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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pendidikan Ilmu Komputer (JPIK) Jurnal Ilmu Komputer (JIK) Jurnal Computech & Bisnis (e-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Science and Technology METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Aptisi Transactions on Management Jurnal Teknologi Terpadu Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Journal of Electronics Technology Exploration (JOeTEx) Journal of Computers for Society
Eddy Prasetyo Nugroho
Universitas Pendidikan Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Pengukuran Kelayakan Simulator Forensik Digital Menggunakan Metode Multimedia Mania Eddy Prasetyo Nugroho; Irawan Afrianto; Rini Nuraini Sukmana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1556

Abstract

Pengujian kelayakan suatu media pembelajaran merupakan hal yang penting dilakukan untuk menjamin keberlangsungan, keberlanjutan dan keterikatan (engagement) antara aplikasi dengan penggunanya. Tujuan dari penelitian ini adalah menguji kelayakan dari aplikasi simulator forensik digital sebagai media ajar untuk menginvestigasi keamanan sistem pada lingkungan jaringan komputer menggunakan metode multimedia mania. Bidang jaringan komputer dan internet merupakan lingkungan yang memiliki kerentanan yang sangat tinggi, dimana berbagai macam jenis eksploitasi terhadap lingkungan ini sering terjadi dan menyebabkan kerugian yang besar, sehingga pembelajaran pada bidang ini menjadi suatu hal yang penting dilingkungan sekolah, khususnya pada Sekolah Menengah Kejuruan bidang teknik dan jaringan komputer serta informatika. Multimedia mania merupakan suatu metode pengukuran kelayakan berupa rubrik yang menilai aspek-aspek teknis pada aplikasi multimedia. Rubrik ini digunakan untuk menggali lebih banyak informasi terkait keselarasan media pembelajaran dengan kebutuhan dan kenyamanan pengguna. Penilaian pada rubrik ini terdiri dari 5 aspek penting, yaitu mekanisme, elemen multimedia, struktur informasi, dokumentasi, dan kualitas konten multimedia. Hasil pengujian kelayakan simulator forensik digital di 4 sekolah menegah kejuruan mendapatkan nilai kelayakan sebesar 89,22% dari ahli media dan 96,55% dari penilaian siswa. Hasil ini menunjukkan bahwa simulator forensik digital layak untuk dijadikan sebagai media pembelajaran dan bahan ajar multimedia pada bidang investigasi keamanan jaringan komputer.
Rancang Bangun Aplikasi Real-Time Information Angkutan Kota dengan Location-based Service Menggunakan Metode Prototyping Maulana, Rachman Faiz; Wibisono, Yudi; Nugroho, Eddy Prasetyo
Digital Transformation Technology Vol. 6 No. 1 (2026): Periode Maret 2026
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v6i1.7362

Abstract

Penelitian ini dilatarbelakangi oleh menurunnya minat masyarakat dalam menggunakan angkutan kota di Kota Bandung, salah satunya disebabkan oleh tidak tersedianya informasi posisi angkot secara real-time yang membuat penumpang sulit memperkirakan waktu tunggu. Ketidakpastian ini berdampak pada rendahnya kenyamanan dan kepercayaan pengguna terhadap layanan angkutan kota. Untuk menjawab permasalahan tersebut, penelitian ini bertujuan merancang dan membangun aplikasi real-time information untuk angkutan kota berbasis Android dengan memanfaatkan teknologi Location-Based Service (LBS). Tujuan utama pengembangan adalah menyediakan informasi posisi angkot secara langsung, memudahkan navigasi penumpang, dan meningkatkan transparansi serta kualitas layanan. Metode Prototyping digunakan agar proses pengembangan dapat dilakukan secara iteratif melalui umpan balik pengguna hingga menghasilkan desain yang sesuai kebutuhan. Pengujian sistem menggunakan metode Black Box menunjukkan seluruh fitur berjalan sesuai dengan spesifikasi. Evaluasi usability dengan System Usability Scale (SUS) melibatkan 15 responden dan menghasilkan skor rata-rata 73,67 yang berada pada kategori Acceptable dan Grade C. Hasil ini menunjukkan bahwa aplikasi dapat diterima dengan baik, cukup mudah dipahami, dan layak digunakan oleh pengguna. Secara keseluruhan, penelitian ini berhasil menghasilkan prototipe aplikasi yang berfungsi sesuai tujuan serta berpotensi mendukung peningkatan kualitas layanan angkutan kota, meskipun masih terbuka peluang pengembangan lebih lanjut untuk penyempurnaan fitur dan pengalaman pengguna.
Visual Trend Analysis of E-Commerce Thumbnails Using Parallel Computing for Image Big Data Muhamad Tio Ariyanto; Haris Maulana; Muhammad Rifky Afandi; Eddy Prasetyo Nugroho
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.19194

Abstract

The rapid growth of e-commerce platforms has led to the massive accumulation of product thumbnail images, making manual visual analysis inefficient and conventional sequential processing methods insufficient to handle such data volumes in a timely manner. Given the crucial role of thumbnails in influencing consumer purchasing decisions, computational strategies are required to accelerate the analysis process without compromising classification accuracy. This study applies a parallel computing approach combined with deep learning to improve the efficiency of visual trend analysis using two primary datasets: 2,608 images for model training and validation, and 40,254 images for large-scale inference. The proposed framework integrates parallel image preprocessing on multi-core CPUs, the development of a Convolutional Neural Network based on MobileNetV2 using a transfer learning approach, and batch-based parallel inference on GPUs. The developed model demonstrates stable and convergent performance, achieving a training accuracy of 0.85 and a validation accuracy of 0.83. Efficiency testing during the preprocessing stage shows that the parallel approach is more effective under large data workloads, providing a speed improvement of up to 1.58×. During the inference stage, predictions for 500 images can be completed in 1.84 seconds compared to 41.76 seconds using the sequential method, resulting in a significant computational speedup of 22.8×. Big data analysis reveals a polarization of visual strategies, where technology product categories are dominated by infographic-style thumbnails, fashion categories rely heavily on human model representations, and household product categories emphasize clean product visuals supported by promotional elements. This study concludes that the application of parallel computing significantly enhances the efficiency and scalability of visual big data analysis in e-commerce and supports more operational and strategic mapping of visual trends.
Pengembangan Sistem Monitoring Prestasi Mahasiswa Berbasis Data Management Framework Mia Karisma Haq; Rani Megasari; Eddy Prasetyo Nugroho
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.485

Abstract

The issue of limited structured data has often hindered the role of academic advisors in effectively monitoring students’ participation and achievements. At Universitas Pendidikan Indonesia (UPI), student achievement data, both academic and non-academic, is still largely processed manually through forms or online messaging groups, which does not support comprehensive analysis. This study aims to develop a Student Achievement Monitoring System based on the Data Management Framework (DAMA-DMBOK) to ensure that data management is standardized, integrated, and supports data-driven decision-making. The research method includes data collection through literature studies, observation, and interviews; designing the data architecture; formulating key performance indicators (KPIs); developing data visualization and reporting features; and evaluating data management maturity using the Data Management Maturity Assessment (DMMA). The implementation results show that the system has successfully increased the maturity level of data management in key areas such as Data Modeling and Design, Data Storage and Operations, Data Integration & Interoperability, Metadata Management, and Business Intelligence, reaching the Optimizing level. With its analytical dashboard, reporting features, and dynamic data filters, the system supports academic advisors in monitoring student achievement more accurately, continuously, and in a well-documented manner. This study is expected to serve as a reference for developing more adaptive and integrated student achievement monitoring systems at the study program level in higher education institutions.
Integrasi YOLOv11 dan Intersection-Based Method Untuk Estimasi Karakteristik Parkir Berdasarkan Parking Lot Surveillance Video Muhammad Kamal Robbani; Yudi Wibisono; Eddy Prasetyo Nugroho
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.502

Abstract

The rapid growth of vehicles without a corresponding increase in parking space availability has led to various issues such as traffic congestion, fuel waste, and excessive emissions. This study develops a computer vision-based parking analysis system using the YOLOv11 model to automatically detect vehicles in parking areas. The system integrates an intersection-based method and the BoT-SORT object tracking algorithm to classify parking spot availability. The classification results are then used to extract parking characteristic data. Video data were obtained from a publicly accessible livestream on YouTube in Kusatsu, Japan, and used for training and evaluating the model. The model achieved an mAP@50-95 of 0.926 under bright lighting conditions and 0.859 in low-light conditions. Additionally, estimation accuracy was evaluated using MAE and R² metrics, showing promising results, with MAE of 1.27 and R² of 0.989 during daytime, and MAE of 0.91 and R² of 0.91 at night.
Sistem Pendukung Keputusan Penerima Bantuan Zakat Menggunakan Random Forest dan Fuzzy Analytical Hierarchy Process Azka Naufal Nurrahman; Eddy Prasetyo Nugroho; Yudi Ahmad Hambali
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.510

Abstract

Salah satu tantangan utama dalam penyaluran zakat adalah keterbatasan dana yang tersedia dibandingkan dengan jumlah pengajuan bantuan yang terus meningkat. Kondisi ini menyebabkan lembaga seperti BAZNAS harus melakukan seleksi secara ketat dan menentukan prioritas penerima bantuan secara adil. Dalam upaya menjawab permasalahan tersebut, penelitian ini mengembangkan Sistem Pendukung Keputusan (SPK) menggunakan kombinasi metode Random Forest dan Fuzzy Analytical Hierarchy Process (F-AHP). Metode Random Forest digunakan untuk melakukan klasifikasi kelayakan calon penerima zakat berdasarkan lima kriteria utama, yaitu pendapatan, jumlah tanggungan, status tempat tinggal, Riwayat penerimaan bantuan, dan nominal ajuan. Sementara itu, Fuzzy Analytical Hierarchy Process (F-AHP).digunakan untuk menentukan tingkat prioritas dari calon yang telah dinyatakan layak. Penelitian ini menggunakan data dari BAZNAS Kota Bandung, yang dikumpulkan melalui observasi, wawancara. Hasil evaluasi menunjukkan bahwa model Random Forest yang dibangun memiliki tingkat akurasi sebesar 88,98%, dengan precision dan recall masing-masing di atas 93%, menunjukkan performa klasifikasi yang tinggi. Selanjutnya Proses pembobotan Fuzzy Analytical Hierarchy Process (F-AHP) menghasilkan bobot dominan pada kriteria pendapatan sebesar 0,408, diikuti oleh status tempat tinggal dan jumlah tanggungan. Sistem ini berhasil membantu proses seleksi dan pemeringkatan calon penerima zakat secara objektif, efisien, dan transparan. Dengan pendekatan ini, diharapkan pengelola zakat dapat mengoptimalkan distribusi zakat sesuai dengan prioritas kepada mereka yang membutuhkan.
Predictive Classification Model dalam Tahapan Framework NIJ untuk Otomatisasi Investigasi Digital Forensik (Studi Kasus: Cyberbullying) Khana Yusdiana; Rizky Rahman J.P; Eddy Prasetyo Nugroho
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.521

Abstract

This study aims to apply the National Institute of Justice framework in the digital forensic process for conversations retrieved from LINE and Telegram applications, as well as to explore the utilization of a Predictive Classification Model for automated text-based comment classification in cyberbullying cases. Cyberbullying is a growing form of digital crime, particularly on private and encrypted instant messaging platforms that are difficult to monitor. The research employs two machine learning algorithms within the PCM framework Complement Naive Bayes and Random Forest to detect potentially abusive comments. The forensic process follows several stages: Preparation, Evidence Assessment, Evidence Acquisition, Evidence Examination, and Documenting and Reporting, with a secure and forensically sound data extraction approach from both applications. Due to data limitations from LINE and Telegram, the classification analysis is conducted using an Instagram comment dataset that reflects the cyberbullying context. Evaluation results show that the Complement Naive Bayes model outperforms Random Forest, achieving an accuracy of 86% with balanced F1-scores, while Random Forest achieves 75% accuracy. These findings support the use of PCM as an effective aid for automatically identifying high-risk content on social media. The integration of digital forensics and artificial intelligence has significant potential to enhance the effectiveness of cyberbullying investigations. Keywords: Cyberbullying, Predictive Classification Model, Complement Naive Bayes, Random Forest, LINE, Telegram, Digital Forensics, National Institute of Justice
Design Blockchain Architecture for Population Data Management to Realize a Smart City in Cimahi, West Java, Indonesia Eddy Prasetyo Nugroho; Irawan Afrianto; Erna Piantari; Ani Anisyah; Dwi Novia Al Husaeni; Ibrahim Danial Bisulthon; Irham Jundurrahmaan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27493

Abstract

Smart city as a concept of city development which integrates information and communication technology with the intention of optimizing city management becomes a major goal for Indonesia, especially through the movement towards 100 Smart Cities. However, population data management is crucial in achieving this for optimal planning and management. Personal data protection becomes a crucial challenge with the rapid population growth and mobility in cities. The need for a more reliable protection system is very necessary. This research proposes a blockchain architecture that not only manages digital identities but also population data. The focus is population administration in Cimahi City, West Java, with the hope of providing security, transparency, and a strong audit trail for all population data. The contribution of this research is to design a blockchain architecture specifically for population data management, meeting the needs of population administration in cities, especially the city of Cimahi. Through a blockchain architecture development approach, this research considers the diverse administrative needs of the population and applies a blockchain model that enables data security and integrity. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This research also shows that the use of blockchain technology specifically for population data management can be a reliable and innovative solution in ensuring the security and reliability of data important for smart city development.However, this research has limited access to central data, so the data obtained is still very limited. Therefore, further research is needed to follow up on these limitations. Apart from that, this research is also expected to provide knowledge and solutions in securing data, especially population data in government environments.
Pemodelan Sistem Deteksi Intrusi pada Sistem Smart Home Pemantauan Konsumsi Energi Listrik Berbasis Machine Learning Eddy Prasetyo Nugroho; Sabian Annaya Havid; Muhammad Nursalman
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp42-49

Abstract

The occurrence of electricity usage that exceeds the power capacity of the home requires a smart home system that can monitor electricity consumption efficiently. This smart home system is built based on the Internet of Things (IoT) which can help electricity users at home to evaluate usage more easily and in an integrated manner. The development of this IoT-based smart home system uses the ESP32 Micro Controller Unit (MCU) and the PZEM-004T v.3.0 sensor. The reading results from the system can be seen on the front end of the web-based application and the LCD module on the controller system. To obtain the efficiency of electricity usage, an electricity usage leakage detection system is needed or in this case, it is called an intrusion detection system or Intrusion Detection System (IDS). The development of IDS by identifying anomalies based on electricity usage. The IDS model utilizes Machine Learning with a labelling process pattern as a preprocess using the Isolation Forest unsupervised learning algorithm and the classification process using the Random Forest supervised learning algorithm with Anomaly and Normal status. Evaluation of the IDS model on the dataset that has gone through labelling gives quite good results with an accuracy value of 99.63 %. IDS Model is ready to be tested in the implementation of classifying recorded data in real-time against several electrical energy load scenarios in the future.
Co-Authors Al Fathin, Deva Shofa Amadudin, Muhammad Nur Yasin Ani Anisyah Anisyah , Ani Anisyah, Ani Arif Zuhud R Asep Wahyudin Azka Naufal Nurrahman Caesar Firdaus Dr Munir Dwi Novia Al Husaeni Eggy Ryana Agustian Enjang Ali Nurdin Enjun Junaeti Erna Piantari Fathimah, Nusuki Syari’ati Haris Maulana Havid, Sabian Annaya Hidayat, Kurniawan Ibrahim Danial Bisulthon Imam Wiyanto Irawan Afrianto Irham Jundurrahmaan Kesuma, M Salman Khana Yusdiana Kusnendar, Jajang Mahkota, Fachri Veryawan Mamat Rahmatullah Maulana, Rachman Faiz megasari, Rani Megasari Mia Karisma Haq Muhamad Tio Ariyanto Muhammad Adnan Khairi A.S. Muhammad Kamal Robbani Muhammad Nursalman Muhammad Rifky Afandi Nuris, Nuris Nurkhofifah, Eva Nursalman, Muhammad Panji Nugroho Rachman, Irfan Haydar Ramadhani, Muhammad Satria Rani Megasari Rini Nuraini Sukmana Rizky Rachman J. Rizky Rachman Judiputra Rizky Rahman J.P Robbani, Zuhal Sabian Annaya Havid Siregar, Herbert Suryoatmojo, Jason Rafif Pangestu Turnip, Franklin Umar Syahid Aulia Rahman Wahyudin Wahyudin Waslaluddin MT Wibisono, Yudi Yosafat Yudha, Apri Anggara Yudi Ahmad Hambali