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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 Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Dan Sistem Informasi Bisnis ILKOMNIKA: Journal of Computer Science and Applied Informatics 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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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.
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.
Implementasi Multi-Factor Authentication Pada Aplikasi Berbasis Website dan Pengembangan Company Profile PT Raflesia Berjaya Properti Haeruddin Haeruddin; Stefanus Eko Prasetyo; Avista Mindy
Jurnal Pengabdian Masyarakat Indonesia (JPMI) Vol. 1 No. 6 (2024): Agustus
Publisher : Publikasi Inspirasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/jpmi.v1i6.2051

Abstract

PT Raflesia Berjaya Properti (PT RBP), sebuah pengembang properti di Batam, menghadapi tantangan kritis dalam keamanan data dan branding perusahaan. Untuk mengatasi hal ini, Program Pengabdian Kepada Masyarakat (PKM) dilaksanakan dengan dua tujuan utama yaitu meningkatkan keamanan sistem informasi dan pembuatan website profil perusahaan untuk mengingkatkan branding. Fokus keamanan melibatkan penerapan Multi-Factor Authentication (MFA) menggunakan Auth0 untuk mencegah akses tidak sah dan melindungi data sensitif. Peningkatan branding melalui pembuatan website profil perusahaan yang profesional untuk meningkatkan visibilitas PT RBP secara global dan meningkatkan kepercayaan. Pengembangan website dan MFA ini menggunakan metodologi Network Development Life Cycle (NDLC), yang meliputi tahap analisis, desain, implementasi, dan pengujian. Implementasi MFA berhasil mengurangi risiko akses tidak sah, dan website profil perusahaan yang baru meningkatkan kehadiran PT RBP di pasar global. Meskipun terdapat beberapa tantangan adaptasi bagi pengguna MFA dan kemungkinan penyesuaian fitur website, PKM ini secara signifikan meningkatkan keamanan data dan branding PT RBP untuk memastikan keberlanjutan operasional dan daya saing pasar yang lebih baik.
Penyusunan Sertifikasi ISO 27001 Di PT. Pundi Mas Berjaya Haeruddin Haeruddin; Stefanus Eko Prasetyo; Ari Wahyuni Kaharuddin
Jurnal Pengabdian Masyarakat Indonesia (JPMI) Vol. 1 No. 6 (2024): Agustus
Publisher : Publikasi Inspirasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/jpmi.v1i6.2052

Abstract

Persiapan untuk sertifikasi ISO 27001 di PT Pundi Mas Berjaya merupakan proses yang melibatkan pemahaman mendalam tentang persyaratan standar, penyusunan SOP yang terstruktur, penilaian risiko yang cermat, kolaborasi antar departemen, dan dokumentasi yang teliti. Hasil dari persiapan ini menunjukkan komitmen perusahaan dalam meningkatkan keamanan informasi secara menyeluruh. Saran yang diambil adalah untuk terus berkomitmen pada peningkatan kontinu dalam manajemen keamanan informasi, sesuai dengan prinsip-prinsip ISO 27001. Dengan demikian, persiapan ini bukan hanya memenuhi persyaratan formal, tetapi juga membawa dampak positif dalam memperkuat keamanan informasi di PT Pundi Mas Berjaya.
Penerapan Logika Fuzzy Untuk Analisis Tingkat Kepuasan Layanan Pelabuhan Domestik Sekupang Kota Batam Joni Eka Candra; Noviardi, Refli; Eko Prasetyo, Stefanus; Burhan, Rifa’atul M.; Rushadi
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3558

Abstract

Penelitian ini bertujuan untuk menganalisis dan mengetahui seberapa besar pengaruh tingkat layanan yang diberikan terhadap respon kepuasan bagi para penumpang kapal di pelabuhan domestik Sekupang kota Batam. Dalam kelancaran penelitian ini digunakan metode studi kepustakaan, observasi, dan penyebaran kuesioner dengan menggunakan skala likert, serta metode fuzzy. Berdasarkan analisis dan pembahasan yang dilakukan terhadap 50 orang responden, dengan metode skala likert responden menyatakan cukup puas. Ini berarti pelabuhan sekupang Kota Batam, cukup berhasil memberikan layanan yang terbaik atau cukup memuaskan kepada para penumpang, baik dari Dimensi Kehandalan, Dimensi Daya Tanggap, Dimensi Kepastian, Dimensi Empati dan Dimensi Berwujud secara keseluruhan konsumen merasa cukup puas, dengan rata-rata presentase sebesar 71 % (35,5 dari 50 responden). Tidak jauh berbeda dengan hasil uji coba Fuzzy Logic metode Mamdani dilihat dari hasil nilai output untuk kepuasan konsumen sebesar 150 (dengan range 50-250) yang artinya tingkat kepuasan konsumen cukup puas akan pelayanan yang diberikan oleh Pelabuhan Sekupang Kota Batam.
Generalization Analysis of a Long Short-Term Memory Model for Cross-Domain Malware Detection Prasetyo, Stefanus Eko; Haeruddin, Haeruddin; Jason, Jason
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.856

Abstract

The increasing diversity of malware targeting heterogeneous computing environments poses significant challenges to conventional detection approaches that rely on domain-specific assumptions. In particular, detection models optimized for a single dataset often exhibit limited robustness when applied to data with different structural and behavioral characteristics. This study analyzes the generalization capability of a Long Short-Term Memory (LSTM) model for behavior-based malware detection across multiple domains. A fixed two-layer LSTM architecture is evaluated using one primary dataset, CIC-MalMem-2022, and four additional datasets representing Android applications, Internet of Things network traffic, botnet behavior, and static Windows Portable Executable analysis. Although each dataset undergoes a dataset-specific preprocessing pipeline, all experiments employ an identical model architecture and hyperparameter configuration to ensure consistent and comparable evaluation. Model performance is assessed using standard classification metrics, supported by single train–test evaluation and five-fold cross-validation to examine performance stability and robustness. The experimental results demonstrate that the LSTM model maintains consistently high detection performance across datasets with diverse characteristics, including both sequential and non-sequential data representations. These findings indicate that the model effectively captures fundamental malware behavior patterns that generalize beyond a single domain, highlighting its potential applicability in heterogeneous cybersecurity environments where cross-domain robustness is required. At the same time, the evaluation is conducted under controlled experimental conditions and does not explicitly address adversarial adaptation or fully dynamic runtime deployment, which should be considered when interpreting the results for practical operational use.
Analisis Kinerja Smart Door Hybrid Haar Cascade dan ArcFace pada Raspberry Gautama Wijaya; Stefanus Eko Prasetyo; Haeruddin Haeruddin; Kevin Kevin
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3537

Abstract

Implementing biometric security systems on deep learning devices faces a major challenge in balancing identity verification accuracy with computational resource efficiency. This study presents a performance analysis of a Raspberry Pi 5-based Smartdoor system integrating the detection speed of Haar Cascade with the recognition accuracy of ArcFace. System performance was evaluated based on RAM usage, CPU load, FPS stability, and access Success Rate parameters. Empirical evaluation results indicate that integrating Deep learning ArcFace increased RAM usage by 33.7% and CPU load from 33% to 53%. However, due to the processing capacity of the Raspberry Pi 5, the system maintained stable real-time performance with an average of 18.3 FPS. In terms of security, the Hybrid method proved superior with an access success rate of 73.7%, surpassing the conventional Haar Cascade method which only reached 68.4%. This study concludes that the Hybrid method is a viable solution for home security systems, where the increased computational load is justified by a significant improvement in identity verification reliability.Keyword: Raspberry Pi 5; Smartdoor; Haar Cascade; ArcFace; Computational Performance. AbstrakImplementasi sistem keamanan biometrik pada perangkat deep learning menghadapi tantangan utama dalam menyeimbangkan akurasi verifikasi dengan efisiensi sumber daya. Penelitian ini menyajikan analisis kinerja sistem Smartdoor berbasis Raspberry Pi 5 yang mengintegrasikan kecepatan deteksi Haar Cascade dengan akurasi pengenalan wajah ArcFace. Kinerja sistem dievaluasi berdasarkan parameter penggunaan RAM, beban CPU, stabilitas FPS, dan tingkat keberhasilan akses. Hasil evaluasi empiris menunjukkan bahwa integrasi Deep learning ArcFace meningkatkan penggunaan RAM sebesar 33,7% dan beban CPU dari 33% menjadi 53%. Namun, berkat kapasitas pemrosesan Raspberry Pi 5, sistem mampu mempertahankan stabilitas kinerja real-time dengan rata-rata 18,3 FPS. Dari segi keamanan, metode Hybrid terbukti lebih unggul dengan akurasi pengenalan wajah sebesar 73,7%, melampaui metode konvensional Haar Cascade yang hanya mencapai 68,4%. Penelitian ini menyimpulkan bahwa metode Hybrid merupakan solusi yang layak untuk sistem keamanan rumah, di mana peningkatan beban komputasi terbayar dengan peningkatan reliabilitas verifikasi identitas yang signifikan. 
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 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 Gautama Wijaya Gusti Irawan Haeruddin Haeruddin Haeruddin Haeruddin, . Hansen, Irvan Haryono Haryono Hasanah, Nafisatul Jackson Jackson Jason, Jason Jefri Jefri Jemmy Jemmy Jemmy, Jemmy Jhon Lim Jimmy Cung Johan Johan Jonathan Felix Andrianto Kaharuddin, Ari Wahyuni Kelvin Ariesryo Kevin Chandra Wijaya Kevin Kevin 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