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 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 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 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.

scholarly journals Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Drop Out ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

scholarly journals ANALISIS DATA MINING UNTUK PEMETAAN MAHASISWA YANG MEMBUTUHKAN BIMBINGAN DAN KONSELING MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER

2018 ◽  
Vol 11 (1) ◽  
pp. 14-26
Author(s):  
Hadi Kurnia Saputra
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Keberhasilan mahasiswa dalam menjalani proses pendidikan di Perguruan Tinggi dapat diukur melalui kinerja akademik dan masa penyelesaian studi. Namun, dalam perjalanan mahasiswa mengikuti proses pendidikan tidak bisa lepas dari berbagai permasalahan, baik yang bersumber dari lingkungan pendidikan maupun lingkungan keluarga dan pergaulan. Salah satu upaya yang dilakukan oleh institusi pendidikan untuk mendorong peningkatan kinerja akademik mahasiswa yang rendah adalah dengan memberikan pelayanan Bimbingan dan Konseling (BK). Dengan Data Mining dapat dilakukan klasifikasi mahasiswa yang memiliki kinerja akademik yang baik dan buruk. Algoritma Naïve Bayes Classifier dipilih untuk melakukan klasifikasi mahasiswa yang berkinerja akademik rendah dan dinilai membutuhkan proses Bimbingan dan Konseling. Penelitian yang dilakukan terhadap 507 record set data akademik mahasiswa Tahun Masuk 2008 program Strata-1 Fakultas Teknik Universitas Negeri Padang, algoritma Naïve Bayes mampu mengklasifikasi mahasiswa yang membutuhkan proses Bimbingan dan Konseling. Berdasarkan pengujian yang dilakukan terhadap mahasiswa Fakultas Teknik yang mewakili keseluruhan Program Studi diperoleh akurasi sebesar 81%. Sedangkan pengujian yang dilakukan terhadap mahasiswa setiap Program Studi diperoleh akurasi : Program Studi Pendidikan Teknik Bangunan = 82.1429%, Pendidikan Teknik Elektro = 90.566%, Pendidikan Teknik Elektronika = 84.6154%, Pendidikan Teknik Informatika dan Komputer = 82.9268%, Pendidikan Teknik Mesin = 79.1045%, Pendidikan Teknik Otomotif = 81.8182%, Pendidikan Kesejahteraan Keluarga = 73.1343%.

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