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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi INTEGER: Journal of Information Technology JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Abadimas Adi Buana PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Abdi: Media Pengabdian Kepada Masyarakat Jurnal Ekonomika Dan Bisnis Journal of Informatics and Computer Science (JINACS) Journal Serambi Engineering (JSE) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ricky Eka Putra
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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Model Klasifikasi Serangan DoS pada Jaringan Blockchain Menggunakan Algoritma Proximal Policy Optimization Iffo Elsande Pratama Putra; Ricky Eka Putra
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

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Abstrak— Teknologi blockchain menghadirkan pendekatan baru dalam pengelolaan sistem informasi terdesentralisasi yang mampu menjaga keamanan, transparansi, dan integritas data. Namun, karakteristik tersebut menjadikan teknologi blockchain rentan terhadap ancaman siber, terutama serangan Denial of Service (DoS) yang berfokus pada gangguan ketersediaan layanan melalui pembanjiran lalu lintas pada node blockchain. Penelitian ini bertujuan untuk merancang dan mengembangkan model klasifikasi serangan DoS pada jaringan blockchain dengan menggunakan algoritma Proximal Policy Optimization (PPO). Algoritma PPO merupakan salah satu metode dari reinforcement learning yang dikenal memiliki kestabilan tinggi dan efisiensi dalam proses pembaruan kebijakan. Dataset yang di gunakan dalam penelitian ini ada Blockchain Network Attack Traffic (BNaT), yang mencakup lalu lintas normal dan serangan DoS pada jaringan Ethereum privat. Proses penelitian meliputi tahap pengumpulan data, pre-pemrosesan (preprocessing), pelatihan model, dan evaluasi kinerja menggunakan metrik accuracy, precision, recall, F1-Score, dan Area Under the Curve (AUC). Hasil pengujian menunjukkan bahwa model PPO berhasil mencapai akurasi 99,65% dan F1-Score sebesar 99,65%, dengan nilai AUC mencapai 99,99%. Nilai-nilai tersebut menunjukkan bahwa PPO mampu mengenali pola serangan DoS secara adaptif dan stabil. Oleh karena itu, pendekatan reinforcement learning berbasis PPO dapat menjadi alternatif yang menjanjikan untuk pengembangan sistem deteksi ancaman pada jaringan blockchain yang bersifat dinamis dan kompleks.   Kata Kunci— Blockchain, Denial of Service, Proximal Policy Optimization, Reinforcement Learning, Keamanan Siber
Systematic Literature Review on Optical Character Recognition Methods for Text Extraction Nurcahyo, Krisna Bayu Aditya; Ricky Eka Putra; Yuni Yamasari
Jurnal Serambi Engineering Vol. 11 No. 2 (2026): April 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

The development of technology has driven a significant increase in the need for document digitization and automation of text-based data processing. A systematic review is needed to identify progress related to the development of OCR in text extraction. Therefore, this study presents a systematic literature review on the development and use of OCR in text extraction using the PRISMA method. The study began with an initial search of 38 studies, which were then selected based on established criteria. Seven relevant articles were successfully identified through a focused search using the keywords "Optical Character Recognition/OCR." The results of the literature review analysis show that the Convolutional Neural Network (CNN) method is the most widely used approach in the development of OCR for text extraction. In addition, the analysis results also reveal that OCR has been applied in various fields, including healthcare, public administration, government, transportation, and commercial services. This study also highlights the various benefits as well as several challenges that are still faced in the future development of OCR. These challenges include improving character recognition accuracy and handling font variations as well as image quality. Thus, the insights generated by this research contribute to the development of OCR as a more reliable and effective tool in supporting document digitization processes.
Co-Authors Aditya Prapanca Agus Prihanto Akbar, Rafy Aulia Alit, Ronggo Alizar Hasan Ammar, Muhammad Zhafran Anita Qoiriah ANITA QOIRIAH Ardiansyah, Muhammad Yusriza Asmunin Asmunin Aulia Akbar, Rafy Dewi, Maharani Nirmala Diastian Vinaya Wijanarko Dyantono, Aganda Maulan Dan Fardeen Atallah Kabir Muhammad Handayani Tjandrasa Haqqul Amal Jiddan I Made Suartana Iffo Elsande Pratama Putra Juhrah Singke Mahendra, Yusril Isra Maulana, Dimas Avian Mirwa Adiprahara Anggarani Muhammad Santoso Nanik Suciati Nuhaenibudi As-sajid, Amirah Bilqis Nurcahyo, Krisna Bayu Aditya Permatasari, Dyah Wahyu Prasetyo, Achmad Wafa Purnama, Erlix Rakhmad Putra, Nino Fachrurrozi Erlangga Putri, Rezky Arisanti Rafif Raif, Andi Rhifqy Rahmatulloh, Romadlon Rahulil, Muhammad Raya Sulistyowati Romadhon, Dedy Putra Suartana, I Made Subroto, Jahfal Azzuhri Tilasefana, Rhyosvaldo Aurellio Tri Sudarwanto Tutuk Indriyani Usodoningtyas, Sri Wisnu Shena Arrafi Wiyli Yustanti YUNI YAMASARI