The Development of Bank Application for Debtors Selection by Using Naïve Bayes Classifier Technique

Ginting, Selvia Lorena Br; Adler, Jonas; Ginting, Yogie Rinaldy; Kurniadi, Arif

doi:10.1088/1757-899x/407/1/012177

Cited by 4 publications

(4 citation statements)

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“…Naïve Bayes calculates a class's probability based on its attributes and determines the class with the highest probability [23]. The advantage of classification is that Naïve Bayes requires only a small amount of training data to estimate the parameters (means and variance of the variables) for the classification [24]. Only the variation of the variables for each class should be determined because independent variables are assumed, not the entire covariance matrix [25].…”

Section: Composition Of Rulesmentioning

confidence: 99%

Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection

Wanti¹,

Somantri²

2022

J. Inf. Syst. Eng. Bus. Intell.

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Background: Dental disease detection is essential for the diagnosis of dental diseases. Objective: This research compares the Mamdani fuzzy logic and Naïve Bayes in detecting dental diseases. Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naïve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021. Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naïve Bayes method classifies the type of dental disease by calculating the weight of patients’ answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naïve Bayes method was 82.1%. Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naïve Bayes method. Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naïve Bayes, Prediction

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Section: Composition Of Rulesmentioning

confidence: 99%

Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection

Wanti¹,

Somantri²

2022

J. Inf. Syst. Eng. Bus. Intell.

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“…Kredit bermasalah atau sering disebut kredit macet adalah keadaan di mana nasabah tidak membayar minimum pembayaran yang telah jatuh tempo dan sudah melewati 3 bulan penunggakan [3]. Berdasarkan kondisi tersebut, dibutuhkan suatu sistem yang mampu menyeleksi dengan cepat dan memprediksi secara akurat dengan memanfaatkan informasi yang ada (data historis) sebagai tolok ukur dalam pemberian kredit kepada nasabah yang benarbenar layak (memenuhi persyaratan yang ditetapkan) [4]- [7].…”

Section: Hybrid Classifier System: Support Vector Machines Combined Wunclassified

Hybrid Classifier System: Support Vector Machines Dikombinasikan dengan K-Nearest Neighbors untuk Menentukan Kelayakan Nasabah Bank dalam Pengajuan Kredit

Ginting¹,

Permana²

2018

Komputika

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Riset ini dilakukan dengan maksud membangun aplikasi yang dapat manganalisis data nasabah bank kemudian menentukan kelayakan nasabah tersebut dalam hal pemberian pinjaman, agar terhindar dari masalah kredit macet dikemudian hari. Metode yang digunakan adalah metode hybrid yang menggabungkan 2 teknik klasifikasi Data Mining yaitu Support Vector Machines (SVM) dan K-Nearest Neighbors (KNN). SVM bekerja dengan cara menemukan hyperplane yang optimal dan support vector. Lebih lanjut, algoritma KNN akan melakukan klasifikasi data nasabah bank berdasarkan pengidentifikasian support vector tersebut. Dengan 2000 data latih dan 103 data uji: nilai parameter cost=0,1, gamma=2, sistem mengidentifikasi 1998 support vector, kemudian dengan nilai K=16 sistem memberikan hasil 88,35% data yang cocok (91 data dari 103). Dapat disimpulkan bahwa aplikasi ini bekerja dengan cukup baik dan dapat membantu credit analyst dalam merekomendasikan nasabah yang layak memperoleh pinjaman. Kata Kunci - aplikasi; data mining; klasifikasi; metode hybrid; SVM-KNN

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“…The data is analyzed and calculated using the naive bayes algorithm. Naive bayes algorithm is used to classify data in a particular class, then the pattern can be used to estimate debtors who want to join, so the company can make a decision to accept or reject the debtors selection [10].…”

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

Naive bayes algorithm performance for smartphone sentiment analysis in social media

Sarifah¹

2018

IJARLIT

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Indonesia with a population of 250 million is a large market, Millennials tend to be more adaptive to the development of communication technology [1]. There are lot of opportunities that are used by various groups, one of which is the need to use smartphones that can make it easier for people to exchange information [2]. The shift in sales of smartphone brands in Indonesia is influenced by massive advertising carried out by smartphone vendors (smartphone capitalists) to consumers [3]. The enthusiasm of the community in welcoming this platform is so great, lot of comment about smartphone brand stated by public is an interesting thing to be processed to be information. Utilization of that information requires analytical techniques so that the produced information can help many parties. The method used in this study is Naïve Bayes classification method which is a learning technique for data mining algorithms that uses probability and statistical methods [4]. This method is used to classify comments given by the community to smartphone brands. The comments given in this application will later be classified into positive, negative, and neutral comments. The purpose of this study was to find out how much positive, negative and neutral comments the community gave to smartphone brands, so that later it would facilitate the smartphone brand in providing policies or development in the future.

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The Development of Bank Application for Debtors Selection by Using Naïve Bayes Classifier Technique

Cited by 4 publications

References 0 publications

Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection

Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection

Hybrid Classifier System: Support Vector Machines Dikombinasikan dengan K-Nearest Neighbors untuk Menentukan Kelayakan Nasabah Bank dalam Pengajuan Kredit

Naive bayes algorithm performance for smartphone sentiment analysis in social media

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