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INDONESIA
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 7 No 2 (2024): December" : 5 Documents clear
Improving Prostate Cancer Classification with Random Forest Techniques Warmayana, I Gede Agus Krisna
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.195

Abstract

Prostate cancer is a leading cause of cancer-related mortality among men worldwide, necessitating accurate and efficient classification methods for improved diagnosis and treatment planning. This research explores the application of Random Forest algorithms to classify prostate cancer cases using a dataset comprising 100 samples with features such as radius, texture, perimeter, area, smoothness, compactness, symmetry, and fractal dimension. The study emphasizes the integration of preprocessing, feature selection, model training, and evaluation to enhance classification performance. The model achieved a classification accuracy of 75%, with a high recall of 88% for malignant cases, demonstrating its potential in identifying high-risk patients. However, the model exhibited challenges in predicting benign cases due to class imbalance, as reflected in the low precision (33%) for this minority class. Addressing these limitations, techniques such as data balancing, advanced hyperparameter tuning, and enhanced feature engineering are suggested. This study provides valuable insights into key predictors of prostate cancer and highlights the potential of Random Forest techniques as a robust tool for clinical decision-making. Future work should focus on integrating additional clinical and genomic data to further improve classification accuracy and interpretability.
LSTM Network Application for Forecasting Ethereum Price Changes and Trends Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.196

Abstract

Forecasting Ethereum price changes presents challenges due to the cryptocurrency market’s volatility and rapid fluctuations. This study applies Long Short-Term Memory (LSTM) networks to predict Ethereum price trends using hourly historical data. The LSTM model captures temporal dependencies effectively, achieving moderate accuracy with a Root Mean Squared Error (RMSE) of 11.42. It performs well in stable market conditions, with predicted prices closely aligning with actual values, validating its potential for identifying long-term trends. However, the model struggles during high-volatility periods, failing to predict abrupt price spikes and market crashes accurately. Overfitting is also observed, indicated by disparities between training and test errors, limiting the model’s generalizability to unseen data. To address these issues, this research suggests incorporating features such as trading volumes, market sentiment, macroeconomic indicators, and blockchain metrics to enhance predictive accuracy. Additionally, employing advanced architectures like attention mechanisms, hybrid models, and real-time learning frameworks is recommended to improve adaptability and robustness in dynamic market environments. These enhancements aim to create a more comprehensive and reliable predictive tool. This study contributes to the advancement of predictive analytics in cryptocurrency markets, offering valuable insights for traders, investors, and policymakers navigating the complexities of digital finance.
K-Nearest Neighbors Approach to Classify Diabetes Risk Categories Santiyuda, Kadek Gemilang
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.197

Abstract

The prevalence of diabetes as a chronic disease poses significant challenges worldwide, necessitating accurate and early detection of risk categories to improve management and prevention strategies. This research evaluates the application of the K-Nearest Neighbors (KNN) algorithm to classify diabetes risk categories using the Pima Indian Diabetes dataset. The study implements rigorous preprocessing steps, including handling missing values, normalization, and feature engineering, to optimize the dataset for KNN’s distance-based calculations. Hyperparameter tuning and the exploration of various distance metrics, such as Euclidean and Manhattan, are conducted to enhance model accuracy. The KNN model achieves a moderate accuracy of 66%, with a precision of 0.52 and a recall of 0.58 for the diabetic class, highlighting its effectiveness in general pattern recognition but limited ability to handle imbalanced datasets. The research identifies glucose levels and BMI as key predictors and emphasizes the importance of balanced datasets and advanced feature selection techniques. Future recommendations include integrating additional clinical features and hybrid models to improve diagnostic accuracy and applicability in clinical settings. This study underscores KNN's potential as a foundational tool in machine learning for medical diagnostics, contributing to the broader effort to enhance healthcare outcomes through data-driven decision-making.
Land Suitability Analysis Using the Modified Profile Matching Method Pratistha, Indra; Dewi, Ni Wayan Jeri Kusuma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.198

Abstract

The plantation sector plays a significant role in Indonesia's economy, particularly in coffee production. In the province of West Nusa Tenggara (NTB), coffee production experienced annual fluctuations from 2018 to 2021. One of the causes is the lack of public understanding in utilizing land according to its natural potential, leading to decreased productivity and land degradation. Based on discussions with plantation experts from Politeknik LPP Yogyakarta, this study identifies land characteristics divided into qualitative data, such as drainage and soil texture, and quantitative data, including temperature, rainfall, humidity, elevation, effective soil depth, slope, cation exchange capacity (CEC), base saturation, pH H2O, organic carbon (C-organic) content, and nitrogen (N). The application of the modified profile matching method demonstrates its capability in providing recommendations for coffee crop suitability in East Lombok Regency. Data matching tests between land profile values and coffee crop profile values, involving experts from Politeknik LPP Yogyakarta and the NTB Provincial Agriculture Office, resulted in liberica coffee being ranked first in eight sub-districts. However, in one sub-district, Sembalun, robusta coffee did not rank second, as arabica coffee was preferred.
Decision Tree for Bitcoin Price Prediction Based on Market Factors Wardani, Ni Wayan; Nugraha, Putu Gede Surya Cipta; Erawati, Kadek Nonik
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.199

Abstract

The volatile nature of Bitcoin poses significant challenges for accurate price prediction, which is critical for informed decision-making by investors and policymakers. This study explores the application of decision tree algorithms to predict Bitcoin prices using a dataset comprising historical data on Bitcoin prices, market capitalization, and trading volumes. The research emphasizes feature engineering techniques, including derived metrics such as rolling averages and volatility indices, and integrates ensemble methods like Random Forest and Gradient Boosting to enhance predictive performance. The decision tree model achieved an accuracy of 53%, demonstrating its capability to capture general trends in Bitcoin price movements, particularly during high volatility periods. The study highlights the importance of key features such as the Relative Strength Index (RSI) and Moving Averages (MA14) while identifying limitations in predicting price decreases. Recommendations for future research include integrating external data sources, such as sentiment analysis and macroeconomic indicators, and exploring advanced modeling techniques to improve robustness and accuracy. This research contributes to the growing field of cryptocurrency price prediction by providing interpretable and actionable insights into market dynamics. The findings offer valuable tools for analysts and investors navigating the complexities of the cryptocurrency market.

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