Komparasi Naïve Bayes, Support Vector Machine Dan K-Nearest Neighbor Untuk Mengetahui Akurasi Tertinggi Pada Prediksi Kelancaran Pembayaran Tv Kabel

Lasulika, Mohamad Efendi

doi:10.33096/ilkom.v11i1.408.11-16

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…Data mining merupakan metode untuk menemukan pengetahuan (knowledge) dalam suatu tumpukan data berdimensi tinggi. Dalam Data mining terbagi menjadi beberapa kelompok berdasarkan tugas masing-masing yaitu: Deskripsi, Estimasi, Prediksi, Klasifikasi, Clustering, dan Asosiasi [2].…”

Section: Pendahuluanunclassified

K-Nearest Neighbor Menggunakan Feature Selection Backward Elimination Untuk Prediksi Jumlah Permintaan Darah Pada Pmmi Kota Gorontalo

Lasena

Taliki

Lasulika

et al. 2023

jimik

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The importance of the availability of blood at PMI, it is expected that PMI always maintains the amount of blood supply to meet the need for blood transfusions. Prediction of blood supply is needed to overcome problems related to bloodstock supply at PMI Gorontalo. The application of predicting the number of blood requests with the K-Nearest Neighbor Algorithm can be done to overcome the existing problems. K-NN is a non-parametric algorithm that can be used for classification and regression. The last few decades have been used in prediction cases, but the K-NN algorithm is better if feature selection is applied in selecting features that are not relevant to the model, the feature selection used in this study is Backward Selection. This study aims to determine the error value in predicting the number of requests for blood at the PMI in Gorontalo City. Meanwhile, the purpose of this research is to find the error value of the K-Nearest Neighbor Algorithm and Feature Selection which can be used as a reference for PMI in making policies to make various efforts to maintainbloodstockk in the future.

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Section: Pendahuluanunclassified

K-Nearest Neighbor Menggunakan Feature Selection Backward Elimination Untuk Prediksi Jumlah Permintaan Darah Pada Pmmi Kota Gorontalo

Lasena

Taliki

Lasulika

et al. 2023

jimik

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“…Naive bayes terbukti bisa menghasilkan nilai accuracy dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan jumlah data yang besar [13]. Naive bayes juga merupakan metode dengan teknik probabilitas dimana antara fitur yang satu dengan fitur lainnya dalam satu data tidak saling memiliki keterikatan [14]. Naive bayes mempunyai kemampuan yang sama dengan pohon keputusan (decision tree) dan neural network [15].…”

Section: Naive Bayes Classifierunclassified

Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

Suwati

Yesputra

Sapta

2022

ijcs

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This study aims to predict the smoothness of installment payments in cooperatives, making it easier for staff to analyze credit lending. Lack of prudence in analyzing credit results in customers who are in arrears in paying installments, resulting in bad credit. To minimize errors that exist, it is necessary to evaluate the provision of loans to prospective debtors. By utilizing past member criteria data in the past that will be used to predict smooth payments using data mining. The data mining technique used is the Naive Bayes classifier method. The prediction process uses the naive Bayes method, namely by determining the probability or opportunity based on the previous member's data, and the results are used to help make a decision. The criteria used are member data: employment, income, house status, number of credits, and type of credit. Based on the naive Bayes method, the results obtained are 90.00% accuracy, 0.880% AUC, 83,33% recall, and 100% precision.

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“…Sementara itu data training ditampilkan ke ruang yang berdimensi banyak dimana masing-masing dimensi menjelaskan fitur dari data. Adapun tahap-tahap untuk menghitung K-NN adalah sebagai berikut [10] : a. Ambil nilai K secara acak b. Menghitung jarak antar data dengan persamaan ( 2)…”

Section: K-nearest Neighborunclassified

Komparasi Algoritma Klasifikasi Data Mining Menggunakan Optimize Selection untuk Peminatan Program Studi

Anam¹,

Nurhakim²,

Juliane³

2022

bits

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The selection of a study program is a unique opportunity for a student. STMIK IKMI Cirebon is now a KIP Kuliah provider, offering three study program. The research problem is the unavailability of a model of student interest in the study program, so it is necessary to carry out an interest in the study program by applying an algorithm to the classification model. The algorithm used as a comparison is the Decision Tree algorithm (C4.5), Naive Bayes, k-Nearest Neighbor and Support Vector Machine. The classification model applies the Optimize Selection operator by looking for the dominant attribute in its influence on the specialization of the student study program. Finally, the comparison model will be tested by parametric t-test in order to test the significance of the algorithm. The results of the algorithm accuracy test obtained that the SVM algorithm has the best accuracy with a value of 80.76%. While the algorithm with the lowest accuracy is Naive Bayes with a value of 74.64%. While the other two algorithms have a sequential accuracy rate of 80.47% for Decision Tree and 76.09% for k-NN. The results of this study are used to classify study preference for students in STMIK IKMI Cirebon which is useful for predicting study interest based on the background of students

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Komparasi Naïve Bayes, Support Vector Machine Dan K-Nearest Neighbor Untuk Mengetahui Akurasi Tertinggi Pada Prediksi Kelancaran Pembayaran Tv Kabel

Cited by 7 publications

References 4 publications

K-Nearest Neighbor Menggunakan Feature Selection Backward Elimination Untuk Prediksi Jumlah Permintaan Darah Pada Pmmi Kota Gorontalo

K-Nearest Neighbor Menggunakan Feature Selection Backward Elimination Untuk Prediksi Jumlah Permintaan Darah Pada Pmmi Kota Gorontalo

Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

Komparasi Algoritma Klasifikasi Data Mining Menggunakan Optimize Selection untuk Peminatan Program Studi

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