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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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Rancang Bangun Sistem Pembelajaran SMP Nurul Islam Tanahbaya dengan Metode Agile Scrum Nasywatus Sholichah, Nisa; Parga Zen, Bita; Ahmad Firdaus, Eryan; Mulyana, Dadan
Jurnal Sistem Informasi Galuh Vol 3 No 1 (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.v3i1.4567

Abstract

This study aims to design and develop a learning management system (LMS) at SMP Nurul Islam Tanahbaya using the Agile Scrum methodology. The developed system aims to enhance the effectiveness of the learning process by facilitating digital interaction between students and teachers and by providing an integrated platform for learning management. This study involves several stages, starting with problem identification, data collection, verification of needs and issues, Scrum process and development, testing, and conclusion drawing. The results of the study indicate that the application of the Agile Scrum method in the development of the LMS at SMP Nurul Islam Tanahbaya can improve development completion time and produce a system that meets the needs. This LMS features functionalities such as chats, material management, assignments, announcements, discussion forums, assessments, surveys, and recommendations. The Learning Management System (LMS) developed for SMP Nurul Islam Tanahbaya has been tested using blackbox testing methods. The results of the blackbox testing show that the system is valid and capable of performing all expected tasks effectively. Therefore, the developed LMS is expected to significantly contribute to improving the quality of learning at SMP Nurul Islam Tanahbaya and support a more effective and efficient educational process.
Penerapan Teknologi Generative AI Untuk Pembelajaran Kreatif Di SMP & SMK NU Bogor Ahmad Firdaus, Eryan; Vernando, Deden; Ali Hanan, Rohman; Fahlepy Sinaga, Ryan
Jurnal Pengabdian Masyarakat Nauli Vol. 3 No. 2 (2025): Februari, Jurnal Pengabdian Masyarakat Nauli
Publisher : Marcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/nauli.v3i2.178

Abstract

Program Pengabdian Kepada Masyarakat dan Kuliah Kerja Nyata (KKN) oleh Dosen dan mahasiswa Informatika Universitas Pertahanan Republik Indonesia (UNHAN RI) tahun 2024 dilaksanakan di Desa Tanjungsari, Kecamatan Cijeruk, Kabupaten Bogor. Kegiatan ini bertujuan untuk meningkatkan keterampilan masyarakat dan siswa melalui penerapan teknologi modern, dengan fokus pada sosialisasi Generative Artificial Intelligence (AI) dan pelatihan kedisiplinan. Melalui sosialisasi Generative AI, siswa diperkenalkan dengan konsep, manfaat, dan etika penggunaan teknologi ini, khususnya dalam mendukung kreativitas dan produktivitas. Siswa dilatih untuk memanfaatkan Generative AI dalam pembuatan konten kreatif, identifikasi informasi valid, serta pemecahan masalah. Selain itu, pelatihan Peraturan Baris-Berbaris (PBB) dilakukan untuk menanamkan disiplin, kerjasama, dan kepemimpinan pada siswa. Hasil kegiatan menunjukkan peningkatan pemahaman siswa terhadap teknologi Generative AI, termasuk kemampuan praktis dan kesadaran etis dalam penggunaannya. Pelatihan PBB juga berhasil meningkatkan disiplin, kerjasama tim, dan keterampilan kepemimpinan siswa. Kendala seperti keterbatasan waktu, fasilitas teknologi, dan perbedaan minat siswa menjadi tantangan dalam pelaksanaan kegiatan ini, namun dapat diatasi dengan koordinasi yang baik antara tim PKM dan KKN serta pihak sekolah. Program ini memberikan manfaat jangka panjang bagi siswa dan masyarakat, baik dalam bentuk pengembangan keterampilan teknologi maupun pembentukan karakter yang lebih kuat. Diharapkan kegiatan serupa dapat dilaksanakan dengan persiapan yang lebih matang untuk dampak yang lebih optimal.
Mapping ownership of luxury goods and household assets in cities in Jawa Tengah using logistic regression Hanan, Rohman Ali; Firdaus, Eryan Ahmad; Manurung, Jonson
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.292

Abstract

Ownership of luxury goods and household assets is a crucial issue in the Indonesian economy, particularly in Jawa Tengah, as it reflects complex socio-economic dynamics. This study aims to map the distribution of luxury goods and household assets across regencies and cities in Jawa Tengah and analyze the factors influencing their ownership using logistic regression. Socio-economic disparities in asset ownership are driven by factors such as education, income, and access to information, which contribute to broader social inequality and regional economic development.Using data from the Jawa Tengah Statistics Agency, this study examines variations in asset ownership, including motorcycles, refrigerators, and land, across different regions. Findings indicate that regions with higher motor vehicle ownership tend to exhibit stronger economic welfare compared to those with lower asset ownership. Beyond economic factors, psychological and social aspects, including social status and religious influences, also shape decisions regarding luxury goods acquisition.This research contributes to the literature by addressing the underexplored local context of asset ownership in Indonesia. The findings provide insights for policymakers in designing more inclusive and responsive socio-economic policies, aiming to reduce disparities and promote equitable regional development.
Analisis Sentimen Pengguna Twitter terhadap Konflik Rusia-Ukraina Menggunakan Naïve Bayes dan Lexicon Based Features J. Manurung, Barnes; Parga Zen, Bita; Setiya Rafika Nur, Yohani; Claudio Felle, Roland; Ahmad Firdaus, Eryan
Jurnal Informatika Vol 4 No 1 (2025): Jurnal Informatika
Publisher : LPPM Universitas Nias Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57094/ji.v4i1.2574

Abstract

The conflict between Russia and Ukraine remains one of the major international issues drawing global attention. It began in 2014 with the ousting of President Yanukovych, which triggered a political divide between pro-European Union and pro-Russian factions within Ukraine. Tensions escalated significantly by late 2021, culminating in Russia’s military aggression against Ukraine on February 24, 2022, under President Vladimir Putin’s directive. Twitter, as a social media platform, became a major outlet for the public to express their opinions regarding the conflict. This study aims to analyze public sentiment on Twitter concerning the Russia-Ukraine conflict. The methods used in this research are a combination of the Naïve Bayes algorithm and Lexicon Based Features. Naïve Bayes is utilized to classify sentiment data, while Lexicon Based Features assign weights to positive and negative sentiment words in textual data. The results show that this combined method is effective in categorizing public opinions based on their sentiments towards the conflict. This sentiment analysis provides a broader understanding of global perceptions and may serve as a reference for assessing public opinion in digital media contexts.
Pengembangan Website Landing Page Prodi Informatika Universitas Pertahanan RI Menggunakan Metode Responsive Web Design Amar Ma’ruf, Azzam; Savira, Jihan; Adrian Sitanggang, Johan; Aryaputra Piliang, Rizqullah; Jaya Gainal, Ido; Ahmad Firdaus, Eryan
Jurnal Informatika Vol 4 No 1 (2025): Jurnal Informatika
Publisher : LPPM Universitas Nias Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57094/ji.v4i1.2586

Abstract

The advancement of information technology requires educational institutions to provide effective communication media for delivering academic information to the public. A website plays a key role in this regard, serving as a digital gateway that reflects the institution’s credibility and professionalism. The Informatics Study Program at the Indonesian Defense University needs a landing page website that is not only informative and visually engaging but also accessible across a variety of devices and screen sizes. This study aims to design and develop a landing page using the Responsive Web Design (RWD) approach to ensure that the website display can dynamically adjust on desktops, tablets, and smartphones. RWD was chosen for its ability to deliver an optimal viewing experience through the use of flexible grid layouts, media queries, and responsive user interface elements. The development process also considered aspects of user interface (UI) and user experience (UX) to enhance user comfort and functionality. Testing results show that the website developed with the RWD method can effectively present information on various devices, support intuitive navigation, and improve overall accessibility. This website is expected to serve as a reliable communication medium and strengthen the professional image of the Informatics Study Program at the Indonesian Defense University in the digital space.
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.
Co-Authors Abdurahman Adrian Sitanggang, Johan Ali Hanan, Rohman Alva Dinanda Yansen, Fadhil Amar Ma’ruf, Azzam Andrijana Kusuma, Kanggep Anindito Aryaputra Piliang, Rizqullah Asep Budi Dharmawan Asep Sofyan Wahyudin Bacilius Agung Suburdjati Bacilius Agung Suburdjati Bayu Pamungkas Bintang Maharani Sigalingging, Miranda Bita Parga Zen Chika Santika Nurathilla Claudio Felle, Roland Dadan Mulyana Dadan Mulyana Despawana Dessy Rahmadani, Atika Dhaifullah, Rendi Hanif Dwi Tri Putra, Bainul Fadhil, Muhammad Fadhil Fahlepy Sinaga, Ryan Fajar Aditya Fauzi Faisal Nugraha Fauzi Faisal Nugraha Febrian Wahyu Christanto Gian Febriantama, Rizal Gusmi Putri Utami, Aisyah Hanan, Rohman Ali Harahap, Muhammad Aldho Febrian Herdiana, Oding Herris, Fhatur Robby Tanzil Hondor Saragih IDO JAYA GAINAL Iman Saufik Suasana J. Manurung, Barnes Jaya Gainal, Ido Kanggep Andrijana Kusuma Kanggep Andrijana Kusuma Kurniawan, Ferdian Miko Labiba Sarwoko, Nisrina Mamay Syani Mamay Syani Manurung, Jonson Maulana Sidiq Mochamad Rizal Muttaqin Muhamad Abdul Aziz Muhammad Nur Fikri Nasywatus Sholichah, Nisa Naufal Arits Fikri, Muhammad Naufal Rizki, Muhammad Naufal, Alman Nugraha, Fauzi Faisal Nuriansyah, Agam Prabukusumo, Muhammad Azhar 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 Setyawan, Muhammad Iqbal Shanti Maulani Syani, Mamay Tuti Rohayati Umbu Tamu Ama, Aldian Vernando, Deden Wahyu Christanto, Febrian Wowo Trianto Yohani Setiya Rafika Nur Yudi Permana, Nana Yuki Kirana Zhafirah, Findi