APPLICATION OF PREDICTION TIME OF GRADUATION USING THE NAÏVE BAYES ALGORITHM WITH THE PYTHON PROGRAM

Accreditation is a process to ensure the quality of a university and study program. There are several factors that determine the quality standard of accreditation. One of them is the time of graduation. However, there is no means that can be used to predict early student graduation time. Therefore, this study aims to create a means that can predict early graduation time. In this study, data mining methods were used, namely the Naïve Bayes algorithm. After that, data processing and application development will be carried out using the Python program. The data used in the data mining process is three years of historical data and the data used for the trial are active student data for the second and third years. There are 5 types of patterns with an accuracy value of 81%, 87%, 92%, 92%, and 95%.

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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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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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Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection

International Journal of Quantitative Research and Modeling ◽

10.46336/ijqrm.v1i3.72 ◽

2020 ◽

Vol 1 (3) ◽

pp. 123-134

Author(s):

Budiman Budiman ◽

Reni Nursyanti ◽

R Yadi Rakhman Alamsyah ◽

Imannudin Akbar

Keyword(s):

Data Mining ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Study Program ◽

Data Set ◽

Lower Accuracy ◽

Accuracy Result ◽

Bayes Algorithm

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.

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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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ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES DALAM MEMPREDIKSI KEBERHASILAN SEKOLAH MENGHADAPI UN

JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) ◽

10.33480/jitk.v5i2.1233 ◽

2020 ◽

Vol 5 (2) ◽

pp. 285-290

Author(s):

Yeni Angraini ◽

Siti Fauziah ◽

Jordi Lasmana Putra

Keyword(s):

High School ◽

Junior High School ◽

Naive Bayes ◽

Naïve Bayes ◽

National Examination ◽

National Examinations ◽

C4.5 Algorithm ◽

Student Graduation ◽

Bayes Algorithm ◽

Almost All

The national exam (UN) is one of the determinants of student graduation, both elementary school, junior high school and even high school. There are many businesses that are carried out by schools to prepare their students to face national examinations. In fact almost all schools provide material deepening to their students for subjects tested at the national examination. Therefore, this study was conducted to determine the level of success of the school in preparing students in facing national examinations. The method used is a decision tree with C4.5 algorithm and naïve Bayes algorithm. From the results of the study, the results of the accuracy of the naïve bayes algorithm were as big as 95,50% , while accuracy using the c4.5 algorithm is equal to 78,50%. Then it can be concluded that the predictions generated from the naïve bayes algorithm are better compared to the c4.5 algorithm .

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OPTIMASI DATA MINING MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN C4.5 UNTUK KLASIFIKASI KELULUSAN MAHASISWA

Jurnal Teknologi Informasi dan Komputer ◽

10.36002/jutik.v5i1.634 ◽

2019 ◽

Vol 5 (1) ◽

Author(s):

Ni Luh Ratniasih

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Method Comparison ◽

Naïve Bayes ◽

Bayes Method ◽

C4.5 Algorithm ◽

Long Time ◽

Student Graduation ◽

Small Capacity ◽

Information Values

ABSTRACT Presentation of data to produce information values is often displayed in the form of tabulations. If the data displayed has a small capacity, it may not be difficult to process the information. But if the data presented has a very large capacity, it is feared there are obstacles to absorbing information accurately and quickly. This is because that it takes a long time to read the data displayed in detail until the end of the data. The data to be discussed in this study are data of STMIK STIKOM Bali students. Historical data displayed will be converted into a decision tree. Thus the absorption of information will become easier. This research implements data mining disciplines using the naïve bayes method comparison with C4.5 algorithm which is a method for performing classification techniques and applied with Rapid Miner tools. Keywords : C4.5, KNN, Student Graduation ABSTRAK Penyajian data untuk menghasilkan nilai informasi sering kali ditampilkan dalam bentuk tabulasi. Apabila data yang ditampilkan memiliki kapasitas kecil, mungkin tidak terlalu sulit untuk mencerna kandungan informasi tersebut. Tetapi apabila data yang disajikan memiliki kapasitas yang sangat besar, dikawatirkan adanya kendala untuk menyerap informasi secara tepat dan cepat. Hal ini dikarenakan bahwa dibutuhkan waktu yang cukup lama untuk membaca data yang ditampilkan secara rinci hingga akhir data. Data yang akan dibahas dalam penelitian ini adalah data mahasiswa STMIK STIKOM Bali. Data historis yang ditampilkan akan dikonversi menjadi bentuk pohon keputusan. Dengan demikian penyerapan informasi akan menjadi lebih mudah. Penelitian ini mengimplemen-tasikan disiplin ilmu data mining menggunakan komparasi metode naïve bayes dengan algoritma C4.5 yang merupakan sebuah metode untuk melakukan teknik klasifikasi serta diaplikasikan dengan tools Rapid Miner. Kata kunci : C4.5, KNN, Kelulusan Mahasiswa

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

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i1.1502 ◽

2020 ◽

Vol 4 (1) ◽

pp. 95-101 ◽

Cited By ~ 2

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.

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Uji Performa Algoritma Naïve Bayes untuk Prediksi Masa Studi Mahasiswa

Creative Information Technology Journal ◽

10.24076/citec.2019v6i1.178 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Irkham Widhi Saputro ◽

Bety Wulan Sari

Keyword(s):

Data Mining ◽

Cross Validation ◽

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Study Program ◽

New Students ◽

Using Data ◽

The Many ◽

Fold Cross Validation

Universitas AMIKOM Yogyakarta adalah salah satu perguruan tinggi yang memiliki ribuan mahasiswa baru khususnya pada prodi Informatika. Pada tahun 2012 tercatat ada 1009 mahasiswa baru, dan pada tahun 2013 juga tercatat ada sebanyak 859 mahasiswa baru. Namun sayangnya, dari sekian banyak mahasiswa hanya sekitar 50% saja yang dapat lulus dengan tepat waktu. Data tersebut untuk membuat sistem klasifikasi menggunakan teknik data mining dengan metode Naïve Bayes. Dataset yang akan digunakan sebanyak 300 data yang bersumber dari data alumni angkatan 2012, dan 2013 dengan masing-masing data sebanyak 150. Data yang diperoleh memiliki 144 mahasiswa dengan keterangan lulus tepat waktu, dan 156 mahasiswa dengan keterangan lulus tidak tepat waktu. Proses pengujian akan dilakukan menggunakan metode 10-Fold Cross Validation, dan Confusion Matrix. Hasil pengujian menunjukkan bahwa rata-rata performa dari model Naïve Bayes mempunyai nilai akurasi sebesar 68%, nilai precision sebesar 61.3%, nilai recall sebesar 65.3%, dan nilai f1-score sebesar 61%. Nilai performa dari model dapat dipengaruhi oleh dataset yang digunakan untuk pembuatan model.Kata Kunci — data mining, Naïve Bayes, K-Fold Cross Validation, Confusion MatrixAMIKOM Yogyakarta University is one of the colleges that has thousands of new students, especially in the Informatics study program. In 2012 there were 1009 new students, and in 2013 there were 859 new students. But unfortunately, of the many students only around 50% can graduate on time. The data is to make the classification system using data mining techniques with the Naïve Bayes method. The dataset will be used as much as 300 data sourced from alumni data of 2012, and 2013 with each data as much as 150. The data obtained has 144 students with information passed on time, and 156 students with graduation information not on time. The testing process will be carried out using the 10-Fold Cross Validation, and Confusion Matrix method. The test results show that the average performance of the Naïve Bayes model has an accuracy value of 68%, precision value is 61.3%, recall value is 65.3%, and f1-score is 61%. The performance value of the model can be influenced by the dataset used for modeling.Keywords — data mining, classification, Naïve Bayes, graduation time

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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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