Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1
| Main Authors: | Azahari, Azahari, Yulindawati, Yulindawati, Rosita, Dewi, Mallala, Syamsuddin |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Fakultas Ilmu Komputer, Universitas Brawijaya
, 2020
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| Online Access: |
http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093 http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093/pdf |
| ctrlnum |
article-2093 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="id">Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1</title><creator lang="id">Azahari, Azahari</creator><creator lang="id">Yulindawati, Yulindawati</creator><creator lang="id">Rosita, Dewi</creator><creator lang="id">Mallala, Syamsuddin</creator><description lang="id">Prediksi  kelulusan  dibutuhkan  oleh  manajemen  perguruan  tinggi  dalam  menentukan kebijakan  preventif  terkait  pencegahan  dini  kasus drop  out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor.  Dengan  menggunakan data mining algoritma naive bayes dan neural network dapat  dilakukan  prediksi  kelulusan  mahasiswa di  STMIK  Widya  Cipta  Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer.  Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions.</description><publisher lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</publisher><date>2020-05-22</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>File:application/pdf</type><identifier>http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093</identifier><identifier>10.25126/jtiik.2020732093</identifier><source lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 7 No 3: Juni 2020; 443-452</source><source lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 7 No 3: Juni 2020; 443-452</source><source>2528-6579</source><source>2355-7699</source><source>10.25126/jtiik.202073</source><language>ind</language><relation>http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093/pdf</relation><rights lang="en">Hak Cipta (c) 2020 Jurnal Teknologi Informasi dan Ilmu Komputer</rights><recordID>article-2093</recordID></dc>
|
| language |
ind |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:eJournal |
| author |
Azahari, Azahari Yulindawati, Yulindawati Rosita, Dewi Mallala, Syamsuddin |
| title |
Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1 |
| publisher |
Fakultas Ilmu Komputer, Universitas Brawijaya |
| publishDate |
2020 |
| isbn |
9782020732093 |
| url |
http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093 http://jtiik.ub.ac.id/index.php/jtiik/article/view/2093/pdf |
| contents |
Prediksi kelulusan dibutuhkan oleh manajemen perguruan tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus drop out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor. Dengan menggunakan data mining algoritma naive bayes dan neural network dapat dilakukan prediksi kelulusan mahasiswa di STMIK Widya Cipta Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer. Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions. |
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