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All Journal Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Deden Lasmana
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SOSIALISASI SISTEM INTERVENSI BERBASIS AI UNTUK MENGELOLA TANTRUM ANAK AUTISME DI SLB AUTIS TALANG KELAPA PALEMBANG Nyayu Latifah Husni; Masayu Anisah; Abdurrahman; Selfi Nurhaliza; Deden Lasmana; Dewi Permata Sari
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): Aptekmas Volume 7 Nomor 3 2024
Publisher : Politeknik Negeri Sriwijaya

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Abstract

This community service activity aims to introduce an artificial intelligence (AI)-based intervention device for managing tantrums in children with autism at SLB Autis Talang Kelapa Palembang. Tantrums in children with autism are often caused by their inability to express emotions, which can disrupt the learning process and affect their surrounding environment. Through the socialization of an AI device utilizing MediaPipe technology, teachers and staff were trained to quickly detect early signs of tantrums and provide interventions in the form of music designed to calm autistic children. The results of this activity indicate improved understanding among teachers, staff, and parents in detecting and responding to tantrums more effectively. The implementation of this device has been proven to reduce both the frequency and intensity of tantrums, thereby creating a more conducive learning environment for children with autism.
Deteksi Objek Menggunakan YOLOv5 dan YOLOv8 pada Perangkat Pemantauan Lingkungan Deden Lasmana; Nyayu Latifah Husn; RD. Kusumanto
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 2 (2025): Jurnal Elkolind Vol 12 No 2 (Juli 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i2.7615

Abstract

Penelitian ini membandingkan efektivitas algoritma YOLOv5 dan YOLOv8 dalam mendeteksi objek seperti manusia, hewan, dan tumpukan hewan dalam sistem pemeliharaan berbasis AI. Dataset yang digunakan terdiri dari 5.000 gambar berbasis anotasi, dengan data untuk 70% penelitian, 20% untuk validasi, dan 10% untuk pengujian. Model dievaluasi menggunakan metrik precision, recall, mAP0.5, dan mAP0.5:0.95 setelah 50 epoch. Hasilnya menunjukkan bahwa YOLOv8 memiliki performa keseluruhan yang lebih baik daripada YOLOv5, terutama pada metrik mAP0.5:0.95 dengan peningkatan sebesar 25%, sedangkan YOLOv5 hanya mencapai 10%. Dalam hal mendeteksi objek yang kompleks, YOLOv8 lebih akurat daripada YOLOv5, yang lebih cepat tetapi kurang sensitif terhadap objek yang lebih kecil. Hasilnya, YOLOv8 lebih cocok untuk aplikasi pendeteksi lingkungan secara real-time, meningkatkan sampah yang berkelanjutan, dan berintegrasi dengan konsep smart city.
Co-Authors Abdurahman Abdurrahman Dewi Permata Sari Husni, Nyayu Latifah Masayu Anisah Nyayu Latifah Husn RD. Kusumanto Selfi Nurhaliza