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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) 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) 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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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 arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Bagaskara, Aditya Dwi Hartanti Eko Purwanto Harjanto, Prabandaru Pramu Harsanto Harsanto, Harsanto Harsanto, Malika 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 Rakadipa, Dhimas Arya Rengga, Nikodemos Rifky, Raja Saputra, Ilham Tristadika Setiawan, Gilang Sopingi Sopingi, Sopingi Syafrudin, Andang Tory, Alfa Rado Andre Yusa Saka Wahyu Pamekas, Bondan Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf