dataset, divided into 80% training and 20% testing data. Feature normalization was performed using StandardScaler, while Grid Search was applied to determine the optimal combination of parameters, including the number of neighbors (k), weighting function (weights), and distance metric (metric). The optimized KNN configuration with k = 9, weights = distance, and metric = manhattan achieved an average accuracy of 97.19%, outperforming the baseline accuracy of 93.86%. A paired t-test confirmed that the improvement was statistically significant (p < 0.05). These findings demonstrate that the synergy between feature scaling and parameter tuning can substantially improve both the accuracy and stability of KNN models. The scientific novelty of this study lies in the systematic integration of normalization and parameter optimization through Grid Search, providing an empirical framework that enhances KNN’s robustness across datasets with heterogeneous feature distributions. The proposed approach is recommended for medical data classification and can be adapted to other domains with heterogeneous numerical feature distributions.
Optimization of XGBoost hyperparameters using grid search and random search for credit card default prediction Firdaus, Eryan Ahmad; Manurung, Jonson; Saragih, Hondor; Prabukusumo, Muhammad Azhar
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.468

Abstract

This study explores the optimization of the Extreme Gradient Boosting (XGBoost) algorithm for credit card default prediction through systematic hyperparameter tuning using Grid Search and Random Search methodologies. Utilizing the publicly available Default of Credit Card Clients dataset from the UCI Machine Learning Repository, the research focuses on enhancing model performance by fine-tuning critical parameters such as learning rate, maximum tree depth, number of estimators, subsample ratio, and column sampling rate. The baseline XGBoost model achieved an accuracy of 0.8118, while the tuned models using Grid Search and Random Search improved the accuracy to 0.8183 and 0.8188, respectively. Although the improvement appears modest, the optimized models exhibited enhanced balance between precision and recall, particularly in identifying defaulters within an imbalanced dataset—an essential aspect in credit risk assessment. The results demonstrate that systematic hyperparameter optimization not only improves predictive performance but also contributes to model stability and generalization. Moreover, Random Search proved to be more computationally efficient, achieving near-optimal performance with fewer evaluations than Grid Search, thereby emphasizing its practicality for large-scale financial risk modeling applications. The novelty of this study lies in the comparative evaluation of two optimization techniques within the context of financial risk prediction, providing practical insights into how efficient hyperparameter tuning can enhance the reliability and scalability of machine learning models used in real-world credit risk management systems.
Hyperparameter optimization of graph neural networks for predicting complex network dynamics using bayesian meta-learning Saragih, Hondor; Manurung, Jonson; Prabukusumo, Muhammad Azhar; Firdaus, Eryan Ahmad
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.469

Abstract

The rapid growth of graph-structured data in domains such as transportation, social networks, and biological systems has increased the demand for more adaptive and efficient Graph Neural Network (GNN) architectures. However, GNN performance remains highly sensitive to hyperparameter configurations, which are often tuned through computationally expensive manual or heuristic methods. This study proposes a novel Bayesian Meta-Learning (BML)-based framework for hyperparameter optimization of GNNs aimed at improving the prediction accuracy of complex network dynamics. The framework integrates Bayesian optimization with a meta-learning prior adaptation mechanism, enabling the model to learn optimal hyperparameter distributions across multiple graph tasks. Experimental evaluations conducted on three benchmark datasets—Cora, Citeseer, and PubMed—comprising up to 20,000 nodes with diverse structural complexities, demonstrate that the proposed BML-GNN framework achieves faster convergence, lower validation loss, and higher predictive accuracy than both baseline GNN and traditional Bayesian Optimization approaches. Quantitatively, the BML-GNN model attains an R² score exceeding 0.97 with a significant reduction in RMSE, confirming its strong generalization capability. Although the method shows notable performance improvements, its computational overhead during meta-training and reliance on well-defined prior distributions represent potential limitations. Overall, the integration of Bayesian Meta-Learning provides a robust, scalable, and uncertainty-aware optimization strategy that advances the development of reliable GNN models for complex network modeling and intelligent system design.
Pelatihan dan Demonstrasi Antena Satelit bagi Pramuka Penegak untuk Komunikasi Tanggap Bencana di Kabupaten Pandeglang Provinsi Banten dalam Kegiatan Latsitardanus XLV Tahun 2025 Ahmad Firdaus, Eryan; Manurung, Jonson; Prabukusumo, Muhammad Azhar; Nuriansyah, Agam; Dhaifullah, Rendi Hanif
Jurnal Pengabdian Masyarakat Nauli Vol. 4 No. 1 (2025): Agustus, Jurnal Pengabdian Masyarakat Nauli
Publisher : Marcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/nauli.v4i1.234

Abstract

Kegiatan Latihan Integrasi Taruna Wreda Nusantara (Latsitardanus) XLV tahun 2025 di Provinsi Banten merupakan wadah pengabdian masyarakat yang berfokus pada peningkatan kapasitas generasi muda. Salah satu program yang dilaksanakan adalah pelatihan dan demonstrasi penggunaan antena satelit yang ditujukan bagi Pramuka Penegak di Kabupaten Pandeglang. Program ini bertujuan untuk membekali para anggota Pramuka dengan keterampilan komunikasi tanggap bencana, mengingat pentingnya akses komunikasi yang andal di daerah yang memiliki potensi rawan bencana. Pelatihan ini mencakup pengenalan dasar teknologi antena satelit, prosedur operasional, serta simulasi komunikasi dalam skenario darurat. Melalui demonstrasi langsung, peserta diberikan pemahaman praktis mengenai cara mendirikan dan mengoperasikan perangkat komunikasi satelit. Diharapkan, setelah mengikuti kegiatan ini, Pramuka Penegak dapat berperan aktif sebagai ujung tombak dalam sistem komunikasi darurat, baik untuk mengkoordinasikan bantuan maupun menyampaikan informasi penting saat infrastruktur komunikasi konvensional tidak berfungsi. Program ini menjadi kontribusi nyata Latsitardanus XLV/2025 dalam menciptakan kader-kader muda yang kompeten, tanggap, dan siap siaga untuk membantu masyarakat di lingkungan sekitar.
MENINGKATKAN MOTIVASI BELAJAR SISWA SMA MENGGUNAKAN MEDIA BELAJAR INSTRUCTIONAL GAMES Nugraha, Fauzi Faisal; Firdaus, Eryan Ahmad
Jurnal Ilmu Komputer Ruru Vol. 1 No. 2 (2024): Edisi Juli
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Games that are interesting and fun to play make the interest of students to prefer playing games rather than studying. Submission of teaching materials in a conventional way will be boring to be followed by students in class. It is necessary to improve teaching methods that are able to attract students' interest in learning to be motivated in participating in teaching and learning activities. The design of interactive learning media with the instructional games model aims to increase the interest of students so that they can receive teaching materials in a good and fun way. The method used in this research is Research and Development (R&D). To analyze students' learning motivation, a field survey questionnaire was used which was given to 33 students of Class XI SMA Negeri 1 Plered. There has been an increase of 8.22%, where the initial motivation of students is at the percentage of 62.88% increasing to a percentage of 71.10%, which can also be interpreted that the use of interactive instructional games learning media is able to increase student learning motivation.  
Co-Authors Abdurahman Adrian Sitanggang, Johan Agung Suburdjati, Bacilius Ali Hanan, Rohman Alva Dinanda Yansen, Fadhil Amar Ma’ruf, Azzam Andrijana Kusuma, Kanggep Anindito Aryaputra Piliang, Rizqullah Asep Budi Dharmawan Asep Sofyan Wahyudin Azhar Prabukusumo, Muhammad Bacilius Agung Suburdjati Bayu Pamungkas Bita Parga Zen Chika Santika Nurathilla Claudio Felle, Roland Dadan Mulyana Despawana Dhaifullah, Rendi Hanif Fadhil, Muhammad Fadhil Fahlepy Sinaga, Ryan Fajar Aditya Fauzi Faisal Nugraha Fauzi Faisal Nugraha Febrian Wahyu Christanto Firdaus Laia Gusmi Putri Utami, Aisyah Hanan, Rohman Ali Harahap, Muhammad Aldho Febrian Herdiana, Oding Herris, Fhatur Robby Tanzil Hidayati, Ajeng Hondor Saragih Idul Adha, Rochedi Iman Saufik Suasana J. Manurung, Barnes Jaya Gainal, Ido Kanggep Andrijana Kusuma Kanggep Andrijana Kusuma Kurniawan, Ferdian Miko Mamay Syani Mamay Syani Manurung, Jonson Maulana Sidiq Mochamad Rizal Muttaqin Muhamad Abdul Aziz Muhammad Nur Fikri Nasywatus Sholichah, Nisa Naufal Rizki, Muhammad Naufal, Alman Nugraha, Fauzi Faisal Nuriansyah, Agam Prabukusumo, Muhammad Azhar Pratama Jabir, Yuda Putra Nugraha, Reza Putra, I Made Aditya Pradhana Rian Dwicahya Supriatman Rianti Yunita Kisworini, Rianti Yunita Rifki Adhitama, Rifki Rizky Hadiansyah , Muhammad Rizqi Mahestro Tresna Savira, Jihan Seftian Candra Pratama Setyawan, Muhammad Iqbal Shanti Maulani Syani, Mamay Topik Hidayat Tuti Rohayati Vernando, Deden Wowo Trianto Yohani Setiya Rafika Nur Yudi Permana, Nana Yuki Kirana Zhafirah, Findi