Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal
AGASTA, IRA AMELIA
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sarana Pelaporan Angka Bebas Jentik dan Deteksi Jentik Nyamuk menggunakan Deep Learning YUANA, DIA BITARI MEI; AGASTA, IRA AMELIA; SAPUTRO, MUHAMMAD ADI; ROHMAH, ETIK AINUN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.89-98

Abstract

AbstrakDemam Berdarah Dengue (DBD) masih menjadi masalah kesehatan utama di Indonesia. Kabupaten Jember mencatat 1.627 kasus pada tahun 2024, dengan Angka Bebas Jentik (ABJ) hanya mencapai rata-rata 92%, di bawah standar nasional >95%. Penelitian ini mengembangkan sistem deteksi jentik nyamuk otomatis menggunakan metode Deep Learning berbasis CNN dan GRU. Fitur visual diekstraksi melalui model InceptionV3, kemudian dianalisis secara sekuensial oleh GRU untuk klasifikasi larva. Hasil menunjukkan model mencapai akurasi pelatihan dan pengujian dengan performa optimal pada epoch ke-20 sebesar 99.19%, loss 0.0419. Jika dibandingkan dengan metode sebelumnya (AOA) yang hanya mencapai 84%, pendekatan ini terbukti lebih akurat dan tahan terhadap variasi kondisi data.Kata kunci: Demam Berdarah Dengue, Aedes aegypti, Angka Bebas Jentik, Deep Learning, Gated Recurrent Unit, Deteksi OtomatisAbstractDengue Hemorrhagic Fever (DHF) remains a major public health issue in Indonesia. In 2024, Jember Regency recorded 1,627 cases, with the Larvae Free Index (LFI) averaging only 92%, below the national standard of >95%. This study developed an automatic mosquito larvae detection system using a Deep Learning approach based on CNN and GRU. Visual features were extracted using the InceptionV3 model and then analyzed sequentially by the GRU for larval classification. The results showed that the model achieved optimal training and testing performance at the 20th epoch with 99.19% accuracy and a loss of 0.0419. Compared to the previous method AOA, which achieved only 84% accuracy, this approach proved to be more accurate and robust against variations in data conditions.Keywords: Dengue Hemorrhagic Fever, Aedes aegypti, Larvae-Free Rate, Deep Learning, Gated Recurrent Unit, Automated Detection
Co-Authors Muhammad Adi Saputro Rohmah, Etik Ainun Yuana, Dia Bitari Mei