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All Journal Ilmu dan Budaya Buletin Al-Turas Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) SMATIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J I M P - Jurnal Informatika Merdeka Pasuruan INTECOMS: Journal of Information Technology and Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika Jurnal Teknologi Dirgantara JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Mantik Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Informatika C.I.T. Medicom Journal of Applied Data Sciences Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Smatika Jurnal : STIKI Informatika Jurnal
Iskandar Fitri
Universitas Putra Indonesia YPTK Padang
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Penerapan Face Recognition Pada Aplikasi Akademik Online Budi Tri Utomo; Iskandar Fitri; Eri Mardiani
Informatik : Jurnal Ilmu Komputer Vol 16 No 3 (2020): Desember 2020
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v16i3.2259

Abstract

Di era big data seperti sekarang ini, proses identifikasi biometrik berkembang dengan sangat cepat dan semakin banyak diimplementasikan pada banyak aplikasi. Teknologi pengenalan wajah memanfaatkan kecerdasan artificial intelligence (AI) untuk mengenali wajah. Didalam penelitian ini diajukan sebuah perancangan sistem login akademik online di Universitas Nasional dengan memanfaatkan face recognition secara real time yang berbasis OpenCV dengan algoritma Local Binary Pattern Histogram, dan metode Haar Cassade Clasifier. Sistem akan mendeteksi, mengenali dan menbandingkan wajah yang tertangkap kamera dengan database wajah yang tersimpan. Citra gambar wajah yang digunakan berukuran 480 x 680 pixel berekstensi .jpg dalam bentuk citra RGB yang akan dirubah menjadi citra Grayscale, untuk mempermudah perhitungan nilai histrogram dari setiap wajah yang akan dikenali. Dengan pemodelan sistem seperti ini diharapkan dapat mempermudah mahasiswa untuk login ke akademik online.
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.
Composite Loss-Enhanced DeepLabV3+ for Crack Segmentation in Photogrammetric Mining Imagery Yesri Elva; Iskandar Fitri; Syafri Arlis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7807

Abstract

Crack detection in photogrammetric mining imagery remains challenging due to severe class imbalance, thin and irregular crack morphology, and complex soil textures that hinder accurate boundary delineation. This study proposes a DeepLabV3+ semantic segmentation model with a ResNet-50 backbone trained using a Composite Loss that integrates Focal Tversky Loss, Weighted Cross-Entropy Loss, and Boundary Loss into a unified multi-objective framework. The dataset comprised 425 photogrammetric crack images acquired from open-pit mining sites, divided into 380 training and 45 test images. Data augmentation, including horizontal flip, vertical flip, channel shuffle, and random brightness and contrast adjustment, was applied to improve model generalization under varying image conditions. The proposed configuration was evaluated against Default Loss, Binary Cross-Entropy Loss, and Weighted Cross-Entropy Loss. The model achieved a Global Accuracy of 0.9808, Mean Intersection over Union of 0.7890, and Mean Boundary F1-Score of 0.8642, outperforming all baseline configurations. Boundary fidelity showed the largest improvement, with a Mean BFScore gain of 27.3% over Default Loss, demonstrating the effectiveness of Boundary Loss for precise crack contour delineation. The trained model was also deployed in a GUI-based system supporting automated crack analysis and PDF report generation for practical field application.
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.
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.
Co-Authors Ade Saputra Aditya Nuryudha Iriandi Agung Triayudi Agus Gunaryati Agus Herlan Agus Iskandar Ahmad Khuzaifi Ahmad Muslih Mardia Albaar Rubhasy Albaar Rubhasy Albaar Rubhasy Albaar Rubhasy Aldi Ardiansyah Alhadi Alhadi Andreas Aman Tokan Andri Aningsih Andri Aningsih Andri Fauzan Andrianingsih Anggun Wibowo Ani Rahmawati Anis Supriatin Aris Gunaryarti Aris Gunaryati Ariwirawan Djali Atalya Kurnia Sari Bagas Apriandadwi Putra Bayu Setyo Prayogi Ben Rahman Budi Tri Utomo Budianto Budianto Chairil Rohadi Danang Aji Pangestu Daud Situmorang Denis Taslim Denny Hidayatullah Deny Hidayatullah Deny Hidayatullah Dicky Prasetyo Dimas Dandy Aryarajendra Suprapto Dinul Akhiyar Efan Nuari Eky Pambudi Syiamtoni Eldy Dwi Sentosa Endah Tri Esti Handayani Eri Mardiani Fahmi Apriliansyah Fahri al Rasyid Fauzan Muhammad Fauziah Fauziah Febri Hadi Fhizyel Nazareta Fikri Iqromuddin Firnas Azamta Gabbi Rinzani Gio Fandy H Nainggolan Gunadi Widi Nurcahyo Hanggita Ahmad Humansyah Hanif Rahman Burhani Harfidz Alif Alfatihah Harfidz Alif Alfatihah Hasbulloh Hasbulloh Hendy Dwi Haryoyudhanto Henry Prasetyo Utomo Ilham Wahyudi Siadi Iman Cahyadi Ira Diana Sholihati Johar Saputra Irsandi Kresno Wibowo Kurniadin Nehe Leony Mayank Andiny Marcelino Oktaviansyah Mikhael Kristian Mike Mochammad Alif Kurniawan Mochammad Arya Darmawan Mohammad Aldinugroho Abdullah Muhamad Alfaiz Muhamad Sopiyan Muhammad Faiz Syafii Muhammad Muttaqin Muchlis Muhammad Rafiq Zayana Muhammad Raihan Azis Muhammad Ridwan Muhammad Zulfi Rahmanzi Muhammad Zulfi Rahmanzi Muklis Haryono Nabila Tiara Nuraini Niam Shofi Nor Alip Novi Dian Nathasia Novi Dian Nathasia Novi Dian Nathasia Novi Dian Nathasia Novi Dian Nathasia Novi Dian Nathasia Novi Dian Nathasia Nur Hayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhazmi Hawari Oka Saputra Rafif Ramadhan Al Yarda Richard Ronaldo Marbun Rima Tamara Rima Tamara Aldisa Rima Tamara Aldisa Rini Nuraini Rini Nuraini Rini Nuraini Rini Nuraini Sari Ningsih Savanah Sekar Arum Siti Amalia Nazihah Surosa Sumarhadi Sumarhadi Sumijan Suprapto, Dimas Dandy Aryarajendra Supriyanto Supriyanto Suryadi Suryadi Syafri Arlis Syamsun Nahar Abdullah Syifa Faradilla Fabrianne Teuku Feraldy Ramadhani Tiara Andhiza Tiara Andhiza Tio Lovian Tri Oktafi Sidqi Ucuk Darusalam Vicky Agnes Arundy Willi Akbar Satria Wilman Wilman Winarsih Winarsih Wisnu Andika Putra Yesri Elva Yoga Furqaansyah Yolanda Nur Oktavia Yudhi Setiawan