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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. 3 No. 1 (2025): Journal of Data Science Insights" : 5 Documents clear
Analysis of Sales Data Visualization of Warung Indomie using the Looker Studio Platform Purwenti, Rinda; Bela, Nova Rustiana; Alda, Hutri Rizkiyah; Jihannata, Nabila
Data Science Insights Vol. 3 No. 1 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i1.33

Abstract

Indomie stalls are stalls that serve noodles from Indomie products.because people's tastes are very familiar with indomie, the opportunity to do business in the field of warmindo is large.so research was carried out to analyze the sales data of the indomie stall. The method used is Sales Data Visualization Analysis at Indomie Warung Using the Looker Studio Platform, starting from data collection, data preparation and data exploration. The data taken is secondary data from the Bima Putra website. The attributes used are invoice_id,tanggal_transaksi, jenis_produk, quantity, harga_jual, jenis_pembayaran, jenis_pesanan, and nilai_penjualan.so as to produce several visualizations. From this visualization, it is known that the best-selling Indomie product type is Indomie soup with 682 sales and the non-selling product is Indomie Goreng which sold only 293 from January-August 2022. The favorite product is Indomie Soto Betawi flavor as many as 80 sales. With the overall indomie flavor is 18 flavors. For the type of orders that are widely made, delivery is 51.7% with cash payment, which is 20%.the highest monthly income is July 2022 with a total of 1.4 million and the lowest is April 2022 with a total of 899 Rp. With an overall total of 975 sales. Therefore, this indomie stall can pay attention so that the stock of best-selling goods is always available, increase the promotion, improve the service, comfort and facilities of the stall, and of course the taste of the indomie dish should attract customers. Because the factors that cause the success or not of the business come from the number of sales.In addition, from this information, customers can also know which products can be recommended.
Identification of Diabetes Mellitus Risk in Women using Random Forest Wijaya, Eka
Data Science Insights Vol. 3 No. 1 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i1.95

Abstract

Diabetes Mellitus (DM) is one of the chronic diseases that can cause various serious complications, especially in women. Early risk identification is an important step in preventing the progression of this disease. This study aims to identify the factors influencing the risk of diabetes in women by analyzing data from several parameters, namely the number of pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index (BMI), diabetes pedigree function, and age. A quantitative approach was used in this study with descriptive and inferential statistical analysis methods. The research results show that glucose levels and BMI are the most significant factors in increasing the risk of diabetes, followed by family history of diabetes and age. In addition, the number of pregnancies also has an impact on the risk of diabetes, especially in women with a history of gestational diabetes. This research concludes that the combination of several parameters can be used to predict the risk of diabetes more accurately, especially in women. These results are expected to support early prevention efforts and better clinical decision-making in the management of diabetes.  
Drug Classification using Machine Learning Algorithms fernando, Hengky
Data Science Insights Vol. 3 No. 1 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i1.96

Abstract

The right selection of drugs is a crucial factor in the treatment of various diseases to ensure the effectiveness of therapy and avoid risks that can worsen the patient's condition. This study aims to develop a machine learning-based prediction model to classify the appropriate type of drug based on patient characteristics. Several machine learning algorithms are tested to determine the most optimal model. The results of the analysis show that the Random Forest algorithm provides the best performance with the highest level of accuracy in predicting the right type of drug. Thus, the Random Forest-based model is recommended to be implemented as a decision support tool in the selection of drug therapies that is more accurate and efficient.
Implementation of k-Means Algorithm for Coffee Sales Clustering Kevin, Edbert
Data Science Insights Vol. 3 No. 1 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i1.102

Abstract

Coffee is one of the most widely consumed beverages worldwide, with a rich history spanning centuries. Coffee is derived from the beans of the Coffea species, primarily Coffea arabica and Coffea canephora (robusta), and is prized not only for its stimulating effects but also for its complex flavor profile. This paper examines the diverse roles of coffee in human culture, its impact on health, and the global coffee industry. Coffee contains bioactive compounds, including caffeine, antioxidants, and diterpenes, which have been studied for their potential health benefits, such as improved cognitive function and reduced risk of certain chronic diseases. However, excessive consumption can lead to negative effects, including sleep disturbances and cardiovascular problems. In addition, the environmental and social impacts of coffee cultivation, including issues related to sustainability, fair trade, and climate change, are critically examined. The paper concludes with a discussion of emerging trends in coffee research, including innovations in processing methods, the rise of specialty coffees, and the growing importance of ethical sourcing in an increasingly globalized market. This comprehensive review emphasizes the need for a balanced understanding of coffee’s benefits and challenges, highlighting its role as a cultural staple and a commodity in the global economy.
Prediction of Heart Disease Attack Risk using Deep Learning Algorithm Mishel, Michelle Virya Effendy
Data Science Insights Vol. 3 No. 1 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i1.107

Abstract

The heart is a muscular organ that acts as the main pump in the human circulatory system, pumping oxygen-rich blood throughout the body and returning blood containing carbon dioxide to be purified. Coronary heart disease, caused by arterial blockages due to plaque buildup (fat, cholesterol, and other substances), is often the leading cause of heart attacks as blood flow to the heart muscle is reduced. This condition is one of the leading causes of death worldwide, making it necessary to have an accurate method to detect this disease early. This study aims to help predict the risk of heart disease based on gender using data mining. Data mining facilitates heart disease diagnosis, particularly in helping doctors determine whether a patient suffers from heart disease based on early symptoms that appear. The author uses five data mining algorithms: Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Deep Learning. The research results show that the Deep Learning model is the best algorithm for predicting heart disease symptoms. Additionally, using the right predictive model can help reduce the risk of delayed diagnosis. Therefore, the predictive model with this algorithm is recommended for implementation in hospitals to help detect heart disease symptoms in patients more accurately and efficiently. This way, early diagnosis can be made to improve patient recovery chances and reduce mortality rates due to heart disease.

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