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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 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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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.
Performance Analysis YOLO11n Model for Chili Leaf Diseases Detection Rayyan Nur Fauzan; Ervin Yohannes; Ricky Eka Putra; Avirmed Enkhbat
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p14-25

Abstract

Chili plants are a strategic agricultural commodity with high economic value, yet their production is often disrupted by disease attacks causing significant yield reduction. Early and accurate detection of chili leaf diseases is crucial for implementing precision agriculture practices. This research implements the YOLO11n (You Only Look Once version 11 nano) model for automated chili leaf disease detection using the "Chili Plant Leaf Disease and Growth Stage Dataset from Bangladesh" containing 1,856 high-resolution images across six categories: Bacterial Spot, Cercospora Leaf Spot, Curl Virus, Healthy Leaf, Nutrition Deficiency, and White Spot. The model was trained for 100 epochs on Google Colab with Tesla T4 GPU using 640×640 pixel input resolution. Evaluation results demonstrate excellent detection performance with precision of 83.7%, recall of 84.3%, mAP@0.5 of 92.3%, and mAP@0.5:0.95 of 74.5%. Per-class analysis reveals that Nutrition Deficiency achieved the highest performance (mAP@0.5 = 99.2%), while Curl Virus presented the greatest detection challenge (recall = 55.6%). The lightweight YOLO11n architecture with only 2.58 million parameters and 6.3 GFLOPs, making it highly suitable for deployment on edge devices such as agricultural drones, mobile applications, and IoT monitoring systems. This research contributes to smart agriculture applications by providing an efficient and accurate solution for automated chili leaf disease detection under real field conditions.
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 Avirmed Enkhbat Dewi, Maharani Nirmala Diastian Vinaya Wijanarko Dyantono, Aganda Maulan Dan Ervin Yohannes 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 Rayyan Nur Fauzan 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