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JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Published by Infoteks
ISSN : 26552183     EISSN : 26557290     DOI : 10.33173
Core Subject : Science,
Computer Science & IT
data analysis, natural language processing, artificial intelligence, neural networks, pattern recognition, image processing, genetic algorithm, bioinformatics/biomedical applications, biometrical application, content-based multimedia retrievals, augmented reality, virtual reality, information system, game mobile, dan IT bussiness incubation
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 149 Documents
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Data Analysis of Bitcoin Price Trends Using KNN Prediction Models Kusuma, Aniek Suryanti
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.247

Abstract

This study investigates Bitcoin price trends and evaluates the effectiveness of the K-Nearest Neighbors (KNN) algorithm for predicting price movements in the cryptocurrency market. Leveraging a decade of historical Bitcoin price data, trading volume, and market capitalization, the research assesses the accuracy and reliability of KNN in capturing the complex and volatile nature of Bitcoin price dynamics. The methodology includes data preprocessing, exploratory analysis, and predictive modeling with hyperparameter optimization. The findings reveal that while KNN achieves moderate accuracy (53%), it performs better in identifying price decreases (Class 0) with a recall of 66% compared to price increases (Class 1) with a recall of 40%. The study also highlights key challenges, including Bitcoin's high volatility and multicollinearity among features like Moving Averages. To improve prediction accuracy, the research recommends feature expansion, advanced modeling techniques (e.g., LSTM networks), and the integration of external factors such as market sentiment and macroeconomic indicators. These results contribute to the growing body of knowledge in cryptocurrency forecasting, providing insights for investors, traders, and researchers to navigate the complex cryptocurrency landscape.
Random Forest Methodology for Analyzing Diabetes Risk Factors B, Muslimin; Karim, Syafei; Nurhuda, Asep
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.248

Abstract

Diabetes is a chronic disease posing significant health challenges globally, with rising prevalence due to genetic, lifestyle, and environmental factors. This research employs the Random Forest methodology to analyze diabetes risk factors and predict outcomes using a dataset of 768 patient records. Key attributes such as glucose levels, BMI, blood pressure, and age were evaluated to uncover their contribution to diabetes risk. The study achieved an overall accuracy of 72%, with glucose emerging as the most influential predictor, followed by BMI and age. While the model showed strong performance in identifying non-diabetic cases, moderate precision and recall for diabetic cases highlighted the impact of class imbalance. Feature importance analysis provided actionable insights, emphasizing glucose and BMI monitoring in diabetes management. Despite its strengths, challenges such as class imbalance and feature redundancy were noted, suggesting the need for oversampling techniques, additional variables, and advanced feature engineering. These findings demonstrate the utility of Random Forest in healthcare analytics, supporting predictive and preventive care strategies. Future research should focus on integrating lifestyle factors, expanding datasets, and exploring advanced machine learning models to enhance predictive accuracy and real-world applicability.
Using Prophet for Accurate Time-Series Predictions of Doge Coin Aung, Lynn Htet
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.249

Abstract

Cryptocurrencies, including Dogecoin (DOGE), exhibit extreme price volatility and speculative behavior, making accurate price prediction a significant challenge for traders and analysts. This study applies Facebook Prophet, a robust time-series forecasting model, to predict Dogecoin's price movements using historical price and trading volume data. Prophet's ability to handle irregular datasets, missing values, and complex seasonality makes it well-suited for volatile financial markets. The methodology includes preprocessing the dataset, training Prophet on the “Close” price, and evaluating its predictive performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal Prophet's capability to capture Dogecoin's underlying trends and seasonality, providing actionable insights into market behavior. By comparing Prophet's performance with traditional models like ARIMA and advanced deep learning techniques such as LSTM, the study underscores its strengths and limitations in cryptocurrency forecasting, contributing to the growing research on cryptocurrency analytics and offering a reliable framework for understanding and predicting price dynamics in highly volatile markets like Dogecoin.
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.
Classifying UKT Fee Relief Eligibility Using K Nearest Neighbors Algorithm Anggara Putra, I Wayan Kintara; Agastyar Priatdana, Gde Yoga
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.251

Abstract

This research develops a K-Nearest Neighbors (KNN)-based classification model to determine the eligibility of students for Tuition Assistance (UKT) based on socio-economic factors, including parental income, family size, parental occupation, number of dependents, and housing conditions. The goal is to automate the process of identifying students eligible for financial aid, enhancing both the efficiency and fairness in resource allocation. The model was trained using a dataset consisting of both categorical and numerical features, with the target variable being binary: "Eligible" (1) or "Not Eligible" (0) for UKT relief. The KNN model achieved an overall accuracy of 92%, with strong performance in predicting the "Eligible" class. However, the "Not Eligible" class showed lower performance, particularly in terms of recall and F1-score, suggesting the presence of class imbalance. To address this issue, techniques such as class balancing, resampling, or adjusting KNN parameters are suggested to improve the model's ability to correctly classify minority instances. Additionally, exploring ensemble methods like Random Forest or XGBoost may provide more robust results. This study highlights the importance of addressing class imbalance and using appropriate evaluation metrics beyond accuracy when building classification models for imbalanced datasets.
Decision Tree Model for Predicting Ethereum Price Movements Based on Trends Sri Murdhani, I Dewa Ayu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.252

Abstract

This research investigates the application of a Decision Tree model for predicting Ethereum price movements using historical trend data. The dataset includes key attributes such as open, high, low, close prices, and trading volume, offering insights into market dynamics. The research emphasizes preprocessing and feature engineering techniques, including normalization and the introduction of derived metrics like moving averages and Relative Strength Index (RSI). Despite the model's simplicity and interpretability, it achieved an accuracy of 49.10%, indicating limited effectiveness in capturing non-linear relationships in volatile cryptocurrency markets. Analysis reveals challenges in distinguishing price trends and handling data imbalances, leading to suboptimal performance. These findings highlight the complexities of financial prediction and underscore the need for advanced machine learning methods. Future work should explore ensemble models, richer datasets incorporating sentiment analysis, and resampling techniques to improve robustness and predictive accuracy. This research contributes to the growing literature on machine learning applications in cryptocurrency analytics.
Efficient Wine Quality Prediction and Classification Using LightGBM Model B, Muslimin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.253

Abstract

This study develops an efficient machine learning model using the Light Gradient Boosting Machine (LightGBM) algorithm to predict and classify wine quality based on physicochemical properties. The dataset used in this research consists of multiple chemical attributes, including alcohol content, acidity levels, sulphates, and phenolic compounds, which collectively influence wine quality. The preprocessing stage involved data cleaning, outlier treatment, feature scaling, and handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). Feature selection was conducted using mutual information and recursive feature elimination to identify the most influential predictors. The optimized LightGBM model achieved superior performance with 100% accuracy, precision, recall, and F1-score across all quality classes, outperforming traditional algorithms such as Random Forest, SVM, and Logistic Regression. Feature importance analysis revealed that Proline, Flavanoids, and Magnesium were the most significant attributes contributing to wine classification. These findings demonstrate that LightGBM is a robust and scalable solution for wine quality prediction, offering an efficient, data-driven alternative to traditional sensory evaluations. The proposed model can enhance quality control processes in the wine industry by providing accurate and interpretable insights into the chemical determinants of wine quality.
Naive Bayes Classifier for Accurate Diabetes Diagnosis and Analysis Aung, Lynn Htet
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.254

Abstract

Diabetes mellitus is a chronic metabolic disorder with rising global prevalence, necessitating early and accurate diagnostic tools to mitigate complications. This study investigates the Naive Bayes classifier's efficacy for diabetes diagnosis, leveraging a dataset of 768 patient records encompassing clinical and demographic attributes, such as glucose levels, BMI, and insulin. Data preprocessing steps, including imputation, scaling, and normalization, ensure data quality, while feature selection identifies key predictors to enhance model performance. The classifier achieved an accuracy of 77%, with a weighted F1-score of 0.77, demonstrating robust performance for the "Not Worthy" class but moderate results for the "Worthy" class due to class imbalance and overlapping features. Ensemble methods, such as bagging and boosting, were explored to address these challenges, further improving robustness and recall. The study highlights the Naive Bayes classifier as a cost-effective, computationally efficient tool for real-time diabetes detection, with potential for deployment in resource-limited healthcare settings. Future research should focus on class balancing, advanced feature engineering, and validation on larger, diverse datasets to enhance diagnostic reliability and scalability.
Time Series Prediction of Doge Coin Prices Using LSTM Networks Kusuma, Aniek Suryanti; Wardani, Ni Wayan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.255

Abstract

This research explores the application of Long Short-Term Memory (LSTM) networks for predicting Dogecoin prices, addressing the challenges of cryptocurrency market volatility and non-linearity. A historical dataset spanning November 2017 to the present, including features such as opening and closing prices, daily highs and lows, and trading volume, was used for model development. Data preprocessing involved handling missing values, normalization, and structuring the data into a supervised learning format. The LSTM model was designed with optimized hyperparameters, trained using the Adam optimizer, and evaluated against metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Benchmarking with traditional models like ARIMA and SVR demonstrated the LSTM model's superior performance in capturing temporal dependencies and adapting to high volatility. Despite its robust performance, the study highlights limitations, including the exclusion of external factors like market sentiment and a dataset limited to specific timeframes. Future research could integrate broader datasets and advanced features to enhance model precision. This work contributes to the field of cryptocurrency forecasting, offering insights for traders, investors, and researchers navigating volatile markets.
Decision Support System for Tourist Destination Selection in Buleleng Using the Analytical Hierarchy Process (AHP) Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 2 (2023): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.256

Abstract

Tourism is a key driver of regional economic development and cultural sustainability, particularly in destinations with diverse natural and cultural assets such as Buleleng, Bali. Yet, selecting the most suitable tourist location is often difficult because it involves various decision factors, including accessibility, attractiveness, available facilities, safety, cost, cleanliness, popularity, and visitor density, which can lead to decisions based on personal bias rather than objective evaluation. To address this challenge, this study develops a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) to systematically assess and rank tourist destinations in Buleleng based on eight priority criteria. The proposed approach provides a structured weighting mechanism and ensures logical consistency in comparisons, indicated by a Consistency Ratio (CR) of 0.000. The analysis results reveal that Pura Ulun Danu Beratan is the most recommended destination, followed by Lovina Beach and Sekumpul Waterfall, supported by their strong appeal and adequate supporting infrastructure. Future development of this system may involve incorporating real-time visitor data, sentiment analysis from online travel reviews, and GIS-based visualization, as well as deployment in web or mobile platforms to increase usability for travelers and local tourism planners.

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