scholarly journals FEASIBILITY TEST OF POOR RICE RECIPIENTS IN BENCOY SUKABUMI VILLAGE USING NAIVE BAYES

2021 ◽  
Vol 17 (1) ◽  
pp. 93-98
Author(s):  
Taufik Hidayatulloh ◽  
Ardi Winardi ◽  
Lestari Yusuf ◽  
Satia Suhada ◽  
Saeful Bahri
Keyword(s):  
Naive Bayes ◽  
Secondary Data ◽  
Naïve Bayes ◽  
Calculation Algorithm ◽  
Distribution Method ◽  
Work Plan ◽  
The Government ◽  
Bayes Algorithm ◽  
Feasibility Test ◽  
The Right

A regional head must have a work plan every regional head must have a work plan which is sure to be of benefit to the community. Assisting is a definite work plan in every region. A lot of assistance is usually given from the government to the community and must be managed by the village government so that the aid gets to the right hands. And to improve food security, the people in each region have activities to distribute Poor Rice as a subsidy from the government. In the distribution method, sometimes there are constraints in data collection so that poor rice or what we usually call Raskin is not suitable for distribution. Because of this, a way is needed so that the distribution is appropriate or not in the community in accepting the Raskin so that government assistance can be delivered properly and on target. By using secondary data obtained from Bencoy Village, 205 data were obtained containing the attributes of the eligibility category of Raskin recipients, and 6 categories of attributes were found with the classification method of the Naïve Bayes algorithm. The accuracy value obtained is 96.59%, proving that the prediction using the Naive Bayes algorithm has a good performance. The next results obtained are in the form of AUC value which after being calculated produces a value of 0.999 and this results in an application which is an implementation with a flow that is adjusted to the calculation algorithm in the form of a web-based application.

scholarly journals PENERAPAN DATAMINING DENGAN METODE KLASIFIKASI UNTUK STRATEGI PENJUALAN PRODUK DI UD.SELAMAT SELULAR

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sinta Maulina Dewi ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Data Collection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Collection Method ◽  
Data Collection Method ◽  
Sales Strategy ◽  
Bayes Algorithm ◽  
Small Shop ◽  
The Right

In the current era of globalization, developments in various fields of business are accelerating. Both in the culinary field and other fields. One of the most sought after business developments is in the field of counters or credit sales. UD.Selamat Selular was founded in 2010, which only has a small shop with no employees to date which has more than 20 employees. This business continues to develop in ever-increasing business competition. Therefore a sales strategy is needed so that it is not inferior to other trading businesses. In this research, it is necessary to test the previous data in order to find out the right sales strategy using Naïve Bayes. The data collection method was conducted by questionnaire and interview with a questionnaire of 160 respondents. From the results of this study it can be concluded that the model formed using the Naïve Bayes algorithm produces an algorithm of 0.650 so that it is classified as Excellent Classification.Keywords: Datamining, Naïve Bayes, Sales Strategy.

scholarly journals PREDIKSI PEMILIHAN JURUSAN DIPERGURUAN TINGGI

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Dian Agustini ◽  
Muthia Farida ◽  
Auliya Rahman
Keyword(s):  
High School Students ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Limited Information ◽  
Prospective Students ◽  
School Students ◽  
The Government ◽  
Bayes Algorithm ◽  
Selection Of

The education sector is one of the fields that gets the most attention from the government, especially when graduating from high school students. It is expected that these graduates will continue to pursue higher education. Various information about majors in universities have been widely available but have not been able to meet the needs of prospective students. There are three main problems experienced by prospective students, namely limited knowledge of the majors to be followed, limited information available, and limited quantitative recommendations that can be used by prospective students.This study tries to overcome these problems by producing predictions of departmental recommendations using the Naive Bayes algorithm and incorporating criteria that influence the selection of majors in the form of abilities, interests, and also preferences for certain fields. An approach to user preferences is used so that the recommendations approach the desired results. This is done by giving the criteria weighting to the user. Keywords: Data Mining, Predictions, Universities, Naive Bayes

scholarly journals SENTIMENT ANALYSIS OF TWITTER DATA ON REMOTE LEARNING USING NAÏVE BAYES ALGORITHM

2021 ◽  
Vol 3 (3) ◽  
pp. 203-210
Author(s):  
Putri Rana Khairina ◽  
Desti Fitriati
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Public Sentiment ◽  
Global Pandemic ◽  
Distance Learning System ◽  
Twitter Users ◽  
The Government ◽  
Bayes Algorithm

Covid-19 is widespread, resulting in a global pandemic. Distance Learning System (DLS) is considered as a solution but, the reality of the implementation of DLS is not in accordance with the expectations of the community. Many Twitter users wrote their opinions on DLS. The tendency of public sentiment can be used as a way to improve the existing education system in Indonesia and can be an input for the government in improving the DLS method that is being implemented. Thus, this study produced a system that can analyze tweet sentiment towards DLS. The tweet was obtained using the Twitter API. The method used is Naïve Bayes for the process of classification of positive, negative, and neutral sentiments using 600 data. Then, data sharing is done 80% data training and 20% data testing that will be in the text preprocessing first. The accuracy of sentiment analysis of DLS using the Naïve Bayes method using 3-fold Cross-Validation produces an average of 93%.

scholarly journals Pengaruh Pertumbuhan Ekonomi Terhadap Tingkat Kesejahteraan Masyarakat Kecamatan Selong Kabupaten Lombok Timur – Nusa Tenggara Barat Menggunakan Algoritma Naive Bayes

2021 ◽  
Vol 4 (1) ◽  
pp. 20-28
Author(s):  
Yahya Yahya ◽  
◽  
Hariman Bahtiar ◽  
Keyword(s):  
Naive Bayes ◽  
Clean Energy ◽  
Well Being ◽  
Good Health ◽  
Naïve Bayes ◽  
Decent Work ◽  
Responsible Consumption ◽  
Climate Action ◽  
The Government ◽  
Bayes Algorithm

Sustainable Development Goals (SDGs) is one of the world's programs to overcome several problems that are currently the world's issues. The world's issues that want to be addressed include: eliminating poverty, eliminating hunger, building good health and well-being, providing quality education, enforcing gender equality, improving clean water and sanitation, growing affordable and clean energy, creating decent work and growth. economy, improve industry, innovation and infrastructure, reduce inequality, mobilize sustainable cities and communities, influence responsible consumption and production, regulate climate action, promote life under water, advance life on land, ensure peace, justice and strong institutions , build partnerships to achieve goals. The target of seventeen components that will be completed in the world is planned to be achieved in 2030. All components that become world problems will be used as part of the target in this research. One of the research focuses is the economic component. The data obtained in Selong District, especially the economic component, will be managed and processed using the Naive Bayes algorithm. After processing the data using the Naive Bayes algorithm, the accuracy rate of closeness to the real situation is 93.45%. From the data obtained 93.45% or 0.9345 x the amount of data (kk) = 0.9345 x 1130 kk = 1056 families which shows the community is prosperous and 6.55% x 1130 = 74 families which states that people are not prosperous and can used as a reference in poverty alleviation through programs launched by the government.

scholarly journals Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain

2020 ◽  
Vol 1 (1) ◽  
pp. 19-26
Author(s):  
Rakhmi Khalida ◽  
Siti Setiawati
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Traffic Violations ◽  
The Government ◽  
Bayes Algorithm ◽  
User Friendly

Abstract   The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impression. Twitter social media is one of the objects to express opinions because it is user friendly, updated topics, and openly accesses tweets. Opinions on Twitter are collected, then the preprocessing stage is performed, then the selection of information gain features helps reduce noise caused by irrelevant labels, the next step is the classification of sentiments with the Naïve Bayes algorithm and finally polarity sentiments. This research resulted in an accuracy of 41.82%, a precision of 50.51% and a recall of 45.45%.   Keywords: Sentiment analysis, E-ticketing, Information Gain, Naive Bayes   Abstrak   Pemerintah Indonesia melakukan langkah perubahan untuk memperbaiki sistem pelayanan publik dalam pelanggaran berlalu-lintas yaitu dengan menerapkan sistem e-Tilang. Sistem ini menjadi solusi mendisiplinkan para pengendara kendaraan bermotor dari banyaknya melakukan pelanggaran berlalu-lintas. Keberadaan e-Tilang juga menjadi solusi mencegah kenakalan penegak hukum dari pungutan liar, istilah damai ditempat, hingga akuntabilitas uang denda. Dalam penelitian ini melakukan analisis sentimen tentang sistem e-Tilang atau opinion mining untuk mengelompokan ragam komentar masyarakat yang memberikan kesan positif, negatif atau netral. Media sosial Twitter menjadi salah satu objek untuk menyampaikan opini karena user friendly, topik ter-update, dan terbuka mengakses tweet. Opini pada twitter dikumpulkan, lalu dilakukan tahapan preprocessing, selanjutnya dengan seleksi fitur information gain membantu mengurangi noise yang disebabkan oleh label-label yang tidak relevan, tahap selanjutnya adalah klasifikasi sentimen dengan algoritma Naïve Bayes dan terakhir sentimen polarity. Penelitian ini menghasilkan accuracy 41,82%, presisi 50,51% dan recall 45,45%.   Kata kunci: Analisis sentimen, E-Tilang, Information Gain, Naive Bayes

scholarly journals Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 19-25
Author(s):  
Frizka Fitriana ◽  
Ema Utami ◽  
Hanif Al Fatta
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Bayes Algorithm ◽  
Health Organization ◽  
The Right ◽  
The Impact

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.

scholarly journals Implementasi Algoritma Naive Bayes Untuk Memprediksi Predikat Ketuntasan Belajar Siswa Pasca Pandemi Covid 19

2021 ◽  
Vol 4 (1) ◽  
pp. 29-38
Author(s):  
Muhammad Saiful ◽  
◽  
Samsuddin Samsuddin ◽  
Keyword(s):  
Online Learning ◽  
Probabilistic Reasoning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Learning Activities ◽  
Student Knowledge ◽  
Face To Face ◽  
Bayes Algorithm ◽  
The Right

During the Covid-19 pandemic, SMA Negeri 3 Selong changed learning activities from what was originally face-to-face, but currently learning is being transferred to the Online Learning System (SPADA) using several existing platforms. Judging from the level of plurality of students' thinking patterns during the implementation of online learning there are many problems that arise, one of which is the instability of the internet network, the various hendpone media devices owned by students and the lack of student knowledge in using online platforms. The purpose of this study was to determine the indicator of the problem in the predicate of student learning completeness of class XII SMAN 3 Selong during the post-COVID-19 pandemic. The method used to solve this problem is the Naïve Bayes algorithm. Naive Bayes is a method of probabilistic reasoning. And in the future the results of this study are expected to be able to provide the right solution in solving problems in online learning.

scholarly journals Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online

2020 ◽  
Vol 10 (2) ◽  
pp. 64-68
Author(s):  
Mir Habeebullah Shah Quadri ◽  
R. K. Selvakumar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
User Reviews ◽  
Bayes Algorithm ◽  
The Right ◽  
Processing Techniques

Both sellers and buyers heavily depend on the opinions of customers in purchasing and selling products online. When it comes to text-based data, sentiment analysis of user reviews has become a prominent facet of machine learning. Text data is generally unstructured which makes opinion mining very challenging. A wide array of pre-processing and post-processing techniques need to be applied. But the major challenge is selecting the right classifier for the job. Naïve Bayes algorithm is a commonly used machine learning classifier when it comes to opinion mining and sentiment analysis. The focus of this survey is to observe and analyze the performance of Naïve Bayes algorithm in sentiment analysis of user reviews online. Recent research from a wide array of use-cases such as sentiment analysis of movie reviews, product reviews, book reviews, blog posts, microblogs and other sources of data have been taken into account. The results show that Naïve Bayes algorithm performs exceptionally well with accuracies between 75% to 99% across the board.

scholarly journals Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5

2020 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Hermanto Wahono ◽  
Dwiza Riana
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Blood Donation ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
K Nearest Neighbors ◽  
C4.5 Algorithm ◽  
Auc Value ◽  
Bayes Algorithm ◽  
Feasibility Test

Blood donation is a process of taking blood from donors that is declared feasible, in terms of various factors including age, weight, blood pressure, hemoglobin levels, and donor status which are taken into consideration during the feasibility test. This study was conducted to find the most appropriate method with high accuracy and Area Under Curve (AUC) values using 3710 blood donor datasets from the Bekasi City PMI, processed using the Naïve Bayes algorithm method, K-Nearest Neighbors and Decision Tree C4.5. The analysis shows that the Decision Tree C4.5 algorithm shows higher accuracy of 93.83% compared to Naïve Bayes algorithm which shows an accuracy value of 85.15% and the K-Nearest Neighbors algorithm with an accuracy value of 84.10%. In addition to these values, Decision Tree C4.5 is also visually superior where the Decision Tree has an output model tree that shows attribute relationships and has an AUC value of 0.978, Naïve Bayes with an AUC value of 0.927 and K-Nearest Neighbors with an AUC value of 0.816.

scholarly journals IMPLEMENTASI ALGORITMA NAIVE BAYES DAN RANDOM FOREST DALAM MEMPREDIKSI PRESTASI AKADEMIK MAHASISWA UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH

2020 ◽  
Vol 4 (1) ◽  
pp. 50
Author(s):  
Bustami Yusuf ◽  
Muthmainna Qalbi ◽  
Basrul Basrul ◽  
Ima Dwitawati ◽  
Malahayati Malahayati ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Student Achievement ◽  
Random Forest ◽  
Naive Bayes ◽  
Secondary Data ◽  
Naïve Bayes ◽  
Random Forest Algorithm ◽  
Two Factors ◽  
Bayes Algorithm ◽  
The Individual

Academic achievement is determined by two factors, namely internal factors originating from within the individual in this case students and external factors that come from outside the individual or things that are influenced by the environment. There are many ways to find an academic achievement, one of which uses data mining which aims to predict or classify data using a classification algorithm. This study aims to 1) find out how to apply the Naive Bayes algorithm to student achievement, and 2) see the accuracy of the Naive Bayes algorithm to student achievement. This type of research is secondary data in the form of student data obtained from the information technology center and the Ar-Raniry UIN database. This research uses Naive Bayes algorithm and random forest algorithm. The results obtained from this study indicate the highest correlation value in the initial IP variable of r = 0.783 and the leave variable has a very weak correlation level of r = 0.054. The accuracy value of Naive Bayes algorithm after cleaning is 78.0% and Random Forest algorithm variable is 76.7%.

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