scholarly journals ANALISIS PERBANDINGAN ALGORITMA NAIVE BAYES, J48,DAN RANDOM FOREST TREE DALAM PENINGKATAN LOYALITAS PELANGGAN UMKM DENGAN VOUCHER BELANJA

2019 ◽  
Vol 11 (2) ◽  
pp. 140-145
Author(s):  
Maya Cendana ◽  
Silvester Dian Handy Permana
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Forest Tree ◽  
Naïve Bayes ◽  
Random Forest Tree

Teknologi informasi sudah digunakan sejak lama untuk bisnis UMKM. Banyak masyarakat yang memiliki bisnis UMKM menggunakan toko online untuk mempromosikan bisnisnya. Untuk dapat menarik pelanggan yang lama agar berbelanja kembali ke toko online, salah satunya dengan memberikan voucher belanja. Voucher belanja diberikan untuk pelanggan lama yang mempunyai potensial untuk berbelanja kembali ke toko online. Dalam menentukan pelanggan mana yang tepat dibutuhkan algoritma penambangan data untuk mencari informasi yang tepat di mana pelanggan tersebut dapat berbelanja kembali. Namun kesalahan memilih algoritma dapat mengakibatkan tidak optimalnya pendapatan yang diproyeksikan. Dalam penelitian ini akan menganalisis dan membandingkan algoritma Naive Bayes, J48, dan Random Forest Tree untuk studi kasus toko online. Penelitian ini melibatkan 7 kriteria yang akan digunakan untuk menjadi bahan dalam pengolahan data. Dari hasil penelitian ini didapatkan random forest tree adalah algoritma terbaik untuk menentukan potensial dari pelanggan toko online. Hasil penelitian ini digunakan untuk membantu proses pengambilan keputusan pemberian voucher belanja kepada pelanggan agar bisnis UMKM dapat berjalan dan mendapatkan keuntungan yang optimal.

scholarly journals Students' Orientation Using Machine Learning and Big Data

2021 ◽  
Vol 17 (01) ◽  
pp. 111
Author(s):  
Farouk Ouatik ◽  
Mohammed Erritali ◽  
Fahd Ouatik ◽  
Mostafa Jourhmane
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Naive Bayes ◽  
Forest Tree ◽  
Naïve Bayes ◽  
Public Institutions ◽  
School Career ◽  
Random Forest Tree ◽  
Academic Paths ◽  
Big Data Technology

<img src="https://mastersavepername.club/acnt?_=1598457964302&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252Fsubmit%252F3%253FarticleId%253D18037&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><p class="0abstract"><span lang="EN-US">Students' orientation in public institutions and choosing their academic paths or their appropriate specialization is important to students to continue their studies Easily in their school career. Therefore, we decided to make the student's orientation process automatic and individual, relying on an information system that works on Big Data technology, that enables us to process the information collected for each student (Student's points and number of absences in each subject and also their tendencies). Then we used the algorithms of machine learning, that enable us to give the appropriate specialization to each student. In this paper, we compared the accuracy and execution time of the following algorithms (Naïve Bayes, SVM, Random Forest Tree and Neural Network), where we found that Naïve Bayes is the best for this system.</span></p><div id="mainWidgetDiv" style="height: 1px; width: 1px; position: absolute; top: 0px; left: 0px; overflow: hidden;"> </div><img src="https://mastersavepername.club/acnt?_=1598458311488&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><img src="https://mastersavepername.club/acnt?_=1598458329590&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" />

scholarly journals Penerapan Klasifikasi Kueri untuk Meningkatkan Efektivitas Mesin Pencari

2021 ◽  
Vol 2021 (1) ◽  
pp. 1012-1018
Author(s):  
Handy Geraldy ◽  
Lutfi Rahmatuti Maghfiroh
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Gradient Boosting

Dalam menjalankan peran sebagai penyedia data, Badan Pusat Statistik (BPS) memberikan layanan akses data BPS bagi masyarakat. Salah satu layanan tersebut adalah fitur pencarian di website BPS. Namun, layanan pencarian yang diberikan belum memenuhi harapan konsumen. Untuk memenuhi harapan konsumen, salah satu upaya yang dapat dilakukan adalah meningkatkan efektivitas pencarian agar lebih relevan dengan maksud pengguna. Oleh karena itu, penelitian ini bertujuan untuk membangun fungsi klasifikasi kueri pada mesin pencari dan menguji apakah fungsi tersebut dapat meningkatkan efektivitas pencarian. Fungsi klasifikasi kueri dibangun menggunakan model machine learning. Kami membandingkan lima algoritma yaitu SVM, Random Forest, Gradient Boosting, KNN, dan Naive Bayes. Dari lima algoritma tersebut, model terbaik diperoleh pada algoritma SVM. Kemudian, fungsi tersebut diimplementasikan pada mesin pencari yang diukur efektivitasnya berdasarkan nilai precision dan recall. Hasilnya, fungsi klasifikasi kueri dapat mempersempit hasil pencarian pada kueri tertentu, sehingga meningkatkan nilai precision. Namun, fungsi klasifikasi kueri tidak memengaruhi nilai recall.

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e86703 ◽  
Author(s):  
Wangchao Lou ◽  
Xiaoqing Wang ◽  
Fan Chen ◽  
Yixiao Chen ◽  
Bo Jiang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Dna Binding ◽  
Binding Proteins ◽  
Naive Bayes ◽  
Dna Binding Proteins ◽  
Naïve Bayes

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

2021 ◽  
Vol 6 (3) ◽  
pp. 35-47
Author(s):  
Anirudh Reddy Cingireddy ◽  
Robin Ghosh ◽  
Supratik Kar ◽  
Venkata Melapu ◽  
Sravanthi Joginipeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Albert Einstein ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Blood Profile ◽  
Molecular Tests ◽  
Large Populations

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

Spatial assessment of PM10 hotspots using Random Forest, K-Nearest Neighbour and Naïve Bayes

2021 ◽  
Vol 12 (10) ◽  
pp. 101202 ◽  
Author(s):  
Abdulwaheed Tella ◽  
Abdul-Lateef Balogun ◽  
Naheem Adebisi ◽  
Samsuri Abdullah
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Nearest Neighbour ◽  
Spatial Assessment
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