scholarly journals Using Data Mining with C4.5 Algorithm for Student Department Selection at MTs N Kaliangkrik

2021 ◽  
Vol 1 (1) ◽  
pp. 22-36
Author(s):  
Ardhin Primadewi
Keyword(s):  
Data Mining ◽  
Test Score ◽  
General Knowledge ◽  
General Intelligence ◽  
Test Results ◽  
Language Knowledge ◽  
C4.5 Algorithm ◽  
System Input ◽  
Using Data ◽  
Selection Of

Psychological tests can determine the characteristics of behavior, personality, attitudes, interests, motivation, attention, perceptions, thinking power, intelligence, fantasies of students. MTs N Kaliangkrik routinely conducts tests for the selection of majors on its students assisted by Pelita Harapan Bangsa Magelang. In the implementation of the test for students at MTs N Kaliangkrik, processing and calculating the score still used Ms. Excel which requires extra time to recap and know the test results and the school needs to recap the existing results. The system developed applies data mining using the C4.5 Algorithm to predict the selection of majors. The test that is used as system input is the grade IX test score of MTs N Kaliangkrik which includes verbal, non-verbal, general intelligence, language knowledge, definite knowledge, general knowledge, and qualitative power tests. The accuracy of the similarity in the system reaches 80% (good) so that the system is suitable for use as a prediction tool for selecting majors in other schools.

scholarly journals PREDICTION FOR COOPERATIVE CREDIT ELIGIBILITY USING DATA MINING CLASSIFICATION WITH C4.5 ALGORITHM

2021 ◽  
Vol 2 (2) ◽  
pp. 67-74
Author(s):  
Yogiek Indra Kurniawan ◽  
Annastalia Fatikasari ◽  
Muhammad Luthfi Hidayat ◽  
Mohamad Waluyo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Method ◽  
Test Results ◽  
Savings And Loans ◽  
Manual Method ◽  
Average Value ◽  
C4.5 Algorithm ◽  
History Of ◽  
Using Data

BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa

2014 ◽  
Vol 6 (1) ◽  
pp. 15-20 ◽  
Author(s):  
David Hartanto Kamagi ◽  
Seng Hansun
Keyword(s):  
Data Mining ◽  
Drop Out ◽  
Time Pass ◽  
Academic Fields ◽  
C4.5 Algorithm ◽  
Index Terms ◽  
Using Data ◽  
Academic Year ◽  
Source Of Information ◽  
Test Result

Graduation Information is important for Universitas Multimedia Nusantara  which engaged in education. The data of graduated students from each academic year is an important part as a source of information to make a decision for BAAK (Bureau of Academic and Student Administration). With this information, a prediction can be made for students who are still active whether they can graduate on time, fast, late or drop out with the implementation of data mining. The purpose of this study is to make a prediction of students’ graduation with C4.5 algorithm as a reference for making policies and actions of academic fields (BAAK) in reducing students who graduated late and did not pass. From the research, the category of IPS semester one to semester six, gender, origin of high school, and number of credits, can predict the graduation of students with conditions quickly pass, pass on time, pass late and drop out, using data mining with C4.5 algorithm. Category of semester six is the highly influential on the predicted outcome of graduation. With the application test result, accuracy of the graduation prediction acquired is 87.5%. Index Terms-Data mining, C4.5 algorithm, Universitas Multimedia Nusantara, prediction.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals Derivation and Performance of Standardized Enhanced Liver Fibrosis (ELF) Test Thresholds for the Detection and Prognosis of Liver Fibrosis

2019 ◽  
Vol 3 (5) ◽  
pp. 815-826 ◽  
Author(s):  
James Day ◽  
Preya Patel ◽  
Julie Parkes ◽  
William Rosenberg
Keyword(s):  
Liver Fibrosis ◽  
Delphi Panel ◽  
Test Results ◽  
Hazard Ratios ◽  
Test Thresholds ◽  
And Performance ◽  
Using Data ◽  
Relationship Of ◽  
The Relationship ◽  
Selection Of

Abstract Introduction Noninvasive tests are increasingly used to assess liver fibrosis and determine prognosis but suggested test thresholds vary. We describe the selection of standardized thresholds for the Enhanced Liver Fibrosis (ELF) test for the detection of liver fibrosis and for prognostication in chronic liver disease. Methods A Delphi method was used to identify thresholds for the ELF test to predict histological liver fibrosis stages, including cirrhosis, using data derived from 921 patients in the EUROGOLF cohort. These thresholds were then used to determine the prognostic performance of ELF in a subset of 457 patients followed for a mean of 5 years. Results The Delphi panel selected sensitivity of 85% for the detection of fibrosis and >95% specificity for cirrhosis. The corresponding thresholds were 7.7, 9.8, and 11.3. Eighty-five percent of patients with mild or worse fibrosis had an ELF score ≥7.7. The sensitivity for cirrhosis of ELF ≥9.8 was 76%. ELF ≥11.3 was 97% specific for cirrhosis. ELF scores show a near-linear relationship with Ishak fibrosis stages. Relative to the <7.7 group, the hazard ratios for a liver-related outcome at 5 years were 21.00 (95% CI, 2.68–164.65) and 71.04 (95% CI, 9.4–536.7) in the 9.8 to <11.3 and ≥11.3 subgroups, respectively. Conclusion The selection of standard thresholds for detection and prognosis of liver fibrosis is described and their performance reported. These thresholds should prove useful in both interpreting and explaining test results and when considering the relationship of ELF score to Ishak stage in the context of monitoring.

scholarly journals Goods Stock Management using the K-Means Algorithm Method

2020 ◽  
Vol 10 (1) ◽  
pp. 22-45
Author(s):  
Dhio Saputra
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Test Results ◽  
Stock Management ◽  
Clustering Method ◽  
Sales Data ◽  
Using Data ◽  
Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

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