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All Journal Jurnal Algoritma Jurnal Informatika dan Teknik Elektro Terapan JUPITER (Jurnal Pendidikan Teknik Elektro) PROCEEDING IC-ITECHS 2014 SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Indonesian Journal of Applied Informatics KOMPUTIKA - Jurnal Sistem Komputer JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi 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 Journal of Comprehensive Science Proceeding of International Conference Health, Science And Technology (ICOHETECH) Adopsi Teknologi dan Sistem Informasi Innovative: Journal Of Social Science Research Journal of Software Engineering and Information System (SEIS) SmartComp
Pradana, Afu Ichsan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Other

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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.
Intelligent Surveillance for Mask Regulation in Healthcare Using the YOLOv11 Algorithm Pradana, Afu Ichsan; Harsanto; Aboobaider, Burhanuddin Bin Mohd; Harsanto, Malika
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/23mc9656

Abstract

The use of face masks in healthcare settings is a crucial measure in preventing the spread of infectious diseases, particularly since the outbreak of the COVID-19 pandemic. However, public compliance with mask-wearing remains a challenge despite the implementation of various regulations. This study aims to design and develop an automatic mask-wearing detection system by leveraging the YOLOv11 algorithm, which is renowned for its superior speed and accuracy in object detection. The methodology involved collecting a dataset of facial images with and without masks, data labeling, model training using YOLOv11, and evaluating the system's performance in real-world conditions. Test results demonstrate that the system can perform real-time mask detection with a mean Average Precision (mAP) of 0.9, establishing it as an effective solution for supporting health protocol monitoring in medical facilities. Consequently, this system not only enhances monitoring efficiency but also has the potential to minimize the risk of infection spread through an intelligent technological approach.
Optimalisasi Teknologi IoT untuk Penyemprotan Tanaman Padi Pujiati Edy Santoso, Elysa Mei; Pradana, Afu Ichsan; Maulindar, Joni
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19422

Abstract

Pertanian modern menuntut efisiensi dalam pengelolaan sumber daya, termasuk proses penyemprotan air, nutrisi, dan pestisida. Penelitian ini merancang sistem penyemprotan otomatis berbasis IoT pada tanaman padi menggunakan NodeMCU ESP8266, sensor DHT11, soil moisture sensor, dan LDR. Data sensor digunakan untuk mengaktifkan pompa penyemprot secara otomatis berdasarkan kondisi lingkungan. Sistem terhubung dengan aplikasi Blynk untuk pemantauan real-time. Hasil menunjukkan sistem mampu mendeteksi perubahan suhu, kelembapan, dan cahaya secara akurat, dengan respon otomatis rata-rata di bawah 2 detik. Contohnya, pompa aktif saat kelembapan tanah <30% dan berhenti setelah mencapai ambang batas. Pemantauan melalui Blynk berjalan stabil. Sistem terbukti efektif dan potensial untuk otomatisasi pertanian skala kecil. Ke depan, pengembangan dapat mencakup integrasi machine learning untuk prediksi kebutuhan penyemprotan serta pengendalian berbasis zona lahan guna meningkatkan efisiensi dan skalabilitas.
Implementasi IOT Untuk Deteksi Kebocoran Gas dan Peringatan Dini di Rumah Tangga Jordan, Alles Tio; Purwanto, Eko; Pradana, Afu Ichsan
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19428

Abstract

Kebocoran gas di lingkungan rumah tangga merupakan masalah serius yang dapat menimbulkan risiko kebakaran dan keracunan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem deteksi kebocoran gas berbasis Internet of Things (IoT) menggunakan mikrokontroler NodeMCU ESP8266 dan sensor gas MQ-2. Sistem yang dikembangkan mampu mendeteksi keberadaan gas LPG, karbon monoksida, dan asap secara real-time serta memberikan peringatan melalui alarm lokal dan notifikasi ke perangkat mobile menggunakan platform Blynk. Pengujian sistem menunjukkan bahwa notifikasi peringatan dapat dikirimkan dalam waktu kurang dari 2 detik setelah deteksi gas, dengan tingkat akurasi dan kestabilan koneksi yang memadai. Sistem ini terbukti efektif dalam meningkatkan keamanan lingkungan rumah tangga dengan memberikan peringatan dini secara cepat dan akurat. Selain itu, konsumsi daya yang efisien memungkinkan pemakaian jangka panjang tanpa perlu penggantian sumber daya secara sering. Penelitian ini membuka peluang pengembangan lebih lanjut dengan penambahan fitur kalibrasi otomatis dan integrasi dengan sistem smart home untuk peningkatan keselamatan yang lebih komprehensif.
Penerapan Metode Content-Based Filtering Pada Sistem Rekomendasi Pemilihan Produk Obat Studi Kasus : Apotek Hero Farma Indriyani, Tiara; Pradana, Afu Ichsan; Hartanti, Dwi
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30498

Abstract

Selecting the right drug product is an important aspect in pharmaceutical services, especially for customers who do not yet have a deep understanding of the content and function of each drug. The limited number of pharmacists at Hero Farma Pharmacy often causes the service process to be inefficient, especially when still relying on manual recommendation methods that take a long time. This study aims to design a recommendation system that can assist in drug selection by implementing the content-based filtering method. This system is built by processing product attributes such as drug name, category, indication, dosage form, and price to form a profile of each product. The level of similarity between products is then calculated using the TF-IDF and Cosine Similarity methods. The data used in this study were obtained from Hero Farma Pharmacy located in Surakarta, with samples of 10 different types of drugs. The implementation results show that the system can produce recommendations with a good level of accuracy, where Promag drug obtained the highest Cosine Similarity value of 43.03% based on the query entered. This system successfully provides drug recommendations that have similar characteristics based on user needs and can help customers in making decisions correctly and improve the work efficiency of pharmacy staff.
Intelligent Traffic Sign Detection Using Yolov9 Pradana, Afu Ichsan; Harsanto, Harsanto; Maulindar, Joni
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4205

Abstract

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.
Optimalisasi Akurasi Model Identifikasi Penyakit Pada Daun Padi Dengan Fine-Tuning YOLOv11 Untuk Ketahanan Pangan Berkelanjutan Harsanto; Pradana, Afu Ichsan; Wahyu Pamekas, Bondan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2945

Abstract

Rice is one of Indonesia's main food commodities, whose productivity often declines due to leaf disease. Early detection of rice leaf disease is an important aspect of maintaining sustainable food security. This study aims to optimize the accuracy of early identification of rice leaf disease by fine-tuning the YOLOv11 model. The research stages included dataset collection, annotation, data preprocessing, data augmentation, model training, fine-tuning, and model performance evaluation. The results showed an improvement in model performance after fine-tuning, with the overall recall value increasing from 0.760 to 0.788 and mAP from 0.764 to 0.785. The confusion matrix also shows a more stable prediction distribution in the fine-tuned model compared to the initial model. Thus, fine-tuning YOLOv11 has proven to be effective in improving the accuracy of early identification of rice leaf diseases and has the potential to support the application of artificial intelligence in the agricultural sector to strengthen food security in Indonesia.
The Internet of Things-Based Work Equipment Lending System at PT Namasindo Plas Syafrudin, Andang; Pradana, Afu Ichsan; Hartanti, Dwi
Journal of Comprehensive Science Vol. 5 No. 2 (2026): Journal of Comprehensive Science
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v5i2.3996

Abstract

The borrowing of work tools plays an essential role in supporting production activities at PT Namasindo Plas. The current borrowing process is still carried out manually using logbooks and spreadsheets, which often leads to issues such as inaccurate records, inconsistencies in inventory data, and the risk of tool loss. This research aims to develop an Internet of Things (IoT)-based work tool borrowing system equipped with Radio Frequency Identification (RFID) technology to enable automatic identification of tools and users, as well as real-time recording of all borrowing activities. The system was developed using the Waterfall method, which includes requirement analysis, system design, device assembly, testing, and implementation. The system utilizes an RFID module, an ESP8266 microcontroller, and an online database to store and manage borrowing information. The test results show that the system successfully reads RFID tags, records borrowing and returning transactions automatically, and updates tool status in real time. The implementation of this system improves data accuracy, speeds up the borrowing process, and reduces the potential for tool loss, thereby enhancing the operational efficiency of the warehouse at PT Namasindo Plas.
Co-Authors Aboobaider, Burhanuddin bin Mohd Aji, Sindhu Purnomo Akmal, Baasith Khoiruddin Ardiansah, Ricky arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Dwi Hartanti Eko Purwanto Fadhilah, Dayinta Fajrin Gadissa, Tiur Bunga Harjanto, Prabandaru Pramu Harsanto Harsanto, Harsanto Harsanto, Malika Imaduddin, Mohamad Indriyani, Tiara Insaan, Nashiirotul Jordan, Alles Tio Junantoro, Hagai Kusuma, Adri Surya Maulindar, Joni Nugroho, Nasrun Nurchim Nurchim Pamekas, Bondan Wahyu Permana, Aditya Candra Permatasari, Hanifah Pipin Widyaningsih Pratama, Fandi Aziz Pujiati Edy Santoso, Elysa Mei Putra, Enggar Wijaya Putra, Wihan Perkasa Nugraha Rahmah, Ila Aulia Rakadipa, Dhimas Arya Ramadana, Muhamad Ilhamsyah Amara Rengga, Nikodemos Rifky, Raja Rudi Susanto Setiawan, Gilang Setiawan, M. Qurdi Ari Sopingi Sopingi, Sopingi Suryani, Fajar Syafrudin, Andang Tory, Alfa Rado Andre Yusa Saka Wahyu Pamekas, Bondan Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf