Prediksi Angka Kelahiran Bayi  Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes

The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to determine the number of infant birth rates for the coming year using the Naive Bayes algorithm. By looking at the prediction patterns of each variable and testing training data on testing data. It is hoped that the Naive Bayes algorithm can solve the problem in Tridaya Sakti Village in handling and overcoming the calculation of infant birth rates and can help the Tridaya Sakti Village in regulating population growth in the coming years. The results obtained from the data that have been taken and calculated by Data Mining using the Naive Bayes algorithm produce an information that can be used as a reference to find out the number of births. Performance and time in data processing are more effective and efficient as well as more accurate and accurate predictions of the number of baby births. Keywords: Naive Bayes, Birth of a Baby, Prediction Abstrak Angka kelahiran merupakan suatu hal yang dapat mempengaruhi peningkatan pertumbuhan penduduk. Jumlah penduduk yang besar merupakan beban bagi pembangunan. Menurut Teori Malthus yang menyatakan bahwa pertumbuhan jumlah penduduk yang besar bukanlah kesejahteraan yang didapat tapi justru kemelaratan akan ditemui bilamana jumlah penduduk tidak dikendalikan dengan baik. Jumlah angka kelahiran bayi di Desa Tridaya Sakti setiap tahunnya semakin bertambah. Maka dari itu Data Mining dengan menggunakan algoritman Naive Bayes dapat membantu dalam perhitungan memprediksi angka kelahiran bayi di Desa Tridaya Sakti. Data Mining dalam memprediksi jumlah angka kelahiran bayi bertujuan untuk mengetahui jumlah angka kelahiran bayi tahun yang akan mendatang mengunakan algoritma Naive Bayes. Dengan melihat pola prediksi dari setiap variabel dan melakukan pengujian data training terhadap data testing. Diharapkan algoritma Naive Bayes ini dapat menyelesaikan permasalahan di Desa Tridaya Sakti dalam menangani dan mengatasi perhitungan angka kelahiran bayi dan dapat membantu pihak Desa Tridaya Sakti dalam mengatur pertumbuhan jumlah penduduk tahun yang akan mendatang. Hasil yang diperoleh dari data yang sudah diambil dan dihitung dengan Data Mining mengunakan algoritam Naive Bayes menghasilkan sebuah informasi yang dapat digunakan sebagai acuan untuk mengetahui jumlah angka kelahiran bayi. Kinerja dan waktu dalam proses pengolahan data lebih efektif dan efesien serta dari prediksi jumlah kelahiran bayi lebih tepat dan akurat. Kata Kunci: Naive Bayes, Kelahiran Bayi, Prediks

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IMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA STUDI MAHASISWA (STUDI KASUS : UNIVERSITAS DHYANA PURA)

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v4i2.964 ◽

2021 ◽

Vol 4 (2) ◽

pp. 202-209

Author(s):

Kelvin Hennry Loudry Malelak ◽

I Made Dwi Ardiada ◽

Gerson Feoh

Keyword(s):

Data Mining ◽

Undergraduate Students ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Accuracy Rate ◽

Testing Data ◽

Study Programs ◽

Bayes Algorithm ◽

Academic Year

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. Is known that in fact in the 2015/2016 academic year there were 744 people who were accepted as students. Of the 744 people who were accepted, 405 people had completed a study period of about 4 years and the remaining 39 people completed their studies for 5 years and 300 of them did not continue their studies. Based on the problem on, so This study implements a classification that can help Dhyana Pura University in predicting the length of study for students who are currently studying in various study programs at Dhyana Pura University. The author's method serves in the classification to predict long student study period is the Naive Bayes algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 968 training data and 193 data testing data with naive Bayes has succeeded in obtaining an accuracy rate of 100% which also has very good parameters.

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Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen

Prosiding Seminar Nasional Riset Information Science (SENARIS) ◽

10.30645/senaris.v1i0.34 ◽

2019 ◽

Vol 1 ◽

pp. 287

Author(s):

Desi Ratna Sari ◽

Dedy Hartama ◽

Irfan Sudahri Damanik ◽

Anjar Wanto

Keyword(s):

Higher Education ◽

Student Satisfaction ◽

Teaching Methods ◽

Naive Bayes ◽

Teaching Method ◽

Higher Education Institutions ◽

Naïve Bayes ◽

Training Data ◽

Testing Data ◽

Bayes Algorithm

This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.

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Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020732093 ◽

2020 ◽

Vol 7 (3) ◽

pp. 443

Author(s):

Azahari Azahari ◽

Yulindawati Yulindawati ◽

Dewi Rosita ◽

Syamsuddin Mallala

Keyword(s):

Neural Network ◽

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Drop Out ◽

Training Data ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Testing Data ◽

Target Data

Prediksi kelulusan dibutuhkan oleh manajemen perguruan tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus drop out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor. Dengan menggunakan data mining algoritma naive bayes dan neural network dapat dilakukan prediksi kelulusan mahasiswa di STMIK Widya Cipta Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer. Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.<div><div>AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions. </div></div>

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Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i2.2937 ◽

2021 ◽

Vol 5 (2) ◽

pp. 640

Author(s):

Mulkan Azhari ◽

Zakaria Situmorang ◽

Rika Rosnelly

Keyword(s):

Random Forest ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Random Forest Algorithm ◽

Svm Algorithm ◽

Testing Data ◽

C4.5 Algorithm ◽

Bayes Algorithm ◽

Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

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Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

Journal of Applied Intelligent System ◽

10.33633/jais.v3i1.1988 ◽

2018 ◽

Vol 3 (1) ◽

pp. 39-48 ◽

Cited By ~ 5

Author(s):

Arya Kusuma ◽

De Rosal Ignatius Moses Setiadi ◽

M. Dalvin Marno Putra

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Bayes Classifier ◽

Nutritional Content ◽

Classification Technique ◽

Computational Speed ◽

Testing Data ◽

Bayes Algorithm ◽

Histogram Feature

Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

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Tweet Netizen Prediction Using Random Forest, Decision Tree, Naïve Bayes, And Ensemble Algorithm (Case Study The Governor Of DKI Jakarta)

SinkrOn ◽

10.33395/sinkron.v5i1.10565 ◽

2020 ◽

Vol 5 (1) ◽

pp. 9-20

Author(s):

Antonius Yadi Kuntoro

Keyword(s):

Social Media ◽

Random Forest ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Testing Data ◽

Negative Comments ◽

Ensemble Algorithm ◽

Bayes Algorithm

Abstract — The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here. Keywords — Decision Tree, Naïve Bayes, Random Forest, Set, Twitter.

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Comparison Analysis of K-Nearest Neighbor and Naïve Bayes in Determining Talent of Adolescence

International Journal of Artificial Intelligence Research ◽

10.29099/ijair.v4i1.118 ◽

2020 ◽

Vol 4 (1) ◽

Author(s):

Yessi Jusman ◽

Widdya Rahmalina ◽

Juni Zarman

Keyword(s):

Nearest Neighbor ◽

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Training Data ◽

K Nearest Neighbor ◽

Combined Training ◽

Testing Data ◽

Bayes Algorithm ◽

Children's Interests

Adolescence always searches for the identity to shape the personality character. This paper aims to use the artificial intelligent analysis to determine the talent of the adolescence. This study uses a sample of children aged 10-18 years with testing data consisting of 100 respondents. The algorithm used for analysis is the K-Nearest Neigbor and Naive Bayes algorithm. The analysis results are performance of accuracy results of both algorithms of classification. In knowing the accurate algorithm in determining children's interests and talents, it can be seen from the accuracy of the data with the confusion matrix using the RapidMiner software for training data, testing data, and combined training and testing data. This study concludes that the K-Nearest Neighbor algorithm is better than Naive Bayes in terms of classification accuracy.

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IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v2i1.972 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Delisman Laia ◽

Efori Buulolo ◽

Matias Julyus Fika Sirait

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Transportation Industry ◽

Data Set ◽

Data Mining Algorithms ◽

Taxi Service ◽

Bayes Algorithm ◽

Using Data

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes

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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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Application of Data Mining with Classification Methods for Promotion of New Student Admissions at Muhammadiyah University of Sidoarjo Using Web-Based Naïve Bayes Algorithm

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i2.1062 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Vianti Widyasari ◽

Arief Senja Fitrani

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Classification Method ◽

New School ◽

School Year ◽

New Students ◽

Bayes Algorithm ◽

New Student

The University of Muhammadiyah Sidoarjo (UMSIDA) is one of Indonesia's superior and innovative private colleges in developing IPTEKS based on Islamic values for community welfare. UMSIDA that has stood long enough with the number of students received in each year is quite a lot. Each new school year opening, this private college regularly organizes new student admissions (PMB) activities. Admission for new students (PMB) at UMSIDA can be done at pmb.umsida.ac.id. Therefore, research aims to create data mining applications classification method with the algorithm Naïve Bayes. This research uses the classification method used to Megukur accuracy level. To predict the promotion of new students receiving Muhammadiyah Sidoarjo University (UMSIDA) can be done using the Naïve Bayes algorithm with 7 predefined variables. Offline and online predictor of the dataset of 2601 data is divided into 2 as many as 70% of 2000 Training data and as much as 30% from 601 of Testing data.

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