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All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Indonesian Journal of Health Science Madani: Multidisciplinary Scientific Journal
Sinaga, Albert Samuel
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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. 
Penggunaan Metode K-Means Clustering Pemetaan dan Klasterisasi Tempat Wisata di Kabupaten Deli Serdang Beatrice, Chelsea; Inggrid, Antonia; Sinaga, Kezia Theodora; Sinaga, Albert Samuel
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 3 (2025): April 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15276418

Abstract

A problem that often arises is the lack of a clear system for categorizing and classifying tourist attractions based on geographical characteristics. Tourist route planning, supporting infrastructure development, and targeted promotion strategies all become more difficult because of this. As a result, a data-driven approach is needed to more systematically analyze and map tourist attractions in Deli Serdang Regency. This research aims to map and cluster tourist attractions in Deli Serdang Regency based on spatial characteristics and entrance ticket prices using the K-Means Clustering method. This method is used to group tourist sites into several categories based on the similarity of attributes, which include geographic coordinates (latitude and longitude), ticket prices, and distance to the city center as a reference location. The data were analyzed using a spatial statistics approach using Python, while spatial visualization of the clustering results was done with the help of QGIS software to facilitate interpretation of the area. The results of the analysis show that tourist attractions in Deli Serdang Regency can be grouped into three main clusters, each representing tourist groups that are very close, close, and far from the city center, and have differences in the range of admission prices. Evaluation of cluster quality using the Davies-Bouldin Index (DBI) resulted in a value of 1.377, which indicates that cluster formation has been quite good, although there is room for improvement. Thus, this study succeeded in mapping and clustering tourist attractions in Deli Serdang spatially using the K-Means Clustering approach, which is expected to contribute to tourism promotion planning and tourist destination development in the region.
Pemantauan Kualitas Udara di Kota Medan Menggunakan Peta Kendali Multivariat T² Hotelling Sihombing, Christoffel; Harahap, Adi Guanawan; Sinaga, Albert Samuel
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 8 No. 3 (2025): Volume 8 Nomor 3 Tahun 2025 (July - September)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v8i3.6164

Abstract

Penelitian ini bertujuan untuk memantau dan menganalisis tingkat pencemaran udara di Kota Medan menggunakan pendekatan peta kendali multivariat T² Hotelling. Data yang digunakan merupakan data sekunder yang diperoleh dari AQI.in selama periode 1 April hingga 29 Mei 2025, mencakup enam jenis polutan utama: PM2.5, PM10, CO, SO2, NO2, dan O3. Metodologi yang digunakan meliputi statistik deskriptif, transformasi data dengan Z-score, penerapan peta kendali T² Hotelling, serta dekomposisi MYT untuk mengetahui kontribusi masing-masing variabel terhadap fluktuasi kualitas udara. Hasil penelitian menunjukkan bahwa pada tanggal 14 Mei dan 19 Mei 2025, tingkat polusi udara melebihi batas kendali atas. Polutan NO2, SO2, dan O3 merupakan penyumbang utama pada 14 Mei, sementara PM10 dan O3 dominan pada 19 Mei. Peta kendali T² Hotelling terbukti efektif dalam mendeteksi pergeseran kualitas udara secara simultan.
Co-Authors Beatrice, Chelsea Harahap, Adi Guanawan Harefa, Clara Jocelyn Inggrid, Antonia Sarah, Auta Shinta Sihombing, Christoffel Simorangkir, Agnes Monica Sinaga, Kezia Theodora Surbakti, Christina Amanda Syairal Fahmy Dalimunthe