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All Journal Format : Jurnal Imiah Teknik Informatika Journal Collabits
Karima, Inna Sabily
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Computer Science & IT Engineering

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Penerapan Machine Learning untuk memprediksi Resiko Pengidap Penyakit Jantung menggunakan Algoritma decision tree Karima, Inna Sabily
FORMAT Vol 14, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i1.007

Abstract

Heart disease remains a leading cause of mortality worldwide, necessitating innovative approaches to improve diagnosis and management. This study aims to enhance the prediction of heart disease risk using machine learning, particularly the Decision Tree algorithm. A publicly available dataset containing 303 entries with 14 features related to heart disease risk factors, such as age, cholesterol levels, blood pressure, and electrocardiogram results, was utilized. The data underwent preprocessing steps, including normalization, handling outliers, and standardization, to ensure optimal model performance. The Decision Tree algorithm was trained on 80% of the dataset and evaluated on the remaining 20%. The model achieved an accuracy of 80%, with a balanced F1-score of 0.82, demonstrating its effectiveness in predicting heart disease risk. Feature importance analysis revealed that cholesterol levels, age, and resting blood pressure were the most influential predictors. The Decision Tree's interpretability provides valuable insights for medical practitioners, enabling more accurate and transparent risk assessments. This study highlights the potential of machine learning in medical diagnostics, particularly in identifying high-risk individuals for early intervention and better patient outcomes.
Comparative Analysis of Consumer Experience Against Business Strategy in E-Commerce: Case Study of Shopee and Tokopedia Hakim, Lukman; Karima, Inna Sabily; Oktavian, Rahmat; Muarif, Muhammad Riski; Hidayat, Ahmad
Journal Collabits Vol 1, No 2 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i2.27242

Abstract

The purpose of this research is to compare customer experiences in e-commerce. Electronic commerce is a global phenomenon that has changed the way businesses interact with consumers. It involves buying, selling and exchanging goods and services through online platforms. E-commerce has grown rapidly along with advances in information technology in business strategies. However, the challenges faced in e-commerce include the security of online transactions, intense competition, and the need to build customer trust. Examples of e- commerce are SHOPEE, LAZADA, TOKOPEDIA and others. It's just that in this research theresearcher will compare the 3 studies in terms of customer experience. SHOPEE, LAZADA AND TOKOPEDIA are three e-commerce platforms operating in Southeast Asia, including Indonesia. By using quantitative methods.
Co-Authors Ahmad Hidayat Lukman Hakim Muarif, Muhammad Riski Oktavian, Rahmat