Evaluasi Telemarketing Kartu Kredit Bank Menggunakan Algoritma Genetika untuk Seleksi Fitur dan Naive Bayes

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Ekka Pujo Ariesanto Akhmad
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Standard Process ◽  
Industry Standard

<strong> </strong>Bagian pemasaran bank sudah menampung data dari nasabah atau pelanggan bank dengan cara memasarkan atau mensosialisasikan kartu kredit lewat telepon (telemarketing). Evaluasi telemarketing kartu kredit yang sudah dilakukan bank masih kurang membawa hasil dan berdaya guna. Salah satu cara yang tepat untuk evaluasi laporan telemarketing kartu kredit bank adalah menggunakan teknik data mining. Tujuan penggunaan data mining untuk mengetahui kecenderungan dan pola nasabah yang berpeluang untuk berlangganan kartu kredit yang ditawarkan bank. Metode penelitian menggunakan Cross Industry Standard Process for Data Mining (CRISP-DM) dengan Algoritma Genetika untuk Seleksi Fitur (GAFS) dan Naive Bayes (NB). Hasil penelitian menunjukkan jumlah atribut pada dataset telemarketing kartu kredit bank sejumlah 15 atribut terdiri dari 14 atribut biasa dan 1 atribut spesial. Dataset telemarketing bank mengandung data berdimensi tinggi, sehingga diterapkan metode GAFS. Setelah menerapkan metode GAFS diperoleh 7 atribut optimal terdiri dari 6 atribut biasa dan 1 atribut spesial. Enam atribut biasa meliputi pekerjaan, balance, rumah, pinjaman, durasi, poutcome. Sedangkan atribut spesial adalah target. Hasil penelitian menunjukkan algoritma NB mempunyai nilai akurasi <em>86,71</em>%. Algoritma GAFS dan NB meningkatkan nilai akurasi menjadi <em>90,27</em>% untuk prediksi nasabah bank yang mengambil kartu kredit.

scholarly journals PENERAPAN ALGORITMA NAÏVE BAYES UNTUK MEMPREDIKSI KEPUTUSAN CALON NASABAH DAN NASABAH TETAPBANK BRI SYARIAH MENERIMA PENAWARAN PROGRAM DEPOSITO BERJANGKA

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Wahyu Nurjaya WK ◽  
Yusrina Adani
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Preparation ◽  
Standard Process ◽  
Industry Standard

Bank BRI Syariah memiliki banyak produk yang menarik untuk ditawarkan kepada calon nasabah maupun nasabah tetap berupa produk jangka panjang atau jangka pendek, yang menawarkan banyak keuntungan bagi nasabah itu sendiri. Salah satu produknya adalah Deposito berjangka yang merupakan produk investasi dengan menyimpan uang dan penarikanya hanya bisa dilakukan pada kurun waktu tertentu yang telah di janjikan oleh pihak bank dengan persetujuan nasabah. Dengan telemarketing yang baik oleh pihak bank maka diharapkan calon nasabah dan nasabah tetap mengetahui produk ini.Telemarketing adalah salah satu cara dalam mempromosikan produk-produk atau jasa layanan yang ada di bank. Seorang telemarketing bank harus dapat membuat target nasabah, nasabah mana yang berpotensi untuk meningkatkan deposito dengan melihat data-data nasabah bank yang telah tersimpan dalam database. Dikarenakan database nasabah sangat besar, maka tidak mungkin untuk mencari pola prediksi calon nasabah atau nasabah tetap yang berminat untuk program Deposito dengan cara konvensional.Berdasarkan hal tersebut, pengelolaan data yang sangat besar bisa diatasi dengan memanfaatkan Data Mining yaitu proses iteratif dan interaktif untuk menentukan pola atau model baru yang sempurna, bermanfaat dan dapat dimengerti dalam suatu database yang sangat besar. Data Mining berisi pencarian trend pola yang diinginkan dalam database besar untuk membantu pengambilan keputusan diwaktu yang akan datang. Dengan menggunakan Data Mining diharapkan dapat mengoptimasikan proses prediksi data nasabah oleh seorang telemarketing, sehingga dia mampu menawarkan deposito dengan target calon nasabah atau nasabah tetap yang tepat sasaran. Adapun Teknik Klasifikasi Data Mining menggunakan algoritma Naïve Bayes. Naïve Bayes bekerja sangat efektif saat diuji pada dataset yang besar untuk menentukan pola dimasa lalu dan mencari fungsi yang akan menjadi pola penilaian data dimasa yang akan datang. Untuk mencapai hasil yang diharapkan metode CRISP-DM (Cross Industry Standard Process for Data Mining) sangat cocok sebagai solusi, melalui proses business understanding, data understanding, data preparation, modeling, evaluation dan deployment. Dengan ini hasil prediksi akan lebih akurat, sehingga untuk target telemarketing produk Deposito Bank BRI Syariah akan tepat sasaran.

scholarly journals Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Munawir Munawir ◽  
Taufiq Iqbal
Keyword(s):  
Data Mining ◽  
Research Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Standard Data ◽  
Industry Standard ◽  
Bayes Algorithm ◽  
Using Data ◽  
Banda Aceh

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm. 

scholarly journals Data Mining untuk Klasifikasi Penderita Kanker Payudara Berdasarkan Data dari University Medical Center Menggunakan Algoritma Naïve Bayes

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Ibnu Ramadhan ◽  
Kurniawati Kurniawati
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Naive Bayes ◽  
Medical Center ◽  
Naïve Bayes ◽  
Probabilistic Prediction ◽  
Industry Standard ◽  
Prediction Technique ◽  
Bayes Algorithm ◽  
The University

The breast cancer sufferers data from the University Medical Center is data about patients suffering from breast cancer based on certain characteristics. This data has abundant information so that data mining can be done with the aim of digging deeper information which of course can be useful in the future. The data class itself is divided into 2 groups: recurrence and non-relapse classes. The technique used in this study is classification using the Naive Bayes algorithm. Naive Bayes is a simple probabilistic prediction technique based on the implementation of Bayes rules with a strong assumption of independence on features. The tool used to find accuracy values is RapidMiner 9.3. Data attributes consist of Class, Age, Menopause, Tumor-Size, Inv-Nodes, Node-Caps, Deg-Malig, Breast, Breast-Quad and Irradiant. In terms of methods, this study uses the CRISP-DM (Cross Industry Standard Process for Data Mining) method. This research is used as information in making decisions to determine policies taken in dealing with patients with breast cancer

scholarly journals OPTIMASI PARTICLE SWARM OPTIMIZATION PADA NAÏVE BAYES UNTUK PREDIKSI KONDISI KELAHIRAN BAYI

2021 ◽  
Vol 2 (1) ◽  
pp. 28-33
Author(s):  
Handini Arga Damar Rani
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Cross Validation ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Process Data ◽  
Standard Process ◽  
Swarm Optimization ◽  
Industry Standard ◽  
Fold Cross Validation

Tehnik data mining dapat digunakan dalam berbagai bidang salah satunya dalam aspek data mining, buat memperkirakan sebuah penyakit dari data rekam medis pasien. Teknik riset yang dipakai pada riset ini mengikuti berbagai tahapan model “Cross-Industry Standard Process Data Mining” (CRISP-DM). Melalui metode klasifikasi dalam data mining, atribut data seperti usia, tekanan darah, berat badan, posisi janin, dan tinggi fundus uteri bisa dipakai buat memperkirakan kemungkinan penyakit pasien. Maka dari itu, peneliti menggunakan metode klasifikasi Naive Bayesian dan optimasi “Particle Swarm Optimization” (PSO) untuk prediksi kelahiran bayi guna mengecek prediksi status kelahiran bayi. Dari hasil prediksi itu bisa dipakai buat menetapkan rata-rata hasil kelahiran bayi setiap bulannya. Data yang kami pakai adalah jumlah ibu hamil 165. Selama pengujian digunakan perhitungan akurasi, akurasi, recall, dan AUC chart, dan model prediksi dievaluasi menggunakan 10 “fold cross-validation”. Dengan nilai akurasi 91,82% dan precission 100% serta recall 81,50% dan nilai AUC 0.90 termasuk kategori excellent classification pada model yang diujikan

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 Prediction of benign and malignant breast cancer using data mining techniques

2018 ◽  
Vol 12 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal ◽  
BB Tiwari
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Low Income ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Low Income Countries ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Rbf Network

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

scholarly journals Analisa Komparasi Algoritma Decision Tree C4.5 dan Naïve Bayes untuk Prediksi Churn Berdasarkan Kelas Pelanggan Retail

2019 ◽  
Vol 3 (3) ◽  
pp. 103
Author(s):  
Ni Wayan Wardani ◽  
Ni Kadek Ariasih
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes

Pelanggan adalah salah satu aset utama bagi perusahaan ritel. Perusahaan harus dapat mengenali bagaimana karakter pelanggan mereka sehingga mereka dapat mempertahankan pelanggan yang sudah ada agar tidak berhenti membeli dan pindah ke perusahaan ritel yang bersaing (churn). Salah satu model yang tepat untuk mengenali karakter pelanggan adalah model RFM (Recency, Frekuensi, Moneter). Model RFM mampu menghasilkan kelas pelanggan dan di setiap kelas pelanggan dapat dianalisis atau diprediksi dengan konsep data mining apakah pelanggan tetap sebagai pelanggan atau churn. Data yang digunakan berasal dari data pelanggan dan data penjualan di UD. Mawar Sari. Kelas pelanggan UD Mawar Sari yang dihasilkan dari model RFM adalah Dormant, Everyday, Golden dan Superstar. Konsep data mining dengan membangun model prediksi dalam penelitian ini menggunakan algoritma Decision Tree C4.5 dan Naïve Bayes. Di semua kelas pelanggan kinerja Algoritma Naïve Bayes lebih baik daripada Algoritma Decision Tree C4.5 dengan Recall 95,92%, Precision 84,15%, dan Accuracy 83,49% dan kelas pelanggan yang memiliki potensi churn tinggi adalah Dormant B, Dormant E, dan Dormant F.Kata Kunci: Prediksi Churn, RFM, C4.5, Naïve Bayes

scholarly journals Algoritma Naïve Bayes Untuk Memprediksi Kredit Macet Pada Koperasi Simpan Pinjam

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Diah Puspitasari ◽  
Syifa Sintia Al Khautsar ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Predictive Value ◽  
Naive Bayes ◽  
False Negative ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Naïve Bayes ◽  
Data Mining Technique ◽  
Application Form ◽  
Using Data

Cooperatives are a forum that can help people, especially small and medium-sized communities. Cooperatives play an important role in the economic growth of the community such as the price of basic commodities which are relatively cheap and there are also cooperatives that offer borrowing and storing money for the community. Constraints that have been felt by this cooperative are that borrowers find it difficult to repay loan installments, causing bad credit. Because the cooperative in conducting credit analysis is carried out in a personal manner, namely by filling out the loan application form along with the requirements and conducting a field survey. Therefore there is a need for an evaluation to be carried out in lending to borrowers. To minimize these problems, it is necessary to detect customer criteria that are used to predict bad loans and to determine whether or not the elites are eligible to take credit using data mining. The data mining technique used is classification with the Naive Bayes method. Based on testing the accuracy of the resulting model obtained accuracy level of 59%, sensitivity (True Positive Rate (TP Rate) or Recall) of 46.80%, specificity (False Negative Rate (FN Rate or Precision) of 69.81%, Positive Predictive Value (PPV) of 57.89%, and Negative Predictive Value (NPV) of 59.67%.

scholarly journals Aplicación de los árboles de decisión en la identificación de patrones de lesiones fatales por causa externa en el municipio de Pasto, Colombia

2017 ◽  
Vol 19 (3) ◽  
pp. 388
Author(s):  
Ricardo Timaran-Pereira ◽  
Andrés Calderón-Romero ◽  
Arsenio Hidalgo-Troya
Keyword(s):  
Data Mining ◽  
Standard Process ◽  
Industry Standard ◽  
America Latina

Introducción: La Organización Panamericana de la Salud (OPS) desde el año 1993 y la Organización Mundial de la Salud (OMS) en 1996, aceptaron que la violencia es un problema de salud pública, situación que se corrobora en el Informe de Violencia y Salud, en el cual América Latina presentó una tasa de homicidios de 18 por cada 100.000 personas, y es considerada como una de las regiones más violentas del mundo. Objetivo: Detectar patrones delictivos con técnicas de minería de datos en el Observatorio del Delito del municipio de Pasto (Colombia). Materiales y métodos: Se aplicó Cross Industry Standard Process for Data Mining (CRISP-DM), una de las metodologías utilizadas en el desarrollo de proyectos de minería de datos en los ambientes académico e industrial. La fuente de información fue el Observatorio del Delito del municipio de Pasto, donde está almacenadas las cifras históricas, limpias y transformadas sobre las lesiones de causa externa (fatales y no fatales), registrados en 11 años. Resultados: Se construyó un modelo de clasificación basado en árboles de decisión que permitió descubrir patrones de muertes por causa externa. Para el caso de homicidios, estos sucedieron en su mayoría en la Comuna 5 de Pasto, los fines de semana, en la madrugada, en el segundo semestre del año, en la vía pública y las víctimas fueron hombres adultos, de oficios varios, la causa de los homicidios fueron riñas y se produjeron con arma de fuego. Conclusión: El conocimiento generado ayudará a los organismos gubernamentales y de seguridad a tomar decisiones eficaces en lo relacionado a la implementación de planes de prevención de delitos y seguridad ciudadana.

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.

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