Article Per Year (5 Year)
p-Index From 2020 - 2025
6.31
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Gst. Ayu Vida Mastrika Giri
Department Of Computer Science, FMIPA, Universitas Udayana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 56 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 56 Documents
Search

 2   3   4   5   6 
Cover & Table of Contents JELIKU Vol. 13 No. 1 Gst Ayu Vida Mastrika Giri
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cover & Table of Contents JELIKU Vol. 12 No. 3 Gst Ayu Vida Mastrika Giri
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques Mastrika Giri, Gst Ayu Vida; Radhitya, Made Leo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.96665

Abstract

 Electroencephalogram (EEG) records brain activity as electrical currents to discern emotions. As interest in human-computer emotional connections rises, reliable and implementable emotion recognition algorithms are essential. This study classifies EEG waves using machine and deep learning. A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. The models were assessed for accuracy, precision, recall, and F1-Score. SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. SVM and CNN surpassed other approaches in accuracy, precision, recall, and F1-Score. The research shows that machine learning and deep learning can classify EEG signals to identify emotions. High accuracy results, especially from SVM and CNN, suggest these models could be used in emotion-aware human-computer interaction systems. This study adds to EEG-based emotion classification research by revealing model selection and parameter tweaking strategies for better categorization.
Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases Mastrika Giri, Gst. Ayu Vida; Musdar, Izmy Alwiah; Angriani, Husni; Taruk, Medi
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.111

Abstract

This study explores the application of machine learning techniques in the agricultural domain, focusing on the classification of two common diseases in mango leaves: Powdery Mildew and Sooty Mould. Utilizing the MangoLeafBD dataset, the research employs a Gradient Boosting Classifier, enhanced with mean shift image segmentation and Hu moments for feature extraction. The performance of the model was rigorously evaluated through 5-fold cross-validation, yielding insights into its accuracy, precision, recall, and F1-score. The results demonstrate moderate success, with the highest accuracy and precision observed in the initial fold, indicating the model's potential for reliable disease identification. The study addresses the challenge of distinguishing between diseases with similar symptomatic appearances, offering a novel, data-driven approach for disease management in mango cultivation. This research contributes to the growing field of precision agriculture, highlighting the potential of machine learning in enhancing disease diagnosis and treatment strategies, thus supporting sustainable agricultural practices.
Deteksi Pneumonia Dengan Ekstraksi Fitur Gray-Level Co-occurrence Matrix (GLCM) Dan Support Vector Machine (SVM) Arianata Putra, I Gusti Bagus Sutha; Mastrika Giri, Gst. Ayu Vida
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, 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.p08

Abstract

Pneumonia, a prevalent lung disease globally, poses significant challenges in accurate diagnosis despite its severity. This paper proposes a novel approach leveraging Support Vector Machine (SVM) classification and Gray-Level Co-occurrence Matrix (GLCM) analysis on chest X-ray images to aid in pneumonia diagnosis. By extracting pneumonia-indicative features from digital X-ray images using Gray-Level Co-occurrence Matrix (GLCM) and employing Support Vector Machine (SVM) for classification, the study aims to enhance pneumonia diagnosis effectiveness, particularly crucial in regions with limited healthcare resources. The proposed method focuses on identifying characteristic patterns indicative of pneumonia in chest X-ray images and distinguishing between normal and pneumonia-affected images based on GLCM-extracted features. Furthermore, the study evaluates the impact of hyperparameter tuning using grid search on the proposed diagnostic system's performance, including accuracy, sensitivity, and specificity. By achieving these objectives, the research aims to contribute significantly to the development of more accurate and effective diagnostic tools for pneumonia, especially in resource-constrained areas. Keywords: Gray-Level Co-occurrence Matrix (GLCM), Machine Learning, Pneumonia, Support Vector Machine, X-Ray
Implementasi Algoritma KNN untuk Memprediksi Performa Siswa Sekolah Dhita, I Made Ryan Prana; Mastrika Giri, Gst. Ayu Vida
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the factors that influences students' graduation rates is their performance in learning. Predicting graduation rates based on student performance has the benefit of analyzing academically underperforming students and providing support to students who face difficulties in the learning process. There are several factors to consider in predicting students' graduation rates, such as academic grades, attitudes, and social factors. However, these factors alone are not sufficient to effectively predict students' performance, and educators also struggle to identify which factors affect students' performance.To predict the performance of school students, the K-Nearest Neighbor (KNN) method is utilized. The K-Nearest Neighbor method is often used in classifying students' performance due to its simplicity and ability to produce significant and competitive results. In this research, the prediction of students' graduation rates is carried out using the KNN method.The results of implementing the prediction of students' performance using the KNN method can serve as a reference for students to improve their achievements and assist educators in considering future teaching materials. Keywords: KNN, K-Nearest Neighbor, Students Performance, Student
Klasifikasi Lagu Daerah di Indonesia dengan Metode Machine Learning Vida Mastrika Giri, Gst Ayu; Leo Radhitya, Made
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Keunikan dari lagu daerah yang mencerminkan daerah asal adalah diiringi dengan alat musik daerah dan dinyanyikan dengan bahasa daerah masing-masing. Ciri khas lagu daerah dapat dilihat dari fitur-fitur musik seperti spectral centroid dan Mel Frequency Cepstral Coefficients (MFCC) karena dimainkan dengan alat musik berbeda dan memiliki timbre yang berbeda pula. Dengan menggunakan fitur-fitur musik tersebut dan algoritma machine learning, lagu-lagu daerah dapat diklasifikasi berdasarkan daerah asalnya. Pada penelitian ini digunakan sebuah dataset lagu daerah Indonesia yang bernama IRSD: Indonesian Regional Song Dataset yang terdiri dari 67 fitur musik yang diantaranya adalah MFCC, energy, dan spectral centroid dari 500 lagu daerah dari 10 provinsi di Indonesia. Metode machine learning yang akan digunakan untuk klasifikasi adalah SVM dan K-NN untuk menghasilkan nilai klasifikasi yang baik dengan waktu eksekusi yang cepat. Dengan menggunakan nilai K=3 dan 5-fold cross validation, metode K-NN menghasilkan nilai akurasi 0,69. Klasifikasi dengan metode SVM menggunakan kernel RBF dan 5-fold cross validation menghasilkan nilai akurasi 0,73. Pada penelitian kali ini, metode SVM dapat mengklasifikasi lagu daerah lebih baik daripada metode K-NN. Keywords: K-Nearest Neighbor, klasifikasi, lagu daerah Indonesia, machine learning, Support Vector Machine
Perancangan Sistem Steganografi Berbasis Transformasi Wavelet Diskrit Terintegrasi Algoritma Rijndael dan QR-Code Pratama Putra, I Putu Rizky; Mastrika Giri, Gst. Ayu Vida
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, 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.p04

Abstract

The advancement of technology has been the primary driving force behind the transformative shifts across various domains of human life, spanning from the era of industrial revolution to the present digital age. Within the digital epoch, the pivotal role of information and communication technology in shaping the global societal framework is unequivocal. Nonetheless, the rapid progression of technology introduces novel challenges such as safeguarding personal data integrity and combating unauthorized access to individual information. Addressing these challenges entails the adoption of sophisticated techniques, including compression methodologies like Discrete Wavelet Transform (DWT), renowned for its efficacy in multimedia data compression with high rates. Furthermore, cryptographic algorithms such as Rijndael offer viable solutions to enhance data security through a series of encryption operations, encompassing substitution, permutation, and iterative rounds applied to each block. The amalgamation of DWT and Rijndael culminates in data representation via QR codes. Additionally, this research encompasses the development of a user interface design to facilitate the seamless implementation and utilization of the system, ultimately aiming to fortify data security effectively. Keywords: Discrete Wavelet Transform, multimedia data, Rijndael algorithm, QR-code, user interface
Pengembangan Sistem Monitoring Bimbingan Tugas Akhir Berbasis Website Vida Mastrika Giri, Gst Ayu
Jurnal Ilmu Komputer Vol 16 No 2 (2023): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2023.v16.i02.p08

Abstract

Tugas akhir adalah salah satu syarat yang dibutuhkan mahasiswa untuk menyelesaikan pendidikan sarjana. Sejak pandemi Covid-19 di tahun 2019, kegiatan bimbingan tugas akhir telah berubah dari harus dilakukan secara tatap muka hingga saat ini bimbingan tugas akhir dapat dilakukan dengan tidak bertatap muka langsung. Berbagai cara telah dilakukan oleh mahasiswa dan dosen pembimbing untuk mengatasi masalah bimbingan tugas akhir yang tidak dilaksanakan secara tatap muka, seperti menggunakan e-mail, platform chatting online, melakukan pertemuan online, atau berbagi dokumen di cloud storage. Dalam beberapa kasus, proses bimbingan tugas akhir tidak dapat dilakukan dengan baik karena tidak ada catatan kemajuan kegiatan bimbingan yang jelas. Dosen pembimbing tidak memiliki catatan bimbingan yang lengkap dan terkadang kesulitan menghubungi mahasiswa yang harus menyelesaikan tugas akhirnya. Sistem Monitoring bimbingan tugas akhir berbasis website yang dapat diakses secara online oleh mahasiswa dan dosen pembimbing dapat mengatasi permasalahan pencatatan kemajuan tugas akhir mahasiswa. Penelitian ini akan membahas proses pengembangan sistem bimbingan tugas akhir berbasis website di Program Studi Informatika Universitas Udayana.
ANALISA APLIKASI CLOUD BASE GUESTAPS UNTUK UPSELLING DAN BOOKING ENGINE PADA PT. GUESTPRO TEKNOLOGI INDONESIA I Made Ryan Prana Dhita; Gst. Ayu Vida Mastrika Giri; I Gede Arta Wibawa
Jurnal Pengabdian Informatika Vol. 3 No. 1 (2024): JUPITA Volume 3 Nomor 1, November 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Submitted: Revised: Accepted:1VOLUME xx NOMOR xx, BULANxx TAHUNxxxxANALISA APLIKASI CLOUD BASE GUESTAPS UNTUKUPSELLING DAN BOOKING ENGINE PADA PT. GUESTPROTEKNOLOGI INDONESIAI. M. R. P.Dhita1, G. A. V. M. Giri 2, dan I. G. A. Wibawa3ABSTRAKJurnal ini menjelaskan proses integrasi GuestApps dengan Property Management System (PMS) untukmeningkatkan efisiensi pengelolaan properti. Ini meliputi pengaturan produk dan layanan untuk MerchantHotel, seperti Room Dining, Spa, Things To Do, Voucher, dan Voucher Site, serta pengaturan untukMerchant Restaurant. Selain itu, jurnal ini menguraikan cara mengonfigurasi kebijakan (policy) yang berlakubagi tamu, termasuk kebijakan kamar (Room Policy) dan kebijakan pembatalan (Cancellation Policy). Padatahap lain, jurnal ini juga menjelaskan cara membuat dan mengelola tarif (Rates) dalam Booking Engine &Website. Ini mencakup pengaturan Room Type, yang melibatkan pengaturan kamar, tempat tidur, gambar,fasilitas, dan tarif (Room Rates). Selanjutnya, jurnal ini menjelaskan konfigurasi Room Rate Plan untukmengelola harga dan ketersediaan kamar. Seluruh proses ini dijelaskan dalam konteks penggunaanGuestApps untuk meningkatkan penjualan langsung dan efisiensi pengelolaan properti. Dengan pemahamanyang baik tentang langkah-langkah ini, properti dapat mengoptimalkan layanan kepada tamu danmemaksimalkan potensi pendapatan.
Co-Authors Agus Harjoko Agus Muliantara Al Habib Muhammad Anak Agung Istri Ngurah Eka Karyawati Andika Putra, Ida Bagus Angriani, Husni Arianata Putra, I Gusti Bagus Sutha Dhita, I Made Ryan Prana Edo Krishnanda Aditya Gede Agung Aji Andar Sakti Giri, I Nyoman Yusha Tresnatama Gusto Gibeon Ginting I Gede Arta Wibawa I Gede Arta Wibawa I Gede Diva Dwijayana I Gede Laksmana Yudha I Gede Santi Astawa I Gusti Ngurah Anom Cahyadi Putra I Ketut Gede Suhartana I Ketut Gede Suhartana I Komang Ari Mogi I Komang Ari Mogi I Made Ryan Prana Dhita I Made Widiartha I Made Yoga Mahendra I Putu Ananta Wijaya I WAYAN SANTIYASA I Wayan Supriana izmy alwiah musdar Jaya, I Nyoman Wiratma Kadek Nanda Banyu Permana Komang Arsa Wiguna Leo Radhitya, Made Luh Arida Ayu Rahning Putri Luh Ristiario Muhammad Arief Budiman Ngurah Agus Sanjaya ER Ni Ketut Intan Setiawati Ni Made Elvina Aryadhika Putri Ni Putu Novia Ardiyanti Ni Putu Suci Paramita Nyoman Hendradinata Dharma Pasha Renaisan Prasetya, Putu Rikky Mahendra Pratama Putra, I Putu Rizky Putu Audy Cipta Pratiwi Radhitya, Made Leo Raharja, Made Agung Siaka, Made Bayu Maha Krisna Sutanti, Putu Asri Sri Taruk, Medi Wiratama Putra, I Putu Andi Yoga, I Putu Harta