scholarly journals Analisis Cluster Hasil Uji Kompetensi Lembaga Sertifikasi Profesi (LSP) Melalui Teknologi Data Mining

2021 ◽  
Vol 19 (2) ◽  
pp. 76-81
Author(s):  
Raditya Danar Dana ◽  
Ahmad Faqih
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Data Cleaning ◽  
Quality Of Education ◽  
Data Mining Approach ◽  
Collection Data ◽  
Competency Tests ◽  
Cluster 2 ◽  
Made In

The implementation of the Competency Test at the LSP institution in higher education is an effort to ensure that students have abilities in certain fields according to predetermined competency standards. Education providers are required to always strive to improve the quality and quality of education with the aim that the student's academic performance will always improve. From the results of observations made in the research location, it was found a problem with the high number of failures in the implementation of the competency test. This study aims to conduct cluster analysis of the data resulting from the implementation of competency tests with the Data Mining approach through several stages in the form of data collection, data cleaning, data transformation, data modeling and data evaluation. This study resulted in grouping the results of competency tests which were divided into 3 clusters, namely cluster 1 as much as 38%, cluster 2 as much as 32% and cluster 3 as much as 30%..

scholarly journals Survey on data mining approach for analysis and prediction of student performance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 467 ◽  
Author(s):  
Chala Simon ◽  
Ybralem Bugusa
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Quality Of Education ◽  
Survey Paper ◽  
Data Mining Approach ◽  
Student Academic Performance

The quality of education is measured by the academic performance of students and the results they produce. Since the student academic performance is the made up of the environmental, psychological, socio-economic and other factors, it is challenging to measure the aca- demic performance of students. Such difficulties can be reduced by investigation of various factors that influence the student perfor- mance. Many researchers have been used different approaches to identifying the variables that help to predict students’ performance. This survey paper examines various data mining methodologies that have been used to analyze and predict students’ performance.   

scholarly journals Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering

KOMTEKINFO ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 1-9
Author(s):  
Andri Nofiar ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Palm Oil ◽  
Laboratory Testing ◽  
Quality Data ◽  
Test Results ◽  
Clustering Method ◽  
Data Mining Approach ◽  
Cluster 2

The classification of the quality of palm oil in PT Tasma Puja is still done by laboratory testing and then the data is saved manually in Excel. The method of grouping takes time and allows data to be lost. With the development of knowledge, it can be replaced by a data mining approach that can be used to classify the quality of palm oil based on its standards. The k-Means clustering method can be applied to classify the quality of palm oil based on water, dirt and free fatty acids. The data used is the quality data of palm oil in December 2017 as many as 31 data with criteria of good, very good and not good. The test results contained 3 clusters, namely cluster 0 for good categories amounted to 12 data, cluster 1 for very good category amounted to 13 data and cluster 2 for less good categories amounted to 6 data. The k-Means clustering method can be used for data processing using the concept of data mining in grouping data according to criteria.

A STUDY ON IMPLEMENTATION OF CONTINUOUS AND COMPREHENSIVE EVALUATION IN UPPER PRIMARY SCHOOLS OF KERALA

2016 ◽  
pp. 168-176
Author(s):  
R.G Kothari ◽  
Mary Vineetha Thomas
Keyword(s):  
Primary Schools ◽  
Comprehensive Evaluation ◽  
Secondary Level ◽  
The State ◽  
Quality Of Education ◽  
Government Schools ◽  
Curriculum Framework ◽  
Powerful Means ◽  
Made In

Evaluation is widely acknowledged as a powerful means of improving the quality of education. The introduction of Continuous and Comprehensive Evaluation (CCE) is considered as one of the major steps taken in this regard to improve and strengthen the quality of learner evaluation. The state of Kerala has been going through a series of educational reforms over the last decade or so and the introduction of CCE in the state is one among them. As emphasized by Kerala Curriculum Framework (2007) the implementation of new evaluation practices focusing on CCE was introduced right from primary to secondary level. Though the state has made all-out efforts to implement CCE in its true spirit, the questions that remain unanswered are that whether CCE has been actually and effectively implemented in all classes, what problems are being faced by teachers while implementing CCE. The present paper is a brief attempt made in this regard and is directed towards answering these questions and giving suggestions for the same. The study has been conducted on teachers of upper primary government schools of Kerala.

Fuzzy Miner

2011 ◽  
pp. 299-321 ◽  
Author(s):  
Nikos Pelekis ◽  
Babis Theodoulidis ◽  
Ioannis Kopanakis ◽  
Yannis Theodoridis
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Internet Routing ◽  
Data Mining Approach ◽  
New Information

QOSP Quality of Service Open Shortest Path First based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their appli-cations for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute-selection prob-lem in internet routing.

Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

2010 ◽  
Vol 37 (7) ◽  
pp. 5259-5264 ◽  
Author(s):  
Seyed Mohammad Seyed Hosseini ◽  
Anahita Maleki ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Customer Loyalty ◽  
Data Mining Approach ◽  
Using Data

THE QUALITY OF EDUCATION, SUCCESSES AND SHORTCOMINGS IN THE MATERIAL-TACHNICAL BASE OF SCHOOLS(on the example of Namangan region public education)

2021 ◽  
Vol 4 (8) ◽  
pp. 39-45
Author(s):  
Lutfillo P. Mamasaidov ◽  
Keyword(s):  
Public Education ◽  
Extracurricular Activities ◽  
Quality Of Education ◽  
Technical Base ◽  
Made In

This article highlights the data about reforms made in Namangan region Publiceducation, successes and shortcomings in schools' material-technical base. Besides, it also analyzes facts about the quality of education in the initial years, schools' need for qualified staff and resources, opportunities created for pupils in schools whose hours were prolonged, problems related to extracurricular activities and how successfully they were tackled

ADAPTIVE DATA MINING APPROACH FOR FLOW REDIRECTION IN MULTIHOMED MOBILE ROUTER

2010 ◽  
Vol 09 (05) ◽  
pp. 737-758 ◽  
Author(s):  
MIHUI KIM ◽  
YUKYONG JUNG ◽  
KIJOON CHAE
Keyword(s):  
Data Mining ◽  
Decision Models ◽  
Data Mining Approach ◽  
Efficient Resource ◽  
Mobile Router ◽  
Network Status ◽  
Interface Type ◽  
Physical Signal ◽  
Selection Of

On multihomed mobile router with several interfaces toward Internet, it is important to provide an efficient flow redirection (FR) method that changes the served interface according to each network status or user movement. However, currently as the mobile devices roams, the interface is selected as only the physical signal strength, thus efficient resource use or quality of service (QoS) support is not guaranteed. In this paper, we propose an adaptive data mining approach that provides the selection of influential attributes for FR and the proper FR decision model as the interface type. We analyze assuming network protocols in order to heuristically extract the FR candidate attributes for estimating the required QoS. We abstract theoretically the FR influential attributes through decision-tree algorithm, and finally obtain the proper FR decision models per each interface. Our simulation results show that our FR approach provides improved performances in comparison with current handover.

scholarly journals Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Shuo Li ◽  
Zhanjie Song ◽  
Wenhuan Lu ◽  
Daniel Sun ◽  
Jianguo Wei
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Least Significant Bit ◽  
Speech Watermarking ◽  
Big Data Mining ◽  
Data Mining Approach ◽  
Watermark Embedding ◽  
Data Infrastructures ◽  
Original Speech

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.

scholarly journals Predictive Modelling for Quality Prediction and Assurance of Extrusion Blow Molding

2019 ◽  
Vol 8 (11) ◽  
pp. 1364-1368
Keyword(s):  
Data Mining ◽  
Predictive Modelling ◽  
Molding Process ◽  
K Nearest Neighbors ◽  
Blow Molding ◽  
Data Mining Approach ◽  
Complex Relationships ◽  
Using Data ◽  
Extrusion Blow Molding

Extrusion Blow Molding process plays an important role in manufacturing of hollow products with wide variety of materials like polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC). Extrusion blow molded products are rejected due to the occurrence of defects such as die lines, blowouts, shrinkage, over weight of part. The complex relationships that exist between the process variables, and causes of defects are investigated for 1 litre container made of highdensity polyethylene (HDPE) using data mining techniques in order to reduce scrap. In this paper Data Mining approach is implemented by applying Decision Tree, k-Nearest Neighbors, Rule Induction and Vote techniques in RapidMiner for quality assurance and prediction of the quality of the extrusion blow molded product

scholarly journals Student Dropout Prediction & Educational Data Mining

2019 ◽  
Vol 9 (2) ◽  
pp. 5190-5192
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Social Factors ◽  
Educational Data Mining ◽  
Quality Of Education ◽  
Educational Organization ◽  
Student Dropout ◽  
Underperforming Students ◽  
The Subject

Educational data like students performance is very important to study and analyze and to improve the quality of education. The study concerned to data mining techniques with educational data is known as Educational Data Mining (EDM). This study finds knowledge and interesting patterns in educational organization. Students performance are the subject mainly concerned to find the qualitative model based on student’s personal and social factors then classify and predict the student performance. Proper counseling to underperforming students can reduce dropout ratio and help them to continue their studies.

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