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 Klasifikasi Peminatan Siswa SMA Menggunakan Metode Naive Bayes

2016 ◽  
Vol 2 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Husni Naparin
Keyword(s):  
High School ◽  
High School Students ◽  
Junior High School ◽  
Test Scores ◽  
Naive Bayes ◽  
Junior High ◽  
Naïve Bayes ◽  
School Students ◽  
Average Value ◽  
The Government

The Government through 2013 curriculum wants to prepare students who are ready earlier than expected to develop his talents, so that the government create a system of specialization of class X with a variety of considerations which is considered very mature, with a value based on the level of the previous studies, the value of the psychological test, the data of interest, and value specialization. It is necessary to study to classify the high school students specialization 2013 curriculum, 2013. In this study, algorithm that used is Naive Bayes. Probability Bayes principle or principles is the principle that is based on the observation and focused on the use of traditional methods. Classification of Naive Bayes assumption that holds the relationship between independent features or attributes that make it more effective for the categorization, a simple, fast and produces a high degree of accuracy. The step of research include data collection and testing of Naive Bayes algorithm. In this study, the dataset used is desirable student Department, the Department of the Results of Psychotest, average value of Mathematics of the students when they were in the first to the fifth semester of junior high school, Math test scores, the average value of IPA of the student when they were in the first to the fifth semester of junior high school, IPA test scores, the average value of IPS of the student when they were in the first to the fifth semester of junior high school and IPS test scores. This study aims to determine the Clasification Results using Naive Bayes Method of determining the appropriate students’ specialization on 2013 curriculum, based on data value of SMAN 2 Banjarmasin. The significance of this study can be used by the school to perform accurate algorithm as a tool to calssify the corresponding specialization in 2013 curriculum and to provide an overview and understanding of Naive Bayes prediction methods, as a case study of the value of all the students of SMAN 2 Banjarmasin andto determine their specialization and it is also expected to become a tool determines the appropriate specialization curriculum in 2013 for students of SMAN 2 Banjarmasin. The result of the try out by using Naïve Bayes method to assess high school students’ specialization reached the assessment result that has the highest accuration level 99.47% and AUC value 1.000.

scholarly journals Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease

2019 ◽  
Vol 2 (2) ◽  
pp. 56-60
Author(s):  
Irfan Santiko ◽  
Ikhsan Honggo
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Progressive Decline ◽  
Bayes Algorithm ◽  
Test Algorithm ◽  
Selection Testing ◽  
Selection Of

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.

scholarly journals COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

2021 ◽  
Vol 4 (1) ◽  
pp. 33-39
Author(s):  
Budi Pangestu ◽  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
K Nearest Neighbors ◽  
Selection Of

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

scholarly journals Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

2020 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Yohanes Christopher Tapidingan ◽  
Debby Paseru
Keyword(s):  
High School ◽  
High School Students ◽  
Junior High School ◽  
Cross Validation ◽  
Naive Bayes ◽  
Junior High ◽  
Naïve Bayes ◽  
Junior High School Students ◽  
Classification Methods ◽  
School Students

Stress is generally defined as a state where someone is mentally disturbed as the response to the adversity that he/she experiences. Junior High School students usually are not aware of the stress that they encounter. This research aims to compare two classification methods of KNN and Naïve Bayes to determine stress level. The data of this research were gathered from 254 respondents from Catholic Junior High School of Don Bosco Bitung. The tests of k-cross validation and percentage split from the data showed that Naïve Bayes method excelled KNN method. With k=3, KNN accuracy reached 86.61% at the highest and Naïve Bayes reached 87.40%. Meanwhile, based on percentage split test, the average of Naïve Bayes accuracy was higher than KNN with percentage of 88.31%. Moreover, for the precision and recall, Naïve Bayes was higher than KNN with 88.30% and 87.40% seen from the k-cross validation.

scholarly journals Penentuan Penilaian Kredit Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization

2017 ◽  
Vol 1 (1) ◽  
pp. 48
Author(s):  
Rinawati Rinawati
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Credit Ratings ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Data Mining Algorithms ◽  
Auc Value ◽  
Bayes Algorithm ◽  
Selection Of

Bad credit is one of the credit risk faced by the financial and banking industry. Bad credit can be avoided by means of an accurate credit analysis of the debtor. The accuracy of credit ratings is crucial to the profitability of financial institutions. Improved accuracy of credit ratings can be done by doing the selection of attributes, because the selection of attributes reduce the dimensionality of the data so that operation of the data mining algorithms can be run more effectively and more cepat.Banyak research has been conducted to determine credit ratings. One of the methods most widely used method of Naive Bayes. In this study will be used method Naive Bayes and will do the selection of attributes by using particle swarm optimization to determine credit ratings. After testing the results obtained are Naive Bayes produce accuracy value of 72.40% and AUC value of 0.765. Then be optimized by using particle swarm optimization results show values higher accuracy is equal to 75.90% and AUC value of 0.773. So as to achieve the increased accuracy of 3.5%, and increased the AUC of 0.008. By looking at the accuracy and AUC values, the Naive Bayes algorithm based on particle swarm optimization into the classification category enough.

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 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.

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