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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sarjana Teknik Informatika Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains Dan Teknologi (SAINTEKBU) Jurnal Komputer Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JSiI (Jurnal Sistem Informasi) Jurnal Ilmiah Mandala Education (JIME) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal E-Komtek Journal of Computer Networks, Architecture and High Performance Computing Jurnal Computer Science and Information Technology (CoSciTech) Humanism : Jurnal Pengabdian Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Techno Jurnal Pengabdian Informatika (JUPITA) Jurnal Rekayasa elektrika Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Prosiding Seminar Nasional CORISINDO
Abdul Fadlil
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Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Sistem Monitoring dan Estimasi Konsumsi Listrik untuk Rumah Tangga Berbasis IoT dengan Antarmuka React Basri, Mhd.; Anton Yudhana; Abdul Fadlil
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.66675

Abstract

Konsumsi energi listrik rumah tangga di Indonesia terus meningkat, mencapai 1.337 kWh per kapita pada 2023, naik 13,98% dari tahun sebelumnya. Penelitian ini mengembangkan sistem monitoring konsumsi listrik berbasis Internet of Things (IoT) menggunakan sensor PZEM-004T dan mikrokontroler ESP32, yang mampu mengukur tegangan, arus, daya aktif, dan energi kumulatif secara akurat. Backend dibangun dengan Node.js dan database real-time, sementara antarmuka frontend menggunakan React.js untuk menampilkan visualisasi data yang interaktif dan responsif. Dashboard menampilkan informasi penting seperti estimasi biaya (Rp14.673), konsumsi real-time (34,90W), konsumsi saat ini (10 kWh), konsumsi kumulatif (1450,500 kWh), serta pemantauan beban peralatan rumah tangga. Sistem menunjukkan status konsumsi “EFISIEN” dan berhasil meningkatkan kesadaran pengguna, terbukti dari pengurangan konsumsi energi rata-rata sebesar 16,8%. Akurasi sensor mencapai 98,5% untuk daya dan 97,2% untuk energi. Survei menunjukkan tingkat kepuasan pengguna sebesar 89,1%, dengan antarmuka dinilai mudah digunakan (4,4/5,0). Hasil penelitian membuktikan bahwa integrasi sensor PZEM dengan teknologi IoT dan React mampu menghasilkan solusi monitoring energi yang akurat, real-time, dan mendukung pengelolaan energi rumah tangga yang efisien dan berkelanjutan.
Pemanfaatan Artificial Intelligence untuk Peningkatan Literasi Digital pada Pengajaran di Lingkungan Perbatasan Muhammad, Muhammad; Dzulqarnain, Muhammad Faqih; Abdul Fadlil; Sutikno, Tole
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5606

Abstract

The utilization of technology, particularly Artificial Intelligence (AI), in education is crucial, especially in border regions that often face challenges related to digital literacy. This study aims to explore and implement AI technology to enhance digital literacy in teaching within border environments. The method used is community service involving training on the use of AI to support the learning process in several schools in border areas. In this activity, AI applications such as text-to-speech, automatic answer analysis, and providing improvement recommendations are used to assist students with special needs and enhance the effectiveness of learning evaluations. Survey results conducted with 40 respondents show that 100% agree that AI can help analyze student answers and provide improvement recommendations. Furthermore, 87.5% of participants stated that the digitalization of traditional knowledge through AI can help preserve local culture for future generations. Additionally, 50% of respondents agreed that the use of AI technologies, such as text-to-speech, can improve inclusivity in education, particularly for students with special needs. The survey results indicate that AI improves accessibility to education and supports more effective learning management in areas with limited technology access. This community service demonstrates that the application of AI has the potential to improve the quality of education in border regions, while also promoting the preservation of local culture and inclusivity within the education system.
Performance Evaluation of Otsu and Sauvola Thresholding for Structured Document Binarization Darpito, Muhammad Noko; Kartika Firdausy; Abdul Fadlil
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40245

Abstract

Purpose: Digitizing public administration records, particularly structured forms such as the Transport of Plants and Wildlife Abroad (Surat Angkut Tumbuhan dan Satwa Liar Luar Negeri / SATS-LN), necessitates meticulous preparation for precise subsequent analysis. Most of the photos in the SATS-LN archives are scanned, and they have inconsistent lighting, varying resolution, and background noise, which makes it difficult to separate the text from the backdrop and read it clearly. This work identifies the optimal SATS-LN binarization approach for preserving textual structure and suppressing background artifacts. Methods: A four-stage pipeline is used. First, Detectron2 localizes seven important SATS-LN fields. Second, binarization is investigated with global Otsu and adaptive Sauvola thresholding under three parameter configurations. Third, following binarization, Contrast-Limited Adaptive Histogram Equalization (CLAHE) boosts local contrast. Finally, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Difference from Reference for Distortion (DRD), Precision, Recall, F1-score, and Foreground Ratio are assessed on 200 annotated SATS-LN documents (150 scanner-based/DOC and 50 camera captured/CAM). Result: The acquisition domain and assessment model affect binarization performance on 200 SATS-LN documents (150 DOC scans and 50 CAM images). Global Otsu_T10 has the highest median PSNR (21.19 dB) and the lowest median MSE (494.69), indicating a visually cleaner background. However, segmentation-based metrics show better stroke preservation with Sauvola, as Sauvola_k05 has the strongest DOC text–background separation (F1 = 0.938). In the CAM domain, where illumination variability dominates, Sauvola performs better across structural and segmentation indicators, with Sauvola_k04 performing best overall (F1 = 0.980) and mitigating the over-segmentation tendency of strict global thresholds. The Sauvola window (25x25) and CLAHE clip limit (1.0) results suggest using Sauvola_k05 for DOC and Sauvola_k04 for CAM to preserve text integrity and reduce background artifacts. Novelty: This study presents a novel field-level binarization assessment that combines automated cropping and ground-truth evaluation, providing practical guidance for robust preprocessing that supports scalable, reliable, and cross-device public document digitization.
Comparative Evaluation of Thresholding Methods for Optimized Digital Document Parsing Accuracy Muhammad Noko Darpito; Kartika Firdausy; Abdul Fadlil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6982

Abstract

Automated parsing of semi-structured documents has become increasingly important, particularly in standardized formats like SATS-LN, which contain fixed-layout fields such as permit number, addresses, validity period, and item types. This study investigates the impact of two thresholding methods Otsu and Sauvola on object detection accuracy using Faster R-CNN with Detectron2. A dataset of 200 SATS-LN documents, captured via scanner and camera, was augmented into 3,600 images and labeled for seven key fields. Image quality was evaluated using PSNR, SSIM, and MSE, while detection performance was measured through mAP, AP50, AP75, AR@100, precision, recall, and F1-score. Results showed that Sauvola preserved structural layout more effectively (SSIM: 0.76 for scanner, 0.47 for camera), although Otsu achieved higher PSNR on scanned images. Sauvola attained the highest macro and weighted F1-score (0.998), with near-perfect label detection and consistent performance across augmentations. Overall, Sauvola is more reliable for enhancing segmentation and detection in layout-based document processing.
Co-Authors Abdul Azis Agung Tri Lestari, agungyappi Anton Yudhana Arief Budiman Arief Setyo Nugroho Asno Azzawagama Firdaus Aznar Abdillah, Muhamad Basir, Azhar Basri, Mhd. Darpito, Muhammad Noko Deni Kuswandani Devi Puspita Sari Dianda Rifaldi Edi Ismanto Fahmi Auliya Tsani Fitrah, Fitrah Juliansyah Hanif, Kharis Hudaiby Herman Ikhsan Hidayat Imam Riadi Jamaludin Dwi Laspandi Joko Supriyanto Kartika Firdausy Kitagawa, Kodai Muhamad Rosidin Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Kunta Biddinika Muhammad Muhammad Muhammad Noko Darpito Muhammad Rizki Setyawan Murinto Nadia Wati Aprianti Nilam Tri Astuti Nur Tyas Anggraeni Pahlevi, Ryan Fitrian Rezki Rinday Zildjiani Salji Rio Anggara RR. Ella Evrita Hestiandari Rusydi Umar Safiq Rosad Siti Helmiyah Sunardi - sunardi sunardi Sunardin Sunardin Suprianto - Tiara Widyakunthaningrum Tole Sutikno Tri Stiyo Famuji Tristanti, Novi Wahyu Sapto Aji Yuwono Fitri Widodo