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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of ICT Research and Applications JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) International Journal of Engineering and Emerging Technology Indonesian Journal of Electrical Engineering and Computer Science Jurnal Bali Membangun Bali Makara Journal of Technology Jurnal Ilmiah Teknologi dan Komputer (JITTER) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I ketut Gede Darma Putra
Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana
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RANCANG BANGUN APLIKASI SISTEM PAKAR PENGENALAN INDIVIDU DENGAN ILMU PALMISTRI MENGGUNAKAN METODE FUZZY LOGIC DAN CERTAINTY FACTOR Arya Widyaningrat, Made Gunawan; Darma Putra, I Ketut Gede; Eka Pratama, I Putu Agus
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 1 No 2 (2020): JITTER, Vol.1, No.2, December 2020
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1001.646 KB) | DOI: 10.24843/jitter.v1i2.67801

Abstract

Sistem Pakar Pengenalan Individu Dengan Ilmu Palmistri dikembangkan untuk masyarakat dapat mengenal dirinya sendiri. Sistem Pakar Pengenalan Individu Dengan Ilmu Palmistri dapat menganalisa telapak tangan individu dan menerjemahkannya menjadi sifat-sifat, kondisi mental/psikologis yang dimiliki individu tersebut. Menurut penelitian telapak tangan sendiri unik karena telapak tangan berubah-ubah mengikuti beranjaknya umur individu itu sendiri. Metode yang digunakan untuk mendapatkan hasil perhitungan yang akurat, yaitu Metode Fuzzy Logic yang dikombinasikan dengan Metode Certainty Factor. Metode Fuzzy Logic berfungsi untuk mendapatkan nilai bobot dari user, sedangkan Metode Certainty Factor berfungsi untuk mendapatkan hasil akhir dari kombinasi nilai yang dimasukkan oleh pakar (zi) dan nilai yang dimasukkan oleh user (wi). Sistem Pakar Pengenalan Individu Dengan Ilmu Palmistri diimplementasikan berbasis web.
Multi Task Deep Learning with Transformer Encoder Decoder for Semantic Segmentation Indah, Komang Ayu Triana; Darma Putra, I Ketut Gede; Sudarma, Made; Hartati, Rukmi Sari
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.1978

Abstract

Visual understanding is one of the core elements of computer vision consisting of image classification, object detection, and segmentation. The system applies a multilayer process to obtain complex image and video understanding using deep learning methods to convert the images to text. Therefore, this study aimed to extract video in the form of frames followed by the application of Transformer and Inception V3 architectures to the image captioning process. The synchronization was based on Multi-task Deep Learning method developed by combining Convolutional Neural Network (CNN) system in the image area, Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in the sentence area, Caption Content Network (CCN), and Relational Network Context (RCN). Moreover, Transformer Encoder-Decoder architecture was used in the process of labeling and determining the relationships between objects. The results of the image-to-text conversion process were determined by comparing prospective translated text with one or more references. This was achieved using accuracy and loss validation tables to provide graphical comparisons between the number of epochs and losses. The test results showed that the validation data accuracy was 70.166% while the loss was 22,648% and this showed more epoch iterations led to greater validation accuracy.Keywords— Visual Understanding, Transformer, Encoder, Decoder
Intelligent Web-Based Application for Personalized Obesity Management Wijayakusuma, I Gusti Ngurah Lanang; Sudarma, Made; I Ketut Gede Darma Putra; Oka Sudana; Minho Jo
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9151

Abstract

Obesity is a serious global problem due to its association with various chronic diseases. This study explores the utilization of machine learning in particular deep learning technology to predict Body Mass Index (BMI) from individual photos to create an efficient solution for assessing obesity. Using the ResNet152 model and K-Fold Cross Validation, this application integrates filters on individual photos to improve prediction accuracy. The application was developed using React JS for the front end, PHP and MySQL for the backend and database management, and Python as the core of the machine learning system. The application that tested using blackbox method, to see all features is functioning and the web application prototipe is passed all the test scenario.
Automated Generation of Folklore Short Stories Using T5 Transformer Model Pirade, Evangelika; Darma Putra, I Ketut Gede; Singgih Putri, Desy Purnami
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10582

Abstract

High reading interest plays an important role in increasing knowledge and fostering a stronger literacy culture. With the growing access to information and technology, reading interest is also expected to improve through innovative and interactive platforms. However, traditional reading materials often fail to attract younger generations who are more engaged with digital content. To address this challenge, one of the efforts undertaken is the development of a modern platform that provides a collection of short stories enriched with cultural and educational values, tailored to appeal to contemporary readers. This study aims to design and implement a short story generation system using a Transformer-based language model, specifically T5 (Text-to-Text Transfer Transformer). The model is fine-tuned using a curated dataset of folktales from various regions, with the goal of producing relevant, engaging, and coherent narrative texts. The generation process is supported by pre-processing techniques to structure the data into narrative components such as introduction, conflict, climax, and resolution. The generated stories are then evaluated through human evaluation methods, including questionnaires and User Acceptance Testing (UAT), to assess their quality, coherence, engagement, and cultural relevance. This ensures that the system not only produces technically valid texts but also delivers narratives that are meaningful and enjoyable for readers. Ultimately, this study contributes to the promotion of literacy by presenting local wisdom and traditional values from diverse cultures through stories in a more modern, engaging, and accessible format for the younger generation.
Comparing Data Preprocessing Strategy on T5 Architecture to Classify ICD-10 Diagnosis Lanang Wijayakusuma, I Gusti Ngurah; Sudarma, Made; Darma Putra, I Ketut Gede; Sudana, Oka; Astutik, Dian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6919

Abstract

Manual ICD-10 coding in healthcare systems remains time-consuming, error-prone, and inefficient, particularly in resource-constrained settings. This study investigates the effect of various preprocessing strategies on the performance of the Text-to-Text Transfer Transformer (T5) model for primary diagnosis classification using structured clinical data. A total of 7,263 clinical records were collected from two high-density regions in Bali (Badung and Gianyar) between January 2023 and March 2024, then converted into descriptive text prompts for model training. Four experimental scenarios combined variations of input features and label configurations, comparing T5 with Oversampling against T5 with Easy Data Augmentation (EDA) plus Oversampling. Results showed that T5 with Random Oversampling consistently outperformed the EDA-based configuration across all scenarios, with performance gaps ranging from 8% to 19%. Scenario 4, which excluded body system features and the semantically overlapping E860 label, achieved the highest balance, reaching 84.7% accuracy, 85.1% precision, 84.7% recall, and 84.3% F1-score. Conversely, the EDA-based approach reduced training time by up to 72%, indicating a clear trade-off between performance and efficiency. Both configurations frequently misclassified semantically similar codes within the same ICD-10 categories, underscoring the difficulty of distinguishing clinically related diagnoses. Overall, the results suggest that careful selection of preprocessing strategies can enhance transformer-based medical text classification, while striking a balance between model performance and training efficiency. This work may serve as an initial reference for developing more efficient semi-automated medical coding systems in the Indonesian regional healthcare context.
Determining Tuna Grade Quality Based on Color Using Convolutional Neural Network and k-Nearest Neighbors I Gede Sujana Eka Putra; Ahmad Catur Widyatmoko; I Ketut Gede Darma Putra; Made Sudarma; A. A. K. Oka Sudana
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p02

Abstract

One of the main commodities that Indonesia exports is tuna. Indonesia's inadequate handling of food safety is demonstrated by a number of instances when the United States has rejected Indonesian fishery goods and food poisoning incidences. Fish quality grade is currently determined by manual inspection which has risk human mistake. According to Robert DiGregorio, four tuna grade classifications exist: grade 1, 2+, 2, and 3. The purpose of this study is to assess the tuna meat's quality according to its color. The procedure involves pre-processing images, training datasets, and classifying them using the Convolutional Neural Network (CNN) and k-Nearest Neighbors algorithms. CNN pre-processing involves converting the image into HSV color space and training the CNN model using 240 training datasets and 74 testing datasets. CNN’s accuracy was 84% higher than k-Nearest Neighbors' which was 54%. Additionally, a comparison of the classification accuracy of CNN, VGG (Visual Geometry Group) 16, and AlexNet revealed that CNN outperformed the others with an accuracy of 84%, followed by VGG16 with 70% and AlexNet with 66%.
Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures Ni Luh Gede Pivin Suwirmayanti; I Made Budi Sentana; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; Komang Budiarta
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p02

Abstract

Efforts to preserve Balinese culture can be carried out by integrating art and technology as new steps that need to be developed. This research is motivated by the existence of various forms of God statues which have a central role in Balinese culture. The Deep Learning method is proposed because it has unique features that can be extracted automatically. The technique used in Deep Learning is Convolutional Neural Network (CNN). The training process is first performed to perform the classification process, and then the testing process is performed. We compared our CNN model with two other models, AlexNet and ResNet, based on the experimental results. Using a data split of 70%- 30%, our CNN model has the highest accuracy in managing statue image data. Specifically, our model achieves 97.14% accuracy, while Alexnet and Resnet achieve 24.44% and 33.33%, respectively. Apart from contributing to introducing the Balinese God Statue, this research can also be a basis for developing more comprehensive applications in culture and tourism.
BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification Komang Ayu Triana Indah; I Ketut Gede Darma Putra; Made Sudarma; Rukmi Sari Hartati; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p04

Abstract

The increasing amount of internet content makes it difficult for users to find information using the search function. This problem is overcome by classifying news based on its context to avoid material that has many interpretations. This research combines the Uncased model BiDirectional Encoder Representations from Transformer (BERT) with other models to create a text classification model. Long Short-Term Memory (LSTM) architecture trains a model to categorize news articles about traffic violations. Data was collected through the crawling method from the online media application API through unmodified and modified datasets. The BERT Uncased-LSTM model with the best hyperparameter combination scenario of batch size 16, learning rate 2e-5, and average pooling obtained Precision, Recall, and F1 values of 97.25%, 96.90%, and 98.10%, respectively. The research results show that the test value on the unmodified dataset is higher than on the modified dataset because the selection of words that have high information value in the modified dataset makes it difficult for the model to understand the context in text classification.
Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing I Putu Agus Eka Darma Udayana; Made Sudarma; I Ketut Gede Darma Putra; I Made Sukarsa; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p05

Abstract

Electroencephalogram (EEG) is a non-invasive technology widely used to record the brain's electrical activity. However, noise often contaminates the EEG signal, including ocular artifacts and muscle activity, which can interfere with accurate analysis and interpretation. This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods: Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. The effectiveness of the denoising technique is evaluated by focusing on changes in standard deviation and calculating the Percentage Residual Difference (PRD) value of the EEG signal before and after denoising. The results show that ICA provides better denoising performance than PCA, as reflected by a significant reduction in standard deviation and a lower PRD value. These results indicate that the ICA method can effectively reduce noise and preserve important information from the original signal.
Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS Ricky Aurelius Nurtanto Diaz; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; Naser Jawas
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i01.p06

Abstract

An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.
Co-Authors A. A. K. Oka Sudana Adie Wahyudi Oktavia Gama Agung Udayana Putra Ahmad Catur Widyatmoko Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Kompiang Oka Sudana Anindya Santika Devi Ariana, Anak Agung Gede Bagus Arsa, Dewa Made Sri Arya Widyaningrat, Made Gunawan Astutik, Dian Bagus Yudistira Citra Arum Sari Desak Ayu Savita Desak Ayu Sista Dewi Desy Purnami Singgih Putri Dewa Agung Krishna Arimbawa P Dewa Ayu Nadia Taradhita Dewa Made Sri Asra Dwi Putra Githa Dwi Rusjayanthi, Dwi Erdiawan Erdiawan Erdiawan Erdiawan Erdiawan Erdiawan, Erdiawan G M Arya Sasmita Gede Eridya Bayu Gede Ngurah Pasek Pusia Putra Gede Riska Wiradarma I Dewa Gede Wahya Dhiyatmika I Gede Aditya Nugraha I Gede Galang Surya Prabawa I Gede Hendra Parwata I Gede Suarjana I Gede Sujana Eka Putra I Gede Sujana Eka Putra, I Gede Sujana Eka I Gusti Ayu Agung Diatri Indradewi I Gusti Ayu Triwayuni I Gusti Made Ngurah Ardi Yasa I Gusti Ngurah Dwiva Hardijaya I Kadek Erik Priyanto I Kadek Surya Widiakumara I Ketut Adi Purnawan I Made Agus Dwi Suarjaya I Made Aris Satia Widiatmika I Made Budi Adnyana I Made Budi Sentana I MADE SUDARMA I Made Sukafona I Made Sukarsa I Made Sukarsa I Made Sunia Raharja I Made Suwija Putra I Made Suwija Putra, I Made I Made Yudha Arya Dala I N Satya Kumara I Nyoman Gede Arya Astawa I Nyoman Gunantara I Nyoman Piarsa I Nyoman Putu Suwindra I Nyoman Satria Paliwahet I Putu Adi Purnawan I Putu Agung Bayupati I Putu Agus Eka Darma Udayana I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Agus Eka Pratama I Putu Arya Dharmaadi I Putu Bayu Krisnawan I Putu Indra Permana I Putu Jordi Astika I Putu Satwika Putra I Putu Yoga Pertama Yasa I Wayan Agus Surya Darma I Wayan Budi Sentana I Wayan Gunaya I Wayan Muka I Wayan Ryon Waryanta I Wayan Wahyu Gautama Ida Ayu Dwi Giriantari Ida Ayu Putu Febri Imawati Ida Bagus Nyoman Yoga Ligia Prapta Kadek Adi Praptha Kadek Suar Wibawa Komang Ayu Triana Indah Komang Budiarta Lie Jasa Linawati Linawati Luki Ardiantoro M Sudarma Made Adi Widyatmika Made Sudarma Made Sudarma Made Sudarma Mimin F Rohmah Minho Jo Minho Jo Minho Jo Naser Jawas Ni Kadek Ariasih, Ni Kadek Ni Kadek Dwi Rusjayanthi, Ni Kadek Ni Kadek Riska Sadini Ni Komang Surya Cahyani Putri Ni Komang Sutiari Ni Komang Widyasanti Ni Luh Gede Pivin Suwirmayanti, S.Kom, MT, Ni Luh Gede Pivin Ni Made Ary Esta Dewi W Ni Made Ary Esta Dewi Wirastuti Ni Made Ika Marini Mandenni Ni Putu Ayu Oka Wiastini Ni Putu Chendy Widya Santi Ni Putu Intan Waindika Dharma Ni Putu Ratindia Apriyanti Ni Putu Sutramiani Nyoman Purnama, Nyoman Nyoman Putra Sastra Nyoman S Kumara Nyoman Sumerta Yasa Perdana, I Putu Iduar Pirade, Evangelika Purnamaswari, Anak Agung Arimas Putra, I Made Suwija Putri Isma Oktawiani Putu Githa Pratiwi Putu Manik Prihatini Putu Putri Wrestra Saridewi putu roy nurbhawa Putu Wira Buana Ricky Aurelius Nutanto Diaz, Ricky Aurelius Riskiyanti, Zuraida Malini Cantika Risky Aswi R, Risky Rosalia Hadi Rukmi Sari Hartati Rukmi Sari Hartati Siti Helmyati Sulya Arya Wasika Tri Ginarsa, I Nyoman Adi Wayan Oger Vihikan Wijayakusuma, I Gusti Ngurah Lanang Wira Bhuana Wira Bhuana, Wira Wiranatha, AA.Kt. Agung Cahyawan Yandi Perdana Yogiswara Dharma Putra Yudiadewi, Made Aprisintia Yusliza Binti Mohd Yasin