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Data Science Insights
Published by PT Visi Media Network
ISSN : -     EISSN : 30311268     DOI : https://doi.org/10.63017/jdsi.v3i2
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Data Science Insights, with ISSN 3031-1268 (Online) published by PT Visi Media Network is a journal that publishes Focus & Scope research articles, which include Data Science and Machine Learning; Data Science and AI; Blockchain and Advance Data Science; Cloud computing and Big Data; Business Intelligence and Big Data; Statistical Foundation for Data Science; Probability and Statistics for Data Science; Statistical Inference via Data Science; Big Data and Business Analytics; Statistical Thinking in Business; Data Driven Statistical Methods; Statistical Methods for Spatial Data Analytics; Statistical Techniques for Data Analysis; Data Science in Communication; Information and Communication Technology; Graph Data Management for Social Network Applications; Metadata for Information Management; Information/Data: Organization and Access; Information Science and Electronic Engineering; Big Data and Social Science; Data Communication and Computer Network; ICT & Data Analytics. This journal is published by the PT Visi Media Network, which is published twice a year.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2026): Journal of Data Science Insights" : 5 Documents clear
Prediction of Heart Failure in Patients using Five Types of Algorithms Charles
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.129

Abstract

Heart failure is one of the cardiovascular diseases that has a significant impact on patients' quality of life and requires appropriate medical treatment. With the advancement of technology, the use of machine learning algorithms to predict the risk of heart failure can enhance the efficiency of diagnosis and treatment. This study aims to compare the performance of five machine learning algorithms in predicting heart failure in patients. The algorithms used in this study are K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Deep Learning. The dataset contains patients' medical data, including medical history, symptoms, and clinical test results. The evaluation method was carried out by measuring the accuracy, precision, recall, and F1-score of each algorithm. The results show that the Random Forest algorithm achieved the best performance in terms of accuracy and prediction stability, followed by Deep Learning and Naïve Bayes.
Application of Data Mining to Predict Diabetes Disease Yapnata, Florencia; Abdullah
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.134

Abstract

Early management of diabetes risk and paying attention to the factors that cause someone to have the potential for diabetes are important to minimize cases of diabetes sufferers. Monitoring of Pre-diabetes patients is characterized by increasing certain parameters in the medical record data feature which is an important dynamic part of this study. Data mining techniques in diabetes disease prediction are used to determine a patient's risk of diabetes more quickly and accurately. This study uses the Knowledge Discovery in Database model process consisting of several stages such as Data Selection, Preprocessing, Transformation, Data Mining and Evaluation. The techniques that can be used to overcome these problems are Data Mining using the Navies Bayes algorithm, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest have various evaluation results with several input datasets used.
Liver Disease Prediction using Decision Tree Algorithm Selly
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.138

Abstract

The liver is one of the important organs in the human body that functions to detoxify or neutralize toxins from everything that enters our body, making the body healthier. The liver can be affected by diseases that can disrupt its function; when liver disease attacks, toxins will spread throughout the body, making it unhealthy. Liver disease is a condition caused by viruses, alcohol, lifestyle factors, and others. According to WHO (World Health Organization) data, nearly 1.2 million people die each year, particularly in Southeast Asia and Africa, due to liver disease. Individuals often do not realize or are late in detecting liver disease, so by the time they are examined, the disease is already severe. Early intervention would be better if symptoms are recognized. Data mining can assist in diagnosing liver disease more easily, especially in helping doctors determine whether a patient suffers from liver disease based on symptoms that closely resemble liver conditions. The diagnosis process for liver disease is carried out through classification, resulting in whether the patient has liver disease or not. This study uses five data mining algorithms: Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Deep Learning.
A Review on Support Vector Machine Problems and Solutions Al-Dulaimi, Hiba; Ku-Mahamud, Ku Ruhana
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.217

Abstract

In this paper, a review is presented particularly on Support Vector Machine (SVM) problems, so as to understand these problems and to identify the approaches to solve them. The aim is to organize the main SVM problems in a manner that provides a clear view for the readers. The approaches for solving SVM problems were classified into non-simultaneous and simultaneous approaches based on constraints considered in solving the problems. Algorithms for model selection and feature subset selection are classified into heuristic and non-heuristic approaches. Very promising result can be obtained if the bio-inspired algorithms are simultaneously applied with SVM for classification problem.
The Impact of Rainfall Pattern Dataset Construction on Neural Network Performance for Reservoir Water Level Forecasting Wan Ishak, Wan Hussain; Raja Mohamad, Raja Nurul Mardhiah
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.223

Abstract

Reservoir water level forecasting is a critical component of effective water resources management, supporting flood mitigation, water supply planning, and sustainable reservoir operation, particularly under increasingly variable rainfall conditions. During periods of heavy rainfall, inaccurate or delayed water level prediction may increase flood risk, while during low rainfall seasons, poor forecasting can compromise water storage and operational efficiency. Artificial Neural Networks (ANNs) have been widely adopted for reservoir water level forecasting due to their capability to model nonlinear rainfall–reservoir relationships. However, existing studies largely focus on algorithm selection or architectural enhancement, with limited attention given to how rainfall data representation and dataset construction influence neural network performance. This study addresses this gap by analysing the impact of rainfall pattern dataset construction on ANN performance for reservoir water level forecasting. The primary aim is to evaluate how different rainfall representations affect predictive accuracy when the learning algorithm and training configuration are held constant. Two rainfall pattern datasets were constructed using the same raw rainfall and reservoir water level data from the Timah Tasoh Reservoir, Malaysia. The first dataset represents a compact abstraction of rainfall behaviour using rainfall change indicators derived from day-to-day observations. The second dataset enriches the feature space by incorporating both rainfall change and rainfall intensity categories for each upstream station. In both datasets, the reservoir water level category serves as the prediction target. Prior to model training, redundancy and conflicting data instances were removed to ensure data consistency. A consistent ANN architecture was employed for both datasets and evaluated using 10-fold cross-validation. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The experimental results demonstrate that the enriched rainfall pattern dataset achieved significantly lower RMSE and MAE values compared to the compact rainfall change dataset, indicating improved learning capability and generalisation performance. Although the enriched dataset required higher computational effort, the improvement in forecasting accuracy was substantial. The findings highlight that dataset construction plays a decisive role in neural-network-based reservoir water level forecasting.

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