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All Journal Jurnal Sistem Cerdas Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Nurjanah, Endang
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Analisis Prediksi Stroke Menggunakan Pendekatan Decision Tree dengan Seleksi Fitur dan Neural Network Indah Werdiningsih; Purwanti, Endah; Mardiyana, Iin; Handayani, Arum Tiyas; Suryadewi, Kharristantie Sekarlangit; Nurjanah, Endang; Akhlaqulkarimah, Fildzah; Pramiyas, Naurah Hedy; Yahrani, Fakhrana Almas Syah
Jurnal Sistem Cerdas Vol. 6 No. 3 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i3.310

Abstract

Currently, stroke is the second cause of death globally. According to data from the World Health Organization (WHO), 7.9% of deaths in Indonesia are caused by stroke. Based on these data, analysis of the factors influencing the case growth rate is very useful. This paper analyzes various factors in electronic health records for effective stroke prediction with different machine-learning algorithms including Decision Tree and Neural Networks. This research uses a dataset consisting of 12 features, namely ID, gender, age, history of hypertension, history of heart disease, marital status, type of work, type of residence, average glucose level, BMI (Body Mass Index) number, and status. smoking, and prediction of stroke. These features were analyzed using the Neural Network and Decision Tree methods so that selected features were produced for further analysis using the Neural Network method. The feature selection results consist of 5 features: age, history of hypertension, marital status, average glucose level, and BMI (Body Mass Index) number. The highest accuracy results were obtained using the Neural Network method with a feature selection of 88.75, the second highest was obtained with the neural network method of 87.1875, and the lowest accuracy was obtained with the Decision Tree method which had an accuracy result of 81.25. Based on these accuracy results, it can be obtained that the most optimal results are shown by the Neural Network method with feature selection.
The Implementation of Machine Learning for Software Effort Estimation: A Literature Review Hariyanti, Eva; Paradista , Mirtha Aini; Goyayi, Maria Lauda Joel; Arthalia, Arthalia; Shabirina, Detria Azka; Nurjanah, Endang; Husna, Oktavia Intifada; Yahrani, Fakhrana Almas Syah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.2803

Abstract

Effort estimation is pivotal for the triumph of software development endeavors. The appropriate forecasting approach is vital for aligning software project effort estimation outcomes. This process aids in efficiently distributing resources, charting project strategies, and facilitating informed choices in IT Project Management. Machine learning, a facet of artificial intelligence (AI), is dedicated to crafting algorithms and models that empower computers to enhance their performance based on data and facilitate predictions or decision-making. This study discusses the implementation of machine learning in software development effort estimation. We collected 558 relevant papers on software effort estimation and machine learning techniques. After a quality review process, we identified 40 articles for in-depth review. We categorized machine learning techniques into supervised, unsupervised, and reinforcement learning. The results indicate that using ensemble techniques in supervised and unsupervised learning can improve the accuracy of software effort estimation. Artificial Neural Networks, Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Bootstrap Aggregation are the most commonly used methods. Ensemble techniques also aid in selecting relevant features and preprocessing data to enhance model performance. This study provides insights into implementing machine learning techniques to estimate software effort and highlights the advantages of ensemble technique.
Co-Authors Akhlaqulkarimah, Fildzah Arthalia, Arthalia Endah Purwanti Eva Hariyanti Goyayi, Maria Lauda Joel Handayani, Arum Tiyas Husna, Oktavia Intifada Mardiyana, Iin Paradista , Mirtha Aini Pramiyas, Naurah Hedy Shabirina, Detria Azka Sugeng, Santoso Suryadewi, Kharristantie Sekarlangit Yahrani, Fakhrana Almas Syah