Classification of Customer Loans Using Hybrid Data Mining

Mandala, Eka Praja Wiyata; Rianti, Eva; Defit, Sarjon

doi:10.30595/juita.v10i1.12521

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…ADASYN is also an oversampling method, it is a further development of SMOTE which works more adaptively to make the dataset relatively balanced. The steps of the ADASYN method are as follows [14] A combination of SMOTE with an undersampling gives better performance [15]. One of the popular hybrid methods of oversampling-undersampling technique is SMOTE-ENN which was developed by [13].…”

Section: B Preprocessingmentioning

confidence: 99%

Resampling Technique for Imbalanced Class Handling on Educational Dataset

Rozi¹,

Wibowo²,

Warsito³

2023

JUITA

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Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.

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Section: B Preprocessingmentioning

confidence: 99%

Resampling Technique for Imbalanced Class Handling on Educational Dataset

Rozi¹,

Wibowo²,

Warsito³

2023

JUITA

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“…Penggabungan metode data mining ini disebut dengan hybrid data mining. Hybrid data mining merupakan kombinasi dari beberapa metode fitur seleksi dan klasifikasi untuk membantu proses pengambilan keputusan [7]. Dalam metode hybrid data mining, faktor penting yang mempengaruhi adalah proses pembangkitan berbagai klasifikasi untuk membuat model umum.…”

Section: Pendahuluanunclassified

Hybrid Data Mining berdasarkan Klasterisasi Produk untuk Klasifikasi Penjualan

Putri

Mandala

2022

komtekinfo

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Penjualan yang terjadi pada minimarket mengalami pasang surut sejak pandemi Covid-19. Dalam penelitian ini, didapatkan masalah yaitu banyaknya produk pada mininmarket tidak laku terjual atau sebaliknya, adanya permintaan produk, namun di minimarket tidak tersedia. Data mining bisa menjadi solusi dalam menyelesaikan masalah ini. Penelitian ini mengusulkan metode hybrid data mining dengan menggabungkan algoritma K-Means dan K-Nearest Neighbors. Kombinasi metode ini bekerja dengan dua tahap yaitu melakukan klasterisasi produk yang terjual yang diteruskan dengan melakukan klasifikasi penjualan produk tersebut. Data yang digunakan pada penelitian ini adalah data produk perlengkapan mandi dan mencuci sebanyak 20 produk. Dari hasil penelitian yang dilakukan, terdapat 14 produk yang Banyak Peminat dari 20 produk. Dari 14 produk yang digunakan sebagai data pelatihan, dilakukan klasifikasi penjualan untuk 1 produk baru dengan kriteria stok ≥ 200, terjual ≥ 100 dan harga ≥ Rp. 10.000. Dari hasil pengujian diperoleh klasifikasi produk baru adalah Penjualan Tinggi dengan ketepatan dalam klasifikasi mencapai 85.7143 %. Penggunaan metode hybrid antara algoritma K-Means dan K-Nearest Neighbors memberikan pengaruh yang signifikan dalam menentukan hasil klasifikasi. Untuk penelitian selanjutnya, disarankan adanya proses prediksi dari hasil klasifikasi yang sudah ditemukan, sehingga lebih memiliki pengaruh yang besar dalam penggunaan metode hybrid data mining ini.

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Classification of Customer Loans Using Hybrid Data Mining

Cited by 2 publications

References 16 publications

Resampling Technique for Imbalanced Class Handling on Educational Dataset

Resampling Technique for Imbalanced Class Handling on Educational Dataset

Hybrid Data Mining berdasarkan Klasterisasi Produk untuk Klasifikasi Penjualan

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