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All Journal Journal of Applied Data Sciences Jurnal Sains Informatika Terapan (JSIT)
Iskandar Fitri
Universitas Putra Indonesia YPTK Padang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Perancangan Design Website E-Commerce Dan Pemesanan Pada Rumah Makan Terang Masakan Padang Dengan Mengimplementasikan Bahasa Pemograman PHP Dan Database MySQL Ade Saputra; Iskandar Fitri
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.1089

Abstract

Pengolahan online ini dibutuhkan sebuah sistem secara e-comerce. E-comerce dapat melayanan internet yang dimanfaatkan untuk jual-beli (Nugroho, 2016). Dengan didukung adanya internet e-commerce dapat dimanfaatkan untuk mempromosikan masakan. Dengan adanya e-commerce nanti masakan-masakan pada Rumah Makan Terang Masakan Padang bisa lebih mudah dikenal dan bisa mudah dipromosikan kepada kalangan masyarakat luas, bisa sampai luar daerah Sumbar, luar pulau Sumbar, bahkan bisa sampai Luar Negeri untuk menaikan kembali hasil penjualan yang lebih banyak dari sebelumnya. Pemilik “Rumah Makan Terang Masakan Padang” yang sudah berkecimpung didunia usaha kuliner ini selama bertahun-tahun belum pernah mencoba melakukan media promosi melalui web. Kemajuan teknologi membuat usaha ini harus dapat mengikuti perkembangan di jaman sekarang oleh karena itu perlu dibuatkan situs untuk Rumah Makan Terang Masakan Padang agar mampu bersaing dengan usaha kuliner lainnya yang mungkin telah lebih dikenal di masyarakat, selain itu juga untuk menunjukkan bahwa masakan dari “Rumah Makan Terang Masakan Padang” ini rasanya juga nikmat sehingga menarik perhatian dari masyarakat sekitar dan berguna mengembangkan lagi usaha kuliner ini dan dapat melayani pembeli dari luar daerah bahkan luar negeri.
Adaptive Marker-Controlled Watershed Combined with Voxel Quantification for Automated Fetal Measurement Febri Hadi; Sumijan Sumijan; Iskandar Fitri
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 Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments Dinul Akhiyar; Iskandar Fitri; Gunadi Widi Nurcahyo
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.v7i2.1094

Abstract

Vegetable deterioration in retail environments is often accelerated by improper storage conditions, leading to quality degradation, economic losses, and reduced consumer trust. Early detection of deterioration is therefore essential to enable timely preventive actions before visible spoilage becomes severe. This study proposes an integrated image-based framework for early detection of spinach leaf deterioration by combining K-Means++ for robust color segmentation, Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction, and Convolutional Neural Network (CNN) for classification. K-Means++ improves segmentation stability through optimized centroid initialization, GLCM captures subtle texture variations associated with early spoilage, and CNN enables accurate classification by learning complex visual patterns from segmented images. The dataset consists of 642 spinach leaf images captured under controlled lighting for initial calibration and under varying lighting conditions to simulate real-world retail environments. Experimental results show that the standard K-Means algorithm achieved an average classification accuracy of 77%, while the proposed K-Means++ segmentation improved accuracy to 81.86%. Furthermore, CNN-based validation achieved the highest classification accuracy of 94.82%, demonstrating strong generalization capability. The novelty of this work lies in the optimized integration of K-Means++ segmentation under lighting variability, selective GLCM feature utilization validated through ablation analysis, and end-to-end CNN-based validation with real-time deployment feasibility. The proposed framework offers a practical, scalable, and non-destructive solution for automated freshness monitoring in retail environments and can be extended to other leafy vegetables.
Co-Authors Ade Saputra Dinul Akhiyar Febri Hadi Gunadi Widi Nurcahyo Sumijan Sumijan