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All Journal Techno.Com: Jurnal Teknologi Informasi Scientific Journal of Informatics Jurnal Info Kesehatan
Fulazzaky, Tahira
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology

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Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat Fulazzaky, Tahira; Waode, Yully Sofyah; Fitrianto, Anwar; Erfiani, Erfiani; Pradana, Alfa Nugraha
Techno.Com Vol. 22 No. 4 (2023): November 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i4.9281

Abstract

Tujuan dari penelitian ini adalah untuk mengkaji bagaimana kualitas air dan sanitasi mempengaruhi Kejadian Luar Biasa (KLB) Diare di Provinsi Jawa Barat, Indonesia, menggunakan data Pendataan Potensi Desa (PODES) tahun 2021. Diare merupakan permasalahan serius dalam kesehatan masyarakat Indonesia, terutama pada kelompok anak balita, dan salah satu faktor penyebab utamanya adalah rendahnya kualitas air dan sanitasi. Dalam konteks penelitian ini, kami menerapkan metode algoritma K-Nearest Neighbors (K-NN) untuk mengklasifikasikan wilayah-wilayah yang mengalami KLB Diare. Hasil eksplorasi data menunjukkan variasi yang signifikan dalam jumlah kasus diare di sejumlah kabupaten dan kota yang tersebar di wilayah Jawa Barat. Untuk menangani masalah ketidakseimbangan data, kami menerapkan teknik Pengurangan Acak (Random Under Sampling), Penambahan Acak (Random Over Sampling), dan Synthetic Minority Oversampling Technique (SMOTE).Hasil analisis menunjukkan bahwa model K-NN dengan penggunaan metode SMOTE menghasilkan tingkat akurasi tertinggi, yaitu sebesar 71.28%. Meskipun demikian, nilai F1 score untuk semua model cenderung rendah, yang mengindikasikan adanya tantangan dalam mengklasifikasikan wilayah-wilayah dengan KLB Diare. Penelitian ini memberikan wawasan yang penting mengenai korelasi antara kualitas air, sanitasi, dan KLB Diare di Jawa Barat, serta mengidentifikasi wilayah-wilayah yang memerlukan perhatian lebih dalam upaya pencegahan dan pengendalian penyakit diare. Hasil ini dapat digunakan sebagai dasar untuk merancang program-program kesehatan yang lebih efektif di daerah-daerah dengan tingkat insiden diare yang tinggi. Kata kunci: Algoritma K-Nearest Neighbors (K-NN), SMOTE, Ketidakseimbangan data dan teknik pengambilan sampel ulang, Kualitas air dan sanitasi, Program pencegahan dan pengendalian diare.
Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost Fulazzaky, Tahira; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15937

Abstract

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.
The Influence of Women’s Empowerment on The Preference for Contraceptive Methods in Indonesia: A Multinomial Logistic Regression Modelling Fulazzaky, Tahira; Indahwati, Indahwati; Fitrianto , Anwar; Erfiani, Erfiani; Khikmah, Khusnia Nurul
JURNAL INFO KESEHATAN Vol 22 No 3 (2024): JURNAL INFO KESEHATAN
Publisher : Research and Community Service Unit, Poltekkes Kemenkes Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31965/infokes.Vol22.Iss3.1213

Abstract

The concept of women's empowerment encompasses enabling women to take control of their own lives, independently make choices, and fulfill their complete capabilities. Numerous research studies examined the correlation between the empowerment of women and their reproductive health. In Indonesia, female labor force participation is relatively low. As a result, research on the influence of empowering women on contraceptive method preference in Indonesia makes sense. This research aims to find the multinomial logistic regression model in choosing contraceptive methods for married women in Indonesia and to identify the women’s empowerment traits that most impact contraceptive method choice.  For this study, the researchers utilized secondary data obtained from the 2017 Indonesian Demographic and Health Survey (IDHS). The participants consisted of women between the ages of 15 and 49 who were married. The total number of respondents sampled was 49,216. Variables that significantly affect contraceptive method use include the respondent's current employment, the respondent has bank account or other financial institution accounts, the cumulative count of offspring previously born and beating justified if the wife argues with her husband. The analysis is obtained using the multinomial logistic regression test, independency, multicollinearity, and parameter test, and the selection is made by considering either the smallest value of Akaike's information criterion or the option that achieves the highest level of accuracy. Findings highlight four significant variables: Firstly, employed women are more likely to use contraceptives than the unemployed. Secondly, access to banking services correlates with a higher likelihood of contraceptive use. Thirdly, women with more children tend to prefer long-acting reversible contraceptives. Lastly, endorsement of spousal violence justifiability is linked to conventional contraceptive selection. These results emphasize the roles of employment, financial access, family size, and gender-based violence perceptions in shaping contraceptive choices in Indonesia. Model 3 emerges as the most accurate predictor of preferences after eliminating six variables based on rigorous testing and multicollinearity considerations. These findings underscore the importance of addressing economic empowerment and gender-related issues in Indonesian reproductive health programs and policies. Such a comprehensive approach can enhance women's autonomy, enabling them to make crucial life choices and ultimately improving their overall well-being.         
Co-Authors Agus Mohamad Soleh Alfa Nugraha Pradana ASEP SAEFUDDIN Erfiani Erfiani Fitrianto, Anwar Indahwati Khikmah, Khusnia Nurul Waode, Yully Sofyah