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All Journal Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat JMM (Jurnal Masyarakat Mandiri) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Applied Data Sciences Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Ekonomika Dan Bisnis Jurnal Komtekinfo Sistem Pendukung Keputusan dengan Aplikasi Jurnal Pengabdian Masyarakat Dharma Andalas SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Febri Hadi
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Hendri, Halifia; Rani, Larissa Navia; Enggari, Sofika; Ramadhanu, Agung; Hadi, Febri
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.
Adaptive Marker-Controlled Watershed Combined with Voxel Quantification for Automated Fetal Measurement Hadi, Febri; Sumijan, Sumijan; Fitri, Iskandar
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.v7i2.1224

Abstract

Accurate and consistent fetal biometric measurement is essential for assessing fetal growth and gestational age in prenatal care. However, ultrasound (US) imaging presents several challenges, including speckle noise, shadowing artifacts, and low tissue contrast, which often degrade segmentation accuracy. Classical watershed algorithms, though effective for edge detection, tend to produce over-segmentation in such complex textures. The dataset used in this study consisted of 272 ultrasound images of patients from M. Djamil Hospital, Padang, West Sumatra. The dataset covers various phases of fetal development, from the first trimester to the third trimester. All images correspond exclusively to fetal ultrasound examinations and were used solely for automated fetal biometric analysis. To overcome these issues, this study introduced an Adaptive Marker-Controlled Watershed (AMCW) algorithm combined with Voxel Quantification (VQ) to achieve more reliable and automated fetal measurements. The proposed AMCW method integrates adaptive marker generation based on morphological gradient and local intensity statistics, enabling dynamic control of internal and external markers across varying fetal regions. After segmentation, spatially calibrated pixel-based quantification was employed to estimate the dimensional properties of segmented fetal structures. The method was applied exclusively to 2D B-mode ultrasound datasets across multiple gestational ages, targeting four key fetal parameters: Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Although the present study is limited to 2D ultrasound images, the proposed framework may be extendable to 3D ultrasound data in future research. The combination of adaptive marker-controlled watershed segmentation and voxel-based quantification presents a robust, interpretable, and computationally efficient framework for automated fetal measurement. The CNN achieved a classification accuracy of 98.75% on the independent testing dataset, indicating that the extracted biometric features contain strong discriminative information for automated fetal condition assessment. This hybrid approach minimizes operator dependency and measurement variability aligning with clinical measurement trends.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Ramadhanu, Agung; Mardison, Mardison; Hendri, Halifia; Hadi, Febri; Rani, Larissa Navia; Yuhandri, Yuhandri
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.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Co-Authors , Shary Armonitha Lusinia Ariandi, Vicky Aulia Fitrul Hadi Defra Irawan Devia Kartika Dheatri Agrema Dodi Guswandi Eliza Eliza Enggari, Sofika Erdian, Muhammad Habib Febri Aldi Gushelmi hadi syahputra Hadi Syahputra Hadi, Meswantri Halifia Hendri Harma, Wahri Heri yanto irawan, Defra Irzal Arief Wisky Iskandar Fitri, Iskandar Mardison Mardison Mayola , Liga Meswantri, Meswantri Meta, Monanda Rio Muhammad Afdhal Musli Yanto Musli Yanto Nadya Alinda Rahmi Paisal, Hena Utami Putri Pohan, Nurmaliana Pradani Ayu Widya Purnama Prayogi Wicaksana Rahman, Sepsa Nur Ramadhanu, Agung Randy Permana Rani, Larissa Navia Rima Liana Gema Rizky Rianda Sarjon Defit Satria Yulanda Shary Armonitha Lusinia Sumijan Sumijan Sumijan Syafri Arlis Syafrika Deni Rizki Vicky Ariandi Vicky Ariandi Wulan Ayu Safitri yanto, heri Yenila, Firna Yogi Wiyandra Yuhandri Yuhandri, Yuhandri Yusvi Diana Zefriyenni Zefriyenni, Zefriyenni