SIMULASI KLASIFIKASI  HAMA DAN PENYAKIT PADA JAGUNG DENGAN NAIVE BAYES

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic ◽

10.33558/piksel.v5i2.268 ◽

2018 ◽

Vol 5 (2) ◽

pp. 60-67 ◽

Cited By ~ 1

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

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CNB-MRF: Adapting Correlative Naive Bayes Classifier and MapReduce Framework for Big Data Classification

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i11.10116 ◽

2016 ◽

Vol 11 (11) ◽

pp. 1007 ◽

Cited By ~ 3

Author(s):

Chitrakant Banchhor ◽

N. Srinivasu

Keyword(s):

Big Data ◽

Naive Bayes ◽

Data Classification ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Mapreduce Framework ◽

Big Data Classification

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Analysis of Feature Reduction Techniques for Online News Popularity Prediction

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijo-science.v4i10.165 ◽

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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Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

Studia Universitatis Babe-Bolyai Oeconomica ◽

10.2478/subboec-2019-0009 ◽

2019 ◽

Vol 64 (2) ◽

pp. 53-71

Author(s):

Botond Benedek ◽

Ede László

Keyword(s):

Neural Network ◽

Decision Tree ◽

Naive Bayes ◽

Insurance Industry ◽

Naïve Bayes ◽

Sql Server ◽

Categorical Variables ◽

Automobile Insurance ◽

Price Determination ◽

Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

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PREDIKSI JURUSAN PADA SELEKSI NASIONAL MASUK PERGURUAN TINGGI NEGERI (SNMPTN) MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES

DOUBLECLICK Journal of Computer and Information Technology ◽

10.25273/doubleclick.v4i1.6623 ◽

2020 ◽

Vol 4 (1) ◽

pp. 55

Author(s):

Herliyani Hasanah ◽

Nugroho Arif Sudibyo ◽

Edy Kurniawan

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bahasa Indonesia

SNMPTN merupakan salah satu jalur masuk perguruan tinggi negeri yang banyak diminati siswa karena hanya menggunakan parameter nilai raport, prestasi siswa, dan prestasi sekolah. Setiap jurusan mempunyai nilai diterima minimal yang berbeda-beda, besaran kuota yang ditetapkan LTMP 2019 minimal hanya 20% dari daya tampung program studi di setiap perguruan tinggi negeri. Besarnya minat siswa dan kecilnya jumlah kuota tidak sebanding sehingga menyebabkan persaingan diterima pada jalur ini semakin ketat. Namun, masih banyak siswa yang belum mempertimbangkan parameter tersebut saat mendaftar sehingga kemungkinan diterima pada jalur SNMPTN semakin kecil. Oleh karena itu diperlukan sebuah sistem yang dapat memprediksi kemungkinan diterimanya siswa pada jurusan SNMPTN berdasarkan atribut yang sudah ditentukan. <em>Naïve Bayes</em> diterapkan untuk mencari nilai probabilitas terbesar dalam setiap variabel yang ada. Variabel yang digunakan meliputi nilai rata-rata matematika, bahasa indonesia, dan bahasa inggris semester 1 sampai 5 serta prestasi siswa yang dilampirkan saat mendaftar dan prestasi sekolah. Hasilnya dengan <em>naïve bayes</em> mampu menghasilkan akurasi sebesar 83,3%.

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Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i7.1419 ◽

2019 ◽

Vol 7 (7) ◽

pp. 14-19 ◽

Cited By ~ 1

Author(s):

Asharani S Dandoti ◽

Sunil M Sangve

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Emotion Identification ◽

Bayes Algorithm

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Otomatisasi Peringkasan Dokumen Sebagai Pendukung Sistem Manajemen Surat

Register Jurnal Ilmiah Teknologi Sistem Informasi ◽

10.26594/r.v1i1.400 ◽

2015 ◽

Vol 1 (1) ◽

Author(s):

Ahmad Najibullah ◽

Wang Mingyan

Keyword(s):

Naive Bayes ◽

Naïve Bayes

Peringkasan dokumen adalah proses penyajian kembali dokumen dalam bentuk yang lebih singkat tanpa membuang informasi penting yang terdapat dalam dokumen tersebut. Dalam penelitian ini, peneliti menggunakan metode Naive Bayes untuk menghasilkan ringkasan sebuah dokumen. Objek dalam penelitian ini berupa dokumen yang berbentuk surat. Dalam proses peringkasan dokumen, penghitungan probabilitas didasarkan pada fitur teks yang ada dalam surat, diantaranya adalah frekuensi kata, kata kunci, frase kunci, dan kata yang termasuk dalam kelas entitas atau numerik. Hasil uji coba menunjukkan bahwa tingkat kompresi adalah 53.67% dengan informasi penting yang tersedia dalam ringkasan mencapai 96.67% dari dokumen asli.

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