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I Gede Surya Rahayuda
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Sekretariat JNATIA Gedung FMIPA Lantai 1, Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
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Bali
INDONESIA
Jurnal Nasional Teknologi Informasi dan Aplikasinya
Published by Universitas Udayana
ISSN : 29863929     EISSN : 30321948     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) adalah jurnal yang berfokus pada teori, praktik, dan metodologi semua aspek teknologi di bidang ilmu komputer, informatika dan teknik, serta ide-ide produktif dan inovatif terkait teknologi baru dan teknologi informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah diterbitkan. JNATIA (Jurnal Teknologi Informasi dan Aplikasinya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 339 Documents
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Opposition-Based Dynamic Grey Wolf Optimizer untuk Eksplorasi dan Eksploitasi I Gede Abhijana Prayata Wistara; I Gusti Agung Gede Arya Kadyanan
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.p15

Abstract

Grey Wolf Optimizer (GWO) is a prominent swarm intelligence algorithm, but it exhibits significant drawbacks, including a strong search bias towards the origin and premature convergence on complex multimodal landscapes. To address these limitations, this paper proposes a novel hybrid algorithm, the Opposition-Based Dynamic Grey Wolf Optimizer (OB-DGWO). The proposed method integrates a dynamic prey estimation strategy to mitigate search bias with an Opposition-Based Learning (OBL) mechanism to enhance population diversity and global exploration capabilities. The performance of OB-DGWO was rigorously evaluated against the conventional GWO, GWO with OBL, and the dynamic GWO (DGWO) using standard unimodal and multimodal benchmark functions. Experimental results demonstrate that the proposed OB-DGWO exhibits superior robustness. It successfully overcomes the failure of DGWO on problems with optima at the origin, while demonstrating improved accuracy and consistency on complex multimodal functions where the standard GWO fails. The findings indicate that OB-DGWO provides a more balanced and reliable approach for solving diverse optimization problems
Klasifikasi Jenis Tari Bali Menggunakan Hyperparameter Tuning CNN dan Transfer Learning ResNet18 I Gede Surya Diva Ananda; Ida Ayu Gde Suwiprabayanti Putra; Ida Bagus Gede Sarasvananda
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.p16

Abstract

Balinese dance is a cultural heritage that carries deep philosophical and historical values. In the field of computer vision, image classification of Balinese dance poses a unique challenge due to similarities in movement patterns, costumes, and backgrounds. This research compares two approaches to Balinese dance image classification: a Convolutional Neural Network (CNN) model enhanced with hyperparameter tuning via grid search, and a transfer learning model based on ResNet18. The dataset consists of seven dance classes, each with approximately 240 to 254 images, which are balanced to ensure fair evaluation. The CNN model's hyperparameters, including learning rate, dropout rate, batch size, and optimizer, were optimized using grid search, achieving a top training accuracy of 96.51% and validation accuracy of 72.30%. Meanwhile, the ResNet18 model, leveraging transfer learning from ImageNet, outperformed with a training accuracy of perfect 100% and a validation accuracy of 96.79%. The experimental results suggest that transfer learning significantly boosts performance compared to CNNs trained from scratch, even when carefully tuned. These findings highlight the practical advantage of leveraging pre-trained models in cultural heritage preservation tasks through computer vision.
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.
Efektifitas Hasil Analisis Sentimen Aplikasi SIGNAL Berbasis Lexicon-Based dan Random Forest Nayra Zanetti Windy Rahmantya; I Gusti Ngurah Anom Cahyadi Putra
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.p18

Abstract

SIGNAL (Samsat Digital Nasional) is a digital innovation developed by the Indonesian National Police to simplify vehicle tax payments, STNK validation, and other administrative services online. As the number of users grows, various user opinions are reflected in the form of reviews on the Google Play Store. The research adopts a lexicon-based approach by extracting positive and negative keywords directly from the dataset to classify sentiments in user-generated reviews. A sentiment label is assigned based on the frequency and dominance of positive or negative terms within each review. To evaluate the effectiveness of this lexicon-based classification, the Random Forest machine learning algorithm is employed as a benchmark. These findings indicate that the lexicon-based approach, when built from domain-specific vocabulary, can effectively classify sentiment with minimal computational resources while maintaining competitive performance. This research contributes to the development of lightweight sentiment analysis systems and highlights the potential of hybrid methods for enhancing accuracy.
Penggunaan Deep Learning dengan Convolutional Neural Network untuk Klasifikasi Kondisi Buah Tomat Muhammad Ferry Saputra; I Gusti Agung Gede Arya Kadyanan
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.p19

Abstract

Kualitas dan tingkat kematangan tomat merupakan faktor krusial yang menentukan nilai pasar dan masa simpannya. Hal ini menjadi tantangan signifikan bagi para petani yang seringkali masih mengandalkan proses penyortiran manual yang bersifat subjektif. Penelitian ini mengusulkan sebuah solusi otomatis menggunakan deep learning untuk meningkatkan efisiensi dan objektivitas penyortiran. Kami mengembangkan sebuah model Convolutional Neural Network (CNN) untuk mengklasifikasikan kondisi tomat ke dalam tiga kategori berbeda: matang, mentah, dan busuk. Dataset yang digunakan bersumber dari Kaggle, terdiri dari 300 gambar untuk pelatihan dan 60 gambar untuk pengujian, dengan distribusi yang seimbang di ketiga kelas tersebut. Arsitektur CNN dioptimalkan menggunakan metode RandomSearch pada ruang hyperparameter yang telah didefinisikan. Model terbaik yang dihasilkan berhasil mencapai akurasi keseluruhan sebesar 80% pada data pengujian. Analisis dari confusion matrix menunjukkan performa yang sangat baik dalam mengidentifikasi tomat matang (recall 100%) dan mentah (recall 95%), meskipun menghadapi tantangan dalam mengidentifikasi tomat busuk secara tepat (recall 45%). Hasil ini menunjukkan potensi besar dari sistem berbasis CNN sebagai alat yang mudah diakses dan efektif bagi para petani untuk mengotomatisasi pengendalian kualitas, mengurangi kerugian pascapanen, dan meningkatkan produktivitas secara keseluruhan
Perancangan Ulang Tampilan Elemen “Liquid Glass” pada iOS 26 dengan Metode Design Thinking I Wayan Brahmani Novus Abasan; I Gede Santi Astawa
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.p20

Abstract

As a pioneer in mobile interface design, Apple introduced a unique and elegant element called “Liquid Glass”. Although this element is considered aesthetically attractive, many users and developers complain about usability and text readability issues. This study offers a solution by redesigning “Liquid Glass” to achieve high usability and readability. Using the Design Thinking method, this study identified the main problems users face in using this element. From a survey of 15 iOS users, this redesign scored 81 (grade B) on the System Usability method.
Analisis Sentimen Komentar Universitas di Indonesia Menggunakan Metode Naive Bayes dan SVM Benediktus Silaban; Ida Ayu Gde Suwiprabayanti Putra
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.p21

Abstract

This research aims to analyze public sentiment towards Universitas Indonesia based on user reviews collected from Google Maps. In the era of digital information, online reviews serve as invaluable feedback channels, significantly influencing an institution's reputation and prospective student choices. This study employs a sentiment analysis approach to automatically classify reviews into positive, negative, and neutral categories. The methodology involves several key stages: data collection from Google Maps, comprehensive text preprocessing (including cleaning, tokenization, stopword removal, and stemming), and feature extraction using Term FrequencyInverse Document Frequency (TF-IDF). For classification, two prominent machine learning algorithms, Support Vector Machine (SVM) and Multinomial Naive Bayes, are utilized. Both models are trained and evaluated on the processed dataset to assess their performance in accurately classifying sentiment. A comparative analysis will be conducted to highlight the strengths and weaknesses of each algorithm in this specific context. The findings are expected to provide Universitas Indonesia with actionable insights into public perception, identify areas for improvement, and contribute to the understanding of sentiment analysis applications in educational contexts.
Rekomendasi Video Game Menggunakan Metode Collaborative Filtering dengan K-NN Kendrick Raphael Ticoalu; Ida Ayu Gde Suwiprabayanti Putra
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.p22

Abstract

As the digital age progresses, the more technology affects various aspects in our lives for example entertainment through video games. A problem arises where there are too many video games to choose from, so there is a need to find methods to narrow down the choices. This study implements a collaborative filtering-based video game recommendation system to analyze user preferences based on playtime data. The system processes user-game interaction data from a secondary dataset containing 14.3 million players and 50.9 million games, constructing a sparse matrix to map user playtime behavior. By identifying similar users through kNN, the system recommends games frequently played by users with comparable preferences. Evaluation on 100 sample users achieved an impressive mean precision of 88.12%, indicating that most recommended games were among the users' top 20 most-played titles. This study hopes to further enable people in finding more fun experiences in their lives.
Analisis Sentimen Pengguna X dan YouTube Terhadap Carmen Hearts2Hearts Menggunakan Metode IndoBERT Fellycia Caroline; Syalsabilla Valentisyesa; Muhammad Rizky Pribadi
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.p23

Abstract

The rapid growth of social media has increased the amount of public opinion expressed online, particularly on platforms such as X and YouTube, where users actively share their views regarding public figures and entertainment topics. This study aims to analyze public sentiment toward Carmen, a member of the K-pop group Hearts2Hearts, using the IndoBERT model for sentiment classification. Data were collected from X and YouTube comments through web scraping techniques and combined into a single dataset to obtain more diverse opinions. The research process involved several stages, including text preprocessing, manual sentiment labeling, dataset splitting, model training, and evaluation. The preprocessing stage consisted of duplicate data removal, case folding, noise removal, tokenization, stopword removal, and stemming to improve data quality before classification. The dataset was categorized into three sentiment classes: positive, neutral, and negative, then divided into training and testing data using an 80:20 ratio. The IndoBERT model was trained using transformer-based deep learning to understand the context of Indonesian-language text more effectively. Evaluation results showed that the model achieved an accuracy of 72.41%, precision of 75.82%, recall of 72.41%, and F1-score of 71.15%, indicating that IndoBERT performs effectively in classifying sentiment on Indonesian social media data despite challenges such as informal language and ambiguous expressions.

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