Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

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PENGELOMPOKAN SEBARAN TRANSFORMATOR DISTRIBUSI BERDASARKAN KAPASITAS DAYA MENGGUNAKAN METODE NAÏVE BAYES Studi Kasus: PT. PLN RAYON KOTA SAMARINDA

Jurnal Komputer dan Informatika ◽

10.35508/jicon.v8i1.2185 ◽

2020 ◽

Vol 8 (1) ◽

pp. 36-44

Author(s):

Sentikom 2019

Keyword(s):

Naive Bayes ◽

Electrical Energy ◽

Naïve Bayes ◽

Training Data ◽

Bayes Method ◽

Voltage Level ◽

Human Needs ◽

Calculation Results ◽

Industrial Needs ◽

Naive Bayes Method

Human needs for energy are mostly obtained from electrical energy, both for daily needs and for industrial needs. PT. PLN (Persero) is one of the state electricity companies that serves the community's need for electricity. Transformer or better known as "transformer" or "transformer" is actually an electrical device that converts AC power at one voltage level to one voltage level based on the principle of electromagnetic induction without changing its frequency. Because of the lack of distribution of transformers around the Samarinda area, it can result in electricity demand services to the community. Therefore we need a method that can facilitate the distribution of PT. PLN Rayon Kota Samarinda, one of the methods is by applying Naïve Bayes. The purpose of this study is to facilitate the distribution in each region and the type of transformer used. The results of calculations using the Naïve Bayes method, the results obtained the probability of grouping training data is P (160) = 0.006441224, P (100) = 0.016304348, P (80) = 0.001610306, P (50) = 0.001610306, P (40) = 0.000402576, P P (20) = 0,000679348. From the calculation results, it appears that the probability value P (100) is more dominant, then 100 is recommended for real consumption which is used as training data. The Naïve Bayes method produces an accuracy rate of 92%.

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Prediction of User Loyalty Using the Naive Bayes Method in the "Goprint" Online Printing Marketplace

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i03.p03 ◽

2020 ◽

Vol 8 (3) ◽

pp. 227

Author(s):

Gede Widiastawan ◽

I Gusti Agung Gede Arya Kadyanan

Keyword(s):

Decision Making ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Test Results ◽

Bayes Method ◽

Testing Data ◽

Bayes Algorithm ◽

Naive Bayes Method

Goprint is an Online Printing Marketplace that connects printing services with users who want to print documents quickly without the need to queue. In the span of time from April 2019 to September 2019 it was found that the number of Goprint users reached 407 users, 24 partners, and 256 orders. From transactions that have been carried out by users, not a few orders are often canceled due to ineffective Goprint features or poor partner performance. This causes Goprint users to feel dissatisfied with the services provided by the Goprint application. The Naive Bayes algorithm is one of the algorithms used for classification or grouping of data, but can also be used for decision making. With this algorithm and the problems that occur, the authors make a system to predict the loyalty of Goprint users to anticipate users who stop leaving Goprint because they are not satisfied or loyal users. The data used as training data is 20 and testing data is 10. From the test results it is found that the value of precision is 80%, 100% recall, and 90% accuracy.

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Perbandingan Metode Klasifikasi Berita Hoaks Berbahasa Indonesia Berbasis Pembelajaran Mesin

Repositor ◽

10.22219/repositor.v2i5.692 ◽

2020 ◽

Vol 2 (5) ◽

pp. 675

Author(s):

Muhammad Athaillah ◽

Yufiz Azhar ◽

Yuda Munarko

Keyword(s):

Test Data ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Features Selection ◽

Comparison Of Algorithms ◽

Bayes Algorithm

AbstrakKlasifiaksi berita hoaks merupakan salah satu aplikasi kategorisasi teks. Berita hoaks harus diklasifikasikan karena berita hoaks dapat mempengaruhi tindakan dan pola pikir pembaca. Dalam proses klasifikasi pada penelitian ini menggunakan beberapa tahapan yaitu praproses, ekstraksi fitur, seleksi fitur dan klasifikasi. Penelitian ini bertujuan membandingkan dua algoritma yaitu algoritma Naïve Bayes dan Multinomial Naïve Bayes, manakah dari kedua algoritma tersebut yang lebih efektif dalam mengklasifikasikan berita hoaks. Data yang digunakan dalam penelitian ini berasal dari www.trunbackhoax.id untuk data berita hoaks sebanyak 100 artikel dan data berita non-hoaks berasal dari kompas.com, detik.com berjumlah 100 artikel. Data latih berjumlah 140 artikel dan data uji berjumlah 60 artikel. Hasil perbandingan algoritma Naïve Bayes memiliki nilai F1-score sebesar 0,93 dan nilai F1-score Multinomial Naïve Bayes sebesar 0,92. Abstarct Classification hoax news is one of text categorizations applications. Hoax news must be classified because the hoax news can influence the reader actions and thinking patterns. Classification process in this reseacrh uses several stages, namely preprocessing, features extraxtion, features selection and classification. This research to compare Naïve Bayes algorithm and Multinomial Naïve Bayes algorithm, which of the two algorithms is more effective on classifying hoax news. The data from this research from turnbackhoax.id as hoax news of 100 articles and non-hoax news from kompas.com, detik.com of 100 articles. Training data 140 articles dan test data 60 articles. The result of the comparison of algorithms Naïve Bayes has an F1-score value of 0,93 and Naïve Bayes has an F1-score value of 0,92.

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Application Design of Dengue Hemorrhagic Fever Patients Screening Using Naive Bayes Method

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.34.20 ◽

2017 ◽

Vol 34 ◽

pp. 20-28

Author(s):

Cindy Astelia Ramadhan Suparman ◽

Endah Purwanti ◽

Prihartini Widiyanti

Keyword(s):

Dengue Hemorrhagic Fever ◽

Naive Bayes ◽

Public Health Problem ◽

Hemorrhagic Fever ◽

Naïve Bayes ◽

Training Data ◽

Design System ◽

Design Calculation ◽

Bayes Method ◽

Naive Bayes Method

Dengue Hemorrhagic Fever is a disease which is endemic in most districts / cities still becomes a public health problem in Indonesia. The awareness of people to the dengue viral infection and its symptoms are needed to decrease the fatality of this disease. The community need to be known the symptoms thereby they could intervened and prevent from falling in to worse condition. This study was conducted to design system which could diagnose the onset of the disease with 3 levels of possibilities namely Grade 1 Dengue Hemorrhagic Fever, Grade 2 Dengue Hemorrhagic Fever, and Non Dengue Hemorrhagic Fever. The system is build based on patient medical records of Dr. Wahidin Sudiro Husodo General Hospital, Mojokerto, East Java using the Naive Bayes method. The method of this study including several steps such as collecting data, preprocessing data, designing database, interface design, calculation and processing data, classification and analyzing data and evaluating application. Determining the results of the application diagnose requires posterior calculation which searches the highest values in three degrees as the results of the initial diagnose. The application as a device for an early diagnosis of dengue hemorrhagic fever has a high accuracy value of 97% out of the 30 tested data. The homogenization of the training data and the test data by sex and age can be considered in future research.

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Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v5i1.44379 ◽

2021 ◽

Vol 5 (1) ◽

pp. 32

Author(s):

Hartatik Hartatik

Keyword(s):

Data Mining ◽

Big Data ◽

Prediction Models ◽

Naive Bayes ◽

Program Planning ◽

Naïve Bayes ◽

Bayes Method ◽

Student Graduation ◽

Bayes Algorithm ◽

Naive Bayes Method

Abstrak :Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma naive bayes. Algoritma Naive bayes merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa. Peneliti dalam hal ini menerapkan metode Naive bayes menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi naive bayes methods berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode Naive bayes sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial sebesar 85% dengan selisih akurasi 10%.__________________________Abstract : Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.

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Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i3.183 ◽

2020 ◽

Vol 5 (3) ◽

pp. 291

Author(s):

Hanif Rahman Burhani ◽

Iskandar Fitri ◽

Andrianingsih Andrianingsih

Keyword(s):

Expert System ◽

Naive Bayes ◽

Comparison Method ◽

Naïve Bayes ◽

Training Data ◽

Certainty Factor ◽

Bayes Method ◽

Lack Of Information ◽

Statistics And Probability ◽

Naive Bayes Method

Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.Keywords:Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHP.

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Penerapan Algoritma Naïve Bayes Dalam Penentuan Kelayakan Penerima Kartu Jakarta Pintar Plus

Jurnal Teknik Komputer ◽

10.31294/jtk.v6i1.6680 ◽

2020 ◽

Vol 6 (1) ◽

pp. 15-20

Author(s):

Endah Widya Ningsih ◽

Hardiyan Hardiyan

Keyword(s):

Smart Card ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Bayes Method ◽

Using Data ◽

Naive Bayes Method

The eligibility of Jakarta Smart Card Plus recipients is still not on target due to subjective receipts. Schools has an important role in making decisions about the eligibility of Jakarta Smart Plus Card recipients. Therefore, the authors make this research using data that already exists or is called training data. The author uses the Naïve Bayes method with 7 independent attributes to knowing eligibility. The author also uses the another application Rapidmined 5.3 to test the accuracy of the Naïve Bayes method. The result of this research the accuracy of determining the eligibility of Jakarta Smart Plus Card recipients are good with 98.88% with an error of presentation 2.22%. So it can be concluded that the naive bayes method can help detrermine the eligibility of jakarta smart plus card recipients. Keywords: Jakarta Smart Card, Naïve Bayes, eligibility

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Prediction analysis of student interest in design learning using Naïve Bayes method

SinkrOn ◽

10.33395/sinkron.v5i2.10726 ◽

2021 ◽

Vol 5 (2) ◽

pp. 208-212

Author(s):

Jefri Junifer Pangaribuan ◽

Ferawaty Ferawaty

Keyword(s):

Test Data ◽

Naive Bayes ◽

Naïve Bayes ◽

Student Interest ◽

Experiment Data ◽

Bayes Method ◽

Design Learning ◽

The Subject ◽

Existing Data ◽

Naive Bayes Method

Students' interest in a field is usually seen from the grades resulting from students' learning in the classroom. This is a common thing for parents and teachers to do. In the research conducted by researchers this time is in the field of design studied by students in the classroom in one semester, namely the subjects Photoshop and CorelDraw. The grades taken are grades that include the value of theory, practical grades, assignments, and quiz assignments obtained by students in the classroom. The four grades will be calculated until they get a provision on whether or not the student graduates in the subject. These values will be studied by researchers using the Naïve Bayes method, so that it can be known how much can be said to pass in this design lesson. Researchers conducted research using Rapid Miner program, where the data will be divided into 2 parts, namely by using some existing data as learning samples and the rest as test data. The results obtained from the experiment were 146 students graduated in the field of design, and 119 students failed. This suggests the experiment using the Naïve Bayes method was successful if the experiment data was entered a lot

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ANALISIS MODEL NAIVE BAYES UNTUK IDENTIFIKASI PENGGOLONGAN DAYA LISTRIK DI KOTA LHOKSUMAWE

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v2i1.971 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Muhammad Saidi ◽

Fajriana Fajriana ◽

Wahyu Fuadi ◽

Ermatita Ermatita ◽

Iwan Pahendra

Keyword(s):

Naive Bayes ◽

Electrical Power ◽

Naïve Bayes ◽

Training Data ◽

Bayes Method ◽

Customer Data ◽

Poor Households ◽

House Area ◽

Naive Bayes Method

Electricity subsidy is provided for all 450 VA power household customers and 900 VA power household customers who are poor and disadvantaged. However, there are many facts that household customers with 450 VA power are capable and 900 VA power household customers consist of capable households, boarding houses or luxury rented. Households are able to use more electricity than poor households. This paper describe to the identification of household customers' electrical power in the Lhokseumawe city to facilitate PLN in classifying customer power by using the Naive Bayes method. Naive bayes value variables used in this study are: monthly income, highest diploma, last job, house area, subscription fee and government registered household. The classification of household customer power is grouped into three categories, namely low (450 VA down), medium (900 VA) and high (above 1300 VA).. Based on household customer data that is used as training data, the Naive Bayes method is able to classify the customer data tested. So the Naive Bayes method successfully predicts the magnitude of the probability of household electrical power with an accuracy percentage of 80%.Keywords: Electricity, Naive Bayes, CBS, low birth weight, subsidy

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Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v4i1.3860 ◽

2020 ◽

Vol 4 (1) ◽

pp. 151-160

Author(s):

Muhathir Muhathir ◽

Muhammad Hamdani Santoso

Keyword(s):

Feature Extraction ◽

Natural Resources ◽

Naive Bayes ◽

Naïve Bayes ◽

Fresh Fruit ◽

Tropical Fruits ◽

Bayes Method ◽

Bayes Algorithm ◽

Naive Bayes Method

Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the Naïve Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the Naïve Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%.Keywords – Apple, Avocado, Apricot, Banana, Naïve Bayes, HOG.

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