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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro
Catherine Olivia Sereati
Atma Jaya Catholic University of Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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KLASIFIKASI GENRE MUSIK DENGAN MENGGUNAKAN METODE MACHINE LEARNING Catherine Olivia Sereati; Hilarius Radix W. C; Lanny W Pandjaitan; Maria Angela Kartwidjaja
Jetri : Jurnal Ilmiah Teknik Elektro Jetri, Volume 21, Nomor 1, Agustus 2023
Publisher : Website

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jetri.v21i1.15339

Abstract

This research discusses how to classify music genres using machine learning.. Music of the same genre usually shares specific characteristics related to instrumentation, rhythmic structure, and musical pitch. Music genres can be done using the Machine Learning method. The Machine Learning algorithms used in this study are K-Nearest Neighbor, Random Forest, and Naïve Bayes. Furthermore, this study will compare the accuracy of the three methods. The primary data used in this study is the result of downloading from the internet. Machine learning programming is used through the Google Colab platform with the Python programming language. From the analysis and testing results, it was found that the Random Forest method has the highest level of accuracy
Tomato leaf disease recognition system using Faster R-CNN Karel Octavianus Bachri; Bryan Santoso; Duma Kristina Yanti Hutapea; Catherine Olivia Sereati; Lanny W. Pandjaitan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.25519

Abstract

The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results.
Co-Authors Bachri, Karel Octavianus Bryan Santoso Duma Kristina Yanti Hutapea Hilarius Radix W. C Lanny W Pandjaitan Lanny W. Pandjaitan Maria Angela Kartwidjaja