Uncovering Hidden Information Within University's Student Enrollment Data Using Data Mining

ABSTRACT The problem that often occurs in the world of education is the high level of student drop outs. This must be addressed so that it does not get worse. There are several efforts that can be made to overcome these problems including finding hidden information from student data stacks in the database. The discovery of hidden information can be done by utilizing data mining techniques with the K-Nearst Neighbor algorithm. STIKOM Bali as one of the universities certainly needs to look for hidden information stored in a database pile that can later be used as a reference in making decisions to overcome student drop-out problems. The results of the research have been done in the form of designing applications using Data Flow Diagrams, Conceptual Databases, Designing Base Models and Table Structures. From the design that has been done and continued with the implementation stage of the K-Nearst Neighbor algorithm on the application. The application that has been built can classify students who are classified as prospective drop outs. Keywords: K-Nearst Neightbor, Klasifikasi, Drop Out ABSTRAK Permasalahan yang banyak terjadi dalam dunia pendidikan adalah tingginya tingkat drop out mahasiswa. Hal ini harus segera ditanggulangi supaya tidak bertambah buruk. Ada beberapa upaya yang dapat dilakukan untuk mengatasi permasalahan tersebut diantaranya menemukan informasi tersembunyi dari tumpukan data mahasiswa dalam database. Penemuan informasi tersembunyi dapat dilakukan dengan cara memanfaatkan teknik data mining dengan algoritma K-Nearst Neighbor. STIKOM Bali sebagai salah satu perguruan tinggi tentunya perlu mencari informasi tersembunyi yang tersimpan dalam tumpukan database yang nantinya dapat dijadikan acuan dalam pengambilan keputusan untuk menanggulangi permasalahan drop out mahasiswa. Hasil penelitian yang telah dilakukan berupa perancangan aplikasi dengan menggunakan Data Flow Diagram, Konseptual Database, Perancangan Basis Model dan Struktur tabel. Dari perancangan yang telah dilakukan dan dilanjutkan dengan tahap implementasi algoritma K-Nearst Neighbor pada aplikasi.Aplikasi yang telah dibangun dapat menglasifikasikan mahasiswa yang tergolong calon drop out. Kata Kunci : K-Nearst Neighbor, Klasifikasi, Drop out

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A Study on Detection of Small Size Malicious Code using Data Mining Method

Jouranl of Information and Security ◽

10.33778/kcsa.2019.19.1.011 ◽

2019 ◽

Vol 19 (1) ◽

pp. 11-17

Author(s):

Taek-Hyun Lee ◽

◽

Ho Kook Kwang

Keyword(s):

Data Mining ◽

Malicious Code ◽

Mining Method ◽

Data Mining Method ◽

Using Data

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A Study on the Analysis of Employment Decision Factor of the Visually Impaired using Data Mining Technique

Disability & Employment ◽

10.15707/disem.2013.23.1.011 ◽

2013 ◽

Vol 23 (1) ◽

pp. 273-302 ◽

Cited By ~ 6

Author(s):

임은정 ◽

신현욱 ◽

김성진

Keyword(s):

Data Mining ◽

Visually Impaired ◽

Data Mining Technique ◽

Mining Technique ◽

Employment Decision ◽

Using Data

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Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.468 ◽

2017 ◽

Vol 7 (11) ◽

pp. 79

Author(s):

Sujata Mulik

Keyword(s):

Data Mining ◽

Crop Yield ◽

Crop Production ◽

Climatic Factors ◽

Crop Productivity ◽

Yield Prediction ◽

Data Mining Techniques ◽

Agriculture Sector ◽

Using Data ◽

Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction. The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops.

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

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

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

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