scholarly journals Diagnosis of Diabetes Using Naïve Bayes Classifier Method

2021 ◽  
Vol 1 (1) ◽  
pp. 22-29
Author(s):  
Tasya Ardhian Nisaa ◽  
Shavira Maya Ningrum ◽  
Berlianda Adha Haque
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Bayes Classifier ◽  
Genetic Inheritance ◽  
Data Mining Techniques ◽  
Python Programming Language ◽  
Python Programming

Not a few people suffer from diabetes, diabetes is usually caused by genetic inheritance from parents and grandparents. Not only from heredity but many criteria or characteristics can determine a person has diabetes. This research was conducted by looking for a dataset on Kaggle that contains criteria for someone diagnosed or undiagnosed with diabetes such as age, gender, weakness, polyuria, polydipsia, and others. Furthermore, from these criteria, predictions are calculated using the Naive Bayes classification method where this method is one of the data mining techniques. This prediction calculation uses the Python programming language. From these criteria, each criterion is grouped with similarities and the results of the program that have been made can diagnose someone with diabetes. The prediction calculations that have been carried out have resulted in 90% accuracy, 93% precision, 89% recall, 92% specificity, and 91% F1-Score.

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 Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 2675
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 lang="EN-US">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>

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

scholarly journals APPLICATION OF NAIVE BAYES CLASSIFIER ALGORITHM IN DETERMINING NEW STUDENT ADMISSION PROMOTION STRATEGIES

2019 ◽  
Vol 1 (1) ◽  
pp. 14-28
Author(s):  
Ahmad Haidar Mirza
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Statistical Techniques ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Promotion Strategies ◽  
Effectiveness And Efficiency ◽  
New Student

Data Mining is a process that uses statistical techniques, mathematics, artificial intelligence, machine learning to extract and identify useful information and related knowledge from large databases. Data mining is the process of finding new patterns in data by filtering large amounts of data. Data mining uses pattern recognition technology that is similar to statistical techniques and mathematical techniques. The patterns found can provide useful information for generating economic benefits, effectiveness and efficiency. Algorithm Naive Bayes Classifier is one method of data mining that can be used to support effective and efficient promotion strategies. The Naive Bayes Classifier algorithm is used to predict the interest of the study based on the calculations performed. The data used are new student registration data from 2014 until 2016 at Bina Darma University. The results of this study are new models that are expected to provide important information can be used to assist the Marketing Team of Bina Darma University Palembang in policy making and implementation of appropriate marketing strategy. The results obtained are expected to help to support the promotion strategies that impact on the effectiveness and efficiency of promotion and increase the number of new students who will register.

scholarly journals KLASIFIKASI KARAKTERISTIK KONSUMEN SEPEDA MOTOR MERK T DI JAWA BARAT MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER PADA DATA MINING

2017 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Jaka Aulia Pratama ◽  
Zulhanif Zulhanif ◽  
Yadi Suprijadi
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Model Classification ◽  
Automatic Transmissions ◽  
Market Planning ◽  
Customer Classification

PT. JKL has a role as a main dealer of T’s brand are handling three types of motorcycle products in West Java. These are type of Sport, CUB, and Scooter(Automatic Transmissions). The company records the buyer of T’s brand motorcycle in the Customer Database (CDB). CDB collected from 2011 to 2013 yielded information of consumer characteristics which is necessary in market planning. Consumer characteristics are classified into two groups: Repeated Order and New Customer. Classification methods used in the study of Data Mining is the Naïve Bayes Classifier. Model classification is done by calculating the conditional probability to choose the greatest value of probability. The accuracy of the classification is 83% and the error classification is 17%.

scholarly journals Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer

2018 ◽  
Vol 5 (3) ◽  
pp. 269
Author(s):  
Yoga Dwitya Pramudita ◽  
Sigit Susanto Putro ◽  
Nurul Makhmud
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Easy Access ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
News Stories ◽  
Long Time ◽  
Sports News

<p>Dokumen berita olahraga dalam bentuk web kini memiliki jumlah yang besar dalam kurun waktu singkat. Untuk kemudahan akses dokumen perlu melakukan pengelompokan dokumen berita kedalam beberapa kategori. Hal tersebut bertujuan agar berita olahraga tersusun sesuai dengan kategori yang ditentukan. Berita dapat dikelompokkan secara manual oleh manusia, akan tetapi hal tersebut membutuhkan waktu yang lama untuk melakukan kategorisasi. Metode klasifikasi diusulkan dalam penelitian ini untuk melakukan pengkategorian secara otomatis dokumen berita. Tujuan dilakukannya klasifikasi adalah untuk mempercepat dan mempermudah dalam pemberian kategori, sehingga dapat meningkatkan efisiensi waktu. Pada penelitian ini menggunakan metode klasifikasi Naïve Bayes Classifier. Sebelum dilakukan klasifikasi ada proses preprocessing dengan menggunakan Enhanced Confix Striping Stemmer.  Hal ini bertujuan untuk mengembalikan ke bentuk kata dasar, sehingga data berkurang dan proses komputasi menjadi lebih efisien. Pengujian dilakukan menggunakan 18 berita olahraga yang dipilih secara acak oleh user atau tester, dari 18 berita yang diujikan terdapat 14 berita yang bernilai benar atau relevan dengan analisis yang dilakukan use atau tester pada berita uji. Dari penelitian ini dapat disimpulkan bahwa Aplikasi Klasifikasi Berita Olahraga menggunakan Metode Naïve Bayes dengan Enhanced Confix Striping Stemmer mampu mengklasifikasi berita olahraga sesuai dengan kategori masing-masing, seperti Sepak Bola, Basket, Raket, Formula 1, Moto GP dan olahraga lainnya dengan keakuratan sebesar 77%.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p>Web-based sports news currently has a considerable amount of documents. News documents need to be grouped into multiple categories for easy access. The goal is that sports news is structured according to the specified category. News can be grouped manually by humans, but it takes a long time to categorize if it involves large documents. Classification method is proposed in this research to categorize automatically news document. The purpose of doing the classification is to accelerate and simplify the granting of categories, thereby increasing the efficiency of time. In this research using the Naïve Bayes Classifier classification method. Prior to classification there is a preprocessing process using Enhanced Confix Striping Stemmer. It aims to return to the basic word form, so the data is reduced and the computing process becomes more efficient. From the test using 18 sports news randomly selected by the user or tester, there are 14 news stories that are true or relevant to the analysis by the user or the tester on the test news. This study concludes that the Sports News Classification Application using the Naïve Bayes Method with Enhanced Confix Striping Stemmer is able to classify sports news according to their respective categories, such as Football, Basket, Racquet, Formula 1, Moto GP and other sports with accuracy of 77%.</p>

scholarly journals Analisa Pola Penyalahgunaan Facebook Sebagai Alat Kejahatan Trafficking Menggunakan Data Mining

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Linda Jayanti ◽  
Steven R. Sentinuwo ◽  
Oktavian A. Lantang ◽  
Agustinus Jacobus
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Abstrak - Facebook memungkinkan penggunanya berinteraksi dengan orang yang kita kenal maupun orang yang tidak kita kenal, dimana hal tersebut dapat membuka peluang bagi kejahatan dunia maya seperti, penculikan, perdagangan manusia (trafficking), hingga pembunuhan. IOM mecatat bahwa korban perdagangan orang atau trafficking di Indonesia mencapai 74.616 hingga I juta per tahun, dimana tindak kejahatan teersebut banyak dilakukan melalui facebook sebagai medianya. Data teks (status) yang berada di halaman facebook sangat besar. Dengan menggunakan Teknik pengolahan data dari ilmu Data Mining, terutama di bidangtext mining, penulis memanfaatkannya untuk mengidentifikasi data teks (status facebook) yang terindikasi sebagai proses kejahatan trafficking dengan memakai salah satu teknik klasifikasi dengan teorema naïve bayes classifier (NBC).   Kata kunci : facebook, trafficking, data mining, text mining, klasifikasi, naïve bayes classifier.

scholarly journals PENERAPAN ALGORITMA KLASIFIKASI SEBAGAI PENDUKUNG KEPUTUSAN PEMBERIAN BEASISWA MAHASISWA

2018 ◽  
Author(s):  
Heni Sulistiani
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Beasiswa merupakan bantuan pemerintah maupun swasta berupa sejumlah uang yang diberikan kepada siswa yang sedang atau yang akan mengikuti pendidikan di sekolah. Beasiswa diberikan dengan harapan dapat menumbuhkan dan meningkatkan semangat mahasiswa untuk berprestasi dilakukan dengan memberikan penghargaan berupa beasiswa tiap semester. Banyaknya calon mahasiswa yang mengajukan beasiswa tersebut dan melebihi kuota yang diberikan mengakibatkan proses penyeleksian penerima memakan waktu yang lama karena penyeleksian harus sesuai dengan kriteria agar penerima beasiswa tepat sasaran. Dalam hal ini penggunaan metode data mining sangatlah tepat untuk menemukan pola di dalam pengolahan datanya. Karena data mining melakukan ekstraksi untuk mendapatkan informasi penting yang sifatnya implisit dan sebelumnya tidak diketahui, dari suatu data. Classifier Naive Bayes memberikan proses penyeleksian yang cepat dan algoritmanya mudah dimengerti. Dalam beberapa penelitian, pendekatan dengan menggunakan Naive Bayes memiliki kinerja yang cukup tinggi untuk mengklasifikasikan data metode Naive Bayes Classifier memiliki keunggulan yaitu kesederhanaan dalam komputasinya. Penelitian ini berfokus pada penerapan algoritma klasifikasi Naive Bayes sebagai pendukung keputusan pemilihan beasiswa Bidikmisi bagi calon mahasiswa untuk klasifikasi pemilihan beasiswa agar mempercepat proses penyeleksian dan tidak terjadi kesalahan dalam penentuan calon penerima beasiswa. Pengujian dilakukan dengan menggunakan teknik pengukuran akurasi dan melihat dari matriks konfusi. Hasil menunjukkan bahwa dengan menggunakan algoritma naive bayes, nilai akurasi mencapai 80%.

scholarly journals Klasifikasi Karya Ilmiah (Tugas Akhir) Mahasiswa Menggunakan Metode Naive Bayes Classifier (NBC)

SISTEMASI ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 268
Author(s):  
Nurdin Nurdin ◽  
M Suhendri ◽  
Yesy Afrilia ◽  
Rizal Rizal
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Simple Algorithm ◽  
Scientific Work ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Final Project ◽  
Scientific Papers

ABSTRACTThe final project or thesis is the result of research that addresses a problem according to the student's field of science. By increasing the number of graduates, the number of final project documents produced will also be even greater. The large number of scientific papers or final project documents will be difficult to find according to the topic if they are not grouped. A large number of documents will not be effective if classification is done manually. This study makes a scientific paper classification application aimed at classifying the scientific work (final project) of students in the field of Informatics Engineering. This application was built by implementing the Naive Bayes Classifier algorithm based on background parameters and will be classified into 5 categories, namely image processing, data mining, decision making systems, geographic information systems and expert systems. With the research stages, namely data collection, preprocessing, calculation of the Naive Bayes Classifier method, implementation and system testing. This study uses 170 scientific papers, which are divided into 150 data for training and 20 data for testing. The results of this study illustrate that the Naive Bayes Classifier algorithm is a simple algorithm that can be used to classify scientific papers with an average accuracy of 86.68% and the average processing time required in each test is 5.7406 seconds / test.Keywords:scientific work, naive bayes classifier, classification,training, testing ABSTRAKTugas akhir atau skripsi merupakan hasil penelitian yang membahas suatu masalah sesuai bidang ilmu dari mahasiswa. Dengan bertambah jumlah lulusan, maka jumlah dokumen tugas akhir yang dihasilkan juga akan semakin besar. Jumlah dokumen karya ilmiah atau tugas akhir yang besar akan sulit dicari sesuai dengan topik jika tidak dikelompokkan. Jumlah dokumen yang besar akan tidak efektif jika dilakukan klasifikasi secara manual. Penelitian ini membuat aplikasi klasifikasi karya ilmiah bertujuan untuk mengklasifikasikan karya ilmiah (tugas akhir) mahasiswa dalam bidang ilmu Teknik Informatika. Aplikasi ini dibangun dengan mengimplementasikan algoritma Naive Bayes Classifier berdasarkan parameter latar belakang dan akan diklasifikasikan menjadi 5 kategori yaitu pengolahan citra, data mining, sistem pengambilan keputusan, sistem informasi geografis dan sistem pakar. Dengan tahapan penelitian yaitu pengumpulan data, preprocessing, perhitungan metode Naive Bayes Classifier,implementasi dan pengujian sistem.Penelitian ini menggunakan data sebanyak 170 data karya ilmiah, yang dibagi menjadi 150 data untuk pelatihan dan 20 data untuk pengujian. Hasil penelitian ini menggambarkan bahwa algoritma Naive Bayes Classifier merupakan algoritma sederhana yang mampu digunakan untuk melakukan klasifikasi karya ilmiah dengan rata-rata akurasi 86,68% serta rata-rata waktu proses yang dibutuhkan dalam setiap pengujian yaitu 5,7406 detik/pengujian.Kata Kunci:Karya ilmiah, Naive bayes classifier, Klasifikasi, Pelatihan, Pengujian.

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