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All Journal Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Al-Qardh MEDIA BINA ILMIAH Journal of Mathematics: Theory and Applications Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Jurnal Pengabdian Sumber Daya Manusia Media Bina Ilmiah
Yuana Sukmawaty
Universitas Lambung Mangkurat
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Journal : Journal of Mathematics: Theory and Applications

 1 
K-Means Clustering dan Principal Component Analysis (PCA) Dalam Radial Basis Function Neural Network (RBFNN) Untuk Klasifikasi Data Multivariat Hayqal Hazmi Qastari Hayqal; Oni Soesanto; Yuana Sukmawaty
Journal of Mathematics: Theory and Applications Volume 4, Nomor 1, 2022
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1089.188 KB) | DOI: 10.31605/jomta.v4i1.1757

Abstract

Pada penelitian ini dilakukan uji simulasi data berskala besar sehingga diperlukan metode yang handal untuk permasalahan klasifikasi salah satunya adalah Radial Basis Function Neural Network (RBFNN). Untuk training data RBFNN menggunakan struktur khusus yang melibatkan dimensi tinggi pada hidden layer. Dengan struktur RBFNN yang khusus tersebut maka seringkali menimbulkan permasalahan karena hidden layernya terlalu besar, sehingga diperlukan penambahan metode penyederhaan jaringan seperti PCA dan K-Means Clustering. Metode PCA digunakan untuk mereduksi dimensi input pada RBFNN sedangkan metode K-Means Clustering digunakan untuk penentuan inisialisasi center awal pada RBFNN. Pada hasil percobaan metode PCA dihasilkan komponen utama ke-1 dan ke-2 dengan masing-masing mewakili 55.2288% dan 27.3108% dari seluruh variabilitas, secara kumulatif kedua komponen utama menyatakan sebesar 82.5396% dan hasil percobaan perulangan iterasi di metode penelitian ini didapatkan hasil rata-rata proses akurasi PC dan Klas terbaik berada pada PC-2 Klas-3 dengan akurasi di atas 90% untuk proses training dan testing dengan akurasi kesalahan klasifikasi di bawah 10%.
Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit Sri Elina Herni Yulianti; Oni Soesanto; Yuana Sukmawaty
Journal of Mathematics: Theory and Applications Volume 4, Nomor 1, 2022
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.128 KB) | DOI: 10.31605/jomta.v4i1.1792

Abstract

Bad credit card is a problem of inability of credit card users to pay credit card bills that can cause losses to both parties concerned. In order to avoid losses caused by bad credit cards, the provider must conduct a careful analysis of prospective or old customers using credit cards. This study aims to classify bad credit card customers using machine learning techniques, namely classification techniques. One of the classification techniques used is the XGBoost method which is useful for regression analysis and classification based on the Gradient Boosting Decision Tree (GBDT), the XGBoost method has several hyperparameters that can be configured to improve the performance of the model. Hyperparameter tuning method used is grid search cross validation which is then validated using 10-Fold Cross Validation. XGBoost hyperparameters configured include n_estimators, max_depth, subsample, gamma, colsample_bylevel, min_child_weight and learning_rate. Based on the results of this study proves that the use of algorithms with hyperparameter tuning can improve the performance of eXtreme Gradient Boosting algorithm in the process of classification of credit card customers with an accuracy of 80.039%, precision of 81.338% and a recall value of 96.854%. Keywords: XGBoost, classification, Accuracy, Precision, Recall
Co-Authors Annisa Halida Annisa Halida Azkia Azkia Dewi Anggraini Dewi Anggraini Dewi Sri Susanti Farah, Nadya Fitriani Fitriani Fuad Muhajirin Farid Hayqal Hazmi Qastari Hayqal Lestia, Aprida Siska Nabila Septiani Nur Salam Sekar Wahyuningtias Selvi Annisa Siti Jubaidah Soesanto, Oni Sri Elina Herni Yulianti Syifa Ajeria Thaibatun Nissa Winda Adinda Tanjung Yeni Rahkmawati Yunita Alisia