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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sains dan Teknologi Jurnal Buana Informatika Jurnal Ilmiah Kursor JUTIK : Jurnal Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal RESISTOR (Rekayasa Sistem Komputer) WIDYA LAKSANA Seminar Nasional Aplikasi Iptek (SINAPTEK) Jurnal Pengabdian Informatika (JUPITA) Paradharma: Jurnal Aplikasi IPTEK Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Wayan Supriana
Universitas Udayana
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Sistem Rekomendasi Personalized Music dengan Metode Jenis Gaya Ida Ayu Tri Sabina Putri; I Wayan Supriana
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025
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.2025.v03.i02.p21

Abstract

The rapid growth of the digital era has led to an increase in online music platforms and music users. However, this abundance of choices has resulted in information overload, making it challenging for users to find their favorite music easily. Thus, the aim of this study was to propose an effective music recommendation method that considers user attributes, music genres, and temporal dynamics. The research utilized a collaborative filtering approach, leveraging user data and music preferences to generate relevant recommendations. Thus, the proposed method aimed to address the issue of information overload and provide more personalized and accurate music recommendations. The results of the recommendation experiments demonstrated the positive effects of integrating these three perspectives. The recommendations generated from this approach were able to assist users in finding their preferred music more conveniently, thereby enhancing user satisfaction with online music platforms. 
Implementasi Algoritma Yolo untuk Deteksi Kebusukan pada Sayur Kembang Kol Alexander Ibrahim; I Wayan Supriana
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p19

Abstract

This research utilizes the YOLOv8 algorithm to detect spoilage in cauliflower vegetables. Image data was collected from Google, processed using Roboflow, and tested using Google Colab. The study results indicate an accuracy of 59%, recall of 58%, and MAP of 60%. The YOLOv8 algorithm significantly contributes to image recognition and visual data processing. Additionally, the article discusses the application of the YOLOv8 algorithm for object detection in 360-degree panoramic images. The training process was conducted to recognize objects in the images, and evaluation was performed using a confusion matrix and mAP50. The evaluation results demonstrate the model's good performance in object recognition. Several references cited in the article are also included. 
Case-Based Reasoning untuk Diagnosis Penyakit Campak Menggunakan Metode Bayesian Model I Wayan Adhi Surya Gemilang; I Wayan Supriana
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.p16

Abstract

In the medical world, measles is an infectious disease that has long been known and is still a global health problem. Measles is divided into two main types, rubeola measles and rubella measles.This study is conducted to build a diagnose system for measles disease with CaseBased Reasoning (CBR). Case-based reasoning (CBR) is a method in artificial intelligence that solves problems by analyzing solutions from similar cases that have occurred before. CBR can eliminate the need to extract models or sets of rules. Knowledge acquisition in CBR is based on a collection of experiences or previous cases. The Bayesian model is used as indexing to find the type of measles CBR in this study. The test was carried out by using 35 cases that were stored in case base and 20 case bases serve as a new case. 
Perancangan UI/UX Website Pengenalan Budaya Bali dengan Metode User Centered Design Ida Ayu Made Putri Santiani; I Wayan Supriana
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.p17

Abstract

Culture is a collection of beliefs and traditions passed down from generation to generation by a group of people. The island of Bali is one of the places rich in culture and traditions. Each district in Bali has its own unique culture and traditions. This cultural diversity and tradition serve as an attraction for domestic and international tourists who want to visit the island of Bali. To help tourists get to know more about Balinese culture and find places to eat according to their preferences, this research aims to develop a website-based application. This website is developed using the User-Centered Design method. This website can be a solution for tourists who want to visit Bali to see local traditions and culture or enjoy the typical dishes found on the island of Bali. 
Analisis Perbandingan Kualitas Citra Hasil Steganografi DCT dan LSB Berdasarkan Parameter RMSE dan PSNR I Putu Krisnawan Putra; I Wayan Supriana
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.p20

Abstract

Steganography is a technique of hiding secret messages in digital media, one of the digital media that is often used is images. Two commonly used steganography methods are DCT (Discrete Cosine Transform) and LSB (Least Significant Bit). This research aims to analyze and compare the image quality of DCT and LSB steganography results based on RMSE (Root Mean Square Error) and PSNR (Peak Signal to Noise Ratio) parameters. DCT and LSB methods are implemented on several images with several variations of secret messages. RMSE and PSNR values are calculated for each stego image and analyzed to see which method produces better image quality. The results show that the LSB method produces lower RMSE values and higher PSNR values compared to the DCT method on all tested images. This shows that the LSB method is significantly better in maintaining the quality of the stego image compared to the DCT method. 
Klasifikasi Ulasan Aplikasi TikTok Menggunakan Algoritma K-Nearest Neighbor dan Chi Square Sandrina Ferani Aisyah Putri; I Wayan Supriana
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 2 (2024): JNATIA Vol. 2, No. 2, Februari 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.i02.p16

Abstract

TikTok application has achieved extraordinary popularity among users around the world, which has been downloaded by more than 500 million users with 16 million reviews and received a rating of 4.4 out of 5 on the Google Play Store. In this study, we will analyze user sentiment towards the TikTok application reviews. These reviews can be a benchmark for users to find out information about user experience and become a race for application developers to improve performance or quality. For that we need a method to describe the reviewer efficiently so that it is easier to understand the reviewer. In this study, the authors used a comparison of the KNN algorithm with the effect of feature selection to carry out the classification. Classification of application reviews into two classes, positive reviews, and negative reviews. In this classification, it is found that using Chi Square feature selection can produce the highest accuracy, with k = 9 value of 86.22% whereas without Chi Square feature selection it only produces the highest accuracy with k = 11 value of 77.04%. 
Dampak Penggunaan Anotasi Penamaan yang Berbeda pada Kinerja NER I Made Widi Arsa Ari Saputra; I Wayan Supriana
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.p16

Abstract

In developing the NER model, naming annotations are used as an important part of the training process. The impact of using different naming annotations on NER performance has been a concern in the research community. So, the writer wants to once again, test the impact of using different naming annotations using the spaCy library on English documents. Using 2 naming schemes namely BIO and IOBES, using the spaCy model to get 0.96 accuracy for BIO and 0.95 for IOBES. 
Analisa Sistem Rekomendasi Konten Youtube Berdasarkan Durasi Menonton Menggunakan Content-Based Filtering I Gede Ngurah Wahyu Ananta; I Wayan Supriana
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.p16

Abstract

In today's era, the internet is a facility in social life that causes phobias, complete and necessary information is difficult to obtain again. However, how YouTube provides consistently using an algorithm designed for YouTube content recommendations which is an online video media can witness important moments instantly to individuals who are not on television media so that all users can get useful information and entertainment from website media. For some reason Youtube is used as social media with the highest user level from Instagram. Therefore, we make an experiment to categorize the right content to be a crucial factor in producing accurate and meaningful recommendations. In a system analysis, it recommends content on Youtube based on individual categories using the basic concept of the content based filtering algorithm and how it is implemented in the context of YouTube. The model training is carried out using the cosine similarity method which aims to compare the similarity between the contents of these representations. Evaluation of the model can provide insight into how effective the algorithm is in producing relevant recommendations. The steps in the recommendation system analysis are literature study, data collection, model training, and model evaluation by increasing understanding of content-based filtering algorithms. 
Analisis Trade-off Pendekatan Greedy dan Metaheuristic dalam Seleksi Fitur Terhadap Model Ensemble Anak Agung Gede Ngurah Ananda Wirasena; I Wayan Supriana; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p11

Abstract

The increasing volume and dimensionality of medical data pose challenges for effective machine learning model development. Feature selection techniques (FST) are crucial for improving model performance, computational efficiency, and interpretability. This study analyzes the trade-off between greedy and metaheuristic FST approaches in optimizing Decision Tree-based ensemble models. We compare Mutual Information-Sequential Backward Selection (MI-SBS) as a greedy method and Binary Grey Wolf Optimization (BGWO) as a metaheuristic method. FST fitness is evaluated using a Decision Tree Classifier with 5-fold cross-validation. Final classification performance is assessed using AdaBoost and XGBoost on three distinct medical datasets. Results indicate that MI-SBS offers faster feature selection and stable accuracy, often outperforming the baseline. BGWO, while slower in selection, achieves greater feature reduction, leading to significantly faster final model training at the cost of a minor accuracy decrease. This research provides insights into selecting appropriate FST based on desired trade-offs between computational efficiency and classification accuracy in health informatics.
Analisis Kekuatan Kata Sandi Berbasis Konteks Bahasa Indonesia Menggunakan Machine Learning Putu Dena Satwika Sandi; I Wayan Supriana
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p17

Abstract

The widespread reliance on password authentication is persistently undermined by users creating contextually weak passwords, a vulnerability often overlooked by standard, English-centric password strength meters. This research addresses this security gap by developing and evaluating a machine learning model specifically tailored for password strength analysis within the Indonesian linguistic context. We trained a Decision Tree classifier and benchmarked it against a robust XGBoost model using a dataset enriched with local passwords and contextual features, including a custom heuristic score and Levenshtein similarity to a comprehensive Indonesian dictionary. To overcome severe class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. While the XGBoost model achieved superior predictive performance, the most significant finding emerged from the feature importance analysis, which revealed that our custom heuristic score and the password's length were the two most dominant predictors. This study successfully validates that a context-aware machine learning approach can effectively analyze password strength, underscoring the critical need to integrate local linguistic patterns into security mechanisms and providing a robust foundation for developing more secure authentication systems for Indonesian users.
Co-Authors Adiartika, Made Harry Dananjaya Aditya Chandra Nugraha Agus Muliantara Aisyah Putri, Sandrina Ferani Alexander Ibrahim Anak Agung Gede Ngurah Ananda Wirasena Anak Agung Istri Dewi Lestari Anak Agung Istri Ngurah Eka Karyawati Anak Agung Istri Ngurah Eka Karyawati Ananta, I Gede Ngurah Wahyu Brahmantha, Gede Putra Aditya Cokorda Pramartha Cokorda Rai Adi Pramartha Devan Bramantya Dharmawangsa, I Gusti Ngurah Agung Eka Putra, I Gede Ariawan Gerson Feoh Giri, Gst. Ayu Vida Mastrika Giri, I Nyoman Yusha Tresnatama Gst. Ayu Vida Mastrika Giri Hanggara Diatha, Putu I Dewa Agung Cahya Putra I Gede Ariawan Eka Putra I Gede Ngurah Wahyu Ananta I Gede Putu Mahasatya Katyayana I Gede Santi Astawa I Gede Wilantara Jaya I Gusti Agung Gede Arya Kadyanan I Gusti Bagus Ngurah Agung Brian Wijaya I Gusti Ngurah Anom Cahyadi Putra I Ketut Agus Pranata Muliawan I Ketut Gede Suhartana I Ketut Gede Suhartana I Komang Ari Mogi I Made Arya Dwisada I Made Satria Bimantara I Made Satria Bimantara I Made Wahyu Purnama Putra I Made Widi Arsa Ari Saputra I Made Widiartha I Made Widiartha I Nyoman Adiputra I P G. Adiatmika I Putu Gede Hendra Suputra I Putu Gede Maysa Putra I Putu Krisnawan Putra I Wayan Adhi Surya Gemilang I WAYAN SANTIYASA I Wayan Wikananda Adikara Ida Ayu Gede Suwiprabayanti Ida Ayu Made Putri Santiani Ida Ayu Tri Sabina Putri Ida Bagus Gede Dwidasmara Ida Bagus Made Mahendra IWK Teja Sukmana Jeremi Herodian Abednigo Jhordi, Rafif Karlina Surya Witanto Kiki Dwi Prebiana Komang Arjuntama Satria Komang Dhiyo Yonatha Wijaya Krisphino Saputra Nurbidin Liana, Laurensia Luh Arida Ayu Rahning Putri Luh Gede Astuti Luh Gede Astuti Mahardika, I Gede Teguh Maheswara, Pande Putu Devo Punda Marissa Audina Mrs Megawati Muhammad Firdaus Zulkarnain Muliawan, I Ketut Agus Pranata Ni Kadek Rika Dwi Utami Ni Komang Santi Cahyani Ni Made Putri Wahyuni Ni Putu Dian Kartika Sari Ni Wayan Anti Andari Nyoman Putra Prasetya Wardhana Partamayasa, I Wayan Gede Prashanti, Ni Putu Vidya Vira Prebiana, Kiki Dwi Putra, I Made Wahyu Purnama Putra, I Putu Krisnawan Putra, IGN Anom Cahyadi Putu Dena Satwika Sandi Putu Nadya Putri Astina Putu Wida Gunawan Radhitya, Made Leo Raharja, Made Agung Saifulloh Rahman Sandrina Ferani Aisyah Putri Sang Putu Febri Wira Pratama Santiani, Ida Ayu Made Putri Saputra, I Made Widi Arsa Ari Saputra, I Made Widi Arsa Ari Saputra Simarmata, Ivan Luis subanar subanar Sudarmawan, I Komang Roni Surya Gemilang, I Wayan Adhi Susy Purnawati Tpoy, Getzbie Alfredo Utama, I Putu Yasa Utari, Ni Nyoman Grisyana Valentin Gea Affila Pradika Wahyu Ananta, I Gede Ngurah Wardhana, Nyoman Putra Prasetya Wati, Zulfazazalia Putri Candra Wiantari, Ni Wayan Zulfazazalia Putri Candra Wati