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All Journal Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Semantik Jurnal Buana Informatika Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Luh Arida Ayu Rahning Putri
Universitas Udayana
Humanities Computer Science & IT Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Klasifikasi Kematangan Buah Manggis Dengan Algoritma Support Vector Machine (SVM) I Kadek Angga Kusuma Diatmika; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p21

Abstract

This research developed an automatic mangosteen fruit maturity classification system utilizing image processing techniques and machine learning algorithms. The proposed system employed the Support Vector Machine (SVM) classifier with feature extraction based on the Hue, Saturation, and Value (HSV) color space from mangosteen fruit images. A dataset consisting of 140 mangosteen fruit images, with 70 ripe and 70 unripe samples, was constructed. Preprocessing steps, including cropping and resizing, were applied to standardize the image dimensions. The RGB color images were converted to the HSV color, and the mean values of Hue, Saturation, and Value were extracted as features for classification. The SVM algorithm with a linear kernel was trained using these features to discriminate between ripe and unripe mangosteen fruits. Evaluation using a confusion matrix demonstrated the system's high classification accuracy of 96%, with satisfactory precision, and recall for both classes. The proposed system exhibits potential for application in the agricultural industry, enabling automated quality assessment, postharvest management, and maximizing the commercial value of mangosteen fruits. This technology can assist producers in rapidly and accurately classifying mangosteen fruits. 
Klasifikasi Kematangan Sayuran Pare dengan Metode KNN I Gede Yogananda Adi Baskara; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p23

Abstract

The bitter melon plant (Momordica charantia L) is a vegetable commodity that has commercial potential if cultivated on an agribusiness scale. The bitter melon plant products currently have quite a lot of consumers and have even entered supermarkets. However, the selection of bitter melon vegetables still uses human eye assessment which has the weakness of being subjective and inconsistent, so the level of accuracy is low. Based on these problems, researchers will create a system that is able to classify the level of maturity of bitter melon vegetables using HSV feature extraction with the KNN method at the classification stage and with the help of the Python programming language. In this research, 160 datasets will be used which are divided into 3 types of classes, namelcategy cooked bitter melon vegetables and raw bitter melon vegetables. The dataset is divided into two ories, namely 128 training data and 32 test data. The next stage is testing the data using the K-Nearest Neighbor method using the value k=3. From the test results, an accuracy rate of 88% was obtained. 
Analisis Penggunaan Metode MFCC Dalam Mendeteksi Emosi Pada Musik Indonesia I Komang Sutrisna Eka Wijaya; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i04.p21

Abstract

This research aims to develop a method for detecting emotions in Indonesian music using the MFCC method. The MFCC method is used to identify emotions in music by measuring acoustic features of music, such as tempo, pitch, and intensity. The study uses a dataset of 40 Indonesian music samples from various regions, which are analyzed to detect emotions. The confusion matrix is used to calculate the precision of emotion prediction. The results show that the MFCC method is effective in detecting emotions in Indonesian music. The research also highlights the importance of using a representative dataset to improve the accuracy of emotion identification in music. This study provides insights into the challenges and opportunities of using the MFCC method for emotion detection in Indonesian music. 
Klasifikasi Genre Musik Menggunakan Support Vector Machine Berdasarkan Spectral Features I Gusti Agung Ngurah Diputra Wiraguna; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i03.p20

Abstract

This research focuses on music genre classification based on spectral features and SupportVector Machine (SVM). Features such as Spectral Centroid, Spectral Rolloff, Spectral Flux, and Spectral Bandwidth are extracted from MP3 music audio. The dataset comprising 4 music genres is utilized for training and testing the system. The extracted spectral features are fed into the SVM classifier to predict the genre of test samples. Python and machine learning are both used in developing the system while the experimental results demonstrate the effectiveness of SVM in accurately classifying music genres based on the current extracted features. The proposed approach contributes to automated music genre classification systems, facilitating music organization, recommendation, and retrieval. This research promotes advancements in music information retrieval and enhances user experience in music-related applications. 
Co-Authors Agus Muliantara Anak Agung Istri Ngurah Eka Karyawati Anak Agung Istri Ngurah Eka Karyawati Ari Putra, I Kadek Riski Ariyawan, Made Dwi Artayani, Adis Luh Sankhya Bayu Yudistira Ramadhan Bhavanta, I Made Adika Christian Valentino Cokorda Pramartha Cokorda Rai Adi Pramartha Culio, Shelomita Putrinda Diatmika, I Kadek Angga Kusuma Diputra Wiraguna, I Gusti Agung Ngurah Dwi Payana, I Kadek Krisna Eka Wijaya, I Komang Sutrisna Enga Prinda Adu Gede Sudimahendra Genaldy Septianto Mbuik Giri, I Nyoman Yusha Tresnatama Gst. Ayu Vida Mastrika Giri Guna, Putu Wahyu Tirta Gusti Agus Sakah Aditia Gusto Gibeon Ginting I Dewa Agung Adwitya Prawangsa I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu I Gede Arta Wibawa I Gede Erwin Winata Pratama I Gede Santi Astawa I Gede Tendi Ariyanto I Gede Yogananda Adi Baskara I Gusti Agung Gede Arya Kadyanan I Gusti Agung Ngurah Diputra Wiraguna I Gusti Ayu Riyana Astarani I Gusti Ngurah Adhiwangsa Devananda I Gusti Ngurah Anom Cahyadi Putra I Gusti Ngurah Febri Ananda Krisna I Kadek Angga Kusuma Diatmika I Kadek Krisna Dwi Payana I Kadek Riski Ari Putra I Ketut Adian Jayaditya I Ketut Gede Suhartana I Komang Arya Ganda Wiguna I Komang Kumara Saduadnyana I Komang Sutrisna Eka Wijaya I Made Suma Gunawan I Made Teja Sarmandana I Made Widiartha I Made Widiartha I Putu Gede Hendra Suputra I Putu Indie Surya Jayadi I Putu Rama Anadya I Wayan Gede Adi Palguna I WAYAN SANTIYASA I Wayan Supriana Ida Ayu Gde Suwiprabayanti Putra Ida Bagus Ari Widhiana Ida Bagus Made Mahendra Julianti, Syelvia Kadek Cahya Dewi Kadek Vincky Sedana Kompiang Gede Sukadharma Luh Gede Astuti Matthew Novan Sidharta Ngurah Agus Sanjaya ER Ngurah Diputra Wiraguna, I Gusti Agung Ni Putu Sintia Wati Ni Putu Subhasini Dewi Sukma Ni Wayan Windayani Palguna, I Wayan Gede Adi Pradnyaditha, Kadek Yoga Vidya Pratama, I Gede Erwin Winata Putu Audy Cipta Pratiwi Putu Risky Andrean Rizky, Muhammad Firyanul Satria Mahagangga, Made Dhandy Sidharta, Matthew Novan Sri Hartati Sudimahendra, Gede Wahyu Ramadhan Wayan Gede Suka Parwita Wayan Kiki Oktalao Wikardiyan, Aditya Yana, Santa Yasa, I Gede Cahya Purnama