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All Journal Journal of Technology and System Information Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan
Basuki Rahmat
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT

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Implementasi YOLOv8 Pada Robot Deteksi Objek Azka Avicenna Rasjid; Basuki Rahmat; Andreas Nugroho Sihananto
Journal of Technology and System Information Vol. 1 No. 3 (2024): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v1i3.2969

Abstract

Pendeteksian objek merupakan salah satu tantangan utama dalam pengembangan robotika, khususnya untuk aplikasi yang membutuhkan identifikasi berbagai objek dalam lingkungan yang beragam. Penelitian ini ditujukan untuk implementasi YOLOv8 pada Robot Deteksi Objek. Metode penelitian mencakup pelatihan YOLOv8 menggunakan dataset yang terdiri dari 150 gambar untuk setiap kelas objek. Kinerja model dievaluasi berdasarkan metrik presisi (P), recall (R), mean Average Precision (mAP) pada threshold 50% (mAP50), dan mAP50-95. YOLOv8 bertujuan untuk mendeteksi objek dengan 7 sampel kelas objek yaitu: botol, kursi, manusia, pot, galon, tong sampah, dan ember. Hasil evaluasi menunjukkan bahwa model YOLOv8 memberikan kinerja yang sangat baik dengan presisi dan recall mendekati 1 untuk semua kelas objek. Secara khusus, kursi, manusia, dan tong sampah mencapai nilai P dan R sebesar 0.994 atau lebih, dengan mAP50-95 masing-masing sebesar 0.891, 0.874, dan 0.894. Botol dan ember juga menunjukkan hasil yang baik dengan mAP50-95 masing-masing sebesar 0.857 dan 0.905. Sementara itu, galon dan pot masing-masing memiliki mAP50-95 sebesar 0.908 dan 0.705.
Implementasi Metode CNN Dan K-Nearest Neighbor Untuk Klasifikasi Tingkat Kematangan Tanaman Cabai Rawit Muhammad Rifki Bahrul Ulum; Basuki Rahmat; Made Hanindia Prami Swari
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 3 (2024): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i3.131

Abstract

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.
Implementasi Algoritma K-Means dan Knearest Neighbors (KNN) Untuk Identifikasi Penyakit Tuberkulosis Pada Paru-Paru Rachmadhany Iman; Basuki Rahmat; Achmad Junaidi
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 3 (2024): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i3.77

Abstract

In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.
Co-Authors Achmad Junaidi Andreas Nugroho Sihananto Azka Avicenna Rasjid Made Hanindia Prami Swari Muhammad Rifki Bahrul Ulum Rachmadhany Iman