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All Journal Jurnal Sains dan Teknologi Jurnal Krisnadana
Ida Bagus Ary Indra Iswara
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Computer Science & IT Control & Systems Engineering Education

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Image Classification of Traditional Musical Instruments from East Nusa Tenggara Using CNN and VGG19 Marvin Gilbrand Adu; I Gede Totok Suryawan; Ida Bagus Ary Indra Iswara
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/tt9bdq83

Abstract

This research is motivated by the declining ability of the younger generation to recognize the physical forms of traditional musical instruments from East Nusa Tenggara (NTT). This study aims to apply a Convolutional Neural Network (CNN)- based Deep Learning algorithm to classify seven types of musical instruments: Sasando, Moko, Gong, Gendang, Foydoa, Jungga, and Knobe Oh. A dataset of 610 images was used, split 80% for training and 20% for testing, and subjected to preprocessing steps, including resizing and data augmentation. The performance of a Custom CNN architecture was compared against VGG19 (fine-tuning) across variations of learning rates (0.001, 0.0001, and 0.00001). The results showed that the Custom CNN achieved optimal performance at a learning rate of 0.0001, with a training accuracy of 91.63% and a validation accuracy of 82.50%. Meanwhile, the fine-tuned VGG19 model achieved 100% training accuracy and 93.33% validation accuracy. Confusion Matrix evaluation on the test data demonstrated that the best model achieved 100% accuracy, indicating that the system successfully extracts and recognizes the visual features of each instrument.
Design and Construction of a SaaS-Based Multi-tenant Information System for Drone Activity Recording and Inventory I Wayan Juli Budiarta; Ida Bagus Ary Indra Iswara; I Made Sukafona; Ni Wayan Sumartini Saraswati; I Putu Agus Eka Darma Udayana
JST (Jurnal Sains dan Teknologi) Vol. 15 No. 1 (2026): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jst-undiksha.v15i1.106703

Abstract

Drone flight activity and inventory recording in uncrewed aerial vehicle (UAV) service providers is still dominated by manual methods and separate local systems. This condition risks recording errors and hinders operational data integration and access. This study aims to develop a SaaS-based multi-tenant information system for drone activity and inventory recording. This type of research is Research and Development with a Rapid Application Development (RAD) design. The research subjects included one drone company owner to inform the formulation of system requirements, while the researcher conducted trials independently using an access load simulation. Data were collected through interviews, observations, and literature reviews, with Apache JMeter as the testing tool and the stress testing method. Data analysis was conducted descriptively and qualitatively. The results of the study show the system is stable at low to medium loads and remains functional at high loads despite performance degradation. The study's conclusions indicate that the system can replace manual recording in web-based drone operational management. This study provides an overview of the application of a multi-tenant SaaS architecture to support centralized drone flight and inventory data management, while ensuring security and data isolation between organizations.
Co-Authors I Gede Totok Suryawan I Made Sukafona I Putu Agus Eka Darma Udayana I Wayan Juli Budiarta Marvin Gilbrand Adu Saraswati, Ni Wayan Sumartini