scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN KREDIT USAHA MIKRO PADA BANK MANDIRI GOMBONG

Respati ◽  
2017 ◽  
Vol 10 (30) ◽  
Author(s):  
Ika Nur Fajri ◽  
Bambang Soedijono W ◽  
Syamsul A Syahdan
Keyword(s):  
Neural Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayesian Classification ◽  
Naive Bayesian ◽  
Naïve Bayesian

ABSTRAKKetepatan dan kecepatan dalam mengambil keputusan menjadi suatu keharusan pada proses penentuan kredit sehingga akan banyak nasabah yang akan menerima hasil, apakah diterima atau ditolak pengajuan kreditnya, karena semakin banyak nasabah yang mengajukan kredit.Penelitian ini mengimplementasikan algoritma naïve bayes untuk membantu menentukan siapa yang berhak mendapatkan kredit khususnya Kredit Usaha Mikro. Algoritma Naive Bayes merupakan salah satu algoritma yang terdapat pada teknik klasifikasi. Bayesian classification adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Bayesian classification didasarkan pada teorema bayes yang memiliki kemampuan klasifikasi serupa dengan decission tree dan neural network. Bayesian classification terbukti memiliki akurasi dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan data yang benar. (Kusrini dan Luthfi, 2009).Hasil penelitian ini menunjukkan tingkat akurasi naïve bayes dalam memecahkan masalah pengajuan kredit sebesar 85,33 %.Kata kunci :SPK, Naive Bayesian, Klasifikasi

scholarly journals Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification

2021 ◽  
Vol 748 (1) ◽  
pp. 012034
Author(s):  
Novriadi Antonius Siagian ◽  
Sutarman Wage ◽  
Sawaluddin
Keyword(s):  
Water Quality ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayesian Classification ◽  
Classification Model ◽  
Bayes Method ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Increase In Accuracy ◽  
Naive Bayes Method

Abstract The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been able to provide better accuracy than conventional Naïve Bayesian. Where an increase in the highest accuracy value obtained from the Water Quality dataset is equal to 88.57% in the Weight Naïve Bayesian classification model, while the lowest accuracy value is obtained from the Haberman dataset which is 78.95% in the conventional Naïve Bayesian classification model. The increase in accuracy of the Weight Naïve Bayesian classification model in the Water Quality dataset is 2.9%. While the increase in accuracy value in the Haberman dataset is 1.8%. If done the average accuracy of each dataset using the Weight Naïve Bayesian classification model is 2.35%. Based on the testing that has been done on all test data, it can be said that the Weight Naïve Bayesian classification model can provide better accuracy values than those produced by the conventional Naïve Bayesian classification model.

scholarly journals Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification

2013 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Sandi Fajar Rodiyansyah ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Support Vector ◽  
Bayes Classifier ◽  
Traffic Jam ◽  
Naive Bayesian ◽  
Naïve Bayesian

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine  AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

scholarly journals Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Sandi Fajar Rodiyansyah ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Support Vector ◽  
Bayes Classifier ◽  
Traffic Jam ◽  
Naive Bayesian ◽  
Naïve Bayesian

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

scholarly journals Improved Distance Functions for Instance-Based Text Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Khalil El Hindi ◽  
Bayan Abu Shawar ◽  
Reem Aljulaidan ◽  
Hussien Alsalamn
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Distance Measure ◽  
Naïve Bayes ◽  
Distance Functions ◽  
Distance Measures ◽  
Specific Class ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Word Frequencies

Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures’ performance and propose new ones that exploit word frequencies and the ordinal relationship between them. In particular, we propose new distance measures that are based on the value distance metric (VDM) and the inverted specific-class distance measure (ISCDM). The proposed measures are suitable for documents represented as vectors of word frequencies. We compare these measures’ performance with their original counterparts and with powerful Naïve Bayesian-based text classification algorithms. We evaluate the proposed distance measures using the kNN algorithm on 18 benchmark text classification datasets. Our empirical results reveal that the distance metrics for nominal values render better classification results for text classification than the Euclidean distance measure for numeric values. Furthermore, our results indicate that ISCDM substantially outperforms VDM, but it is also more susceptible to make use of the ordinal nature of term-frequencies than VDM. Thus, we were able to propose more ISCDM-based distance measures for text classification than VDM-based measures. We also compare the proposed distance measures with Naïve Bayesian-based text classification, namely, multinomial Naïve Bayes (MNB), complement Naïve Bayes (CNB), and the one-versus-all-but-one (OVA) model. It turned out that when kNN uses some of the proposed measures, it outperforms NB-based text classifiers for most datasets.

scholarly journals Students' learning style detection using tree augmented naive Bayes

2018 ◽  
Vol 5 (7) ◽  
pp. 172108 ◽  
Author(s):  
Ling Xiao Li ◽  
Siti Soraya Abdul Rahman
Keyword(s):  
Learning Styles ◽  
Bayesian Network ◽  
Learning Style ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Learning Environment ◽  
Detection Accuracy ◽  
Naive Bayesian ◽  
Bayes Network ◽  
Naïve Bayesian

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

scholarly journals Sistem Absensi dan Monitoring Marketing Arteri Pondok Indah Divisi Used Car Menggunakan Metode Algoritma K-Nearest Neighbor dan Naive Bayes pada PT BCA Finance

Respati ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 129
Author(s):  
Andri Kusuma Wardana, Febriani, Arief Sabarudin, Muhammad Rahman Saleh
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Signature Detection ◽  
Used Car ◽  
Returning To Work ◽  
Testing Accuracy

INTISASI Di era globalisasi sekarang ini, seiring dengan semakin berkembangnya teknologi, banyak orang berharap agar segala sesuatu menjadi lebih praktis, saat ini dibutuhkan sistem untuk melakukan tracking yang mereka lakukan saat bekerja. Salah satu solusi dari masalah ini adalah adanya sistem monitoring terhadap sales dalam melakukan pekerjaannya dalam penjualan, sehingga di sini penulis akan menggunakan k-nearest neighbor dan Naive Bayesian sebagai metode untuk klasifikasi dalam proses absensi sales. Uji coba telah dilakukan untuk menguji fungsionalitas dari sistem yang dibuat. Pengujian akurasi untuk pendeteksi tanda tangan sebagai validasi dalam absensi dengan metode klasifikasi Naive Bayesian memberikan hasil dengan tingkat akurasi yang baik. Dengan sistem absen tanda tangan ini setiap sales tidak dapat melakukan absen jika data akun saat login tidak sesuai. GPS dapat digunakan untuk mengetahui posisi letak  keberadaan sales dalam melakukan tracking pekerjaan yang akan di rekam setiap 10 menit sekali. Sistem tracing dengan GPS ini berfungsi untuk mengetahui posisi sales saat melakukan absen, istirahat, kembali bekerja, absen pulang, dan tracking per 10 menit. Kata Kunci : k-nearest neighbor, naive  bayes, gps.                                                   ABSTRACT In today's era of globalization, along with the development of technology, many people hope that everything becomes more practical, now a system is needed to track what they do while working. One solution to this problem is the existence of a monitoring system for sales in doing their work in sales, so here the author will use k-nearest neighbor and Naive Bayesian as a method for classification in the sales attendance process. Trials have been carried out to test the functionality of the system created. Testing accuracy for signature detection as validation in attendance with the Naive Bayesian classification method gives results with a good level of accuracy. With this signature absent system, every salesperson cannot perform an absence if the account data at login does not match. GPS can be used to find out where the sales are in tracking jobs which will be recorded every 10 minutes. This tracing system with GPS functions to find out the position of sales when taking absences, resting, returning to work, absent from home, and tracking every 10 minutes. Keywords: k-nearest neighbor, naive bayes, gps.

scholarly journals Customer Behavior Analysis using Weighted Selective Naive Bayesian

2020 ◽  
Vol 9 (4) ◽  
pp. 1110-1115
Keyword(s):  
Performance Prediction ◽  
Business Processes ◽  
Naive Bayes ◽  
Deep Understanding ◽  
Naïve Bayes ◽  
Customer Behavior ◽  
Naive Bayesian ◽  
Bayes Model ◽  
Naïve Bayesian ◽  
Customer Analysis

The predominant success of each enterprise relies upon the efficient analysis of customer behavior and a deep understanding of customers' needs. Performing better customer analysis provides an effective analysis of potential customers, better decision-making, improved business processes, the measure of customers churn and enhance customer retention. Efficient customer analysis can be performed using the Naive Bayes (NB), which is a simple classifier used for predicting customer behavior. However, the performance prediction of Naive Bayes is strongly affected in some real-time datasets which involve the presence of correlated attributes and this creates a breach of the assumption made by the Naive Bayes model on the dataset. To enhance the performance prediction of the NB and to eliminate correlated variables, this research suggests a simple WSNB (weighted Selective Naive Bayesian) method that uses the C4.5 DT for selecting the attributes with high information values. Then the selected weighted attributes are further used with the Naive Bayes for improving performance prediction. The Experimental approach is tested with a bank client dataset, which indicates that WSNB makes better predictions than the standard Naive Bayes. Also, WSNB reduces the running time of the classifier by eliminating the correlated attributes, which in effect minimize the size of learning and testing data

scholarly journals IMPLEMENTASI AUGMENTED REALITY UNTUK IDENTIFIKASI LOGO DAN VIDEO SEBAGAI MEDIA INFORMASI MENGGUNAKAN METODE KLASIFIKASI NAIVE BAYESIAN

2018 ◽  
Vol 9 (1) ◽  
pp. 249-254
Author(s):  
Astrid Novita Putri ◽  
Siti Asmiatun
Keyword(s):  
Augmented Reality ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayesian ◽  
Unity 3D ◽  
Naïve Bayesian

Pada  Universitas Semarang Fakultas Teknologi Informasi dan  Komunikasi setiap  tahun  selalu mempunyai banyak kegiatan seperti kegiatan Seminar, Workshop, Pelatihan, Festifal, dsb. Kegiatan- kegiatan  tersebut  biasanya   didokumentasikan  dalam  bentuk  foto   dan   video.   Sedangkan  untuk dokumentasi publikasi kegiatan dalam bentuk media promosi maupun media informasi belum dilakukan, sehingga masyarakat umum yang kurang familiar tidak dapat mengetahui informasi dengan kegiatan yang ada. Memanfaatkan aplikasi smartphone yang berbasis android, blackberry, dan iphone dapat menggunakan  salah  satu  teknologi  augmented  reality  3D  yang  berfungsi  untuk  mengidentifikasi informasi melalui logo Fakultas TIK dan menerapkannya pada berbagai media cetak atau elektronik. Sehingga dengan adanya perubahan cara promosi tersebut diharapkan dapat menarik minat perhatian masyarakat umum dan masyarakat umum untuk mengetahui informasi mengenai kegiatan di Universitas Semarang khususnya Fakultas Teknologi Informasi dan Komunikasi. Pada penelitian ini, akan membahas bagaimana mengklasifikasikan kegiatan-kegiatan tersebut menggunakan metode naive bayes   menjadi dua  kategori yaitu favorit atau tidak favorit. Berdasarkan data foto dan video kegiatan FTIK tahun 2017 yang telah diimplementasikan menggunakan tools Unity 3D menunjukkan bahwa penerapan Augmented Reality untuk identifikasi logo sebagai media informasi menggunakan metode klasifikasi naive bayes dapat diimplementasikan dengan baik. Diharapkan dengan adanya klasifikasi kegiatan dengan memanfaatkan teknologi  augmented  reality  yang  diimplementasikan menggunakan  tools  Unity  3D, informasi yang dihasilkan akan lebih informatif dan menarik perhatian masyarakat umum.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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