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All Journal Indonesian Journal of Health Science Madani: Multidisciplinary Scientific Journal Parameter: Jurnal Matematika, Statistika dan Terapannya
Simorangkir, Agnes Monica
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Journal : Madani: Multidisciplinary Scientific Journal

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Perbandingan Metode K-Nearest Neighbors (K-NN) dan Regresi Logistik Biner Dalam Memprediksi Kanker Surbakti, Christina Amanda; Sinaga, Albert Samuel; Simorangkir, Agnes Monica; Sarah, Auta Shinta; Harefa, Clara Jocelyn; Dalimunthe, Syairal Fahmy
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 2 (2025): March
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15074826

Abstract

Background: Cancer is one of the diseases with a high mortality rate, so an accurate classification method is needed to support the diagnosis process. This study compares the performance of the K-Nearest Neighbors (KNN) method and Binary Logistic Regression in classifying cancer as malignant or benign. Methods: This study used a secondary dataset from Kaggle consisting of 569 cancer patient data with 11 independent variables covering tumor characteristics. The model was developed using data normalization, training and testing data division, and the K-Fold Cross Validation technique to optimize the K parameter in KNN. Model evaluation was carried out based on accuracy, precision, recall, and the McNemar and ANOVA tests to test the significance of differences in model performance. Results: The KNN model with K=13 showed an accuracy of 95.58%, a precision of 95.83%, and a recall of 97.18%, while Binary Logistic Regression had an accuracy of 94.69%, a precision of 92.86%, and a recall of 92.86%. The McNemar test results showed that there was no significant difference between the two models (p-value = 1), while the ANOVA results showed that all independent variables contributed to the model. Conclusion: Both methods performed well in cancer classification, but KNN with K=13 had a slight advantage in accuracy and recall compared to Binary Logistic Regression. The implementation of this model can support decision support systems in cancer diagnosis to improve the accuracy of classification results. 
Analisis Pengaruh PDRB, Umur Harapan Hidup, Rata-rata Lama Sekolah, dan Pengeluaran Perkapita terhadap Indeks Pembangunan Manusia di Provinsi Lampung Triono, Wira; Simorangkir, Agnes Monica; Putri, Maharani Renika
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 5 (2025): Volume 3, Nomor 5, June 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Human development is a crucial foundation for a country's progress, with the Human Development Index (HDI) as its main benchmark. Lampung Province faces challenges in the form of HDI achievements below the national average and the existence of development inequality between regions. This study aims to analyze the effect of Gross Regional Domestic Product (GRDP), Life Expectancy (UHH), Average Years of Schooling (RLS), and Expenditure per Capita (PP) on HDI in Lampung Province. The method used is multiple linear regression analysis on secondary data from 15 districts/cities in Lampung during the period 2020-2024. The results of the analysis show that the regression model involving the four independent variables is significantly better than the simpler model. The F-test (simultaneous) and t-test (partial) prove that GRDP, RLS, UHH, and PP together and individually have a significant influence on HDI at the 5% significance level. The model also proved to be valid and robust after fulfilling all classical assumption tests, namely residual normality, homoscedasticity, non-autocorrelation, and non-multicollinearity, resulting in the Best Linear Unbiased Estimator (BLUE) model. With an Adjusted R-squared value of 0.9925, this model shows a very high ability to explain variations in HDI. Interpretation of the model shows that Average Years of Schooling (AOLS) has the largest positive influence on HDI improvement.
Co-Authors Haliza, Putri Yusra Harefa, Clara Jocelyn Putri, Maharani Renika Sarah, Auta Shinta Sarah, Auta Shintha Sinaga, Albert Samuel Surbakti, Christina Amanda Syairal Fahmy Dalimunthe Triono, Wira