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All Journal Jurnal Pengabdian UntukMu NegeRI IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science JURTEKSI Building of Informatics, Technology and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) JIM ( Journal International Multidiciplinary)
Yoze Rizki
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : JIM ( Journal International Multidiciplinary)

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Hotspots and Smoke Detection from Forest and Land Fires Using the YOLO Algorithm ( You Only Look Once ) Dicko Andrean; Mitra Unik; Yoze Rizki
JIM - Journal International Multidisciplinary Vol. 1 No. 1 (2023)
Publisher : Rumah Jurnal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jim.v1i1.410

Abstract

The term forest and land fires is used to refer to unplanned, controlled and unwanted fires that destroy vegetated areas and their ecosystems triggered by natural or human causes . Early detection of hotspots can reduce the risk of wider forest and land fires. The use of the Deep Learning YOLO ( You Only Look Once ) algorithm is carried out to detect fire and also the smoke it produces. This study tested in 3 ways, 1) 1341 after data augmentation (496 original data), 2) 608 after data augmentation (253 original data), and 3) 1790 after data augmentation (746 original data). Detection of fire and smoke objects in the form of design, implementation and testing resulted in the YOLOv4 framework successfully producing high confidence of up to 97% in the second test. Based on the test results in this study, it is known that the image datasets used for training data greatly affect object detection and affect the confidence value. The more diverse the shape of the object from the image datasets, the lower the confidence value obtained.
Co-Authors Afdhil Hafid Arbi Haza Nasution, Arbi Haza Arif Budiman Bagas Tri Ramadana Desti Mualfah Diah Angraina Fitri Dicko Andrean Dinia Putri Elvina Rahmadani Evans Fuad Febby Apri Wenando Hari Sepdian Harun Mukhtar Hasanuddin Hasanuddin Hayami, Regiolina Husnul Habibie Januar Al Amien M Djodi Andikarama M. Rizki Amanda Pratama Meiriladiwis Gea Mochamad Hariadi Muhammad Abdul Al Aziz Muhammad Rifaldo Mukhlis Ali Rahman Nasution Rabiah Aminullah Rafi Muhammad Rahmad Firdaus Reny Medikawati T Reny Medikawati Taufik Salhazan Nasution Soni Sunanto Sunanto Sunanto Sunanto Taufiq, Reny Medikawati Unik, Mitra Yulia Fatma Yulia Fatma