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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi Journal Of Artificial Intelligence And Software Engineering
Perkasa, Legawan
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Computer Science & IT

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EDA and Tableau Analysis for Identification of Heart Disease Risk Factors Silmina, Esi Putri; Perkasa, Legawan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6389

Abstract

Heart disease is one of the leading causes of death worldwide, influenced by various risk factors such as high blood pressure, cholesterol levels, and lifestyle. This study analyzes the risk factors for heart disease using the Heart Disease Dataset which includes more than 1,000 records with variables such as age, blood pressure, cholesterol, and alcohol consumption. Exploratory Data Analysis (EDA) was applied to identify patterns and relationships between variables, while Tableau was used to present the results visually and interactively. The results showed that high blood pressure was the main risk factor, with the majority of patients having blood pressure in the range of 130-135 mmHg, which is considered high risk. In addition, high cholesterol levels (200-205 mg/dL) also contributed significantly to the increased risk of heart disease, while alcohol consumption in the "Heavy" category worsened heart health conditions. Data visualization shows an increasing trend in heart disease cases, especially in individuals with a combination of these risk factors. Therefore, this study emphasizes the importance of routine blood pressure and cholesterol monitoring, implementing a healthy diet, regular physical activity, and health education to reduce the incidence of heart disease in the future.
Penerapan Arsitektur Kappa dengan Kafka dan Spark untuk Pemrosesan Data Hipertensi di Media Sosial X Perkasa, Legawan; Hardiani, Tikardiha
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 9 No. 1 (2026): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v9i1.57819

Abstract

Hypertension is one of the major public health problems with a continuously increasing prevalence and is widely discussed on social media platform X. The dynamic and continuously flowing nature of social media data requires a Big Data-based processing approach capable of operating in real-time and in a scalable manner. This study aims to implement a streaming-based Big Data architecture (Kappa Architecture) using Apache Kafka and Apache Spark to process and analyze conversations about hypertension on the social media platform X in real-time. The proposed system integrates the X API as the data source, Apache Kafka as the immutable event log and streaming backbone, Apache Spark Structured Streaming as the real-time data processing engine, and MongoDB as the serving layer. The research methodology includes a literature review, system design, streaming-based data collection, real-time text cleaning and feature extraction, and performance evaluation using throughput, latency, and success rate parameters. A total of 10,000 tweets were collected over a two-month period and processed through a unified streaming pipeline. The implementation results show that the system successfully established a consistent end-to-end processing workflow, enabling real-time data ingestion and processing without separating batch and speed layers. The system achieved an average throughput of 19.23 tweets per second, a latency of approximately 520 seconds, and a success rate of 100%. This study concludes that the Kappa Architecture is effective, stable, and scalable for real-time processing and analysis of social media data in monitoring public health issues such as hypertension.
Co-Authors Hardiani, Tikardiha Silmina, Esi Putri