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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL ILMIAH INFORMATIKA Jurnal Manajemen Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Dan Sistem Informasi Bisnis Prosiding National Conference for Community Service Project JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal E-Komtek JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Digital Teknologi Informasi Infotech: Journal of Technology Information Journal of Computer Networks, Architecture and High Performance Computing Telcomatics Journal of Information System and Technology (JOINT) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Business, Social Sciences and Technology (CoNeScINTech) Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Indonesia (JPMI) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Tekno Jurnal ilmiah teknologi informasi Asia INOVTEK Polbeng - Seri Informatika Smatika Jurnal : STIKI Informatika Jurnal
Prasetyo, Stefanus Eko
Universitas Internasional Batam
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Rancangan Jaringan Highly Available PT Pundi Mas Berjaya (PMB) Prasetyo, Stefanus Eko; Wijaya, Gautama; Hasanah, Nafisatul; Jemmy, Jemmy; Syahfira, Putri
Telcomatics Vol. 8 No. 1 (2023)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v8i1.7359

Abstract

In the digital age, networks are an important factor for implementing communication and exchanging information. Campus networks, which are private networks specifically designed for a particular institution, are essential for the institution because they provide access to the necessary information and services. However, campus networks are also vulnerable to security threats such as cybercrime and malware attacks. Firewall implementation can minimize these threats by controlling access to network services. Pundi Mas Berjaya (PMB), a company that provides software solutions to the global market, requires a highly available and redundant campus network. This research uses Cisco Packet Tracer to design and configure the network required by PMB. Implementing a highly available and redundant campus network with a hierarchical network model will improve the performance of the PMB campus network connectivity and security.
ANALISIS PERBANDINGAN QOS PFSENSE, OPNSENSE, DAN FLEXIWAN MENGGUNAKAN METODE LOAD BALANCING Sugianto, Sugianto; Haeruddin, Haeruddin; Prasetyo, Stefanus Eko
Journal of Information System Management (JOISM) Vol. 6 No. 2 (2025): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v6i2.1984

Abstract

Peningkatan konsumsi internet di Indonesia dari 2018 hingga kuartal pertama 2022 menunjukkan pertumbuhan yang signifikan, dengan jumlah pengguna aktif mencapai 210 juta dari total populasi sebesar 272,682 juta. Angka ini mencerminkan penetrasi internet sebesar 77,02%. Internet menjadi layanan yang sangat penting karena memberikan akses cepat dan efisien terhadap informasi. Seiring dengan meningkatnya jumlah pengguna internet, jumlah Penyedia Layanan Internet (ISP) yang tersedia juga bertambah. Ketersediaan dan kualitas layanan internet menjadi faktor kunci yang mempengaruhi minat masyarakat dalam memanfaatkannya. Untuk mendukung kualitas layanan internet, manajemen bandwidth menjadi aspek yang krusial. Salah satu mekanisme yang digunakan adalah Load Balancing, yaitu metode untuk membagi beban lalu lintas jaringan melalui beberapa gateway yang tersedia sehingga tidak terfokus pada satu ISP. Dalam implementasinya, perangkat seperti pfSense, OPNsense, dan flexiWAN sering digunakan sebagai solusi load balancer karena sifatnya yang open-source, fleksibel, dapat dikonfigurasi melalui antarmuka web, serta mendukung fungsi Load Balancing. Selain pfSense dan OPNsense, solusi inovatif seperti flexiWAN kini juga dapat diadopsi sebagai platform SD-WAN open-source yang menawarkan fleksibilitas tinggi dalam pengelolaan lalu lintas jaringan. Dengan kemampuan integrasi yang baik, flexiWAN memungkinkan perusahaan atau organisasi untuk mengoptimalkan jaringan mereka melalui manajemen lalu lintas yang canggih dan efisien. Penelitian ini membandingkan kinerja Load Balancing dari router pfSense, OPNsense, dan flexiWAN pada Bitbox Open Network Appliance. Hasil penelitian menunjukkan bahwa sistem load balancing pada pfSense memberikan kinerja lebih baik dibandingkan OPNsense berdasarkan nilai QoS, meskipun penggunaan resource CPU pada pfSense lebih tinggi. Sebaliknya, penggunaan RAM pada pfSense lebih rendah dibandingkan OPNsense. Kehadiran teknologi seperti flexiWAN dapat menjadi alternatif baru yang memberikan keunggulan tambahan dalam mendukung pengelolaan bandwidth secara optimal.
The Impact of Ephemeral Content on Digital Marketing Strategies: Efforts to Increase Consumer Engagement and Trust Christina, Lidya; Aklani, Syaeful Anas; Prasetyo, Stefanus Eko
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/y1nkwf45

Abstract

This study examines the influence of ephemeral content on consumer engagement and trust by integrating quantitative and qualitative evidence through a sequential explanatory mixed-method approach. Quantitative data were collected from 201 active social media users using validated measurement scales for ephemeral content, engagement, and trust (Cronbach’s α = 0.882). The statistical analysis consists of Pearson correlation, validity testing, and simple regression. Results show that ephemeral content significantly predicts consumer engagement (β = 0.570, p < 0.001; R² = 0.32) and consumer trust (β = 0.620, p < 0.001; R² = 0.38). These R² values indicate moderate explanatory power, consistent with the correlation coefficients. The qualitative phase involved semi-structured interviews with 20 participants to explain the mechanisms underlying the statistical relationships. Thematic analysis reveals three psychological factors that shape consumer responses: (1) real-time urgency that triggers FoMO-driven attention, (2) authenticity generated through minimally edited and spontaneous content, and (3) interactive features that foster a sense of participation. Integration of both datasets shows that ephemeral content is effective not merely because of its temporary nature, but because it combines immediacy, emotional proximity, and interactive cues that enhance perceived credibility.
Evaluasi Keamanan Sistem Autentikasi Biometrik pada Smartphone dan Rekomendasi Implementasi Optimal Felix Yeovandi; Sabariman Sabariman; Stefanus Eko Prasetyo
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.653

Abstract

Biometric authentication on smartphones is a modern solution for more practical and secure login security. This technology offers advantages such as speed of access and resistance to forgery compared to password-based methods. However, there are various weaknesses, such as the potential for exploitation through malware, spoofing, or brute force attacks that exploit security holes, such as Cancel-After-Match-Fail (CAMF) and Match-After-Lock (MAL). Additionally, hacked biometric data cannot be replaced, leaving users vulnerable to long-term security threats. To overcome these weaknesses, this article recommends a security approach based on Trusted Execution Environment (TEE), AES-256 encryption, spoofing detection based on liveness recognition, anti-tamper mechanisms, and the application of rate limiting. The secure authentication flow implementation is designed to protect biometric data locally without transmission to external servers, ensuring user integrity and privacy is maintained. This flow includes suspicious activity detection, login encryption, and data protection with advanced encryption. Through a combination of these technologies, the biometric authentication system is characterized as being able to significantly maximize security by minimizing the risk of attacks on user data. This research provides evaluation results that the DNN deep neural network model trained with AES-256 is characterized as being able to produce accuracy above 99.9% with less than 5,000 power traces. Then, the implementation of liveness detection is characterized as being able to produce an F1-Score of 97.78% and an HTER of 8.47% in the intra-dataset scenario, as well as an F1-Score of 74.77% and an HTER of 29.05% in the cross-dataset scenario. This combination of technologies provides secure and efficient biometric authentication without compromising user comfort.
An Optimized Lightweight CNN with Randomized Hyperparameter Search for Real-Time Image-Based Malware Detection Prasetyo, Stefanus Eko; Chandra Wijaya, Kevin; Haeruddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7765

Abstract

While image-based malware detection using deep learning has shown promise, existing methodologies predominantly rely on computationally expensive pre-trained architectures (e.g., VGG, ResNet) that create significant bottlenecks for real-time deployment on resource-constrained gateways. This research addresses this critical gap by proposing a streamlined, lightweight custom Convolutional Neural Network (CNN) specifically optimized for real-time operation. The novelty of this work lies in the strategic integration of Randomized Search Cross-Validation (RS-CV) to automate the discovery of an optimal configuration of filters, dense units, and dropout rates, eliminating the inefficiencies and biases of manual hyperparameter tuning. The proposed method transforms binary files into 64x64 grayscale images—reducing computational input by over 90% compared to standard architectures—which are then processed by the optimized custom network. Experimental results demonstrate the scientific significance of this approach, as the model achieved a near-perfect Area Under the Curve (AUC) of 0.9996 and identified threats with an average inference time of only 12–15 milliseconds. Out of 1,068 test samples, only 10 misclassifications were recorded, proving that a mathematically optimized lightweight model can outperform heavy ensemble frameworks in both accuracy and speed. These findings provide a reproducible framework for high-speed, front-line cybersecurity systems capable of detecting obfuscated threats in live network environments.
Evaluasi Efektivitas Teknik Regularisasi Dalam Mengurangi Overfitting Pada Model CNN Prasetyo, Stefanus Eko; Haeruddin, Haeruddin; Elvis, Elvis
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 15, No 2 (2025): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v15i2.4676

Abstract

Penelitian ini bertujuan mengevaluasi dan membandingkan efektivitas berbagai teknik regularisasi seperti regularisasi L1 dan L2, dropout, dan augmentasi data, baik secara terpisah maupun kombinasi, dalam mengatasi overfitting pada model Convolutional Neural Network (CNN) dalam skenario dataset terbatas. Keterbatasan dataset merupakan tantangan utama yang menyebabkan model CNN cenderung mengalami overfitting, di mana performa pada data pelatihan 97.95% akurasi jauh melebihi akurasi validasi 67%. Penelitian ini menggunakan arsitektur CNN dasar yang konsisten dan dataset CIFAR-10. Hasil pengujian teknik regularisasi tunggal menunjukkan bahwa augmentasi data adalah teknik yang paling optimal pada pengujian terpisah. Model dengan augmentasi data mencapai akurasi validasi tertinggi 78.18% dan kesenjangan generalisasi terendah 2.31% di antara semua teknik yang diuji. Sementara itu, ditemukan bahwa penggunaan tingkat regularisasi yang terlalu ekstrem pada teknik regularisasi L1/L2 dapat menyebabkan underfitting karena bobot dipaksa mendekati nol  sehingga model kehilangan kapasitas belajar. Pencapaian kinerja model yang paling superior diperoleh melalui pendekatan kombinasi. Kombinasi antara augmentasi data dan regularisasi L2 menghasilkan akurasi validasi tertinggi sebesar 79.89% dengan kesenjangan generalisasi paling kecil, yaitu 0.38%. Dengan demikian, disimpulkan bahwa pendekatan kombinasi teknik regularisasi adalah strategi paling efektif untuk meningkatkan generalisasi model CNN pada lingkungan dengan dataset terbatas.
Analisis Komparasi Algoritma Machine Learning Untuk Klasifikasi Kualitas Udara Indoor Berbasis Sensor Low-Cost Prasetyo, Stefanus Eko; Hansen, Irvan; Haeruddin, Haeruddin
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9024

Abstract

Indoor Air Quality (IAQ) has a significant impact on occupants’ health and comfort; however, limitations of conventional monitoring systems and the high cost of commercial devices have hindered the widespread implementation of indoor air quality monitoring. Sensor-based IAQ monitoring using low-cost devices provides an affordable solution; however, the resulting data often exhibit variability and noise, making direct interpretation challenging. This study presents a comparative analysis of several machine learning algorithms for indoor air quality classification using sensor data. The dataset was collected from DHT22 and MQ-135 sensors measuring temperature, humidity, and air pollutant levels, resulting in 18,000 samples evenly distributed across three air quality classes: Good, Moderate, and Poor. The proposed methodology includes data preprocessing through median imputation and feature standardization, stratified dataset splitting with a ratio of 70% training, 15% validation, and 15% testing data, and model training using four supervised learning algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that all evaluated models achieved high classification performance, with KNN outperforming other algorithms by achieving an F1-score of 1.00 on the test dataset, while the lowest-performing model still achieved an F1-score above 0.96, indicating a relatively narrow yet consistent performance range among the evaluated algorithms. These findings demonstrate the effectiveness of machine learning approaches for indoor air quality classification using low-cost sensor data under controlled experimental conditions.
Klasifikasi Kematangan Buah Pisang Menggunakan YOLOv12 Berbasis Deep Learning Prasetyo, Stefanus Eko; Wijaya, Gautama; Kwan, Allan
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 5 No. 1 (2026): Februari
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v5i1.7557

Abstract

Sebagai komoditas hortikultura dengan permintaan pasar yang tinggi dan nilai jual strategis, pisang memerlukan penanganan pascapanen yang tepat, khususnya dalam penentuan fase kematangan. Selama ini, proses penyortiran kematangan buah umumnya dilakukan secara konvensional melalui inspeksi visual manual, yang bersifat subjektif dan berpotensi menghasilkan penilaian yang tidak konsisten. Oleh karena itu, penelitian ini berfokus pada perancangan sistem otomatis berbasis deep learning untuk menghasilkan klasifikasi kematangan yang lebih objektif dan terstandar. Algoritma YOLOv12 digunakan sebagai metode utama untuk mendeteksi serta mengklasifikasikan citra buah ke dalam tiga fase, yaitu mentah, matang, dan lewat matang. Data latih dikembangkan melalui proses anotasi serta augmentasi citra untuk meningkatkan variasi visual dan mencegah overfitting. Hasil evaluasi menunjukkan bahwa model mencapai Mean Average Precision (mAP@0.5) sebesar 95,2% dengan waktu deteksi di bawah 50 ms per gambar. Temuan ini menunjukkan potensi penerapan sistem secara real-time pada lingkungan industri penyortiran buah.
The Cyber Threat Landscape in Indonesia: Attacks and Security System Analysis Sama, Hendi; Stefanie; Prasetyo, Stefanus Eko
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2200

Abstract

Cybersecurity in learning systems has become increasingly important as educational institutions rely more heavily on digital platforms such as learning management systems, online assessments, and cloud-based academic services. This rapid digital transformation exposes learning environments to sophisticated cyber threats that may disrupt academic activities and compromise sensitive information. However, many institutions still lack a clear understanding of how users perceive cyber risks and how these perceptions influence the effectiveness of cybersecurity systems. Currently, there is a significant research gap regarding empirical evidence that links user behavioral psychology with technical security outcomes in the Indonesian educational context. This study aims to empirically analyze the relationship between cyber awareness, perceived impact of cyber attacks, and perceived effectiveness of cybersecurity systems in digital learning environments. A quantitative research approach was applied using data collected from 402 respondents. The data were analyzed through descriptive statistics, correlation analysis, regression analysis, and group comparison tests to examine variable relationships and demographic differences. The findings indicate that cyber awareness significantly and positively predicts perceived system effectiveness (β = 0.501, p < 0.001), demonstrating that higher awareness levels enhance overall cybersecurity performance. Conversely, the perceived impact of cyber attacks does not show a significant effect on system effectiveness, suggesting that awareness is more influential than threat perception alone. Additional results reveal gender-based differences in cyber incident experiences, while awareness levels remain similar. The practical implications emphasize the importance of cybersecurity awareness programs, digital safety education, and proactive defense strategies to strengthen protection in learning systems and improve institutional cybersecurity readiness.
Co-Authors ., Arron ., Kennedi Abner Onesimus Sijabat Agung Wijaya Ardyansyah Wijaya Ari Wahyuni Kaharuddin Ari Wibowo Ariesryo, Kelvin Aripradono, Heru Wijayanto Avista Mindy Basri, Germen Benny Benny Burhan, Rifa’atul M. Candra, Boby candra, joni eka Chandra Wijaya, Kevin Christina, Lidya Conny Agustin Dede Hilman Rasyid Dendi, Dendi Deven Lee Dini Sari Melati Dominggo Givarel Elia Elia Elvin Elvin Elvin Tan Elvin Tan Elvis Elvis, Elvis Fadil Mahendra Favian, Felix Febby Anggellya Felix Felix Yeovandi Fiona Livianti Frandika Antonius Saputra Frans Hadinata Gautama Wijaya Gusti Irawan Haeruddin Haeruddin Haeruddin Haeruddin, . Hansen, Irvan Haryono Haryono Hasanah, Nafisatul Jackson Jackson Jefri Jefri Jemmy Jemmy Jemmy, Jemmy Jhon Lim Jimmy Cung Johan Johan Jonathan Felix Andrianto Kaharuddin, Ari Wahyuni Kelvin Ariesryo Kwan, Allan Lau, Wilsen Melisa Melisa Mindy, Avista Mitha Veronica Muhammad Jufri Muhammad Yaasin Nafisatul Hasanah Nafisatul Hasanah Nimatul Mamuriyah Noviardi, Refli Nurul Hassanah Princessa Princessa Puteri, Vier Adinda Putri Syahfira Raja Muhammad Isnu Prayoga Ricardo Ricky Chandra Lee Robby Robby Ruby Shafira Rushadi Sabariman Sabariman Sabariman Sabariman Sama, Hendi Sijabat, Abner Onesimus Sopiyan, Sopiyan Stefanie Steny Steny Steven Lie Sugianto Sugianto Sun Pho Syaeful Anas Aklani, Syaeful Syahfira, Putri Tony Jack Tan Ding Try Windranata Try Windranata Vincent Theo Weni Vivianti Yuki Estrada Yulfan Salimin Yulianto, Andik