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All Journal Epsilon: Jurnal Matematika Murni dan Terapan ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika bit-Tech Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering
KHAERUDIN SALEH
Telkom University
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Transportation Other

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YOLOv8n untuk Deteksi Sampah secara Real-Time pada Aplikasi Bank Sampah Antanita, Yulintyandra Puja; Ardana, Arfio; Alfin, Khoerunnisa; Pratama, Yugo; Purnamasari, Rita; Saleh, Khaerudin
eProceedings of Engineering Vol. 12 No. 6 (2025): Desember 2025
Publisher : eProceedings of Engineering

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Abstract

Pengelolaan sampah merupakan tantangan lingkungan yang signifikan di Indonesia, terutama di kawasan perkotaan dengan tingkat kesadaran pemilahan sampah yang masih rendah. Program bank sampah yang diatur dalam Peraturan Menteri Negara Lingkungan Hidup Republik Indonesia Nomor 13 Tahun 2012 bertujuan untuk mendorong partisipasi masyarakat dalam pengelolaan sampah berbasis komunitas, namun pelaksanaannya masih belum optimal akibat keterbatasan teknologi dan proses administrasi yang masih manual. Penelitian ini mengusulkan pengembangan aplikasi bank sampah berbasis mobile yang mengintegrasikan model YOLOv8n untuk deteksi jenis sampah secara real-time. Dataset terdiri dari sembilan kategori sampah dengan total 4.500 gambar, yang dianotasi dan dibagi menjadi data latih, validasi, dan uji dengan rasio 80:10:10. Model YOLOv8n dilatih menggunakan konfigurasi 70 epoch, learning rate 0,001, dan optimizer AdamW, menghasilkan performa mAP@0.5 sebesar 0,995 dan mAP@0.5:0.95 sebesar 0,785. Pengujian lanjutan menunjukkan kemampuan generalisasi yang baik terhadap variasi bentuk, latar belakang, jarak, dan skenario multi-objek, meskipun performa menurun pada bentuk dan warna yang jarang muncul dalam dataset serta pada deteksi jarak jauh. Hasil penelitian membuktikan bahwa YOLOv8n memiliki potensi tinggi untuk diimplementasikan dalam sistem bank sampah berbasis mobile guna meningkatkan efisiensi pemilahan dan partisipasi masyarakat.. Keywords— Objek Deteksi, YOLOv8n, Computer Vision, Machine learning
Designing a Real-Time TOXMAP Backend Based on FastAPI and Firebase for B3 Waste Gading Aurelia Nabila Lubis; Rita Purnamasari; Khaerudin Saleh
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2888

Abstract

Household waste classified as Hazardous and Toxic Waste (Bahan Berbahaya dan Beracun—B3) poses serious risks to health and the environment if not managed properly. Mismanagement can result in groundwater contamination, soil degradation, and long-term exposure to carcinogens. To address this issue, TOXMAP was developed as a mobile-based system that integrates real-time image classification with location-based disposal guidance. This paper discusses the development of the TOXMAP backend using a FastAPI server to process image input and classify waste using a pre-trained Support Vector Machine (SVM) model. Firebase supports user authentication, image storage, and retrieval of nearby dropbox locations. The Flutter-based frontend enables cross-platform access and supports real-time camera input. Load and integration tests show that the system responds in under one second with good classification accuracy and high user responsiveness. The system architecture effectively combines machine learning inference, cloud-based data handling, and mobile accessibility. FastAPI, Firebase, and SVM were selected to ensure lightweight, responsive, and accurate performance. Testing confirmed strong system stability and efficient computation during iterative use. The SVM model offers a balance between prediction accuracy and resource efficiency. By providing accurate classification and practical location guidance, the TOXMAP system enhances environmental awareness and promotes responsible disposal behavior. This architecture presents a scalable, lightweight, and accessible solution to support better household hazardous waste management and sustainable behavioral change.
Co-Authors Adhi Irianto Mastur, Adhi Irianto Alfin, Khoerunnisa Antanita, Yulintyandra Puja Ardana, Arfio Aritonang , Josua Kevin Sumandoro Dimarta, Rafli Caesario Dita Puspitasari Gading Aurelia Nabila Lubis Gelar Budiman HANAFI, FANIESA SAUFANA Iqbal Afriadi LESTARY, GITA AYU M Darfyma Putra Mahanani, Edo Lutfi Muthia Syafika Haq, Muthia Syafika Nasywan Azrial Fariqin Nor Kumalasari Caecar Pratiwi Nur Ibrahim Parameswara, Prastama Agung Yusuf Purnamasar, Rita Ratri Dwi Atmaja Rifa’i, Irfan Rita Purnamasari Rustam Tito Waluyo Purboyo Yugo Pratama Yulinda Eliskar