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All Journal Jurnal SOLMA Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal ABDINUS : Jurnal Pengabdian Nusantara Journal of Soft Computing Exploration Journal of Data Science Theory and Application Jurnal Informatika: Jurnal Pengembangan IT Journal of Applied Informatics Science
Hepatika Zidny Ilmadina
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Edukasi Anemia dan Penerapan Aplikasi CeSLA (Cegah Stunting Lewat Anemia) Berbasis AI di SMA Negeri 1 Tegal Ilmadina, Hepatika Zidny; Nisa, Juhrotun; Apriliani, Dyah
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.20277

Abstract

Background: Anemia merupakan masalah kesehatan yang banyak dialami remaja putri di Indonesia dan berdampak serius terhadap kualitas hidup serta risiko kesehatan di masa mendatang, termasuk stunting. Akan tetapi deteksi anemia masih bergantung pada metode invasif yang tidak selalu mudah diakses oleh remaja. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi kesehatan remaja melalui edukasi dan implementasi aplikasi CeSLA (Cegah Stunting Lewat Anemia), yakni inovasi teknologi berbasis kecerdasan buatan (AI) untuk deteksi anemia secara non-invasif. Metode: Kegiatan dilaksanakan di SMA Negeri 1 Tegal dan diikuti oleh 24 siswi dari kelas XI. Rangkaian kegiatan meliputi edukasi interaktif, simulasi penggunaan aplikasi, pendampingan, serta evaluasi menggunakan pre-test dan post-test. Hasil: Terdapat peningkatan pemahaman peserta terkait anemia dan implementasi AI dalam deteksi dini, yang ditunjukkan melalui kenaikan skor rata-rata dari 7,0 (pre-test) menjadi 9,1 (post-test). Kesimpulan: Implementasi CeSLA terbukti efektif sebagai media edukatif dan praktis untuk mendeteksi anemia secara mandiri. Kegiatan ini menunjukkan bahwa pendekatan berbasis teknologi dapat meningkatkan kesadaran dan pemantauan kesehatan di kalangan remaja.
Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50 Hepatika Zidny Ilmadina; Muhammad Naufal; Dega Surono Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2785

Abstract

Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results.
Co-Authors Ahmad MAULANA Anisa, Lulu Nadhiatun Ardi, Ardi Susanto Arif Hidayah Dairoh Dairoh Dega Surono Wibowo Dega Surono Wibowo Dega Surono Wibowo, Dega Surono Dyah Apriliani Dyah Apriliani Dyah Apriliani Eflita Yohana Fatkhurrozak, Faqih Ginanjar Wiro Sasmito Ginanjar Wiro Sasmito, Ginanjar Wiro Haqqani, Humam Asathin Hendrawan, Andre Budhi Hidayatullah, Muhammad Fikri Insani, Fariq Fadillah Gusti Juhrotun Nisa, Juhrotun Muhammad Naufal Naovi Magfiroh Rakhmah, Firda Aulia Risqi Dwi Saputri, Berliani Sanjaya, Firman Lukman Saptaryani, Titiek Deasy Saputri, Berliani Risqi Dwi Sharfina Febbi Handayani Syarifudin Syarifudin