scholarly journals Predicted Student Study Period with C4.5 Data Mining Algorithm

2020 ◽  
Vol 4 (2) ◽  
pp. 94
Author(s):  
Agus Supriyanto ◽  
Dwi Maryono ◽  
Febri Liantoni
Keyword(s):  
Data Mining ◽  
College Education ◽  
Data Mining Algorithm ◽  
Average Percentage ◽  
Academic Competition ◽  
New Students ◽  
Student Graduation ◽  
External Organization ◽  
College Type ◽  
Academic Year

Data of alumni from 2012 to 2015 found that the average percentage of students graduating on time was 22%. The comparison between the number of students who graduate on time and new students who enter each year is not comparable, therefore a study is needed to find out the factors that affect student graduation and to prediction of the graduation period of the student through data mining research using the C4.5 algorithm. The data tested was student alumni data from 2012 to 2015. The instruments studied include study period, academic year, GPA, corner focus, gender, intensity of work during college, type of thesis, intensity of campus internal organization, intensity of external organization of campus, UKT group, scholarship status, pre-college education, hobby intensity, intensity of game play, academic competition participation status, non-academic competition participation status, and availability of facilities and infrastructure. The best test results using percentage-split 75% obtained 83.33% accuracy as well as the rules contained in the decision tree.

scholarly journals Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa

2018 ◽  
Vol 17 (3) ◽  
pp. 325
Author(s):  
IGA Sri Melati ◽  
Linawati Linawati ◽  
I.A.D Giriantari
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Educational Institution ◽  
Frequent Pattern ◽  
Classification Model ◽  
Prospective Students ◽  
Use Of Data ◽  
New Students ◽  
New Student ◽  
Academic Year

Admission of new students to an educational institution such as STMIK STIKOM Bali was an activity which is routinely implemented every new academic year. The registration of new student candidates was always increasing from year to year, but not all prospective students continued registration step of a number of prospective students who had passed. It would be too late to take action if a new student enrolled very little. By not knowing the number of registration students, institution cannot measure the time and the number of new admissions target which had been achieved. In this case the use of data mining techniques was expected to provide knowledge or information that was previously hidden in the data warehouse, thus becoming valuable information for the organization or institution. In this study, the classification model and the frequent pattern are made to identify the data pattern and its appearance for the "advanced" or "backward registration" status class. Some task mining was used to predict the prospective student was by classification techniques and techniques Frequent Pattern which extracted model and describe important data classes. The algorithm used is Decision Tree. The software which was used for implementation is WEKA.   Index Term : Data Mining, Classification, Decision Tree, Frequent Pattern

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

THE IMPACT OF ZONATION SYSTEM POLICY IN THE PPDB ON STUDENTS' OBTAINING AT SMA NEGERI 8 SURABAYA

2020 ◽  
Vol 4 (3) ◽  
pp. 525
Author(s):  
Idawati Idawati
Keyword(s):  
Junior High School ◽  
Low Income ◽  
Educational Background ◽  
Quantitative Approach ◽  
National Examination ◽  
New Students ◽  
The Impact ◽  
New Student ◽  
Academic Year ◽  
Descriptive Method

This research was conducted by using a descriptive method with a quantitative approach. The quantitative approach was chosen to be tested theories by examining and measuring variables in the form of relationships, differences, influences, contributions, and the others. The research was carried out by describing the students acquisition data on the new student admission (PPDB) using zoning system based on the academic year 2019-2020 and the student acquisition data on the academic year PPDB 2018-2019 as a comparison. Based on the results of the study, the new students of PPDB using zoning system was considered lower in terms of economic and educational background of parents. There were more parents with less education (elementary & junior high school) in the zoning system than in the rayon system, whereas parents with higher education in the zoning system were fewer than the rayon system.  Likewise, in terms of income, there were more people with the low income in the zoning system than in the rayon system, and those having high income were fewer than in the rayon system. The study showed that the intelligence and the result of National Examination Score (NUN) in the zoning system is lower than in the rayon system. The intelligent level of the students in the zoning system is mostly dominated by the scores under 90-109, while in the rayon system were dominated by the scores above 90-109.  The National Examination Scores (NUN) in the zoning system were evenly distributed from a range of scores 0 to 30, while in the rayon system the scores were dominated by a range of scores 28-30, with the lowest score 24.

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.

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