DATA MINING DENGAN ALGORITMA NEURAL NETWORK DAN VISUALISASI DATA UNTUK PREDIKSI KELULUSAN MAHASISWA

Neni Purwati, Rini Nurlistiani, Oscar Devinsen

Abstract


Graduating on time is the desire of all students, not only that graduating on time is an advantage for both parties, namely students and educational institutions. But the graduation status of students when predicted does not always produce predictions early, so that it can result in graduation not on time. This research was conducted using data of students who graduated for 4 years from 2016-2019. The classification method is an approach to grouping data in data mining, namely classifying data. This classification method can also be used to make predictions for information that has not been previously known. The classification data mining method that will be used is the neural network algorithm. Visualization to display recapitulation data visually more interesting and neural network algorithm to predict student graduation which is difficult to do manually. Attributes used in training data consist of Jenis Kelamin, Asal, Kelas, Jurusan, Umur, IPK, Tanggal Yudicium, Tahun Yudicium and Class Hasil. The attributes that become parameters are 9 attributes, of which 8 are predictor attributes and 1 are results attributes. Training and testing data by changing parameters, namely: Hidden Layer Size: 3, Training Time: 500, Learning Rate: 0.3, Momentum: 0.2 produces a classification showing the level of accuracy using a neural network algorithm is 92.83%. Displays some very complete reporting recapitulation, so that predictions and visualization of the data can help in graduating students and provide recommendations for appropriate actions and must be done by management or the authorities to make decisions.

Keywords


Business Intelligence, Data Mining, Neural Network, Visualization

Full Text:

PDF

References


Ali, I., & Sularto, L. (2019). Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa. Jurnal ICT : Information Communication & Technology. https://doi.org/10.36054/jict-ikmi.v18i1.52

Azis, M. A., & Fazriansyah, A. (2019). PREDIKSI TINGKAT KELULUSAN NILAI MAHASISWA TERHADAP MATAKULIAH WEB PROGRAMMING MENGGUNKAN METODE NEURAL NETWORK. Jurnal Pilar Nusa Mandiri. https://doi.org/10.33480/pilar.v15i2.660

Hermawati, F. A. (2013). Data Mining (P. Christian (ed.)). CV Andi Offset.

Inthachot, M., Boonjing, V., & Intakosum, S. (2016). Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend. In Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3045254

Rohmawan, E. (2018). Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Desicion Tree Dan Artificial Neural Network. Jurnal Ilmiah Matrik.

Rudy Ansari. (2016). PREDIKSI KELULUSAN MAHASISWA DENGAN JARINGAN SYARAF TIRUAN. Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM). https://doi.org/10.20527/jtiulm.v1i1.4

Salmu, S., & Solichin, A. (2017). Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naïve Bayes : Studi Kasus UIN Syarif Hidayatullah Jakarta. Seminar Nasional Multidisiplin Ilmu (SENMI) 2017.

Yumalia, A., & Indrajit, R. E. (2017). Penerapan Konsep Business Intelligence untuk Percepatan Penyelesaian Perkara pada Panmud Perdata Khusus Mahkamah Agung RI. Ikraith-Informatika.




 

 

 

LP4M IBI DARMAJAYA

Jl. Zainal Abidin Pagar Alam No. 93 Labuhan Ratu, Bandar Lampung.Kampus IBI Darmajaya,  Gedung A Lantai 2.Telp. 0721-787214, 781310 Fax. 0721-700261 ext.126

Index by:
      

 

Flag Counter


Creative Commons License

Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International LicenseMy Stats jurnal

www.reliablecounter.com