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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ipteks Terapan : research of applied science and education Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Ekonomika Dan Bisnis Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Intellect : Indonesian Journal of Learning and Technological Innovation SATIN - Sains dan Teknologi Informasi Jurnal Quancom: Jurnal Quantum Komputer Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
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A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis Teri Ade Putra; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1117

Abstract

Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Halifia Hendri; Larissa Navia Rani; Sofika Enggari; Agung Ramadhanu; Febri Hadi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1298

Abstract

Concrete surface defect detection is a critical aspect of maintaining the integrity and safety of infrastructure in civil engineering. Traditional manual inspection methods are time-consuming, prone to human subjectivity, and often limited by physical accessibility, necessitating the development of robust automated solutions. This paper presents an automated pixel-level concrete surface defect detection system utilizing the U-Net deep learning architecture. The primary contribution and novelty of our approach lie in optimizing the network's encoder-decoder structure with skip connections to effectively capture both broad contextual features and precise spatial localization. This overcomes the critical limitations of existing traditional methods, which frequently struggle with complex concrete background textures, inherent noise, and uneven illumination. To validate our approach, the proposed U-Net model is systematically compared against a widely used baseline method, K-Means clustering combined with Gray-Level Co-occurrence Matrix (GLCM) texture analysis. The evaluation was conducted using a comprehensive dataset consisting of 1000 high-resolution concrete images. Experimental results reveal that the deep learning architecture vastly outperforms the traditional baseline. Specifically, the U-Net model achieved an outstanding F1-Score of 92.47%, a precision of 93.18%, and a mean Intersection over Union (mIoU) of 86.55%. In stark contrast, the K-Means and GLCM approach only yielded an F1-Score of 69.83% and an mIoU of 54.21%. These quantitative findings demonstrate that the proposed U-Net-based system not only successfully minimizes false segmentations but also provides a highly reliable, efficient, and scalable computational framework. Ultimately, this research delivers a practical solution that can be seamlessly integrated into continuous automated structural health monitoring systems, paving the way for safer and more proactive civil infrastructure management.
Co-Authors ., Ulfa Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Andry Novrianto Angga Angga Anggara Putra, Febri Antoni Antoni Arsyah Arsyah atiqah, sri Avezrima Rahmamuthi Bayuputra, Ramdani Berta Agus Petra Betriana Roza, Yesi Betriana, Yesi Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Delvi, Syerlin Aprilia Desi Permata Sari Desi Permata Sari Desi Permata Sari Desi Permata Sari Devi Maryuni Devita, Retno Dhia Fadhila Agusty Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Dodi Guswandi Enggari, Sofika Erlanda, Hadrian Fadila Cahyani Putri Fajri Saputra, Charisman Fajrul Islami Febri Hadi Fiki Pratama Firmansyah, Ryan Firna Yenila Fitri Yeni Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Hadi Syahputra Hadi Syahputra Putra Halifia Hendri Hanna Pratiwi Harnaranda, Jefri Hasmaynelis Fitri Hendri, Hallifia Hidayati, Dzil Hidayattullah, Hafis Hikmi, Zakiya Honestya, Gabriela Husna Arsyah, Rahmatul Ilmawan, Fachrul Imrah, Imrah Sari Irfan Rizki Nur Irsyad, As'Ary Sahlul Jehan Harka Johan Harlan Jufriadif Na`am, Jufriadif Kareem, Shahab Wahhab Karseno, Doni Khomsi, Ahmad Larissa Navia Rani M.Iqbal, M.Iqbal Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Mardison Mardison Mardison Marfalino, Hari Masri, Taufik Mokti Isra Mokti Isra Muhammad Idris Muhammad Raihan Zaky Muhammad Raihan Zaky Muhammad Yusuf Nabila Frisca Oktavia Nadia, Nadia Aini Hafizhah Nasution, Amir Salim Khairul Rijal Nasution, Annio Indah Lestari Negoro, Wahyu Saptha Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsih Neni Sri Wahyuni Nengsih Neni Sri Wayuni Ningsih Neni Sri Wayuni Ningsih Ningsih, Neni Sri Wayuni Nurdiansyah, Ali Nurhaliza Nurhaliza Nurjannah, Farah Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama Putra, Ramdani Bayu putri, kamila amaliah Rahmad Rahmad Rahmad, R Raja Ayu Mahessya Rani, Larissa Navia Repelita Witri Rheza Thresya Rianti, Eva Riati, Itin Rindy Citra Dewi Riyan Saputra, Riyan Rizky Gusrianto Rosa, Imelda Rosda Syelly Sajida, Mayang salim, alfajri Saputra, Charisman Fajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sisi Hendriani Sofika Enggari Sofika Enggari Sofika Enggari Sovia, Rini Suci Wahyuni Sularno Sularno Sumijan, S Sutri, Ridwan Syafri Arlis Syafrika Deni Rizki Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syafril, S Syalsabilla, Adinda Teri Ade Putra Tesa Vausia Sandiva Tomi, Zebbil Billian Utama Putra, Kharisma Utari, Utari Armila Vidyanti, Angela Citra Wiratama, Aditya Wirdawati, Wira Witri, Repelita Yagus Valentino Harefa Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yogi Wiyandra Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yuhandri Yuhandri Yulihartati, Sandra Zubaidah, Rima Puti