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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Sistem Informasi dan Sistem Komputer Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Rahmat
Universitas DIPA Makassar
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Implementasi Algoritma SVM Untuk Sistem Deteksi dan Pengawasan Keamanan Kendaraan di Area Parkir Menggunakan Kamera Andi Saenong; Rahmat, Rahmat
Jurnal Sistem Informasi dan Sistem Komputer Vol 9 No 2 (2024): Vol 9 No 2 - 2024
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v9i2.570

Abstract

Keamanan kendaraan merupakan aspek penting dalam mencegah pencurian dan pengrusakan. Peningkatan tindakan pencurian menyebabkan kerugian besar bagi pemilik kendaraan. Pengawasan di lahan parkir terbuka menggunakan metode tradisional seperti CCTV dan jasa parkir memiliki keterbatasan, seperti harus melihat kembali rekaman video. Pengenalan citra dapat meningkatkan keamanan dengan mengenali fitur kendaraan untuk mencegah pencurian. Penelitian ini mengimplementasikan metode pengolahan citra dan klasifikasi SVM (Support Vector Machine). Kamera ditempatkan di lahan parkir terbuka dan citra diolah dengan teknik resize untuk memudahkan pengolahan fitur. ROI (Region of Interest) digunakan untuk mengenali area kendaraan di lahan parkir. Berbagai hyperparameter digunakan untuk meningkatkan akurasi dalam proses klasifikasi antara kendaraan dan lahan parkir kosong. Hasil pengolahan citra dalam mengenali kendaraan mencapai akurasi 99%, menunjukkan bahwa sebagian besar prediksi sesuai dengan kondisi sebenarnya.
Efektivitas Pelatihan Komputer dalam Meningkatkan Kemampuan Teknologi Guru SD: Analisis Berdasarkan Metode Wawancara: Penelitian Rahmat; Sadly Syamsuddin; Erfan Hasmin; Arwansyah; Sitti Harlina; Arham Arifin; Hasyrif SY; Faizal; Imran Djafar
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 3 No. 4 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 3 Nomor 4 (April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v3i4.1057

Abstract

This study evaluated the effectiveness of computer training in improving the technological skills of elementary school teachers at UPTD SDN 168 Inpres Jambua through a participatory evaluation approach. The training was conducted using the blended learning method, and the evaluation used questionnaires, focus group discussions (FGDs), interviews, and observations. The results showed significant improvement in teachers' technology skills. Participant feedback indicated that the relevant training materials and interactive methods were very effective. The findings confirmed the importance of continuous and participatory training and the need to adjust the training program based on participant feedback. Recommendations include increasing interactivity, adapting materials to technological developments and ongoing support for teachers. This study contributes to the development of more effective and sustainable technology training programs for primary school teachers.
OPTIMIZING TOMATO STORAGE-TIME USING SUPPORT VECTOR MACHINE ALGORITHM TO IMPROVE QUALITY AND REDUCE WASTE Rahmat; Sunardi; Fitriani; Andi Saenong; Muhammad Rusdi Rahman; Herman Heriadi; Hernawati
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/6kt3mn85

Abstract

Tomatoes are an agricultural commodity that is susceptible to spoilage, with a limited shelf life if not stored under optimal conditions. Optimizing tomato storage time is very important for improving product quality and reducing waste in distribution. This study aims to implement the Support Vector Machine (SVM) algorithm in predicting the optimal storage time for tomatoes, taking into account environmental factors such as temperature and humidity, as well as tomato ripeness. The dataset used consists of tomato images taken at various ripeness levels, as well as environmental data during storage. The SVM model was trained to classify tomato ripeness conditions and predict the optimal storage duration before significant quality deterioration occurs. The results of the study show that the SVM model has high accuracy in classifying tomato ripeness and can be used to predict the optimal storage time, which in turn can extend the shelf life of tomatoes and reduce crop waste. This research contributes to more efficient and sustainable tomato post-harvest management.
Co-Authors Andi Saenong Arham Arifin Arwansyah Erfan Hasmin Faizal Fitriani Harlina, Sitti Hasyrif SY Herman Heriadi Hernawati Imran Djafar Muhammad Rusdi Rahman Sadly Syamsuddin Sunardi Sunardi