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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
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Search results for , issue "Vol. 2 No. 2 (2024): Journal of Data Science Insights" : 5 Documents clear
Cluster Analysis on Laptop Sales Data and Specifications Using K-Means and K-Medoids Methods Fajar, Ibnu
Data Science Insights Vol. 2 No. 2 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i2.38

Abstract

This research aims to address the challenges in understanding the relationship between laptop specifications and sales prices and to enhance product segmentation based on cluster analysis. By using available laptop specifications and sales price data, this study aims to identify patterns in laptop specifications that influence sales prices using K-Means and K-Medoids cluster analysis. This research employs the K-Means and K-Medoids clustering methods to categorize laptops into several categories based on specifications such as screen size (inches), price, RAM capacity, and weight. The data transformation process, exploratory analysis, model building, and cluster performance evaluation were conducted using the RapidMiner analysis tool. The research results show that the K-Medoids algorithm provides more accurate clustering performance compared to K-Means, with a Davies-Bouldin Index value of -0.665 for K-Medoids and -0.487 for K-Means at configurations k=4 and k=5. A lower Davies-Bouldin Index value indicates that K-Medoids better represents the characteristics of the existing data. The clustering results identify laptop categories based on a combination of specifications and prices, which can be used by manufacturers and sellers to develop more targeted marketing strategies. This research is expected to provide useful insights for the laptop industry in understanding consumer preferences and needs, and to assist in making more informative decisions to improve sales and customer satisfaction.
Predicting Emerging Art Styles in AI-Generated Artworks Willyem, Willy
Data Science Insights Vol. 2 No. 2 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i2.99

Abstract

The development of artificial intelligence (AI) technology has brought significant changes in various fields, including the arts. AI-generated art is no longer just a technical experiment, but has evolved into a recognized artistic medium, creating new opportunities in the exploration of creativity and aesthetics. This study evaluates the prediction of aesthetic trends that develop in artistic creativity using the analysis of artwork datasets generated by Artificial Intelligence (AI) based on Machine Learning. In the digital age, AI has become an essential tool in art exploration, producing works with unique styles, techniques, and aesthetics. The study aims to understand the aesthetic patterns and dynamics that emerge from AI artwork. The results of the research obtained can be seen that the random tree model is an appropriate algorithm in making predictions. Through this approach, this article not only contributes to art and technology literature but also provides insight into how the relationship between humans and AI can shape the contemporary art landscape. This research is expected to be the basis for the development of more inclusive and creative AI technology in the future.
Application of Decision Tree Algorithm for Classification of Rice Yields in Sumatra candra, wily
Data Science Insights Vol. 2 No. 2 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i2.103

Abstract

Rice is the main food crop in Indonesia, most of the agricultural sector in Indonesia is dominated by rice farming including on the island of Sumatra. A common problem that arises is how to find out the areas that produce the most rice each year on the island of Sumatra. This study aims to classify the areas that produce the most rice on the island of Sumatra. The dataset used in this study was taken from Kaggle with a total of 225 data and will be tested using the Decision Tree algorithm and several other algorithms. For data visualization, Tableau will be used to see which areas produce the most rice on the island of Sumatra. By using the research method using the Decision Tree algorithm, an accuracy of 97.78% was obtained with a data split of 0.8 for training data and 0.2 for testing data.
Predicting Forest Fires using Five Machine Learning Algorithms Manik, Rian Delober
Data Science Insights Vol. 2 No. 2 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i2.114

Abstract

This research aims to develop a prediction model for forest fires that occur by utilizing five types of machine learning algorithms, namely Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Naïve Bayes (Kernel), and Rule Induction. The data used in this research was taken from [www.kaggle.com]. By using data pre-processing techniques such as missing value imputation, data normalization, and feature selection techniques, to ensure the quality of the data used in the prediction model. The research results show that each algorithm has different performance in predicting forest fires that occur each month, with some algorithms showing higher levels of accuracy and precision. Further analysis discusses the advantages and disadvantages of each algorithm as well as the practical implications of implementing them in the environment.
Evaluating and Deploying Predictive Models for Weather Classification Lie, Jolin Arfina
Data Science Insights Vol. 2 No. 2 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i2.177

Abstract

Weather is the condition of the atmosphere in a specific location over a relatively short period of time, described through various parameters such as temperature, air pressure, wind speed, humidity, and other atmospheric phenomena. It differs from climate, which refers to the average atmospheric conditions over a large area and a long time period studied under the field of climatology. Weather can vary from hot to cold, wet to dry, and windy to calm. It is influenced by dynamic changes in the Earth’s atmosphere, including warming and cooling processes. In recent years, weather changes have become more frequent and unpredictable, significantly affecting daily human activities. Therefore, an intelligent system capable of detecting and predicting weather conditions is increasingly needed. This study aims to apply classification algorithms to predict weather conditions based on relevant meteorological parameters. The algorithms used include k-Nearest Neighbor, Random Forest, Naïve Bayes, Decision Tree, and Deep Learning. Given the irregularity and complexity of weather patterns, manual prediction becomes unreliable. Although it is impossible to predict the weather with absolute certainty, computational methods can provide reasonably accurate estimations. Based on the evaluation results, the Random Forest algorithm demonstrated the highest accuracy among the tested models. Furthermore, the final model was successfully deployed using Python, enabling real-time predictions on incoming weather data.

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