scholarly journals Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring

JURTEKSI ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 85-92
Author(s):  
Mustika Larasati Sibuea ◽  
Andy Safta
Keyword(s):  
Data Mining ◽  
Student Achievement ◽  
Manhattan Distance ◽  
Data Set ◽  
Euclidian Distance ◽  
Student Failure ◽  
Human Resources Information Systems ◽  
High Level

Abstract: The high level of student success and the low level of student failure is a quality of the education world. The world of education is currently required to have the ability to compete by utilizing all resources owned. In addition to facilities, infrastructure and human resources, information systems are one of the resources that can be used to improve competency skills. Data mining is a process of data analysis to find a dataset of data set. Data mining is able to analyze large amounts of data into information that has meaning for decision supporters. One process of data mining is clustring. Attributes used in the grouping of student achievement are Name, Extracurricular, Value which include Task Value, Uts Value, Value of Uses, total absenteeism, and Attitude value. The case study of 20 students with distance calculation using manhattan distance, chbychep distance and euclidian distance yielded 67% accuracy. Keywords: data mining, clustering, k-means, student achievement Abstrak: Tingginya tingkat keberhasilan siswa dan rendahnya tingkat kegagalan siswa merupakan cemin kualitas dunia pendidikan.Dunia pendidikan saat ini dituntut untuk memiliki kemampuan bersaing dengan memanfaatkan semua sumber daya yang dimiliki. Selain sumber daya sarana, prasarana dan manusia, sistem informasi merupakan salah satu sumber daya yang dapat digunakan untuk meningkatkan kemampuan barsaing. Data mining merupakan proses analisa data untuk menemukan suatu pola dara kumpulan data. Data mining mampu menganalisa jumlah data yang besar menjadi informasi yang mempunyai arti bagi pendukung keputusan. Salah satu proses data mining adalah clustring. Atribut yang digunakan dalam pengelompokan prestasi siswa adalah Nama, Ekstrakulikuler, Nilai yang meliputi Nilai Tugas, Nilai Uts, Nilai Uas, jumlah ketidak hadiran siswa (absensi), dan Nilai sikap. Studi kasus pada 20 siswa dengan perhitungan jarak menggunakan manhattan distance, chbychep distance dan euclidian distance menghasilkan akurasi sebesar 67%. Kata kunci: data mining, clustering, k-means, prestasi siswa

scholarly journals Extracting class diagram from hidden dependencies in data set

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Bogumiła Hnatkowska ◽  
Zbigniew Huzar ◽  
Lech Tuzinkiewicz
Keyword(s):  
Conceptual Model ◽  
Graphical Representation ◽  
Real Data ◽  
Class Diagram ◽  
Data Sets ◽  
Data Set ◽  
Key Concepts ◽  
High Level

A conceptual model is a high-level, graphical representation of a specic do-main, presenting its key concepts and relationships between them. In particular, these dependencies can be inferred from concepts' instances being a part of big raw data les. The paper aims to propose a method for constructing a conceptual model from data frames encompassed in data les. The result is presented in the form of a class diagram. The method is explained with several examples and veried by a case study in which the real data sets are processed. It can also be applied for checking the quality of the data set.

scholarly journals Farmer Preference to High Elevation Rice Technological Packages for Accelerating Technological Dissemination (A case Study in Humbang Hasundutan Regency)

Agro Ekonomi ◽  
2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Setia Sari Girsang ◽  
Agung B Santosa ◽  
Tommy Purba ◽  
Deddy R Siagian ◽  
Khadijah E Ramija
Keyword(s):  
Low Cost ◽  
High Elevation ◽  
Primary Data ◽  
Survey Method ◽  
User Friendliness ◽  
Level Of Satisfaction ◽  
High Level ◽  
Level Of Importance

Accelerating the introduction of a new technological package is needed to increase the productivity of high elevation puddled rice in Humbang Hasundutan. The objectives of the study are to find out the perception of the existence of technological packages and farmers' preference for a new technological package. The study used a survey method with primary data gathered using questionnaires. The criteria of locations and respondents were used to obtain relevant respondents and data concerning their knowledge of high elevation puddled rice cultivation.  The collected data were processed by using Importance Performance Analysis in order to find out the level of Importance and Satisfaction of the indicators and the valued aspects in the technological package components. The results of the study showed that the socio-economic aspects had to be heeded in organizing the technological package. Indicators having a high level of importance and a low level of satisfaction consisted of production cost, quality of seeds, farmer groups empowerment, technology information institution, capital cost, agricultural tools and machines, pest control, sales price, irrigation canals, and farm roads. On the other hand, introducing new superior seeds, productivity attribute and planting age were important indicators for local farmers as to improve the quality of existing seeds. Farmers group expected that the technological package had a high level of productivity, better access to input, low cost, and good user-friendliness in its application.

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.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

scholarly journals Using kites for 3-D mapping of gullies at decimetre-resolution over several square kilometres: a case study on the Kamech catchment, Tunisia

2018 ◽  
Vol 18 (6) ◽  
pp. 1567-1582 ◽  
Author(s):  
Denis Feurer ◽  
Olivier Planchon ◽  
Mohamed Amine El Maaoui ◽  
Abir Ben Slimane ◽  
Mohamed Rached Boussema ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerial Photography ◽  
Airborne Lidar ◽  
Data Set ◽  
Topographic Data ◽  
Independent Field ◽  
Wide Range ◽  
Laser Systems ◽  
High Level

Abstract. Monitoring agricultural areas threatened by soil erosion often requires decimetre topographic information over areas of several square kilometres. Airborne lidar and remotely piloted aircraft system (RPAS) imagery have the ability to provide repeated decimetre-resolution and -accuracy digital elevation models (DEMs) covering these extents, which is unrealistic with ground surveys. However, various factors hamper the dissemination of these technologies in a wide range of situations, including local regulations for RPAS and the cost for airborne laser systems and medium-format RPAS imagery. The goal of this study is to investigate the ability of low-tech kite aerial photography to obtain DEMs with decimetre resolution and accuracy that permit 3-D descriptions of active gullying in cultivated areas of several square kilometres. To this end, we developed and assessed a two-step workflow. First, we used both heuristic experimental approaches in field and numerical simulations to determine the conditions that make a photogrammetric flight possible and effective over several square kilometres with a kite and a consumer-grade camera. Second, we mapped and characterised the entire gully system of a test catchment in 3-D. We showed numerically and experimentally that using a thin and light line for the kite is key for a complete 3-D coverage over several square kilometres. We thus obtained a decimetre-resolution DEM covering 3.18 km2 with a mean error and standard deviation of the error of +7 and 22 cm respectively, hence achieving decimetre accuracy. With this data set, we showed that high-resolution topographic data permit both the detection and characterisation of an entire gully system with a high level of detail and an overall accuracy of 74 % compared to an independent field survey. Kite aerial photography with simple but appropriate equipment is hence an alternative tool that has been proven to be valuable for surveying gullies with sub-metric details in a square-kilometre-scale catchment. This case study suggests that access to high-resolution topographic data on these scales can be given to the community, which may help facilitate a better understanding of gullying processes within a broader spectrum of conditions.

scholarly journals PENERAPAN DATA MINING DALAM MENGELOMPOKAN PRODUKSI JAGUNG MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Nanda Erlangga ◽  
Solikhun Solikhun ◽  
Irawan Irawan
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Domestic Market ◽  
Corn Production ◽  
Low Level ◽  
High Level ◽  
Level Cluster ◽  
Central Statistics

Corn needs are currently experiencing a fairly rapid development can be seen in terms of the domestic market, here researchers want to increase the productivity and quality of corn production. The data that will be used is the data from the Central Statistics Agency. The method in this study is the K-means clustering algorithm and the application used is Rapidminer which will be grouped into 2 clustering, namely high and low. The results of this study are 2 high level cluster provinces, 32 low level cluster provincesKeywords: Corn, Data mining, K-means Clustering c

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

scholarly journals A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Longxiao Li ◽  
Xu Wang ◽  
Jafar Rezaei
Keyword(s):  
Customer Service ◽  
Value Function ◽  
Service Network ◽  
Core Elements ◽  
Delivery Platform ◽  
High Level ◽  
The Relationship ◽  
Best Worst Method

Crowdsourcing delivery is becoming a prevalent tool for tackling delivery problems by building a large labor-intensive service network. In this network, the delivery personnel consist of a large number of people with a complex composition and high level of mobility, creating enormous challenges for the quality of service and the management of a crowdsourcing platform. Hence, we attempt to conduct a competence analysis to determine whether they can provide promised services with high quality, i.e., they are competent for their job. To this end, the competence theory is introduced, and a multicriteria competence analysis (MCCA) approach is developed. To illustrate the MCCA approach, a real-world case study is conducted involving a Chinese takeaway delivery platform, where the Bayesian best-worst method is used to determine the weights of the criteria based on the data collected from managers of the platform company. Also, the competence scores of the personnel involved are collected through surveys and data sources of the company. Given the weights and the competence scores, we use additive value function to identify the overall competence scores of them, which reflects the level of competence for their job. The results show that Skills is the most important competence, while Knowledge is the least important of the four competence dimensions. In subcriteria, four core elements are identified such as punctuality, customer service awareness, responsible, and goods intact. In addition to the importance of criteria, a ranking of a sample of personnel is provided, and almost half of the crowdsourcing delivery personnel’s competence is below the average and vary significantly, while the relationship between the competence level and some other variables is also discussed. Moreover, the developed MCCA approach in this paper can be applied to analyze the competence of personnel in many other industries as well.

Penerapan Metode K-Means Clustering Dalam Menentukan Predikat Kelulusan Mahasiswa Untuk Menganalisa Kualitas Lulusan

JURTEKSI ◽  
2018 ◽  
Vol 4 (2) ◽  
pp. 133-140
Author(s):  
Venny Novita Sari ◽  
Yupianti Yupianti ◽  
Dewi Maharani
Keyword(s):  
Data Mining ◽  
Graduate Students ◽  
Clustering Method ◽  
Study Program ◽  
Data Set ◽  
Study Programs ◽  
Group A ◽  
The University ◽  
Program Graduate

Abstract: The increasing number of students who graduated each year causes a lot of student data that need to be processed, causing difficulties in grouping the data. In this research apply Data Mining by using Clustering method to classify the quality of graduate students of Faculty of Computer Science Dehasen University of Bengkulu based on GPA and Study Program. The algorithm used is K-Means Clustering, where the data are grouped based on the same characteristics will be entered into the same group and the data set entered into the group does not overlap. Information displayed in the form of group ?? a group of graduate students who dominate the Study Program, so it is known to the group that has the best graduate quality. The results of this study will assist the University in analyzing the quality of graduated students and the most potential study programs. Software used to help this grouping is Rapid Miner. Keywords: K-Means Clustering, Study Program, Graduate Quality, Rapid Miner Abstrak: Semakin meningkatnya jumlah mahasiswa yang diluluskan setiap tahunnya menyebabkan banyaknya data mahasiswa yang perlu diolah sehingga menyebabkan kesulitan dalam pengelompokan data tersebut. Pada penelitian ini menerapkan Data Mining dengan menggunakan metode Clustering untuk mengelompokkan kualitas lulusan mahasiswa Fakultas Ilmu Komputer Universitas Dehasen Bengkulu berdasarkan IPK dan Program Studi. Algoritma yang digunakan yaitu K-Means Clustering, dimana data dikelompokkan berdasarkan karakteristik yang sama akan dimasukkan ke dalam kelompok yang sama dan set data yang dimasukkan ke dalam kelompok tidak tumpang tindih. Informasi yang ditampilkan berupa kelompok � kelompok lulusan mahasiswa yang mendominasi Program Studi, sehingga diketahui kelompok yang memiliki kualitas lulusan terbaik. Hasil penelitian ini akan membantu pihak Universitas dalam menganalisa kualitas mahasiswa yang diluluskan dan program studi yang paling berpotensi diminati. Software yang digunakan untuk membantu pengelompokan ini adalah Rapid Miner. Keyword: K-Means Clustering, Program Studi, Kualitas Lulusan, Rapid Miner

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