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Aplikasi Prediksi Kelulusan Tepat Waktu Dengan Algoritma Naïve Bayes (Studi Kasus Program Studiteknik Informatika, UMMI)

Authors: Indra Griha Tofik Isa;

Aplikasi Prediksi Kelulusan Tepat Waktu Dengan Algoritma Naïve Bayes (Studi Kasus Program Studiteknik Informatika, UMMI)

Abstract

Penggunaan data mining sudah menjadi tren dalam pengolahan data karenaketersediaanya data dalam jumlah besar serta semakin banyaknya kebutuhanuntuk mengubah data tersebut menjadi informasi maupun pengetahuan yangberguna. Selain sebagai alat bantu dalam mengekstraksi data, data mining jugadigunakan sebagai pendukung keputusan, baik dalam bidang komersil maupunnon-komersil. Dari sekian banyak algoritma yang digunakan dalam data mining,salah satunya adalah Algoritma Naïve Bayes, dimana dalam algoritma inimerupakan salah satu metode pada Probabilistic Reasoning yang bertujuanuntuk melakukan klasifikasi data pada kelas tertentu. Dalam penelitian yangdilakukan dengan menerapkan tahapan-tahapan Algoritma Naïve Bayes padaperancangan aplikasi untuk memprediksi kelulusan tepat waktu mahasiswaberdasarkan parameter yang terdapat pada Penerimaan Mahasiswa Baru (PMB),Indeks Prestasi Kumulatif (IP) dan Bagian Keuangan. Data yang digunakanadalah data mahasiswa Program Studi Teknik Informatika Tahun Ajaran2016/2017. Perancangan sistem menggunakan pemodelan UML denganimplementasi menggunakan bahasa pemrograman PHP dan basis data MySQL

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid