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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Arsitektur Pendapa Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Kotama, I Nyoman Darma
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Rancangan Plugin Multiplayer Game MOODLE Learning Management System (LMS) berbasis WebSocket pada Google Cloud Server yang Berspesifikasi Rendah Kotama, I Nyoman Darma
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 2 No. 2 (2022): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (646.451 KB) | DOI: 10.55606/juisik.v2i2.241

Abstract

With increasing needs for online or online learning and teaching activities, educational institutions need solutions for hosting their data online. The available options include building their own data center or renting a Virtual Private Server (VPS) service that is widely available. Considering the high setup and maintenance costs, many institutions choose VPS rental options such as Google Cloud Server. Moodle Learning Management System (LMS) is one of the most affordable and open-source online learning software solutions for low-cost servers. This paper will discuss the design and implementation of the Moodle Plugin that utilizes WebSocket technology to accommodate the interactive learning process with Multiplayer Games. The result of the study reports that the plugin can accommodate and simulate an increase of 1% in CPU per 15 connections and an increase of 2MB per 15 connections. Which concluded still an affordable solution for low-cost servers for real-time connection.
Classification Of Superstructure Damage In School Buildings In Nusa Penida Bali Using YOLO V7 Adnyana, Anak Agung Gede Oka Kessawa; Mahardika, I Gede Indra; Wicaksana, Gde Bagus Andhika; Kotama, I Nyoman Darma
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 04 (2024): Informatika dan Sains , 2024
Publisher : SEAN Institute

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

Abstract

Structural damage in school buildings poses significant risks to safety and education quality, particularly in remote areas with limited maintenance resources. This study develops a YOLOv7-based model to detect building pillars and classify structural damages, focusing on school buildings in Nusa Penida, Bali. A dataset of 156 images, derived from an initial 521 images collected during field visits, was curated to include both damaged and intact pillars. Preprocessing and augmentation techniques, including resizing and rotation, were applied to optimize the dataset. Training was conducted over 55 epochs using Google Colab with a T4 GPU, incorporating parameter tuning to address dataset imbalance. Confidence thresholds were set at 0.7 for pillars and 0.2 for rebar detection to enhance sensitivity to underrepresented damage classes. Evaluation metrics, including the F1-score and confusion matrix, confirmed the model’s accuracy and robustness in detecting and classifying structural damages. The results demonstrate the model's potential for real-world applications in damage assessment, particularly in resource-limited settings. Future research should focus on expanding datasets, incorporating multi-class classification, and integrating real-time detection and drone-based imagery to enhance scalability and efficiency. This work contributes to developing efficient, AI-driven solutions for structural health monitoring in critical infrastructure.
Support Vector Machine for Accurate Classification of Diabetes Risk Levels Sugiartawan, Putu; Wardani, Ni Wayan; Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.107740

Abstract

This research explores the application of Support Vector Machines (SVM) for accurately classifying diabetes risk levels based on a publicly available dataset containing 768 instances and 9 attributes, including glucose levels, BMI, blood pressure, and insulin levels. The model's systematic development process involved data preprocessing, feature selection, and hyperparameter optimization to ensure robust performance. Results indicate an overall accuracy of 76%, with high precision and recall for the non-diabetic risk class, but relatively lower performance for the diabetic risk class, highlighting the challenges posed by class imbalance and overlapping data features. To address these issues, future research should incorporate advanced resampling techniques, refined feature engineering, and alternative machine learning models like Random Forest or XGBoost. This research underscores the potential of SVM as a valuable tool for early diabetes detection, offering healthcare professionals a reliable means to identify at-risk individuals and personalize intervention strategies. By bridging theoretical advancements and practical applications, the research contributes to enhancing predictive analytics in medical diagnostics, paving the way for improved patient outcomes and efficient public health management
Binary Classification of Exchange Rate Trends Using Logistic Regression Wardani, Ni Wayan; Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 4 (2025): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.218

Abstract

This study explores the use of logistic regression for binary classification of exchange rate trends, focusing on predicting upward and downward currency movements. Logistic regression, valued for its simplicity and interpretability, models the relationship between historical exchange rate data and macroeconomic indicators like interest rates, inflation, GDP growth, and trade balances. The methodology involves data collection, preprocessing, feature engineering, and model evaluation. Historical data is processed to address missing values, outliers, and noise, ensuring a robust dataset. Feature selection techniques, including mutual information scores and principal component analysis (PCA), identify key predictors, while L1 and L2 regularization enhance generalization. The model, implemented using Python's scikit-learn library, is optimized through grid search for hyperparameter tuning. Performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, indicate strong predictive capability, achieving 99% accuracy in forecasting upward and downward trends. Logistic regression's interpretability aids decision-making, making it a valuable tool for financial forecasting. However, the study notes limitations, such as challenges posed by market volatility and geopolitical factors. Future research suggests incorporating sentiment analysis from financial news and social media, and exploring hybrid models combining logistic regression with ensemble methods or deep learning to improve performance under real-world conditions.
Analisis Kualitatif Elemen Gamifikasi dalam Games Berbasis ICT untuk Anak Usia Dini Aditya, Bayu Rima; Iradianty, Aldilla; Kotama, I Nyoman Darma
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106285

Abstract

Pembelajaran berbasis gamifikasi menghadirkan pengalaman baru bagi peserta didik. Tren penggunaan pendekatan gamifikasi ini semakin diminati di semua level pendidikan, terutama untuk level pendidikan anak usia dini. Penelitian ini bertujuan untuk mengidentifikasi elemen gamifikasi yang paling relevan dalam lingkungan pendidikan anak usia dini (PAUD) melalui kegiatan Focus Group Discussion (FGD) bersama guru PAUD di Indonesia. Hasil analisis temuan menunjukkan bahwa guru PAUD memiliki persepsi dan preferensi positif terhadap pembelajaran PAUD berbasis gamifikasi. Selain itu, hasil penelitian juga mengkonfirmasi bahwa terdapat sembilan elemen gamifikasi yang cocok untuk diterapkan pada pembelajaran berbasis gamifikasi di lingkungan PAUD, yaitu poin dan penghargaan, papan peringkat, lencana, tantangan, levelisasi, penanda progres, avatar, mata uang, dan kejutan. Hasil penelitian ini dapat menjadi dasar untuk penelitian masa depan dalam kaitannya dengan pengembangan prototipe sistem pembelajaran PAUD berbasis gamifikasi dengan fokus pada interaksi, pemberian penghargaan, pemecahan masalah, dan pemberian tantangan. AbstractGame-based learning brings new experiences for students. The trend of using the gamification approach is increasingly in demand at all levels of education, especially for early childhood education. This study aims to identify the most relevant gamification elements in early childhood education through Focus Group Discussion activities with early childhood teachers in Indonesia. The results of the analysis of the findings indicate that teachers have positive perceptions and preferences toward game-based learning in early childhood education. In addition, the results also confirm that there are nine elements of gamification that are suitable to be applied to in the early childhood environment, namely points and rewards, leaderboards, badges, challenges, leveling, progress bar, avatars, currency, and surprises. The results of this study can be used as the basis for further research in relation to developing a prototype of a game-based learning system in early childhood education with a focus on interaction, rewarding, problem solving, and challenges.
Applying K-Nearest Neighbors Algorithm for Wine Prediction and Classification Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.231

Abstract

This study evaluates the performance of a machine learning classification model using a confusion matrix to analyze predictions across three distinct classes. The results show the model achieving a high accuracy of 94.44%, indicating reliable classification performance. The confusion matrix highlights that most instances were classified correctly, with minimal misclassifications observed, particularly in Class 1, where some overlap with other classes was evident. The findings suggest that the model effectively distinguishes between well-separated classes while facing minor challenges with overlapping data distributions. To address these issues, potential improvements such as feature engineering, class balancing, and advanced optimization techniques are recommended. The study underscores the importance of confusion matrix analysis as a diagnostic tool for understanding classification errors and guiding model refinement. Additionally, this research emphasizes the role of high-quality datasets, proper model selection, and hyperparameter tuning in achieving optimal classification accuracy. The outcomes provide a basis for further enhancement of machine learning models in applications requiring multi-class classification. By reducing errors and improving model robustness, this approach can contribute to more accurate and reliable decision-making processes across various domains, including healthcare, finance, and natural language processing.
Pengembangan konsep healing environment dalam Metaverse dengan pendekatan desain arsitektur biofilik Putra, Ida Bagus Gede Parama; Wicaksana, Gde Bagus Andhika; Prabawa, Made Suryanatha; Linggasani, Made Anggita Wahyudi; Kotama, I Nyoman Darma
JURNAL ARSITEKTUR PENDAPA Vol. 6 No. 2 (2023): Vol. 6 No. 2 (2023)
Publisher : Universitas Widya Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37631/pendapa.v6i2.761

Abstract

Metaverse sebuah pengembangan teknologi berupa platform digital yang menghadirkan berbagai macam kemungkinan dalam berkreasi. Ruang Virtual dapat dirancang dengan berbagai pendekatan komunikasi visual, elemen interior maupun arsitektur digital yang tanpa batas. Potensi metaverse telah menghadirkan banyak kemungkinan yang tidak terpikirkan. Berbagai sektor seperti pendidikan, teknologi, iklan, retail, real estate dan sektor kesehatan yang menjadi salah satu sektor yang memiliki potensi dalam pengembangan ruang virtual sebagai media terapi. Pengembangan healing environment pada platform virtual dalam penelitian ini berfokus pada menciptakan zona virtual yang dapat digunakan sebagai ruang penyembuhan dan ruang kontemplasi bagi pengguna dengan keterbatasannya dalam melakukan aktivitas. Fokus penelitian ini yaitu melakukan kajian desain arsitektur biofilik dalam merencanakan ruang virtual untuk ruang penyembuhan bagi seseorang yang membutuhkan kenyamanan khususnya. Penelitian ini menggunakan metode kualitatif dalam mengkaji sumber dalam perencanaan desain ruang virtual, teori tentang healing environment dan desain arsitektur biofilik, Hal ini bertujuan untuk memberikan ruang interaktif untuk berbaur dan melakukan interaksi yang diharapkan memunculkan kepercayaan diri bagi pengguna.
K-Nearest Neighbors Algorithm for Analyzing Doge Coin Market Behavior Batubulan, Kadek Suarjuna; Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.238

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm to analyze Dogecoin's market behavior using historical trading data, including daily metrics such as Open, High, Low, Close, and Volume, spanning from November 2017. As a proximity-based machine learning algorithm, KNN effectively captures short-term market patterns, achieving a low Mean Absolute Error (MAE) of 0.0017, demonstrating its capability in identifying general trends during stable periods. However, the model faces challenges in predicting sudden price shifts caused by external factors like social media sentiment and regulatory news, highlighting its limitations in highly volatile cryptocurrency markets. Preprocessing steps, including normalization and outlier handling, improved the algorithm’s performance, yet its scalability and sensitivity to hyperparameters remain issues. Future research directions include integrating external data sources, such as social media sentiment and macroeconomic indicators, and adopting advanced models like Gradient Boosting Machines (GBMs) or Long Short-Term Memory (LSTM) networks to enhance predictive accuracy and adaptability. These improvements aim to provide more robust insights into Dogecoin's market dynamics, aiding traders and financial analysts in navigating the complexities of cryptocurrency markets.
Predicting Wine Quality Based on Features Using Naive Bayes Classifier Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.244

Abstract

This study explores the application of the Naive Bayes classifier in predicting wine quality based on physicochemical attributes. Leveraging a dataset containing features such as acidity, pH, alcohol content, and sulfur dioxide concentrations, the research aims to address the limitations of traditional sensory evaluation methods, which are often subjective and inconsistent. Data preprocessing, including normalization and feature selection, is performed to ensure the dataset is suitable for machine learning. The Naive Bayes classifier is implemented using Python's scikit-learn library, with hyperparameter tuning conducted to optimize its performance. The model is evaluated on metrics such as accuracy, precision, recall, and F1-score, achieving competitive results compared to other machine learning techniques such as Decision Trees and Support Vector Machines. The findings demonstrate the Naive Bayes classifier’s efficiency in handling high-dimensional data, its computational simplicity, and its potential for real-time quality assessment in the wine industry. This research highlights the role of machine learning in automating and enhancing quality control processes, contributing to the broader integration of data-driven approaches in the agri-food sector. The study underscores the feasibility of using physicochemical features as objective indicators of wine quality, offering a scalable and cost-effective alternative to traditional methods.
Using Random Forest to Classify Financially Eligible Students for UKT Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 4 (2023): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.250

Abstract

This research investigates the use of a Random Forest-based classification model to automate the process of determining students' financial eligibility for the Uang Kuliah Tunggal (UKT) tuition assistance system in Indonesia. By leveraging socioeconomic data such as household income, family size, parental education level, and student performance, the model aims to enhance transparency, fairness, and efficiency in financial aid allocation. The dataset, comprising 1,000 student records with categorical and numerical features, was split into training (80%) and testing (20%) sets. The Random Forest model achieved a high overall accuracy of 90%, with exceptional performance for the Worthy class, attaining a recall of 100% and an F1-score of 0.94, ensuring no eligible students were overlooked. However, the model demonstrated lower recall (60%) for the Not worthy class, indicating room for improvement in addressing class imbalance. Key socioeconomic factors emerged as significant determinants, aligning with traditional UKT criteria. Future work should focus on enhancing model performance through data balancing techniques, feature enrichment, and exploring advanced machine learning algorithms. This research underscores the potential of data-driven approaches to improve the equity and efficiency of tuition assistance systems in higher education.
Co-Authors Aditya, Bayu R Adnyana, Anak Agung Gede Oka Kessawa Aldilla Iradianty Batubulan, Kadek Suarjuna Linggasani, Made Anggita Wahyudi Mahardika, I Gede Indra Prabawa, Made Suryanatha Pradhana, Anak Agung Surya Putra, Ida Bagus Gede Parama PUTU SUGIARTAWAN Wardani, Ni Wayan Wicaksana, Gde Bagus Andhika