Penerapan Data Mining untuk Memprediksi Minat Nasabah Terhadap Produk Asuransi Meninggal Dunia dengan  Metode Naïve Bayes  (Studi Kasus : PT. BNI Life Insurance)

INTISASIPendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. faktor penting nasabah berupa premi, premi ditentukan dalam persentase atau tarif tertentu. Pada perusahaan asuransi pasti memiliki jumlah data, dan data tersebut sangat penting bagi perusahaan untuk mengetahui kriteria nasabah yang berminat pada asurnsi yang dipasarkan. Dengan adanya informasi dari data nasabah yang ada, perusahaan asuransi dapat mengambil suatu keputusan dalam menerapkan stragi perusahaan diantarnya yaitu menjual produk- produk promo untuk meninggatkan pendapatan perusahaan. Data mining merupakan suatu teknologi yang dapat membantu perusahaan dalam menemukan suatu yang sangat penting dari sekumpulan data. Data mining dapat membentu sautu pola atau membuat suatu sifat perilaku bisnisa yang berguna untuk pengambilan keputusan. Dengan menggunakan metode algoritma Naive Bayes diharapkan bisa membantu perusahaan dalam pengelolaan data nasabah dengan cara mengklasifikasi data nasabah untuk memprediksi minat nasabah dengan tingkat akurasi melebihi 80% dalam memilih produk asuransi meninggal dunia. Kata Kunci: asuransi, baïve bayes, prediksi, data mining. ABSTRACTIncome for insurance companies is determined by the amount of premium paid by the customer. Important factors for customers in the form of premiums, premiums are determined in certain percentages or rates. The insurance company certainly has the amount of data, and the data is very important for companies to know the criteria of customers who are interested in the insurance marketed. With the information from existing customer data, the insurance company can make a decision in implementing the company's strategy, which is to sell promo products to increase company revenue. Data mining is a technology that can help companies find a very important set of data. Data mining can form a pattern or create a nature of business behavior that is useful for decision making. By using the Naive Bayes algorithm method, it is expected to be able to assist companies in managing customer data by classifying customer data to predict customer interest with an accuracy rate exceeding 80% in choosing a death insurance product. Keywords: insurance, baïve bayes, predictions, data mining..

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Top-k Feature Selction Untuk Deteksi Penyakit Hepatitis Menggunakan Algoritme Naïve Bayes

Jurnal Buana Informatika ◽

10.24002/jbi.v11i1.2456 ◽

2020 ◽

Vol 11 (1) ◽

pp. 1

Author(s):

Riska Wibowo ◽

Henny Indriyawati

Keyword(s):

Data Mining ◽

Feature Selection ◽

Naive Bayes ◽

Information Gain ◽

Naïve Bayes ◽

Decision Making Process ◽

Accuracy Rate ◽

Chemical Substances ◽

Drugs And Alcohol ◽

Bayes Algorithm

Abstract. Becoming one of the society health problems in the world, hepatitis is an inflammation liver disease caused by a virus, bacterial infection, chemical substances including drugs and alcohol. In this research, for the dataset of hepatitis having high dimensionality, its value for each attribute was calculated using weight information gain method. Then, the attributes were selected by using top-k methods and were classified by using Naïve Bayes Algorithm respectively. This research showed that 9 out of 20 attributes had chosen to be the highest top-9 with an accuracy rate of 85.57%. Later on, this research can be useful for a consideration in a decision making process for various subjects related to feature selection and Naïve Bayes Algorithm method and also for predicting hepatitis.Keywords: data mining, weight information gain, Naïve Bayes algorithmAbstrak. Penyakit hepatitis merupakan masalah kesehatan masyarakat di dunia. Penyakit hepatitis merupakan penyakit peradangan hati yang disebabkan oleh virus, infeksi bakteri, zat-zat kimia termasuk obat-obatan dan alkohol. Pada penelitian ini, dataset hepatitis yang memiliki data berdimensi tinggi akan dihitung nilai bobot dari masing-masing atribut menggunakan metode weight information gain. Setelah dihitung nilai bobot dilakukan pemilihan atribut, atribut yang dipilih menggunakan metode top-k. Kemudian dilakukan klasifikasi menggunakan algoritme Naïve Bayes. Hasil penelitian menunjukkan dari 20 atribut, terpilih top-9 tertinggi dengan nilai akurasi 85.57%. Dengan adanya penelitian ini dapat digunakan sebagai bahan pertimbangan dan pengambilan keputusan pada berbagai bidang yang berkaitan dengan metode feature selection, algoritme Naïve Bayes, dan di dalam memprediksi penyakit hepatitis.Kata Kunci: data mining, weight information gain, algoritma Naïve Bayes

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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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Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1715 ◽

2020 ◽

Vol 12 (2) ◽

pp. 104-107

Author(s):

Nurhayati . ◽

Nuraeny Septianti ◽

Nani Retnowati ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Information Technology ◽

Data Processing ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Processing Data ◽

Student Graduation ◽

Phase Data ◽

Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

BINA INSANI ICT JOURNAL ◽

10.51211/biict.v7i1.1343 ◽

2020 ◽

Vol 7 (1) ◽

pp. 53

Author(s):

Derisma Derisma ◽

Fajri Febrian

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Data Mining ◽

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Accuracy Rate ◽

Cancer Disease ◽

Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar 98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %. Kata kunci: kanker payudara, neural network, support vector machine, naive bayes Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate. Keywords: breast cancer, neural network, support vector machine, naive bayes

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Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v5i1.44379 ◽

2021 ◽

Vol 5 (1) ◽

pp. 32

Author(s):

Hartatik Hartatik

Keyword(s):

Data Mining ◽

Big Data ◽

Prediction Models ◽

Naive Bayes ◽

Program Planning ◽

Naïve Bayes ◽

Bayes Method ◽

Student Graduation ◽

Bayes Algorithm ◽

Naive Bayes Method

Abstrak :Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma naive bayes. Algoritma Naive bayes merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa. Peneliti dalam hal ini menerapkan metode Naive bayes menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi naive bayes methods berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode Naive bayes sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial sebesar 85% dengan selisih akurasi 10%.__________________________Abstract : Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.

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Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

Journal of Innovation Information Technology and Application (JINITA) ◽

10.35970/jinita.v1i01.64 ◽

2019 ◽

Vol 1 (01) ◽

pp. 25-34

Author(s):

Ade Riani ◽

Yessy Susianto ◽

Nur Rahman

Keyword(s):

Data Mining ◽

Heart Rate ◽

Heart Disease ◽

Chest Pain ◽

Naive Bayes ◽

Naïve Bayes ◽

Mining Method ◽

The World ◽

Bayes Algorithm ◽

Exercise Induced

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested.

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The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽

10.31253/te.v4i2.543 ◽

2021 ◽

Vol 4 (2) ◽

pp. 44

Author(s):

Rino Rino

Keyword(s):

Data Mining ◽

Heart Disease ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Set ◽

A Value ◽

C4.5 Algorithm ◽

Calculation Results ◽

Mining Methods ◽

Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

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Analisis Kelayakan Lokasi Promosi Dalam Penerimaan Mahasiswa Baru (PMB) Dengan Algoritma Naïve Bayes & Decission Tree C4.5

Kilat ◽

10.33322/kilat.v10i1.1196 ◽

2021 ◽

Vol 10 (1) ◽

pp. 169-178

Author(s):

Wulan Wulandari

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Algorithms ◽

Tertiary Institution ◽

Public And Private ◽

Measurement Results ◽

Bayes Algorithm ◽

The City ◽

New Student

Competition for new student admissions in every public and private tertiary institution is currently growing rapidly every year, some spend a lot of money on promotional activities, to assist institutions / institutions in obtaining recommendations for the feasibility of promotion locations based on several measurement criteria using the classification algorithms contained in data mining . The algorithm used to compare the measurement of the feasibility of the promotion location of the city and district of Bekasi is Naïve Bayes and Decission Tree C4.5 using four parameters including the number of students in one sub-district, the number of students in one sub-district, the distance of location and last year's enthusiasts using 35 regions / sub-districts in Bekasi city and district. measurement results using the rapidminner, the accuracy value of the Naïve Bayes algorithm is 91.43% and the Decission Tree C4.5 is 94.29%.

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Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020731907 ◽

2020 ◽

Vol 7 (4) ◽

pp. 737

Author(s):

Sitti Aliyah Azzahra ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Emotional Expressions ◽

Tourist Attraction ◽

Test Results ◽

Tourist Attractions ◽

Bayes Algorithm ◽

Labeling Method ◽

The City

Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau emoticon, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan algoritme Naïve Bayes dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%. AbstractTourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews may contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classes. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.

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Comparison of Open Source Data Mining Tools: Naive Bayes Algorithm Example

2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) ◽

10.1109/ebbt.2019.8741664 ◽

2019 ◽

Keyword(s):

Data Mining ◽

Open Source ◽

Naive Bayes ◽

Naïve Bayes ◽

Open Source Data ◽

Source Data ◽

Bayes Algorithm ◽

Mining Tools

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