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

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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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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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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Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students

Indonesian Journal of Information Systems ◽

10.24002/ijis.v2i2.3035 ◽

2020 ◽

Vol 2 (2) ◽

pp. 1

Author(s):

Yohanes Christopher Tapidingan ◽

Debby Paseru

Keyword(s):

High School ◽

High School Students ◽

Junior High School ◽

Cross Validation ◽

Naive Bayes ◽

Junior High ◽

Naïve Bayes ◽

Junior High School Students ◽

Classification Methods ◽

School Students

Stress is generally defined as a state where someone is mentally disturbed as the response to the adversity that he/she experiences. Junior High School students usually are not aware of the stress that they encounter. This research aims to compare two classification methods of KNN and Naïve Bayes to determine stress level. The data of this research were gathered from 254 respondents from Catholic Junior High School of Don Bosco Bitung. The tests of k-cross validation and percentage split from the data showed that Naïve Bayes method excelled KNN method. With k=3, KNN accuracy reached 86.61% at the highest and Naïve Bayes reached 87.40%. Meanwhile, based on percentage split test, the average of Naïve Bayes accuracy was higher than KNN with percentage of 88.31%. Moreover, for the precision and recall, Naïve Bayes was higher than KNN with 88.30% and 87.40% seen from the k-cross validation.

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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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Implementation of Weighted Naive Bayes Algorithm for Major Determination in Indonesian High School

2018 International Seminar on Application for Technology of Information and Communication ◽

10.1109/isemantic.2018.8549761 ◽

2018 ◽

Cited By ~ 1

Author(s):

Erwin Yudi Hidayat ◽

Aulia Sabiq Taufiqurrahman ◽

Ardytha Luthfiarta ◽

Junta Zeniarja ◽

Heru Agus Santoso ◽

...

Keyword(s):

High School ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Algorithm

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Application of the Naïve Bayes Algorithm for Student Graduation Analysis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.23596 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 421

Author(s):

Erick Akhmad Fahmi Alfa’izy ◽

Khairil Anam ◽

Naidah Naing ◽

Rosanita Tritias Utami ◽

Nur Anim Jauhariyah ◽

...

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

College System ◽

Student Graduation ◽

Bayes Algorithm ◽

Using Data ◽

Analysis System ◽

Law Student

Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%.

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Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using Naïve Bayes Algorithm

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.20194169 ◽

2019 ◽

Vol 4 (1) ◽

pp. 15

Author(s):

Admaja Dwi Herlambang ◽

Satrio Hadi Wijoyo ◽

Aditya Rachmadi

Keyword(s):

High School ◽

Student Achievement ◽

Student Learning ◽

Naive Bayes ◽

Computing System ◽

Naïve Bayes ◽

Vocational High School ◽

Intelligent Computing ◽

Bayes Algorithm ◽

Intelligent Computing System

Vocational High School with ICT major need an intelligent computing system that could predict the student learning achievement. The system used fifteen achievement indicators and Naïve Bayes algorithm in data processing. Testing on student achievement data produces the conclusion that is the highest intelligent accuracy values in 53% with lowest accuracy value in 48% based on Naïve Bayes algorithm processing. The result of mining process using Naïve Bayes algorithm can be used to classify the 3rd year student achievement to five categories. These categories are Very Good, Good, Fair, Poor, and Failed. The system testing result showed that this intelligent computing system function was fitted with Vocational High School’s system requirement, system design, and system implementation.

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

Journal of Industrial Engineering Management ◽

10.33536/jiem.v0i0.773 ◽

2021 ◽

pp. 38-43

Author(s):

Priskila Christine Rahayu ◽

Eric Jobiliong ◽

Antonny Antonny

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Quality Standard ◽

Study Data ◽

Naïve Bayes ◽

Application Development ◽

Study Program ◽

Prediction Time ◽

Student Graduation ◽

Bayes Algorithm

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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Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i2.2937 ◽

2021 ◽

Vol 5 (2) ◽

pp. 640

Author(s):

Mulkan Azhari ◽

Zakaria Situmorang ◽

Rika Rosnelly

Keyword(s):

Random Forest ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Random Forest Algorithm ◽

Svm Algorithm ◽

Testing Data ◽

C4.5 Algorithm ◽

Bayes Algorithm ◽

Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

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