Journal articles: 'Graduation prediction'

1

Ahmad, Imam, Heni Sulistiani, and Hendrik Saputra. "The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate." Indonesian Journal of Artificial Intelligence and Data Mining 1, no. 1 (November 25, 2018): 47. http://dx.doi.org/10.24014/ijaidm.v1i1.5654.

Full text

Abstract:

The absence of prediction system that can provide prediction analysis on the graduation rate of students becomes the reason for the research on the prediction of the level of graduation rate of students. Determining predictions of graduation rates of students in large numbers is not possible to do manually because it takes a long time. For that we need an algorithm that can categorize predictions of students' graduation rates in computing. The Fuzzy Method and KNN or K-Nearest Neighbor Methods are selected as the algorithm for the prediction process. In this study using 10 criteria as a material to predict students' graduation rate consisting of: NPM, Student Name, Semester 1 achievement index, Semester 2 achievement index, Semester 3 achievement index, Semester 4 achievement index, SPMB, origin SMA, Gender , and Study Period. Fuzzyfication process aims to change the value of the first semester achievement index until the fourth semester achievement index into three sets of fuzzy values are satisfactory, very satisfying, and cum laude. Make predictions to improve the quality of students and implement KNN method into prediction, where there are some attributes that have preprocess data so that obtained a value, and the value is compared with training data, so as to produce predictions of graduating students will be on time and graduating students will be late. This study produces a prediction of student pass rate and accuracy.

APA, Harvard, Vancouver, ISO, and other styles

2

Marzuqi, Ahmad, Kusuma Ayu Laksitowening, and Ibnu Asror. "Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (April 25, 2021): 682. http://dx.doi.org/10.30865/mib.v5i2.2919.

Full text

Abstract:

Accreditation is a form of assessment of the feasibility and quality of higher education. One of the accreditation assessment factors is the percentage of graduation on time. A low percentage of on-time graduations can affect the assessment of accreditation of study programs. Predicting student graduation can be a solution to this problem. The prediction results can show that students are at risk of not graduating on time. Temporal prediction allows students and study programs to do the necessary treatment early. Prediction of graduation can use the learning analytics method, using a combination of the naïve bayes and the k-nearest neighbor algorithm. The Naïve Bayes algorithm looks for the courses that most influence graduation. The k-nearest neighbor algorithm as a classification method with the attribute limit used is 40% of the total attributes so that the algorithm becomes more effective and efficient. The dataset used is four batches of Telkom University Informatics Engineering student data involving data index of course scores 1, level 2, level 3, and level 4 data. The results obtained from this study are 5 attributes that most influence student graduation. As well as the results of the presentation of the combination naïve bayes and k-nearest neighbor algorithm with the largest percentage yield at level 1 75.40%, level 2 82.08%, level 3 81.91%, and level 4 90.42%.

APA, Harvard, Vancouver, ISO, and other styles

3

Nandel Syofneri, Sarjon Defit, and Sumijan. "Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa." Jurnal Informasi & Teknologi 1, no. 4 (September 26, 2019): 12–17. http://dx.doi.org/10.37034/jidt.v1i4.13.

Full text

Abstract:

Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.

APA, Harvard, Vancouver, ISO, and other styles

4

Suwardika, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 13, no. 3 (October 1, 2019): 177–84. http://dx.doi.org/10.30598/barekengvol13iss3pp177-184ar910.

Full text

Abstract:

Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.

APA, Harvard, Vancouver, ISO, and other styles

5

Suwardika, Gede, I. Ketut Putu Suniantara, and Ni Putu Nanik Hendayanti. "Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart." Jurnal VARIAN 2, no. 2 (April 30, 2019): 37–46. http://dx.doi.org/10.30812/varian.v2i2.361.

Full text

Abstract:

The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.

APA, Harvard, Vancouver, ISO, and other styles

6

Santoso, Heri Bambang. "Fuzzy Decision Tree to Predict Student Success in Their Studies." International Journal of Quantitative Research and Modeling 1, no. 3 (September 3, 2020): 135–44. http://dx.doi.org/10.46336/ijqrm.v1i3.59.

Full text

Abstract:

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

APA, Harvard, Vancouver, ISO, and other styles

7

Satria, Fiqih, Zamhariri Zamhariri, and M. Apun Syaripudin. "Prediksi Ketepatan Waktu Lulus Mahasiswa Menggunakan Algoritma C4.5 Pada Fakultas Dakwah Dan Ilmu Komunikasi UIN Raden Intan Lampung." Jurnal Ilmiah Matrik 22, no. 1 (March 30, 2020): 28–35. http://dx.doi.org/10.33557/jurnalmatrik.v22i1.836.

Full text

Abstract:

Student graduation data is very important for universities because it is used in the accreditation process. Data continues to grow and is ignored because it is rarely used. Data of graduating students can provide useful information if processed optimally. This study processes data using data mining to obtain information in the form of a prediction of student graduation punctuality. The method used is the C4.5 algorithm. The criteria used are gender, regional origin, type of school origin, ranking and entry point. In its application, the C4.5 algorithm can be used in predicting student graduation times with a precision value of 70.70%, 60.4% recall, and 58.2% accuracy. In measuring the performance of the algorithm in pattern recognition or information retrieval it is recommended to use a minimum of two parameters namely precission and recall to detect bias, therefore in this study the F-Measure calculation is used. From the calculation of the F-Measure obtained a value of 71% which means that the C4.5 algorithm is considered good in classifying and predicting students who graduate on time

APA, Harvard, Vancouver, ISO, and other styles

8

Kurniawan, Donny, Anthony Anggrawan, and Hairani Hairani. "Graduation Prediction System On Students Using C4.5 Algorithm." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 19, no. 2 (May 30, 2020): 358–65. http://dx.doi.org/10.30812/matrik.v19i2.685.

Full text

Abstract:

Bumigora University College there are several things that are not balanced between the entry and exit of students who have completed their studies. Students who enter in large numbers, but students who graduate on time below the specified standards. As result, there was a huge accumulation of students in each graduation period. One solution to overcome the problem above needs a data mining based system in monitoring or utilizing student development in predicting graduation using the C4.5 algorithm. The stages of this research began with problem analysis, data collection, data requirement analysis, data design, coding, and testing. The results of this study are the implementation of the C4.5 algorithm for predicting student graduation on time or not. The data used is the data of students who have graduated from 2010 to 2012. The level of acceptance generated using the confusion matrix is 93,103% accuracy using 163 training data and 29 testing data or 85% training data and 15% testing data. The results of research and testing that has been done, C4.5 algorithm is very suitable to be used in student graduation prediction.

APA, Harvard, Vancouver, ISO, and other styles

9

Munawir, Munawir, and Taufiq Iqbal. "Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 3, no. 2 (September 30, 2019): 59. http://dx.doi.org/10.35870/jtik.v3i2.77.

Full text

Abstract:

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm.

APA, Harvard, Vancouver, ISO, and other styles

10

Tatar, Ahmet Emin, and Dilek Düştegör. "Prediction of Academic Performance at Undergraduate Graduation: Course Grades or Grade Point Average?" Applied Sciences 10, no. 14 (July 19, 2020): 4967. http://dx.doi.org/10.3390/app10144967.

Full text

Abstract:

Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction.

APA, Harvard, Vancouver, ISO, and other styles

11

Jananto, Arief, Sulastri Sulastri, Eko Nur Wahyudi, and Sunardi Sunardi. "Data Induk Mahasiswa sebagai Prediktor Ketepatan Waktu Lulus Menggunakan Algoritma CART Klasifikasi Data Mining." Jurnal Sisfokom (Sistem Informasi dan Komputer) 10, no. 1 (February 22, 2021): 71–78. http://dx.doi.org/10.32736/sisfokom.v10i1.991.

Full text

Abstract:

Fakultas Teknologi Informasi Universitas Stikubank (UNISBANK) as one of the faculties in higher education in implementing learning activities has produced a lot of stored data and has graduated many students. The level of timeliness of graduation is important for study programs as an assessment of success. This research tries to dig up the pile of student parent data and graduation data in order to get the pass rate and graduation prediction of active students. By implementing the classification data mining technique and the CART algorithm, it is hoped that a decision tree can be used to predict the class timeliness of graduating from active students. By using the graduation data and student parent data totaling 1018 records, a decision tree model was obtained with an accuracy rate of 63% from the data testing test. Determination of split nodes using the Gini Index which breaks the dataset based on its impurity value. Tests conducted in this study show that the order of the variables in the decision tree is gender, origin school status, parental education, age at entry, city of birth, parent's occupation. The prediction with the resulting model is that 71% of active S1 Information Systems students can graduate on time and 51% for S1 Informatics Engineering students.

APA, Harvard, Vancouver, ISO, and other styles

12

Oztekin, Asil. "A hybrid data analytic approach to predict college graduation status and its determinative factors." Industrial Management & Data Systems 116, no. 8 (September 12, 2016): 1678–99. http://dx.doi.org/10.1108/imds-09-2015-0363.

Full text

Abstract:

Purpose The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status. Design/methodology/approach This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation. Findings The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model. Originality/value This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.

APA, Harvard, Vancouver, ISO, and other styles

13

Kartarina, Kartarina, Ni Ketut Sriwinarti, and Ni luh Putu Juniarti. "Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa." JTIM : Jurnal Teknologi Informasi dan Multimedia 3, no. 2 (August 14, 2021): 107–13. http://dx.doi.org/10.35746/jtim.v3i2.159.

Full text

Abstract:

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

APA, Harvard, Vancouver, ISO, and other styles

14

Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.

Full text

Abstract:

Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.

APA, Harvard, Vancouver, ISO, and other styles

15

Kamagi, David Hartanto, and Seng Hansun. "Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa." Jurnal ULTIMATICS 6, no. 1 (June 1, 2014): 15–20. http://dx.doi.org/10.31937/ti.v6i1.327.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

16

Sesodia, Sanjay, David Molnar, and Graham P. Shaw. "Can We Predict 4-year Graduation in Podiatric Medical School Using Admission Data?" Journal of the American Podiatric Medical Association 102, no. 6 (November 1, 2012): 463–70. http://dx.doi.org/10.7547/1020463.

Full text

Abstract:

Background: This study examined the predictive ability of educational background and demographic variables, available at the admission stage, to identify applicants who will graduate in 4 years from podiatric medical school. Methods: A logistic regression model was used to identify two predictors of 4-year graduation: age at matriculation and total Medical College Admission Test score. The model was cross-validated using a second independent sample from the same population. Cross-validation gives greater confidence that the results could be more generally applied. Results: Total Medical College Admission Test score was the strongest predictor of 4-year graduation, with age at matriculation being a statistically significant but weaker predictor. Conclusions: Despite the model’s capacity to predict 4-year graduation better than random assignment, a sufficient amount of error in prediction remained, suggesting that important predictors are missing from the model. Furthermore, the high rate of false-positives makes it inappropriate to use age and Medical College Admission Test score as admission screens in an attempt to eliminate attrition by not accepting at-risk students. (J Am Podiatr Med Assoc 102(6): 463–470, 2012)

APA, Harvard, Vancouver, ISO, and other styles

17

Jamhur, Hardi. "Pemodelan Prediksi Predikat Kelulusan Mahasiswa Menggunakan Fuzzy C-Means Berbasis Particle Swarm Optimization." Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains 10, no. 1 (April 27, 2020): 13–24. http://dx.doi.org/10.36350/jbs.v10i1.79.

Full text

Abstract:

Analysis and excavation of information on educational data is an inseparable part of organizing student academic systems in universities. The excavation of information is known as Educational Data mining (EDM), which is a discipline that focuses on the application of techniques and data mining devices specifically in the field of education. One of the EDM processes is related to the need for prediction especially to gain knowledge regarding the progress of student studies. Practically the activity of getting information / knowledge about the progress of student studies is difficult to do conventionally considering the size of the data volume is quite large. The approach to data mining clustering on student study progress data for prediction of predicate graduation tends to be not optimal. Fuzzy C-means (FCM) modeling optimized with Particle Swarm Optimization (PSO) can produce a more optimal prediction performance. In the case of prediction of student graduation prediction, the application of PSO-based FCM algorithm model produces more optimal results, with predictive accuracy of 86% while the FCM algorithm modeling is 79%.

APA, Harvard, Vancouver, ISO, and other styles

18

Qin, Lu, and Glenn Allen Phillips. "The Best Three Years of Your Life: A Prediction for Three-Year Graduation." International Journal of Higher Education 8, no. 6 (November 7, 2019): 231. http://dx.doi.org/10.5430/ijhe.v8n6p231.

Full text

Abstract:

The 3-year graduation rate is a rarely measured metric in higher education compared to its 4- or 6- year graduation rate counterparts. For the first time in college (FTIC) students to graduate in three years, they must come with certain skills, abilities, plans, supports, or motivations. This project considers two distinct but interrelated ways of using advanced and novel statistical models, the Log-linear Cognitive Diagnostic Model (LCDM) and the Logistic Regression model (LR), to look at both students’ ability to graduate in three years and the characteristics that contribute to this ability. The results indicate that the LCDM is a reliable and efficient statistical model that can provide accurate prediction of students’ ability to graduate early. In addition, student enrolled credit hours in the semester, transfer credit hours, student high school GPA, and student socioeconomic status (EFC) were statistically significant predictors contributing to three-year graduation. The significant interaction between students’ EFC status and transfer credit hours has a meaningfully practical impact on enrollment strategies and institutional policies. Future studies could use the same LCDM model to consider the degree to which these or other characteristics contribute to 4-, 5-, and 6-year graduation rates. Identification of these characteristics could have a policy, student support, and admissions implications. Additionally, the success of the LCDM model in predicting ability could be used for abilities unrelated to graduation, including the ability to pay off loans, succeed in an internship, or give back financially to a university.

APA, Harvard, Vancouver, ISO, and other styles

19

Noercholis, Achmad Noercholis. "COMPARATIVE ANALYSIS OF 5 ALGORITHM BASED PARTICLE SWARM OPTIMIZATION (PSO) FOR PREDICTION OF GRADUATE TIME GRADUATION." MATICS 12, no. 1 (April 7, 2020): 1. http://dx.doi.org/10.18860/mat.v12i1.8216.

Full text

Abstract:

Graduation information is very important for Higher Education involved in education. The data received by students each year is an important part as a source of information for making decisions on the Higher Education side in admitting new students. The results show the PSO-based K-NN Algorithm at k-optimum = 19 has the best performance of the 5 existing algorithms, with an Accuracy value = 74.08% and an Under Curve Area (AUC) value = 0.788. Attributes of Gender, Semester Achievment Index 1, 2, 4, 6 and 7 as well as Employment Status make a real contribution to the right graduation of students. The addition of the Particle Swarm Optimization (PSO) feature always increases the accuracy value, while the highest increase in accuracy value in the Decision Tree (C4.5) Algorithm is 5.21%, the lowest in the Vector Support Engine Algorithm of 1.79%. The K-Nearest Neighbor (k-NN) algorithm corresponds to the third order, it remains the algorithm that has the best value, the highest accuracy value, this is due to the questionable value before discussing the PSO features. For the evaluation phase, the results of the accuracy are much better if using the overall data training (Angaktan 2007-2011), both with the 2011 Force test data or the 2010-2011 Force. Timely graduation of students begins in the class of 2011, with a value of 98.18% (100% resolution), meaning students start graduating on time

APA, Harvard, Vancouver, ISO, and other styles

20

Rani, Sekar Rizkya, Sundari Retno Andani, and Dedi Suhendro. "Penerapan Algoritma K-Nearest Neighbor untuk Prediksi Kelulusan Siswa pada SMK Anak Bangsa." Prosiding Seminar Nasional Riset Information Science (SENARIS) 1 (September 30, 2019): 670. http://dx.doi.org/10.30645/senaris.v1i0.73.

Full text

Abstract:

Student graduation is very important for world education achievement, student graduation also influences the value of accreditation of an educational unit itself, therefor research on graduation prediction becomes a very interesting thing study, this study proposes the use of the K-Nearest Neighbor method to do predict student graduation at Anak Bangsa Private Vocational School. The result of the research is the value of k=5 with an accuracy rate of 93.55% which is determined as K-Optimal. The value of k=5 is applied to the K-NN algoritma to predict student graduation based on attendance, attitude, and value of knowledge.

APA, Harvard, Vancouver, ISO, and other styles

21

Tracey, Terence J., and William E. Sedlacek. "Prediction of College Graduation Using Noncognitive Variables by Race." Measurement and Evaluation in Counseling and Development 19, no. 4 (January 1987): 177–84. http://dx.doi.org/10.1080/07481756.1987.12022838.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Lagman, Ace C. "Embedding Logistic Regression Model in Decision Support Software for Student Graduation Prediction." Proceedings Journal of Interdisciplinary Research 2 (October 10, 2015): 104–10. http://dx.doi.org/10.21016/irrc.2015.au05ef81o.

Full text

Abstract:

Logistic regression is a predictive modeling technique that finds an association between the independent variables and the logarithm of the odds of a categorical response variable. This is one of the techniques used in analyzing a categorical dependent variable. The study focused on the application of logistic regression in predicting student graduation by generating data models that could early predict and identify students who are prone to not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The student graduation rate is the percentage of a school’s first-time, first-year undergraduate students who complete their program successfully. Most students’ first-year freshmen enrolled at the tertiary level failed to graduate. According to the National Center for Education Statistics, almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate. The colleges and universities consisting of high leaver rates go through a loss of fees and potential alumni contributors.

APA, Harvard, Vancouver, ISO, and other styles

23

Imron, Mohammad, and Satia Angga Kusumah. "Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm." IJIIS: International Journal of Informatics and Information Systems 1, no. 1 (September 1, 2018): 1–8. http://dx.doi.org/10.47738/ijiis.v1i1.17.

Full text

Abstract:

The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.

APA, Harvard, Vancouver, ISO, and other styles

24

Mulia, Isnan, and Muanas Muanas. "Model Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 dan Software Weka." JAS-PT (Jurnal Analisis Sistem Pendidikan Tinggi Indonesia) 5, no. 1 (June 10, 2021): 71. http://dx.doi.org/10.36339/jaspt.v5i1.417.

Full text

Abstract:

In this research, we build a model to predict graduation status of students in Institut Bisnis dan Informatika Kesatuan using C4.5 decision tree algorithm. The prediction model is built using students’ GPA from semester 1 to semester 4, for students with admission year of 2013 to 2016. The prediction model obtained is a decision tree with 26 rules, with the attribute IPS_4 being the attribute that determines the graduation label of students. This prediction model yields an accuracy of 73%, a result that is not good enough. This result is probably due to unbalanced proportion of the data used.

APA, Harvard, Vancouver, ISO, and other styles

25

Yuliansyah, Herman, Rahmasari Adi Putri Imaniati, Anggit Wirasto, and Merlinda Wibowo. "Predicting Students Graduate on Time Using C4.5 Algorithm." Journal of Information Systems Engineering and Business Intelligence 7, no. 1 (April 27, 2021): 67. http://dx.doi.org/10.20473/jisebi.7.1.67-73.

Full text

Abstract:

Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.

APA, Harvard, Vancouver, ISO, and other styles

26

Rahmani, Budi, and Hugo Aprilianto. "Early Model of Student's Graduation Prediction Based on Neural Network." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 2 (June 1, 2014): 465. http://dx.doi.org/10.12928/telkomnika.v12i2.1603.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Rahmani, Budi, and Hugo Aprilianto. "Early Model of Student's Graduation Prediction Based on Neural Network." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 2 (June 1, 2014): 465. http://dx.doi.org/10.12928/telkomnika.v12i2.47.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

., Nurhayati, Nuraeny Septianti, Nani Retnowati, and Arief Wibowo. "Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ." Ultimatics : Jurnal Teknik Informatika 12, no. 2 (December 29, 2020): 104–7. http://dx.doi.org/10.31937/ti.v12i2.1715.

Full text

Abstract:

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%.

APA, Harvard, Vancouver, ISO, and other styles

29

Supriyanto, Agus, Dwi Maryono, and Febri Liantoni. "Predicted Student Study Period with C4.5 Data Mining Algorithm." IJIE (Indonesian Journal of Informatics Education) 4, no. 2 (December 10, 2020): 94. http://dx.doi.org/10.20961/ijie.v4i2.46265.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

30

Haryatmi, Emy, and Sheila Pramita Hervianti. "Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (April 29, 2021): 386–92. http://dx.doi.org/10.29207/resti.v5i2.3007.

Full text

Abstract:

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time.

APA, Harvard, Vancouver, ISO, and other styles

31

Coursol, Diane H., and Edwin E. Wagner. "Prediction of Academic Success in a University Honors Program." Psychological Reports 58, no. 1 (February 1986): 139–42. http://dx.doi.org/10.2466/pr0.1986.58.1.139.

Full text

Abstract:

The utility of cognitive and biographical variables for the prediction of academic success in a University honors program was investigated. Several variables modestly predicted both college grade point average and final graduation from the honors program. Results were generally supportive of previous research.

APA, Harvard, Vancouver, ISO, and other styles

32

Lagman, Ace C., Lourwel P. Alfonso, Marie Luvett I. Goh, Jay-ar P. Lalata, Juan Paulo H. Magcuyao, and Heintjie N. Vicente. "Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model." International Journal of Information and Education Technology 10, no. 10 (2020): 723–27. http://dx.doi.org/10.18178/ijiet.2020.10.10.1449.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Muliono, Rizki, Juanda Hakim Lubis, and Nurul Khairina. "Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time." SinkrOn 4, no. 2 (March 11, 2020): 42. http://dx.doi.org/10.33395/sinkron.v4i2.10480.

Full text

Abstract:

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.

APA, Harvard, Vancouver, ISO, and other styles

34

Sussman, Steve, Louise A. Rohrbach, Silvana Skara, and Clyde W. Dent. "Prospective Prediction of Alternative High School Graduation Status at Emerging Adulthood1." Journal of Applied Social Psychology 34, no. 12 (December 2004): 2452–68. http://dx.doi.org/10.1111/j.1559-1816.2004.tb01986.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Goenner, Cullen F., and Sean M. Snaith. "Accounting for Model Uncertainty in the Prediction of University Graduation Rates." Research in Higher Education 45, no. 1 (February 2004): 25–41. http://dx.doi.org/10.1023/b:rihe.0000010045.13366.a6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Yalidhan, Muhammad Dedek. "IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 5, no. 2 (September 28, 2018): 169. http://dx.doi.org/10.20527/klik.v5i2.152.

Full text

Abstract:

Student’s graduation is one kind of the college accreditation elements by BAN-PT. Because of that. Information System is one of the department in STMIK Banjarbaru, there is no application has been implemented to predict imprecisely of student’s graduation time so far, which causes on time graduation percentage tend low every year. Therefore the accurate student’s graduation prediction can help the committe to choose the correct decisions in order to prevent the imprecisely of student’s graduation time. In this research, the backpropagation algorithm of artificial neural network will be implemented into the application with the output result as delayed and on time graduation. This reseach is using 318 data samples which the 70 % of it will be used as the training data and the other 30 % will be used as testing data. From the calculation of confusion matrix table’s the percentage of the prediction accuracy is 98.97 %.Keywords: student’s graduation, artificial neural network, backpropagation, confusion matrixKelulusan mahasiswa merupakan salah satu elemen dalam standar akreditasi perguruan tinggi oleh BAN-PT. Sistem Informasi adalah salah satu program studi yang ada di STMIK Banjarbaru, selama ini belum ada aplikasi yang diimplementasikan untuk memprediksi ketidaktepatan waktu kelulusan mahasiswanya yang menyebabkan angka kelulusan tepat waktu cenderung rendah setiap tahunnya. Oleh sebab itu, prediksi kelulusan mahasiswa yang akurat dapat membantu pihak Program Studi dalam mengambil keputusan-keputusan yang tepat untuk mencegah ketidaktepatan waktu kelulusan mahasiswanya. Pada penelitian ini, artificial neural network algoritma backpropagation diimplementasikan pada aplikasi yang dibuat dengan output lulus terlambat dan lulus tepat waktu. Penelitian ini menggunakan sebanyak 318 sampel data yang mana 70 % data digunakan sebagai data training dan 30 % data digunakan sebagai data testing. Dari hasil perhitungan tabel confusion matrix diperoleh persentase akurasi prediksi sebesar 98.97 %.Kata kunci: kelulusan mahasiswa, artificial neural network, backpropagation, confusion matrix

APA, Harvard, Vancouver, ISO, and other styles

37

Aesyi, Ulfi Saidata, and Retantyo Wardoyo. "Prediction of Length of Study of Student Applicants Using Case Based Reasoning." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 1 (January 31, 2019): 11. http://dx.doi.org/10.22146/ijccs.28076.

Full text

Abstract:

Graduation is important matter in college. Length of study can be used to evaluate curriculum. It affect accreditation score of the sutdy program. Based on Akreditasi Program Studi Magister Buku V Pedoman Penilaian Instrumen Akreditasi 3rd standard there is rule about students and graduation, such as profile of the graduates including average length of study time and gpa (grade point average) of graduates.In this study, system built to predict Gadjah Mada University Master of Computer Science student applicant’s length of study. It used new case with 13 features from applicant that will be predict as new case, then calculate local similarity using euclidean distance and hamming distance while global similarity using nearest neighbor. Maximum value of global similarity taken as solution while revised will be done if it’s value below threshold.Result of this study show that system can help study program to manage educational process. It show 76% accuracy of 50 data.

APA, Harvard, Vancouver, ISO, and other styles

38

Nanglae, Lalida, Natthakan Iam-On, Tossapon Boongoen, Komkrit Kaewchay, and James Mullaney. "Determining patterns of student graduation using a bi-level learning framework." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 2201–11. http://dx.doi.org/10.11591/eei.v10i4.2502.

Full text

Abstract:

The practice of data science, artificial intelligence (AI) in general, has expanded greatly in terms of both theoretical and application domains. Many existing and new problems have been tackled using different reasoning and learning methods. These include the research subject, generally referred to as education data mining (or EDM). Among many issues that have been studied in this EMD community, student performance and achievement provide an interesting, yet useful result to shaping effective learning style and academic consultation. Specific to this work at Mae Fah Luang University, the pattern of students’ graduation is determined based on their profile of performance in different categories of courses. This course-group approach is picked up to generalize the framework for various undergraduation programmes. In that, a bi-level learning method is proposed in order to predict the length of study before graduation. At the first tier, clustering is applied to derive major types of performance profiles, for which classification models can be developed to refine the prediction further. With the experiments on a real data collection, this framework usually provides accurate predictive outcomes, using several conventional classification techniques.

APA, Harvard, Vancouver, ISO, and other styles

39

Shepperd, James A. "Developing a Prediction Model to Reduce a Growing Number of Psychology Majors." Teaching of Psychology 20, no. 2 (April 1993): 97–101. http://dx.doi.org/10.1207/s15328023top2002_7.

Full text

Abstract:

This article explores problems associated with increased numbers of undergraduate psychology majors and considers strategies available to psychology departments wishing to reduce these numbers. Special attention is given to an approach using multiple regression procedures to develop a prediction model for reducing the number of psychology majors. With a prediction model, a criterion such as psychology GPA at graduation is selected, and predictors of this criterion (e.g., Introductory Psychology grade and first semester GPA) are examined. The prediction model approach is illustrated with data from Holy Cross College.

APA, Harvard, Vancouver, ISO, and other styles

40

Rifai, Mochamad Farid, Hendra Jatnika, and Bowval Valentino. "Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS)." PETIR 12, no. 2 (September 26, 2019): 131–44. http://dx.doi.org/10.33322/petir.v12i2.471.

Full text

Abstract:

This research discusses prediction pass rates the certification microsoft office specialist 2013 version (word and excel) aimed to provide information concerning to pass rates and certification give alternative solutions to determine the program certificationi appropriate to chosen before test certification. Naive bayes used for the classification certification graduation where participants know what information pass and did not finish. Naive bayes is a classification with the probability and statistics to predict opportunities in the future based on the Provided before. In this study, system development CRISP-DM to use of the become more ordered and testing done with the BlackBox to test each function is on the application built. From the study, produce values probability of 0.001042 the accuracy of 99 %. These results, proving that naïve bayes method can be used to assist in a prediction graduation rates participants (word and excel), because it produces quite high accuracy. So participants were able to determine the certification program proper chosen before test certification.

APA, Harvard, Vancouver, ISO, and other styles

41

Nichols, Joe D. "Prediction Indicators for Students Failing the State of Indiana High School Graduation Exam." Preventing School Failure: Alternative Education for Children and Youth 47, no. 3 (January 2003): 112–20. http://dx.doi.org/10.1080/10459880309604439.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Tekin, Ahmet. "Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach." Eurasian Journal of Educational Research 14, no. 54 (February 15, 2014): 207–26. http://dx.doi.org/10.14689/ejer.2014.54.12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Ibrahim, Suleiman Khalifa Arafa, and Mahmoud Ali Ahmed. "Prediction of Students’ Cumulative Grade Point Averages (CGPAs) at Graduation: A Case Study." International Journal of Computer Applications 174, no. 24 (March 18, 2021): 35–44. http://dx.doi.org/10.5120/ijca2021921149.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Maulana, A. "Prediction of student graduation accuracy using decision tree with application of genetic algorithms." IOP Conference Series: Materials Science and Engineering 1073, no. 1 (February 1, 2021): 012055. http://dx.doi.org/10.1088/1757-899x/1073/1/012055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Anjali Devi, S., M. Vishnu Priya, P. Akhila, and N. Vasundhara. "Analysis and prediction of student placement for improving the education standards." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 303. http://dx.doi.org/10.14419/ijet.v7i2.8.10429.

Full text

Abstract:

Students’ academic success can be evaluated based on their performance in the exams conducted by the institutions. In this paper, we propose a scheme where prediction of student final placement can be done based on the marks scored by them in the previous semesters. In order to predict the placement of the student we need some data to analyze. For this purpose we will supply students basic details and their previous academic information into the system which will be used to predict the placement of the student. This is done by generating association rules using apriori algorithm. Admin and user will use this system. Here user will be the student. Admin and user will use their login to access the system. Admin will add academic details of the students, like their SSC, HSC, Graduation marks (up to current semester, Back logs etc.,). User will be the student. Admin and user will use their login to access the system. Admin will add academic details of the students, like their SSC, HSC, Graduation marks (up to current semester, Back logs etc.,). This system can be used in schools, colleges and other educational institutions. This evaluation system is more accurate than other conventional methods. We are using a university data set to predict the placement of the student.

APA, Harvard, Vancouver, ISO, and other styles

46

Sutoyo, Edi, and Ahmad Almaarif. "Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 1 (February 8, 2020): 95–101. http://dx.doi.org/10.29207/resti.v4i1.1502.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

47

Izzah, Abidatul, and Ratna Widyastuti. "Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 2 (July 1, 2016): 60. http://dx.doi.org/10.26594/r.v2i2.548.

Full text

Abstract:

AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS). Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT) untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS). However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution. Therefore, in this study, we used a Decision Tree (DT) technique for generate the rules. So, the research aims to predict courses graduation using hybrid FIS and DT. Dataset used is the posttest score, tasks score, quizzes score, and middle test score from 106 students of the Polytechnic Kediri who took Algorithms and Data Structures. The research started by generating 5 rules by decision tree. The next is implementation of FIS that consist of fuzzification, inference, and defuzzification. The results show that the classifier give a good result in an accuracy, sensitivity, and specificity respectively was 94.33%, 96.55% and 84.21%.Keywords: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediction.

APA, Harvard, Vancouver, ISO, and other styles

48

Purwanto, Eko, Kusrini Kusrini, and Sudarmawan Sudarmawan. "Prediksi Kelulusan Tepat Waktu Menggunakan Metode C4.5 DAN K-NN (Studi Kasus : Mahasiswa Program Studi S1 Ilmu Farmasi, Fakultas Farmasi, Universitas Muhammadiyah Purwokerto)." Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) 20, no. 2 (November 12, 2019): 131. http://dx.doi.org/10.30595/techno.v20i2.5160.

Full text

Abstract:

The graduation profile is one of the key elements for the accreditation standard of higher education. It mirrors the performance of the applied educational system within a period of time. The better it is, the better the accreditation will be. In support of this, a graduation prediction may be conducted to the academic database of the students. It is of pivotal to trace and classify the historical data into the data training and data testing, thus, to predict the on time-graduation. The step is importantly done to help decide the better management of learning processes. This study was therefore done to analyse certain variables applied to predict the on time-graduation using the algorythms of C.45 and K-Nearest Neighbour (K-NN). The data mining was done to the academic database of the students of the Pharmacy study programme, Pharmacy Faculty, Muhammadiyah University of Purwokerto by adding certain variables into the process. The data was then classified into the data training and data testing. Backward selection was done to select the best and most influential variables for the dataset. The study further resulted that by using the algorhythm of C.45 and backward selection, the accuracy of the graduation reached 92.75%. It is different from the acurracy the K-NN and backward selection showed that reached 96.14%. The result confirmed that the KNN showed the better accuracy than the C.45. It considerably benefitted the study programme to make better decisions on increasing the quality of services, in particular that of leraning processes.

APA, Harvard, Vancouver, ISO, and other styles

49

Purnamasari, Evi, Dian Palupi Rini, and Sukemi Sukemi. "The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 5, no. 2 (February 27, 2020): 112. http://dx.doi.org/10.26555/jiteki.v5i2.15272.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Wibowo, Agus Hindarto. "Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method." Logic: Jurnal Rancang Bangun dan Teknologi 20, no. 1 (March 30, 2020): 19–24. http://dx.doi.org/10.31940/logic.v20i1.1580.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Graduation prediction'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles