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All Journal Indonesian Journal of Data and Science Science in Information Technology Letters
Kinasih, Agnes Nola Sekar
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Comparative analysis of decision tree and random forest classifiers for structured data classification in machine learning Kinasih, Agnes Nola Sekar; Handayani, Anik Nur; Ardiansah, Jevri Tri; Damanhuri, Nor Salwa
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1746

Abstract

This study explores the application of machine learning techniques, specifically classification, to improve data analysis outcomes. The primary objective is to evaluate and compare the performance of Decision Tree and Random Forest classifiers in the context of a structured dataset. Using the Elbow Method for optimal clustering alongside decision tree and random forest for classification algorithms, this research investigates the effectiveness of each method in accurately categorizing data. The study employs K-Means clustering to segment the data and Decision Trees and Random Forests for classification tasks. Dataset used in this research was obtained from Kaggle consisting of 13 attributes and 1048575 rows, all of which are numeric. The key results show that Random Forest outperforms Decision Trees in terms of classification accuracy, precision, recall, and F1 score, providing a more robust model for data classification. The performance improvement observed in Random Forest, particularly in handling complex datasets, demonstrates its superiority in generalizing across varied classes. The findings suggest that for applications requiring high accuracy and reliability, Random Forest is preferable to Decision Trees, especially when the dataset exhibits high variability. This research contributes to a deeper understanding of how different machine learning models can be applied to real-world classification problems, offering insights into the selection of the most appropriate model based on specific data characteristics.
Application of Mamdani Fuzzy Logic in Identifying Postpartum Depression Risk Kinasih, Agnes Nola Sekar; Hosen, Moh; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.193

Abstract

Introduction: Postpartum depression (PPD) is a common psychological disorder affecting mothers after childbirth, often underdiagnosed due to the subjective nature of its symptoms. Early detection is crucial to prevent adverse effects on maternal and child health. This study aims to develop an early detection system for PPD risk using Mamdani fuzzy logic, which is well-suited to handle vague and imprecise symptom data. Methods: A fuzzy inference system was designed using the Mamdani method to classify PPD risk into Low, Medium, and High categories. The system was built upon a dataset of 1503 questionnaire responses sourced from Kaggle. Subjective symptoms such as sadness, irritability, sleep disturbances, and bonding difficulties were mapped into fuzzy membership functions. A total of 243 fuzzy rules were defined to reflect realistic combinations of symptoms. The system was implemented and validated in both Python and LabVIEW environments. Results: Experimental validation using 10 test inputs showed consistent results between the two platforms, with a deviation of less than ±1%. This consistency confirms the reliability of the fuzzy logic model in interpreting subjective symptom data. The system demonstrated strong potential for classifying PPD risk based on nuanced input variables. Conclusions: The Mamdani fuzzy logic system offers a reliable and flexible tool for assessing postpartum depression risk. By effectively interpreting ambiguous symptoms, it supports healthcare professionals in identifying at-risk individuals for early intervention. Future enhancements should include expanding the dataset and refining the rule base for broader applicability and improved accuracy.
Co-Authors Anik Nur Handayani Damanhuri, Nor Salwa Hosen, Moh Jevri Tri Ardiansah