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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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Perbandingan mtode 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
Indonesian Journal of Health Science Vol 5 No 2 (2025)
Publisher : PT WIM Solusi Prima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54957/ijhs.v5i2.1456

Abstract

Latar Belakang: Kanker merupakan salah satu penyakit yang memiliki tingkat kematian tinggi, sehingga dibutuhkan metode klasifikasi yang akurat untuk mendukung proses diagnosis. Penelitian ini membandingkan performa metode K-Nearest Neighbors (KNN) dan Regresi Logistik Biner dalam mengklasifikasikan kanker sebagai ganas atau jinak. Metode: Penelitian ini menggunakan dataset sekunder dari Kaggle yang terdiri dari 569 data pasien kanker dengan 11 variabel independen yang mencakup karakteristik tumor. Model dikembangkan dengan menggunakan normalisasi data, pembagian data training dan testing, serta teknik K-Fold Cross Validation untuk optimasi parameter K dalam KNN. Evaluasi model dilakukan berdasarkan akurasi, presisi, recall, serta uji McNemar dan ANOVA untuk menguji signifikansi perbedaan performa model. Hasil: Model KNN dengan K=13 menunjukkan akurasi 95,58%, presisi 95,83%, dan recall 97,18%, sementara Regresi Logistik Biner memiliki akurasi 94,69%, presisi 92,86%, dan recall 92,86%. Hasil uji McNemar menunjukkan bahwa tidak terdapat perbedaan signifikan antara kedua model (p-value = 1), sedangkan hasil ANOVA menunjukkan bahwa semua variabel independen berkontribusi terhadap model. Kesimpulan: Kedua metode menunjukkan performa yang baik dalam klasifikasi kanker, tetapi KNN dengan K=13 memiliki sedikit keunggulan dalam akurasi dan recall dibandingkan Regresi Logistik Biner. Implementasi model ini dapat mendukung sistem pendukung keputusan dalam diagnosis kanker untuk meningkatkan ketepatan hasil klasifikasi.
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.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI NILAI UJIAN MENGGUNAKAN REGRESI LOGISTIK ORDINAL Triono, Wira; Haliza, Putri Yusra; Sarah, Auta Shintha; Simorangkir, Agnes Monica
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 2 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv3i02pp135-146

Abstract

Penelitian ini menganalisis faktor-faktor yang mempengaruhi nilai ujian siswa dengan menggunakan regresi logistikordinal. Pendidikan merupakan kunci utama untuk kemajuan suatu bangsa, dan pemahaman terhadap faktor-faktor yang memengaruhi kinerja siswa sangatlah penting. Penelitian ini mengkaji faktor internal seperti jam belajar, motivasi, jam tidur, dan kehadiran, serta faktor eksternal termasuk pendapatan keluarga dan jenis sekolah. Menggunakan data sekunder dari dataset "Faktor Performa Siswa" yang tersedia di Kaggle, analisis ini menerapkan model logit kumulatif untuk mengidentifikasi hubungan antara variabel-variabel tersebut. Hasil penelitian menunjukkan bahwa jam belajar dan motivasi memiliki dampak signifikan terhadap nilai ujian, memberikan wawasan berharga untuk pengembangan kebijakan pendidikan yang bertujuan meningkatkan hasil belajar siswa. Penelitian ini berkontribusi pada pemahaman tentang bagaimana berbagai faktor saling terkait dalam keberhasilan akademik, serta menyoroti pentingnya intervensi yang terarah dalam pendidikan.
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