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All Journal Teknika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) IJELR: International Journal of Education, Language, and Religion Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization International Journal of Engineering, Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Algoritma Jurnal Malikussaleh Mengabdi INOVTEK Polbeng - Seri Informatika
Zahratul Fitri, Zahratul
Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Bumi Persada
Religion Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Improving Vegetable Production in North Aceh Regency: An Implementation of a Smart Farming Monitoring System Fitri, Zahratul; Meiyanti, Rini; Nunsina, Nunsina; Fitria, Rahma; Munauwar, Muhammad Muaz
JINAV: Journal of Information and Visualization Vol. 6 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav4322

Abstract

This research aims to design and implement a smart farming monitoring system that is appropriate for the local conditions of North Aceh to optimize the production of leading vegetables and facilitate sustainable agricultural transformation. In line with the national agenda toward the digitalization of the agricultural sector, this research is part of a concrete effort to encourage the adoption of smart farming technology at the local level. North Aceh Regency has great horticultural potential, but it is not yet optimal due to the minimal application of technology. This research supports the development of agriculture based on local potential. The study also promotes a participatory and educative approach to increase farmers' digital literacy and reduce the technology gap between conventional and modern technology-adopting farmers. The Smart Farming monitoring system was successfully implemented using soil moisture, air temperature, soil pH, and light intensity sensors integrated into a web-based dashboard and mobile application. The implementation of this system was able to increase vegetable productivity by 18–22%, especially for mustard greens, chili, and tomatoes, compared to conventional methods. The system also contributed to the efficient use of resources, shown by a 25% savings in irrigation water and a 15% reduction in the use of chemical fertilizers. The farmer response was quite positive, although there are still challenges related to digital literacy among some older farmers. Overall, the implementation of Smart Farming in North Aceh Regency had a real impact on increasing productivity and cost efficiency while supporting sustainable agriculture in line with the SDGs.
Detection of Anemia Based on Conjunctival Images Using a Convolutional Neural Network (CNN) Method Sari, Rika Yulia; Fitri, Zahratul; Afrillia, Yesy
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.28906

Abstract

A hemoglobin level below 12 g/dL is the primary indicator of anemia, a condition commonly found in adolescent girls. Laboratory blood tests, as a conventional detection method, are invasive, time-consuming, and costly. This study developed a non-invasive classification system for anemia and non-anemia based on conjunctival images using a Convolutional Neural Network (CNN), implemented on a real-time website. A total of 433 conjunctival images were collected comprising 206 images of anemia and 227 of non-anemia sourced from smartphone cameras and the Kaggle dataset, divided in an 80:10:10 ratio for training, validation, and testing. Preprocessing included resizing to 150 150 pixels, augmentation (flip, rotation, zoom, translation, brightness), and pixel normalization. The CNN architecture consists of three convolutional layers (32, 64, and 128 filters), max pooling, dropout, and a fully connected layer with sigmoid activation, trained using the Adam optimizer and the binary cross-entropy loss function until the 43rd epoch. The model achieved an accuracy of 88.37%, precision of 0.89, recall of 0.88, and an F1-score of 0.88. The model was integrated with a Flask-based REST API and MediaPipe Face Landmarker to automatically detect the conjunctival Region of Interest (ROI) via camera or uploaded images, thereby potentially serving as a fast, practical, and easily accessible tool for the initial screening of anemia among adolescent girls in schools and primary health care facilities.
Co-Authors Ade Irfan Aidilof, Hafizh Al Kautsar Andik Bintoro ANNISA KARIMA Asrianda Asrianda Ayouni, Nour Bakhtiar Bakhtiar Dahlan Abdullah Delianda, Anggun Dewi Anggraini Dita Amelia, Dita Emi Maulani Eva Darnila Fadliani, Fadliani Fakhrurrazi Fuadi, Wahyu Hisyam, Mochammad Irwansyah, Defi Kausar, M Reza Khairul Amna Laksana, Fatih Dwi Leni Maulinda Liza Afra Merdu, Bunga Mawarni Miftahul Jannah Muhammad Muaz Munauwar Muhibuddin Muhibuddin, Muhibuddin Mutammimul Ula Nisa, Miftahul Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nuriana, Nuriana Putri, Rizki Amelia Rahma Fitria, Rahma Rahma, Putri Razi, Ar Rini Meiyanti Rizal S.Si., M.IT, Rizal Rizki Suwanda Safriana Safriana Safwandi Safwandi, Safwandi Samsuar, Samsuar Saptari, Mochamad Ari Sari, Rika Yulia Siregar, Siti Chairani Sukatin, Sukatin Tinambunan, Intan Surur Yesy Afrillia Zainal Abidin Zul Akli