Open Access
Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier
Mujib Ridwan,Hadi Suyono,Moechammad Sarosa +2 more
- 01 Jan 2013
- Vol. 7, Iss: 1, pp 59-64
TL;DR: Pemudian dari klasifikasi tersebut, sistem akan memberikan rekomendasi solusi untuk memandu mahasiswa lulus dalam waktu yang paling tepat dengan nilai optimal berdasarkan histori nilai yang telah ditempuh mahasiswas.
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Abstract: Penelitian ini difokuskan untuk mengevaluasi kinerja akademik mahasiswa pada tahun ke-2 dan diklasifikasikan dalam kategori mahasiswa yang dapat lulus tepat waktu atau tidak. Kemudian dari klasifikasi tersebut, sistem akan memberikan rekomendasi solusi untuk memandu mahasiswa lulus dalam waktu yang paling tepat dengan nilai optimal berdasarkan histori nilai yang telah ditempuh mahasiswa. Input dari sistem ini adalah data induk mahasiswa dan data akademik mahasiswa. Sampel mahasiswa angkatan 2005-2009 yang sudah dinyatakan lulus akan digunakan sebagai data training dan testing. Sedangkan data mahasiswa angkatan 2010-2011 dan belum lulus akan digunakan sebagai data target. Data input akan diproses menggunakan teknik data mining algoritma Naive Bayes Classifier (NBC) untuk membentuk tabel probabilitas sebagai dasar proses klasifikasi kelulusan mahasiswa. Output dari sistem ini berupa klasifikasi kinerja akademik mahasiswa yang diprediksi kelulusannya dan memberikan rekomendasi untuk proses kelulusan tepat waktu atau lulus dalam waktu yang paling tepat dengan nilai optimal. Hasil pengujian menunjukkan bahwa faktor yang paling berpengaruh dalam penentuan klasifikasi kinerja akademik mahasiswa yaitu Indeks Prestasi Komulatif (IPK), Indeks Prestasi (IP) semester 1, IP semester 4, dan jenis kelamin. Sehingga faktor-faktor tersebut dapat digunakan sebagai bahan evaluasi bagi pihak pengelola perguruan tinggi. Pengujian pada data mahasiswa angkatan 2005-2009, algoritma NBC menghasilkan nilai precision, recall, dan accuracy masing-masing 83%, 50%, dan 70%. Kata Kunci—Kinerja akademik mahasiswa, data mining, dan Naive Bayes Classifier.
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Citations
Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga
Alfa Saleh
- 01 Jan 2015
TL;DR: In this paper, a Naive Bayes method was used to predict the magnitude of expected electricity use per household in order to more easily manage the use of electricity, based on 60 household electricity usage data tested with Naive bayes method, be obtained the percentage 78.3333% for the accuracy of the prediction, in which of the 60 household electric usage data successfully classified correctly.
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Mapping Student's Performance Based on Data Mining Approach (A Case Study)☆
TL;DR: This paper focus on mapping students using K-mean Cluster algorithm to reveal the hidden pattern and classifying students based on their demographic (gender, origin, GPA, grade of certain courses), and avverage of course attending.
69
The effect of mining data k-means clustering toward students profile model drop out potential
Windania Purba,Saut Parsaoran Tamba,Jepronel Saragih +2 more
- 01 Apr 2018
TL;DR: In this paper, a K-means clustering method was implemented to cluster the drop out students potentially, based on the model taken was found that students who potentially drop out because of the unexciting students in learning, unsupported parents, diffident students and less of students behavior time.
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Classification of intrusion detection system (IDS) based on computer network
David Ahmad Effendy,Kusrini Kusrini,Sudarmawan Sudarmawan +2 more
- 01 Nov 2017
TL;DR: The result of this reseach shows that the application of k-means clustering method for continuous variabe discretization and feature selection can optimize the performance of naivebayes algorithm in classifying intrusion types.
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Classification of the Period Undergraduate Study Using Back-propagation Neural Network
Purwono Prasetyawan,Imam Ahmad,Rohmat Indra Borman,Ardiansyah,Yogi Aziz Pahlevi,Dwi Ely Kurniawan +5 more
- 01 Oct 2018
TL;DR: The BPNN algorithm is suitable for the classification of undergraduate study periods with accuracy rates above 85% and will be used as a reference in making improvements to the performance of student studies.
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