Research and Implement of Chinese Text Classifier Based on Naïve Bayes Method

2010 ◽  
Author(s):  
Jian Huang ◽  
Zhongdi Cen ◽  
Qiuhong Zheng
Keyword(s):  
Chinese Text ◽  
Bayes Method ◽  
Ve Bayes

scholarly journals Decision Support Systems to Selection of Diet Type Using Fuzzy Sugeno and Naïve Bayes Method

2017 ◽  
Vol 1 (02) ◽  
pp. 115
Author(s):  
Youllia Indrawaty Nurhasanah ◽  
Asep Nana Hermana ◽  
Mahesa Arga Hutama
Keyword(s):  
Decision Support ◽  
Naive Bayes ◽  
Fuzzy Inference ◽  
Linear Equations ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Inference System ◽  
Ve Bayes ◽  
Caloric Requirement ◽  
Bayes Algorithm

Sugeno Fuzzy algorithm is one of the algorithms contained on Fuzzy Inference System, that used to describe the condition between the two pieces of the decisions represented in the form of rules IF - THEN, where the output is constant or linear equations. While the Naive Bayes algorithm is an algorithm that uses data classification to a particular class based on the probability of each data class. Both of these algorithms can be implemented on a Decision Support System (DSS) for diet selection, using Fuzzy Sugeno as an additional determinant of energy and Naive Bayes method as decision maker. This is because the need for food intake and diet has become a problem for humans. To prevent excess intake of food it needs dietary adjustments or so-called diet. But in daily life, people sometimes hard to determine the type of diet that is suitable for them. So we need a system that can determine the type of diet that is suitable for a person. The data that used as a reference for decision support are age, daily caloric requirement, Body Mass Index (BMI), blood pressure, cholesterol, uric acid and blood sugar levels. Results of system testing showed from a sample of 30 data there are 26 appropriate data and 4 inappropriate data to determine the type of diet by the system with the success rate of 86.7%.

scholarly journals Prediksi Lama Studi Mahasiswa Menggunakan Naïve Bayes Berdasarkan Aspek Sosial Ekonomi Mahasiwa

2020 ◽  
Vol 2 (1) ◽  
pp. 41-50
Author(s):  
Desy Pratiwi Ika Putri ◽  
Desi Anggreani ◽  
Aji Prasetya Wibawa
Keyword(s):  
Higher Education ◽  
Formal Education ◽  
Data Sets ◽  
Bayes Method ◽  
Tertiary Institutions ◽  
Ve Bayes ◽  
Quality Of Higher Education ◽  
Study Duration ◽  
The Social

Perguruan tinggi merupakan satuan penyelenggara pendidikan tinggi sebagai tingkat lanjut jenjang pendidikan menengah di jalur pendidikan formal. Kualitas perguruan tinggi, khususnya perguruan tinggi di Indonesia diukur berdasarkan 9 standar utama. Salah satu aspek yang berpengaruh ialah mahasiswa dan lulusan. Ketepatan waktu studi mahasiswa adalah hal yang penting dalam perguruan tinggi. Ketepatan waktu mahasiswa dalam menyelesaikan studi menjadi salah satu penunjang penilaian kualitas perguruan tinggi. Metode Naïve Bayes dapat digunakan untuk memprediksi ketepatan lama studi. Klasifikasi Naïve Bayes dalam penelitian ini menggunakan beberapa variabel yang sangat erat kaitannya dalam menyelesaikan studi khususnya pada aspek sosial ekonomi mahasiswa. Adapun variable dari sisi sosial dan ekonomi tersebut diantaranya jenis kelamin, nilai IPK, tempat lahir, tipe sekolah, jumlah keikutsertaan organisasi, tingkat ekonomi, dan dukungan orang tua. Pada penelitian ini, metode Naïve Bayes diimplementasikan pada kasus prediksi lama studi mahasiswa menggunakan 200 data set. Hasil penelitian menunjukkan tingkat rata-rata akurasi sebesar 80,5% dengan menggunakan K-Fold Cross Validation diperoleh standar deviasi 3,02%.   Higher education is a higher education provider unit as an advanced level of secondary education in the formal education pathway. The quality of tertiary institutions, especially tertiary institutions in Indonesia, is measured according to 9 main standards. One influential aspect is students and graduates. Timeliness of student studies is important in higher education. Timeliness of students in completing their studies is one of the supports for assessing the quality of higher education. The Naïve Bayes method can be used to predict the accuracy of the study duration. Naïve Bayes classification in this study uses several variables that are very closely related in completing studies, especially on the social economic aspects of students. The social and economic variables include gender, GPA, birthplace, type of school, number of organizational participations, economic level, and parent support. In this study, the Naïve Bayes method is implemented in the case of prediction of student study duration using 200 data sets. The results showed an average level of accuracy of 80.5% using K-Fold Cross Validation obtained a standard deviation of 3.02%.

Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

1991 ◽  
Vol 30 (01) ◽  
pp. 15-22 ◽  
Author(s):  
A. Gammerman ◽  
A. R. Thatcher
Keyword(s):  
Abdominal Pain ◽  
Acute Abdominal Pain ◽  
Final Diagnosis ◽  
Bayes Theorem ◽  
Bayes Method ◽  
Hospital Records ◽  
Computer Diagnosis ◽  
Bayes Model ◽  
Chi Squared ◽  
Past Data

The paper describes an application of Bayes’ Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the “Simple Bayes” method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

Determination of the Prospective Processed Cacao Product and Calculation of the Added Value in Agro-Tourism Based on Cacao Agroindustry in Pidie Jaya Regency

2018 ◽  
Vol 22 (1) ◽  
pp. 33-42
Author(s):  
◽  
Tajuddin Bantacut ◽  
Sapta Raharja
Keyword(s):  
System Approach ◽  
Added Value ◽  
Assessment Criteria ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Bayes Method ◽  
Cocoa Products ◽  
Processed Products ◽  
Derivative Products

Abstract Utilization of cocoa bean to be a derivative products in industrial is wide enough, that it is necessary to determine the priority of the processed products development. This study aimed to determine the prospective processed cocoa products with a system approach using Bayes method and assessed the potential of added value by using Hayami method. Based on several assessment criteria indicated that chocolate bar is the priority product that needs to be developed and followed by several other processed products. This development was able to produce the added value of Rp 135.000 per kg of cocoa beans. Result indicated that by processing the cocoa beans into chocolate bar could provide a considerable income for the businessman.

Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation

2019 ◽  
Vol 5 (2) ◽  
pp. 108-117
Author(s):  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Cross Validation ◽  
Online Media ◽  
Bayes Classifier ◽  
Ve Bayes ◽  
Fold Cross Validation

Berita kriminalitas merupakan berita yang selalu menjadi trending topik di setiap media massa, khususnya media massa online. Media massa online terlah menyediakan beberapa fasilitas untuk mempermudah masyarakan dalam mencari sebuah berita berdasarkan topik. Media massa online melabeli suatu berita berdasarkan kategorinya. Namun, media massa online tidak memberikan sub kategori pada berita tersebut. Sebagai contoh jika seorang pengguna membuka kategori kriminal, maka yang ditampilkan adalah semua jenis berita kriminal tanpa memberikan informasi yang spesifik dari jenis kriminalitasnya. Permasalahan tersebut dapat diatasi dengan mengklasifikasikan berita kriminalitas berdasarkan subkategori. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC)  untuk mengklasifikasi berita berdasarkan sub kategorinya. Adapun subkategori terbagi kedalam 5 kategori yaitu korupsi, narkoba, pencurian, pemerkosaan dan pembunuhan. Penelitian ini bertujuan untuk mengetahui kemampuan NBC dalam mengklasifikasi berita dengan melakukan pengujian menggunakan teknik K-Fold Cross Validation dengan nilai K dari 3 sampai 10. Hasil pengujian menyatakan bahwa NBC memiliki kemampuan dalam klasifikasi berita kriminal dengan nilai precision sebesar 98,53 %, nilai recall sebesar 98,44 % dan nilai accuracy sebesar 99,38 %.

Klasifikasi Jenis Penyandang Masalah Kesejahteraan Sosial Menggunakan Naïve Bayes

2020 ◽  
Vol 6 (1) ◽  
pp. 54-61
Author(s):  
Nurhaeni Nurhaeni
Keyword(s):  
Ve Bayes

Kebutuhan utama dalam merumuskan kebijakan, program, dan kegiatan pembangunan kesejahteraan sosial adalah ketersediaan data Penyandang Masalah Kesejahteraan Sosial (PMKS). Setiap individu dalam sebuah rumah tangga sangat miskin cenderung memiliki lebih dari satu kategori jenis PMKS. Hal ini akan mempengaruhi dalam penentuan jenis bantuan yang layak diterima, dimana sebaiknya setiap individu hanya dikategorikan ke dalam satu jenis PMKS. Dengan demikian setiap individu hanya akan menerima satu jenis bantuan.  Jenis PMKS dengan pendekatan keluarga ditentukan berdasarkan 12 kriteria yaitu umur, jenis kelamin, status perkawinan, pekerjaan, pendidikan, kecukupan ASI, imunisasi, makanan sehat, pengobatan, kondisi kecacatan, orang tua tunggal, dan perhatian dari keluarga. Sebagai upaya dalam mengoptimalkan klasifikasi jenis PMKS, digunakan metode Naïve Bayes. Berdasarkan penelitian yang dilakukan, tingkat akurasi dari penggunaan metode naïve bayes adalah sebesar 83,87%. Dengan demikian dapat disimpulkan bahwa metode klasifikasi Naïve Bayes dapat digunakan untuk klasifikasi jenis PMKS.

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