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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal ARTERI : Jurnal Ilmu Kesehatan
MUHAMMAD ADI SAPUTRO
Politeknik Negeri Jember
Computer Science & IT Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Sarana Pelaporan Angka Bebas Jentik dan Deteksi Jentik Nyamuk menggunakan Deep Learning DIA BITARI MEI YUANA; IRA AMELIA AGASTA; MUHAMMAD ADI SAPUTRO; ETIK AINUN ROHMAH
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
My Baby: Optimalisasi Pemantauan Pertumbuhan Balita dan Edukasi Gizi Berbasis Aplikasi Digital Miftahul Jannah; Putri Rahayu Ratri; Dessya Putri Ayu; Ria Chandra Kartika; Surya Dewi Puspita; Yohan Yuanta; Muhammad Adi Saputro
ARTERI : Jurnal Ilmu Kesehatan Vol 7 No 3 (2026): Mei
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/arteri.v7i3.798

Abstract

Growth monitoring of under-five children is an important effort for early detection of growth disorders. However, growth monitoring activities at Community Health Center (CHC) still face several challenges, including manual data recording, limited interpretation of measurement results, and suboptimal nutrition education media for parents. The utilization of digital technology through mobile health applications is an alternative approach to improve the effectiveness of child growth monitoring. This study aimed to develop and evaluate the feasibility of the My Baby application as a digital media for growth monitoring and nutrition education among under-five children. This study employed a Research and Development (R&D) method using the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The study was conducted in Sucopangepok Village, Jelbuk District, Jember Regency, from May to December 2025. The application trial involved 14 respondents consisting of 1 village midwife and 13 CHC cadres. Data were collected using a feasibility evaluation questionnaire and analyzed descriptively. The results showed that the My Baby application was successfully developed with features including child identity recording, anthropometric measurement recording in both offline and online modes, nutritional status interpretation, growth charts, nutrition education materials, two user dashboards (mother and cadre dashboards) and export data into Microsoft Excel. The feasibility evaluation showed that the application achieved a score of 90.3%, categorized as highly feasible. The application was considered easy to use, informative, and beneficial in supporting child growth monitoring and CHC recording activities. The My Baby application has the potential to serve as a digital supporting medium for primary healthcare services in early detection of growth disorders.
Co-Authors Dessya Putri Ayu Dia Bitari Mei Yuana ETIK AINUN ROHMAH IRA AMELIA AGASTA Miftahul Jannah Putri Rahayu Ratri Ria Chandra Kartika Surya Dewi Puspita Yohan Yuanta