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I Putu Adi Pratama
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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 5 Documents
 1 
Search results for , issue "Vol 5 No 4 (2023): June" : 5 Documents clear
GDSS for Selecting Culinary Tourism in Bali Using Profile Matching and Borda Sugiartawan, Putu; Wardani, Ni Wayan
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.246

Abstract

This research proposes a method for determining the best culinary tourism destinations in Bali. Currently, being able to determine the best and potential destinations is a problem for people who want to travel tourism in Bali, both domestic and foreign people. Factors that have a dominant influence in determining potential destinations that still cannot be determined with certainty. This will greatly affect the results of decisions that will be taken by the community in determining the destination to be chosen. For this reason, it is very important to create a model to determine the best destination that can be chosen by the community as a decision support system in making decisions. In this journal Profile Matching has been used to determine the best destination as a category that can be obtained from the rating shown on the selected destination.
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.

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