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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 6 No 2 (2023): December" : 5 Documents clear
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.
Hypertension Classification Using HistGradientBoostingClassifier, HealthD, And Model Optimization Sri Murdhani, I Dewa Ayu
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.257

Abstract

High blood pressure ranks among the world's most common heart-related conditions, carrying serious dangers like strokes and heart attacks. Even with progress in medical testing, spotting it early is tough because of the intricate mix of daily habits and inherited traits. This study seeks to solve the issue of precise hypertension forecasting using machine learning methods tailored for varied health information. Driven by the rising demand for evidence-based health prevention, the research employs the HistGradientBoostingClassifier on a collection of 1,985 patient profiles with eleven lifestyle and bodily indicators, such as age, body mass index, sleep hours, sodium consumption, and tension levels. The key innovation here is the histogram-based boosting approach, which adeptly manages diverse attributes and curbs excessive fitting via timely halting and adjustment techniques. Assessment findings show the model reaches 97% accuracy, maintaining even performance in precision, recall, and F1-score for both hypertensive and non-hypertensive groups. These findings underscore the model's reliability and suitability for inclusion in prompt alert tools for hypertension danger assessment. Upcoming efforts will investigate model clarity through SHAP analysis and pit boosting classifiers against neural network methods to boost understanding and adaptability in practical medical settings.
Hypertension Risk Prediction Using GRU-Based Neural Network with Adam Optimization B, Muslimin; Racmadhani, Budi; Rudito, Rudito
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.258

Abstract

Hypertension remains one of the most prevalent chronic conditions worldwide and continues to be a major contributor to cardiovascular morbidity and mortality. Early identification of individuals at high risk is essential, yet conventional screening approaches often rely on periodic clinical examinations that may overlook subtle lifestyle or behavioral indicators. This study aims to address this challenge by developing a predictive model that estimates hypertension risk using a GRU-based neural network enhanced with the Adam optimization algorithm. The motivation for using this approach stems from the ability of GRU networks to capture nonlinear feature interactions and the effectiveness of Adam in improving training stability and convergence. The proposed system incorporates a structured preprocessing pipeline, feature scaling, and a sequential model architecture to classify individuals into hypertension and non-hypertension groups. The results show that the model achieves strong predictive performance, supported by accuracy trends, loss reduction patterns, and confusion matrix analysis that collectively demonstrate consistent learning behavior. The evaluation indicates that the GRU classifier successfully recognizes relevant health attributes such as stress levels, salt intake, age, sleep duration, and heart rate. Future research may explore expanded datasets, additional health indicators, or hybrid architectures to further enhance accuracy and improve clinical applicability. Overall, this work contributes an interpretable and efficient approach for health risk prediction and supports the development of intelligent digital health monitoring systems.
Random Forest Analysis for Key Factors in Bitcoin Price Prediction Aung, Lynn Htet
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.259

Abstract

This research explores the application of the Random Forest algorithm to predict Bitcoin price fluctuations. Given Bitcoin's high volatility and the influence of various factors such as market sentiment, macroeconomic variables, and blockchain-specific metrics, Random Forest was chosen for its capability to handle complex and non-linear relationships. The dataset includes trading volume, market capitalization, mining difficulty, and social media sentiment indicators. Data preprocessing techniques such as normalization, handling missing values, and adding temporal features were employed to enhance prediction quality. Model evaluation using Mean Absolute Error (MAE = 0.15), Mean Squared Error (MSE = 0.25), and R-squared (R² = 0.85) demonstrates the model's robust performance in capturing intricate market dynamics. The study highlights the importance of feature importance rankings in identifying key drivers of Bitcoin price movements, offering valuable insights for traders, regulators, and investors. Despite its success, areas for improvement include incorporating additional features, such as real-time sentiment analysis and advanced time-series predictors, to further enhance predictive accuracy and applicability across volatile market conditions.
SVM-Based Approach for Predicting Future Ethereum Prices Using Historical Data Rowa, Heruzulkifli
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.260

Abstract

Cryptocurrency markets are volatile and complex, presenting challenges for traditional analysis. This study utilizes a Support Vector Machine (SVM) approach to predict Ethereum’s hourly price movements using historical data, including open, high, low, close prices, and trading volume. Analyzing 34,497 hourly records, the SVM model identifies three market regimes: stable conditions, directional trends, and high-volatility events.Stable conditions dominate 72.7% of the data, marked by consistent price movements and moderate trading volumes, indicating consolidation phases. Directional trends, comprising 15.7%, reflect gradual bullish or bearish price shifts influenced by market sentiment or external factors. High-volatility events, representing 11.5%, are characterized by sharp price spikes or crashes, accompanied by increased trading activity.The Silhouette Score of 0.45 highlights the difficulty of segmenting financial data due to overlapping market states. Despite this, the SVM model effectively captures nonlinear patterns, providing valuable insights into Ethereum's price behavior. This research demonstrates the potential of machine learning in cryptocurrency analysis, enabling better market understanding, improved trading strategies, and enhanced risk management. Future work could integrate advanced features and methods to further boost prediction accuracy and model performance.

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