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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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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.
Implementation of the Simple Additive Weighting (SAW) Method in a Decision Support System for Tourist Destination Selection in North Bali Utama Giri, I Gede Andi; Dewa, Hari Putra Maha
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.261

Abstract

North Bali boasts remarkable natural and cultural attractions, but the process of choosing the best tourist spots is still based on personal opinions and lacks consistency. Without a fair and organized way to evaluate options, visitors and those involved often depend only on trends or individual tastes, which hinders effective promotion and handling of destinations. This research seeks to create a Decision Support System (DSS) that employs the Simple Additive Weighting (SAW) technique to fairly evaluate and rank travel sites using various factors like ease of access, appeal, amenities, hygiene, expenses, fame, security, and crowd levels. The suggested framework combines these prioritized elements to generate an overall rating for each potential location. By examining data from ten well-known spots in North Bali, Pura Ulun Danu Beratan topped the list with a score of (0.8800), showing excellence in most assessed aspects. The findings show that the SAW approach can reliably aid complex choices in tourism oversight, ensuring clear and dependable rankings. This tool offers a flexible and expandable structure suitable for evaluating tourism in other areas. Upcoming efforts will focus on adding live data feeds and visitor input via online or app interfaces to boost the system's precision and ease of use.
Implementation of the TOPSIS Method for a Decision Support System in Recommending Tourist Destinations in Tabanan Anggara Putra, I Wayan Kintara; Agastyar Priatdana, Gde Yoga
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.262

Abstract

Tourism development plays an important role in stimulating regional economic growth, particularly in areas with diverse natural and cultural attractions such as Tabanan Regency in Bali. However, visitors often experience difficulties in selecting destinations that match their preferences due to the presence of multiple decision factors and scattered informational resources, making destination decisions less systematic and potentially inconsistent. This situation highlights the need for a methodical decision support mechanism capable of evaluating tourist destinations based on multiple criteria. Motivated by this issue, this study implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as part of a Decision Support System designed to recommend suitable tourist destinations in Tabanan. The system evaluates nine destinations based on eight criteria, which include accessibility, attractiveness, facility availability, cleanliness, cost, popularity, safety, and visitor density, and applies weight values determined through expert judgment. The evaluation results show that Jatiluwih Rice Terrace has the highest ranking with a closeness coefficient of 0.679126, followed by Ulun Danu Beratan and Tanah Lot, indicating that heritage value and environmental management strongly contribute to recommendation outcomes. The model provides transparent ranking reasoning and can support tourists, planners, and local tourism administrators in making informed decisions. Future development may involve expanding the destination dataset, integrating real-time visitor data, and deploying the system as a mobile application to improve personalization and accessibility.
Digitalization of Bale Beleq in Pejanggik Village Based on a 360-Degree Virtual Reality Tour Website Sandani, Rezi; Mahendra, I Gede Orka; Widya Dharma, I Gusti Ngurah Adi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.263

Abstract

Cultural heritage preservation plays a vital role in maintaining local identity and historical continuity. Bale Beleq, located in Pejanggik Village, is a significant cultural landmark representing the legacy of the Sasak community in Lombok. However, the lack of digital documentation and limited accessibility hinder public engagement and threaten the sustainability of this cultural heritage. Motivated by the need to preserve and promote local traditions through technology, this research develops a digital platform integrating a website and 360° Virtual Reality (VR) tour. The system aims to provide immersive access to cultural information, enabling users to virtually explore Bale Beleq through panoramic visualization, interactive hotspots, and multimedia narration. The system was developed using the Multimedia Development Life Cycle (MDLC) method, encompassing conceptualization, design, material collection, development, testing, and distribution. Functionality testing using the Black Box method confirmed that all features—such as the virtual tour, gallery, historical descriptions, and audio guides—performed effectively according to design specifications. The evaluation showed that over 90% of users rated the system as highly engaging and informative, proving its potential as an effective medium for cultural promotion and education. Future work will focus on expanding multilingual capabilities, optimizing mobile interfaces, and integrating AI-based virtual guides to enhance interactivity and personalized learning experiences.
Hypertension Risk Prediction Using GRU-Based Deep Learning Optimized with Stochastic Gradient Descent Sri Murdhani, I Dewa Ayu; Randhika Kerlania, I Gusti Ayu Agung
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.264

Abstract

Hypertension stands out as a highly common heart disease across the globe, where spotting risks early is vital to curb its prolonged effects. Still, standard check-up approaches usually hinge on unchanging health stats that overlook habit-based risk trends entirely. This gap complicates building precise alert systems for folks with different routines and body profiles. Fueled by the push for a more flexible and trend-focused strategy, the study delves into applying a Gated Recurrent Unit (GRU)-driven neural network to predict hypertension threats using lifestyle and past health data. The model blends sequential trend analysis with two GRU layers, dropout for stability, and L2 limits, tuned via Stochastic Gradient Descent (SGD) with momentum and Nesterov boosts. It lets the network uncover intricate links between factors such as age, salt consumption, stress, BMI, sleep time, family background, and treatment history. Trials on 1,985 patient records reveal solid prediction skills, with top classification rates and well-defined categories in the confusion matrix. The training and validation plots also prove smooth learning without major overfit. Next steps cover enlarging the data with continuous health metrics, incorporating attention tools for clearer insights, and pitting it against cutting-edge optimizers like AdamW and Ranger to enhance broader applicability.
KNN-Based Prediction Model for Assessing Hypertension Risk from Lifestyle Features B, Muslimin; Rowa, Heruzulkifli
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.265

Abstract

Hypertension is one of the most common chronic conditions associated with serious cardiovascular complications, and its prevalence continues to rise due to the influence of lifestyle related factors, motivating the use of data driven approaches for early risk identification. Although various machine learning models have been applied in health analytics, many still face challenges in processing heterogeneous lifestyle attributes, which limits their ability to accurately detect individuals at risk. This study addresses that gap by implementing the K Nearest Neighbors algorithm to predict hypertension using a dataset of 1,985 records containing variables such as age, salt intake, stress score, sleep duration, body mass index, family history, medication use, physical activity, and smoking status. The motivation for selecting KNN lies in its simplicity, adaptability, and strong performance in classification tasks involving structured health data. The contribution of this research includes the development of a lifestyle based hypertension prediction model supported by a preprocessing pipeline and optimized hyperparameters, enabling effective handling of mixed numerical and categorical features. The model is evaluated using accuracy, precision, recall, f1 score, and confusion matrix visualization, achieving an accuracy of 85 percent with balanced performance across both classes, showing that KNN offers reliable generalization for this dataset. Future work involves comparing KNN with ensemble or deep learning models, exploring feature selection techniques, and expanding dataset diversity to improve model robustness and applicability for real world digital health solutions.

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