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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Edu Komputika Journal
Nailul Hasan
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PENDAYAGUNAAN DAN METORSHIP KADER PKK KELURAHAN GEBANG PUTIH SURABAYA DALAM MENGOLAH LIMBAH ORGANIK RUMAH TANGGA SEBAGAI KOMPOS IRIT LAHAN DENGAN EM4 SEBAGAI BIOAKTIVATOR Nur Aini Fauziyah; Dyah Suci Perwitasari; Kusuma Wardhani Mas’udah; Primasari Cahya Wardhani; Nailul Hasan; Pardi Sampe Tola; AR Yelvia Sunarti; Kindriari Nurma Wahyusi
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 6, No 1 (2022): Maret
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v6i1.7756

Abstract

ABSTRAKKegiatan pendayagunaan dan mentoring pengolahan limbah organik rumah tangga pada jurnal ini merupakan bentuk tindak lanjut dari kegiatan penyuluhan yang telah dilakukan sebelumnya (Perwitasari dkk., 2021). Dengan menggunakan metode ringkas yang irit lahan, masyarakat Kelurahan Gebang Putih telah mampu mengolah limbah organik sederhana menjadi kompos siap guna dengan menggunakan EM4 sebagai bioaktivator. Metode yang dikembangkan pada kegiatan pengabdian ini adalah melalui metode mentoring secara online dengan kader PKK yang ada di Kelurahan Gebang Putih, Surabaya.  Meski mentoring tidak bisa dilakukan secara langsung, kader PKK bersama perwakilan warga telah berhasil mendayagunakan sampah organik rumah tangga sebagai kompos organik. Kegiatan ini terbukti mampu meningkatkan nilai guna limbah organik menjadi semakin bernilai ekonomis. Warga menjadi lebih hemat karena tak perlu lagi membeli kompos jika ingin bercocok tanam. Harapannya, kelak kompos akan menjadi salah satu peluang ekonomi yang menjanjikan bagi warga Kelurahan Gebang Putih. Kata Kunci: pendayagunaan; mentoring; EM4.ABSTRACTThe utilization and mentoring activities for simple organic waste processing in this journal are a form of follow-up to the counseling activities that have been carried out previously (Perwitasari et al., 2021). By using a compact method that saves land, the people of Gebang Putih Village have been able to process simple organic waste into ready-to-use compost using EM4 as a bioactivator. The method developed in this service activity is through an online mentoring method with PKK cadres in Gebang Putih Village, Surabaya. Although mentoring cannot be done directly, PKK cadres together with community representatives have succeeded in utilizing household organic waste as organic compost. This activity is proven to be able to increase the use value of organic waste to become more economically valuable. Residents become more efficient because they no longer need to buy compost if they want to grow crops. It is hoped that in the future compost will become a promising economic opportunity for residents of Gebang Putih. Keywords: development; mentoring; EM4.
Performance Analysis of Convolutional Neural Network Methods Using VGG16 and YOLOv8n for Bottle Waste Sorting in Computer Vision Applications Akbar Sujiwa; Nailul Hasan; Fajar Timur; Reffany Choiru Rizkiarna
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.42314

Abstract

This study examines the performance of two artificial intelligence models—VGG16 and Yolov8n—in sorting plastic bottles using computer vision.  The objective is to individually assess the classification and detection performance of these models with constrained computational resources and training data.  The dataset consists of 300 original images for two classes (bottle and other), and is split into 210 training, 45 validation and 45 test images.  The images were taken in different lighting conditions and orientations to simulate the real waste sorting situation . Both models were trained and evaluated on CPU based hardware to simulate a constrained computing environment.  The VGG16 was evaluated using classification metrics like accuracy, precision, recall, and F1-score, while the YOLOv8n was evaluated using object detection metrics like precision, recall, F1-score, mAP@0.5, and frame processing speed (FPS). The accuracy of the VGG16 model was 91% on the test set. The mAP@0.5 of YOLOv8n was 0.56 with an average processing speed of 47.12 FPS, while the average processing speed of VGG16 was 5.17 FPS. These results indicate that VGG16 had a good performance on image-level classification, while YOLOv8n had a higher processing efficiency and better object-localization performance in the studied conditions. Further evaluation on embedded hardware is required to establish the suitability of YOLOv8n for practical real-time waste-sorting applications.
Co-Authors Akbar Sujiwa AR Yelvia Sunarti Dyah Suci Perwitasari Fajar Timur Kindriari Nurma Wahyusi Kusuma Wardhani Mas’udah Nur Aini Fauziyah Pardi Sampe Tola Primasari Cahya Wardhani Reffany Choiru Rizkiarna