Article Per Year (5 Year)
p-Index From 2021 - 2026
3.832
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Teknologi Informasi dan Pendidikan Jurnal Praksis dan Dedikasi Sosial Journal of Applied Data Sciences Pusaka : Journal of Tourism, Hospitality, Travel and Business Event Dharma: Jurnal Pengabdian Masyarakat
I Nyoman Gede Arya Astawa
Politeknik Negeri Bali
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Multilingual Parallel Corpus for Indonesian Low-Resource Languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almu’iini; Arya Astawa, I Nyoman Gede; Andika Dwiyanto, Felix
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3412

Abstract

Indonesia has an extraordinary number of languages, with more than 700 regional languages such as Javanese, Madurese, Balinese, Sundanese, and Bugis. Despite the wealth of languages, digital resources for these languages remain scarce, making the preservation and accessibility of digital languages a significant challenge. Research was conducted to address this gap by building a multilingual parallel corpus consisting of more than 150,000 phrase pairs extracted from Bible translations in five regional languages in Indonesia. Rigorous preprocessing, normalization, and Unicode tokenization were performed to improve data quality and consistency. The encoder-decoder architecture was a key focus in the development of the NMT model. Evaluation focused on forward and backward translation directions, which were measured using BLEU scores. The results show that forward translation consistently outperforms backward translation. The Indonesian Javanese model produced a score of 0.9939 for BLEU-1 and 0.9844 for BLEU-4, indicating a high level of translation quality. In contrast, reverse translation tasks, such as translating from Sundanese to Indonesian, presented significant challenges, with BLEU-4 scores as low as 0.3173. This illustrates the complexity of the translation system from Indonesian to local languages. If future research focuses on transformer-based models and incorporates additional linguistic parameters to enhance the accuracy of natural language processing (NLP) models for Indonesia's underrepresented regional languages, this work provides a dataset that can be utilized for that purpose.
Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions Kurniawan, Fachrul; Astawa, I Nyoman Gede Arya; Atmaja, I Made Ari Dwi Suta; Wibawa, Aji Prasetya
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3249

Abstract

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.
Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network Latumakulita, Luther Alexander; Arya Astawa, I Nyoman Gede; Mairi, Vitrail Gloria; Purnama, Fajar; Wibawa, Aji Prasetya; Jabari, Nida; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1082

Abstract

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.
Comparative Performance Analysis of Modified VGG16 and Slim-CNN for Arabica Coffee Bean Defect Classification Ardian, Yusriel; Astawa, I Nyoman gede Arya; Irawan, Novta Danyel; Pradnyana, I Putu Bagus Arya; Sulistyo, Agung
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3244.85-96

Abstract

Defect detection in Arabica coffee beans is a critical aspect of quality control, particularly for export-oriented commodities that require consistent visual standards and uniform quality across production batches. Black and partial-black defects are known to significantly affect market value, quality perception, and sensory characteristics. Meanwhile, manual inspection processes remain vulnerable to evaluator subjectivity and inter-operator inconsistency.This study aims to conduct a comparative analysis between a Modified VGG16 architecture and Slim-CNN for detecting these two defect categories using a deep learning-based Convolutional Neural Network (CNN) approach. The dataset consists of 4,080 high-resolution images of Arabica green coffee beans captured using a 24.2 MP macro camera under controlled lighting conditions to minimize shadows and visual distortion. To preserve the natural characteristics of the defects, minimal data augmentation was applied using cropping and 15-degree rotation techniques. The Modified VGG16 architecture was simplified by reducing the complexity of the fully connected layers, integrating batch normalization, and applying dropout to enhance training stability and computational efficiency. Slim-CNN was employed as a lightweight comparative model with fewer parameters and lower memory requirements, making it suitable for resource-constrained deployment scenarios. Four training schemes were evaluated using variations in learning rate and epoch number to assess configuration impacts on performance. Experimental results show that Modified VGG16 achieved the highest test accuracy of 86.7% at a learning rate of 0.001 with 3 epochs, demonstrating a strong balance between training and validation accuracy. Slim-CNN exhibited shorter training time and lower computational complexity, although with slightly lower classification accuracy compared to Modified VGG16. These findings highlight a trade-off between classification performance and computational efficiency in selecting CNN architectures for coffee bean defect detection. Although the results demonstrate potential for industrial automatic classification systems, further validation using larger datasets and more comprehensive evaluation schemes is required to improve model generalization. This study contributes to the development of a more measurable, adaptive, and efficient deep learning-based coffee quality inspection system to support agro-export industry requirements.
IMPLEMENTASI CCTV ONLINE UNTUK MENINGKATKAN PEMANTAUAN FASILITAS WARGA BANJAR SAMPALAN Rudiastari, Elina; Ari Dwi Suta Atmaja, I Made; Bagus Catur Bawa, I Gusti Ngurah; Triana Indah, Komang Ayu; Ayu Sukerti, Gusti Nyoman; Arya Astawa, I Nyoman Gede
Jurnal Praksis dan Dedikasi Sosial Vol. 7 No. 1 (2024)
Publisher : Universitas Negeri Malang

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

Abstract

IMPLEMENTATION OF ONLINE CCTV TO IMPROVE MONITORING OFBANJAR SAMPALAN RESIDENTS' FACILITIES Sampalan Hamlet is a tourist destination where many facilities and infrastructure have begun to be built. The problems faced by the Sampalan Traditional Banjar are that there is no supervision of the routes around the Banjar area, there is no documentary evidence that can be used to analyze accidents that occur around the Banjar, there is no supervision to avoid acts of theft at the temple and there is no supervision of the social activities of residents which was carried out in Banjar. The solution offered in this community service activity for the problems experienced is the installation of CCTV. This service activity aims to help access monitoring and supervision of the Banjar area to increase the security and comfort of residents and visiting tourists. This method of implementing community service activities is implemented into three main stages, namely preparation, implementation and evaluation. The results of the evaluation through a questionnaire resulted in 71.4 percent of residents stating that the installed CCTV was very useful, and 50 percent of residents stated that the presence of CCTV greatly facilitated monitoring and supervision around the Banjar. Dusun Sampalan merupakan destinasi wisata dimana fasilitas serta infrastruktur sudah mulai banyak dibangun. Permasalahan yang dihadapi Banjar Adat Sampalan yaitu belum adanya pengawasan terhadap jalur disekitar areal Banjar, tidak adanya bukti dokumentasi yang dapat digunakan untuk menganalisa peristiwa kecelakaan yang terjadi di sekitar Banjar, tidak adanya pengawasan untuk menghindari tindakan pencurian di pura serta tidak adanya pengawasan terhadap aktivitas sosial warga yang dilakukan di Banjar. Solusi yang ditawarkan dalam kegiatan pengabdian kepada masyarakat ini untuk permasalahan yang dialami adalah pemasangan CCTV. Tujuan dari kegiatan pengabdian ini adalah membantu akses monitoring dan pengawasan areal Banjar sehingga meningkatkan keamanan dan kenyamanan warga serta wisatawan yang berkunjung. Metode pelaksanaan kegiatan pengabdian kepada masyarakat ini diimplementasikan menjadi tiga tahapan utama yaitu persiapan, pelaksanaan dan evaluasi. Hasil evaluasi melalui kuisioner menghasilkan 71,4 persen warga menyatakan CCTV yang terpasang tersebut sangat bermanfaat serta 50 persen warga menyatakan bahwa adanya CCTV sangat mempermudah monitoring dan pengawasan di sekitar Banjar.
Peningkatan jalur komunikasi perangkat desa dalam bentuk perluasan akses hotspot pada kantor Desa Sibetan, Kecamatan Bebandem, Kabupaten Karangasem Sukerti, Gusti Nyoman Ayu; Atmaja, I Made Ari Dwi Suta; Bawa, I Gusti Ngurah Bagus Catur; Indah, Komang Ayu Triana; Rudiastari, Elina; Astawa, I Nyoman Gede Arya
Jurnal Praksis dan Dedikasi Sosial Vol. 8 No. 1 (2025)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um022v8i12025p97-106

Abstract

Improving communication channels for village officials by expanding hotspot access in the Sibetan Village administration, Bebandem Subdistrict, Karangasem Regency Sibetan Village is one of the villages located in the Bebandem Subdistrict, Karangasem Regency. Based on survey results, Sibetan Village faces limited access to the internet and hotspots, which slows down administrative processes and public services at the village office. The village officials also experience difficulties in accessing fast and accurate information. In response to these issues, the Computer Network Administration Study Program from the Information Technology Department carried out a community service activity on May 28-29, 2024. The activity involved expanding the hotspot network around the village office to cover a wider area and provide stable and fast Internet access. Additionally, the village officials were trained on the management and maintenance of the hotspot network. The purpose of this activity was to improve communication capabilities and information access for village officials and the general public, making public services in Sibetan Village more effective. The activity was evaluated through user satisfaction surveys using questionnaires and interviews. Based on the survey results from 50 respondents, 50 percent stated that the initiative was implemented appropriately, and 60 percent reported that the expansion of the hotspot made it easier for village officials, especially in administrative services. Interviews with the Head of Sibetan Village indicated that the village officials felt supported by the hotspot expansion initiative. Desa Sibetan merupakan salah satu desa yang terletak di Kecamatan Bebandem, Kabupaten Karangasem. Berdasarkan hasil survei yang dilakukan, Desa Sibetan memiliki keterbatasan akses internet dan hospot sehingga memperlambat proses administrasi dan pelayanan publik di kantor desa serta perangkat desa mengalami kesulitan dalam mengakses informasi yang cepat dan akurat. Mengacu pada permasalahan tersebut, Program Studi Administrasi Jaringan Komputer Jurusan Teknologi Informasi melakukan kegiatan pengabdian masyarakat yang dilaksanakan pada pada tanggal 28-29 Mei 2024 dalam bentuk perluasan jaringan hotspot di sekitar kantor desa untuk mencakup area yang lebih luas dan menyediakan akses internet yang stabil dan cepat. Selain itu, perangkat desa diberikan pelatihan terkait pengelolaan dan pemeliharaan jaringan hotspot. Tujuan dari kegiatan ini adalah meningkatkan kemampuan komunikasi dan akses informasi bagi perangkat desa serta masyarakat secara umum serta layanan publik di Desa Sibetan menjadi lebih efektif. Evaluasi kegiatan ini dilakukan dengan survei kepuasan pengguna menggunakan kuisioner serta wawancara. Berdasarkan hasil survei kepuasan pengguna terhadap 50 responden, 50 persen menyatakan kegiatan sudah diimplementasikan secara tepat serta 60 persen menyatakan adanya perluasan hotspot mempermudah kinerja perangkat desa terutama dalam hal pelayanan administrasi. Hasil wawancara terhadap Kepala Desa Sibetan menyatakan bahwa perangkat desa merasa terbantu dengan diadakannya kegiatan perluasan hotspot ini.
Performance Trade-offs of Synchronous and Asynchronous Replication in Patroni-based PostgreSQL Clusters I Putu Suwidnyana Putra; I Nyoman Gede Arya Astawa; Luh Gede Putri Suardani
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1129

Abstract

This research aims to evaluate the effectiveness of High Availability (HA) architectures and analyze the performance trade-offs of database replication modes in critical environments. The study employs an experimental method using a distributed cluster consisting of four virtualized nodes. PostgreSQL is used as the core database, orchestrated by Patroni and Etcd for failover management, with HAProxy as the load balancer. Performance was measured using pgbench with TPC-B standards under varying concurrent loads (10, 50, and 100 clients). The results demonstrate that the Patroni cluster successfully performed auto-failover with an average Recovery Time Objective (RTO) of under 30 seconds in normal conditions, which increased to 75 seconds during peak workloads due to resource contention. Benchmarking reveals that Asynchronous replication achieved a peak throughput of 247 ops/s, while Synchronous replication guaranteed absolute data integrity (RPO=0) but incurred a significant latency increase, reaching 21.36 seconds under a 30-client load due to 99.5% CPU saturation. This study concludes that the proposed architecture effectively eliminates Single Point of Failure (SPOF), providing a critical reference for system architects in balancing transactional speed and data consistency.
Face Images Classification using VGG-CNN Astawa, I Nyoman Gede Arya; Radhitya, Made Leo; Ardana, I Wayan Raka; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
A Novel Approach to Defect Detection in Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation and Model Optimization Ardian, Yusriel; Irawan, Novta Danyel; Sutoko, Sutoko; Astawa, I Nyoman Gede Arya
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian hyperparameter optimization to improve defect detectability and expedite the training period. During testing, the defined model demonstrated excellent efficiency in defect detection, with a 97.29% confidence level, which was higher than that of the modified VGG16 and Slim-CNN models. The goal of the second optimization was an improvement of the practical application of the model. In terms of the time it takes for a model to be trained, approximately 30% of the time was saved. These findings present a consistent and effective way for the mass production processes of coffee to have quality control procedures automated. The model's ability to detect defects in other agricultural items makes it attractive, thus serving as a practical example of how AI can impact effective management in the inspection processes. The research further enriches the study of deep learning applications in agriculture by demonstrating how to efficiently address specific defect detection problems through an optimized convolutional neural network model.
Social Media Mining with Fuzzy Text Matching: A Knowledge Extraction on Tourism After COVID-19 Pandemic Manuaba, Ida Bagus Putra; Sentana, I Wayan Budi; Astawa, I Nyoman Gede Arya; Suasnawa, I Wayan; Pradnyana, I Putu Bagus Arya
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Social media mining is an emerging technique for analyzing data to extract valuable knowledge related to various domains. However, traditional text matching techniques, such as exact matching, are not always suitable for social media data, which can contain spelling mistakes, abbreviations, and variations in the use of words. Fuzzy matching is a text matching technique that can handle such variations and identify similarities between two texts, even if there are differences in spelling or phrasing. The gap in existing research is the limited use of fuzzy matching in social media mining for tourism recovery analysis. By applying fuzzy matching to social media data related to COVID-19 and tourism recovery, this research seeks to bridge this gap and extract valuable insights related to the impact of the pandemic on tourism recovery. We manually retrieved 19,462 Twitter records and differentiated the data sources using four diver parameters to indicate data related to the impact of COVID-19 on the tourism industry, such as the economy, restrictions, government policies, and vaccination. We conducted text mining analysis on the collected 7,352 words and identified 25 highly recommended words that indicated COVID-19 recovery from a tourism perspective. We separated the four words representing the tourism perspective to perform fuzzy matching as a dataset. We then used the inbound dataset on the fuzzy matching process, with the 7,352-word data collected from the text mining process. The matching process resulted in 18 words representing COVID-19 recovery from a tourism perspective.
Co-Authors Agung Sulistyo Aji Prasetya Wibawa Andika Dwiyanto, Felix Ardina, Cening Ari Dwi Suta Atmaja, I Made Atmaja, I Made Ari Dwi Suta Atmaja, I Made Ari Dwi Suta Atmaja Ayu Sukerti, Gusti Nyoman Bagus Catur Bawa, I Gusti Ngurah Bawa, I Gusti Ngurah Bagus Catur Dessy Hariyanti, NK Didik Dwi Prasetya Dwiyanto, Felix Andika Fachrul Kurniawan Fadhli Almu’iini Ahda Fajar Purnama Felipe P. Vista IV Felix Andika Dwiyanto Frangky J. Paat Gede Sukadarmika Gusti Nyoman Ayu Sukerti Gusti Nyoman Ayu Sukerti, Gusti Nyoman Ayu I Dewa Gede Ary Subagia I ketut Gede Darma Putra I Made Oka Widyantara I MADE SUDARMA I Putu Astawa I Putu Bagus Arya Pradnaya I Putu Bagus Arya Pradnyana I Putu Sutawinaya I Putu Suwidnyana Putra I Wayan Budi Sentana I Wayan Budi Sentana, I Wayan Budi I Wayan Raka Ardana I Wayan Suasnawa I Wayan Suasnawa Ida Bagus Putra Manuaba IGAK Cathur Adhi Irawan, Novta Danyel Islam, Noorul Jabari, Nida Kamarudin, Nur Diyana Karim, Irwan Komang Ayu Triana Indah Linawati Linawati Luh Gede Putri Suardani Luther Latumakulita Mairi, Vitrail Gloria Ngr. Putu Raka Novandra Asta Pradnyana, I Putu Bagus Arya Purnama, Ida Bagus Irawan Putu Gde Sukarata Radhitya, Made Leo Rudiastari, Elina Rukmi Sari Hartati Saroyo Saroyo Sirait, Hasanuddin Suasnawa, I Wayan Sukarata, I Putu Gde Sulistyo, Danang Arbian Sutoko, Sutoko Triadi, Wilfridus Bambang Triana Indah, Komang Ayu Yusriel Ardian