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All Journal Applied Technology and Computing Science Journal Power Elektronik : Jurnal Orang Elektro
R. Akbar Nur Apriyanto
Politeknik Negeri Madiun
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Social Sciences

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IMPLEMENTASI FUZZY C-MEANS CLUSTERING DALAM PENGELOMPOKAN UMKM DI KELURAHAN PANGONGANGAN KOTA MADIUN Lorensa Firda Saskya; R. Akbar Nur Apriyanto
Power Elektronik : Jurnal Orang Elektro Vol 11, No 2 (2022): POWER ELEKTRONIK
Publisher : Politeknik Harapan Bersama Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/polektro.v12i1.3713

Abstract

Logika fuzzy adalah salah satu komponen yang membentuk komputasi lunak, yang merupakan cara mudah untuk memetakan ruang input ke ruang output. Dalam banyak kasus, logika fuzzy digunakan untuk menyelesaikan masalah dari input hingga output. Logika fuzzy sering digunakan untuk menyelesaikan masalah dari input hingga output. Dalam hal ini Fuzzy C-Means Clustering merupakan fuzzy yang akan digunakan dalam jurnal ini. Fuzzy CMeans Clustering (FCM) atau dikenal dengan Fuzzy ISODATA merupakan bagian dari metode KMeans. Derajat keberadaan data dalam suatu kelas atau kelompok ditentukan oleh derajat keanggotaannya. Dalam jurnal ini logika fuzzy digunakan untuk clustering UMKM di Kelurahan Pangongangan Kota Madiun . Berdasarkan hasil implementasi dan pengujian sistem, penerapan metode fuzzy CMeans clustering mampu mengidentifikasi UMKM dengan menggunakan variabel berupa penggajian, aset dan jumlah pegawai serta hasil pengujian sekumpulan data group. terdaftar menunjukkan bahwa mayoritas UMKM di Desa Pangongangan Kota Madiun termasuk dalam Golongan 1
A Dual-Stage Hybrid Vision Framework Using YOLOv8n-Canny Edge Detection for Real-Time Railway Trespassing and Intrusion Monitoring Adiratna Ciptaningrum; Mohammad Erik Echsony; R. Akbar Nur Apriyanto
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8579

Abstract

Intrusion and trespasser detection on railway tracks is a crucial safety measure to prevent accidents and maintain operational reliability. This study proposes a hybrid vision-based approach that integrates YOLOv8n, a lightweight real-time object detection model, with the Canny edge detection algorithm to identify and classify unauthorized objects and individuals on railway tracks. In this context, intrusions refer to inanimate objects such as rocks, fallen trees, or construction materials obstructing the tracks, whereas trespassers refer to humans or other living beings engaging in unauthorized activities near or on the railway line. YOLOv8n is employed as a single-stage detector to localize and classify objects, while Canny edge detection is applied to enhance object contours and improve shape-based differentiation between intrusion and trespasser categories. Experimental results show an average accuracy of 52.37%, indicating moderate detection performance. Although the accuracy remains limited, the findings demonstrate the potential of combining deep learning and traditional image processing techniques to develop an automated monitoring system that supports railway safety and surveillance applications. Further optimization of the dataset, model tuning, and feature enhancement are recommended to improve detection performance.
Co-Authors Adiratna Ciptaningrum Lorensa Firda Saskya