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All Journal Jurnal Algoritma Jurnal Informatika dan Teknik Elektro Terapan PROCEEDING IC-ITECHS 2014 SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) KOMPUTIKA - Jurnal Sistem Komputer JIKA (Jurnal Informatika) Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal of Software Engineering and Information System (SEIS) SmartComp
Pradana, Afu Ichsan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Sistem Monitoring Suhu Penetasan Telur Ayam Berbasis Internet of Things (IoT) Ashidiqi, Andhyka Yoga; Pradana, Afu Ichsan; arsanto, arsanto
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 2 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i2.7557

Abstract

strak – Proses penetasan telur ayam merupakan salah satu tahapan penting dalam budidaya ayam. Suhu merupakan faktor yang sangat kritis dalam proses ini. Suhu yang tidak stabil dapat menyebabkan kematian embrio atau menghasilkan anak ayam yang lemah. Oleh karena itu, diperlukan suatu sistem yang dapat memantau dan mengontrol suhu penetasan secara akurat dan efisien. Penelitian ini bertujuan untuk mengembangkan sistem monitoring suhu penetasan telur ayam berbasis Internet of Things (IoT) yang akurat dan efisien. Sistem ini menggunakan NodeMCU ESP32 sebagai mikrokontroler, sensor DHT11 untuk mengukur suhu dan platform Blynk sebagai antarmuka pengguna. Hasil penelitian menunjukkan bahwa sistem mampu memantau suhu inkubator dengan akurasi ±0,5°C dan mengirimkan data secara real-time ke platform Blynk. Penelitian ini memberikan kontribusi pada pengembangan aplikasi IoT di bidang peternakan, khususnya dalam meningkatkan kualitas dan kuantitas produksi telur tetas. Kesimpulan terhadap pengembangan penggunaan sistem monitoring yang dikembangkan berhasil dalam memantau suhu inkubator secara real-time dan akurat. Data suhu yang diperoleh pun dapat diakses oleh pengguna melalui platform Blynk. Penggunaan NodeMCU ESP32 dan Blynk memungkinkan fleksibilitas dalam mengontrol suhu inkubator secara remote. Pengguna dapat memantau dan mengukur suhu inkubator kapan saja dan dimana saja melalui perangkat yang terhubung dengan internet.
SMART LOKER BERBASIS IOT DENGAN AUTENTIKASI QR CODE TERINTEGRASI DENGAN WEB Permana, Aditya Candra; Pradana, Afu Ichsan; Hartanti, Dwi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6573

Abstract

Locker merupakan tempat penyimpanan barang yang sering ditemukan di pusat perbelanjaan, perkantoran, sarana olahraga dan tempat umum lainnya. Namun, kebanyakan dari loker yang sering dijumpai masih menggunakan kunci fisik yang memiliki kekurangan, seperti rentan akan kehilangan kunci dan keamanan yang kurang. Maka dibuatlah sistem berbasis IoT dengan autentikasi qr-code yang terintegrasi dengan web. Sistem ini dapat digunakan pengguna untuk membuka dan mengunci locker melalui pemindai qr-code yang tampil secara acak oleh website. Metode yang digunakan untuk pengembangan sistem ini menggunakan esp32-cam untuk membaca qr-code, relay untuk mengatur kunci solenoid, dengan website untuk memonitoring ketersediaan dan lokasi locker secara real-time. Hasil dari pengujian yang dilakukan memperlihatkan bahwa sistem mampu memvalidasi QR-Code dan dapat mengendalikan kunci solenoid dengan baik serta integrasi antar perangkat dapat berjalan dengan baik.
Personal Protective Equipment Completeness Monitoring System Using YOLO-Based Computer Vision Akmal, Baasith Khoiruddin; Lestari, Wiji; Pradana, Afu Ichsan
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10172

Abstract

Workplace safety in the construction sector remains a critical concern, primarily due to low compliance with Personal Protective Equipment (PPE) standards. To address this, this study develops and evaluates a real-time PPE monitoring system, conducting a comparative analysis of two state-of-the-art object detection models: YOLOv8s and YOLOv11s. The system is designed to detect three essential PPE items: helmets, masks, and vests, and both models were trained on a custom dataset of 9,202 augmented images over 200 epochs. The final evaluation on an unseen test set revealed highly competitive performance. While YOLOv8s achieved a marginally higher mAP@0.5 (90.8%), YOLOv11s demonstrated superior precision (92.0%) and better performance on the stricter mAP@0.5:0.95 metric (54.4%). Based on this nuanced trade-off and its significantly higher computational efficiency (15% fewer parameters), YOLOv11s was selected as the optimal model. The chosen model achieved a real-time inference speed of approximately 112 FPS. A functional web-based prototype was developed using Flask to demonstrate the system's practical application. These findings confirm that YOLOv11s offers a more balanced and efficient solution for automating PPE compliance monitoring and highlight that a holistic evaluation beyond a single metric is crucial for deploying robust computer vision systems in real-world safety applications.
MODEL KLASIFIKASI JARAK MANHATTAN PADA PENGENALAN CITRA SISTEM BAHASA ISYARAT BAHASA INDONESIA Tory, Alfa Rado Andre Yusa Saka; Pradana, Afu Ichsan; Maulindar, Joni
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9466

Abstract

This study aims to design and implement an image recognition system for Sistem Isyarat Bahasa Indonesia (SIBI) by applying the Manhattan distance classification method. Sign language serves as a vital means of visual communication for individuals with hearing impairments and disabilities. However, public understanding of this language remains limited, often leading to ineffective communication between hearing and non-hearing communities. Therefore, an assistive system capable of accurately recognizing sign language is highly needed. The Manhattan method was selected due to its simplicity and efficiency in calculating distances between data points. The dataset used in this study was obtained from the Kaggle website, consisting of 130 training images and 130 testing images, each representing 26 alphabet letters in the SIBI system. All images underwent initial preprocessing using Jupyter Notebook, including resizing, background removal, and conversion to grayscale to facilitate feature extraction. The grayscale images were then transformed into histograms and normalized to maintain a consistent value scale. The classification process was carried out by computing the Manhattan distance between the test and training image histograms. The system was developed using MATLAB R2015a, featuring a user interface that displays classification results directly. The test results showed that out of 130 test images, 104 were accurately recognized, achieving an accuracy rate of 80%. These findings indicate that the Manhattan method is effective for use in image-based sign language recognition systems. The developed system is expected to serve as an inclusive and educational tool to enhance communication between the hearing-impaired community and the general public. Further development may involve integrating additional methods and expanding the dataset.
Co-Authors Aji, Sindhu Purnomo Akmal, Baasith Khoiruddin arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Dwi Hartanti Harjanto, Prabandaru Pramu Harsanto, Harsanto Insaan, Nashiirotul Junantoro, Hagai Kusuma, Adri Surya Maulindar, Joni Nugroho, Nasrun Nurchim Nurchim Pamekas, Bondan Wahyu Permana, Aditya Candra Permatasari, Hanifah Pipin Widyaningsih Pratama, Fandi Aziz Putra, Enggar Wijaya Putra, Wihan Perkasa Nugraha Rakadipa, Dhimas Arya Rengga, Nikodemos Rifky, Raja Saputra, Ilham Tristadika Setiawan, Gilang Sopingi Sopingi, Sopingi Tory, Alfa Rado Andre Yusa Saka Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf