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All Journal Creative Information Technology Journal SISFOTENIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Eksplora Informatika Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Emha Taufiq Luthfi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : SISFOTENIKA

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PREDIKSI MAHASISWA DROP OUT MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Siti Nurhayati; Kusrini kusrini; Emha Taufiq Luthfi
SISFOTENIKA Vol 5, No 1 (2015): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.86 KB) | DOI: 10.30700/jst.v5i1.25

Abstract

AbstrakTingginya tingkat keberhasilan mahasiswa dan rendahnya tingkat kegagalan mahasiswa dapat mencerminkan kualitas dari suatu perguruan tinggi. Salah satu indikator kegagalan mahasiswa adalah kasus drop out. Untuk mengatasi permasalah, dilakukan prediksi menggunakan metode support vector machine. Support Vector Machine berusaha mencari hyperplane yang optimal dimana dua kelas pola dapat dipisahkan dengan maksimal, parameter yang di gunakan pada Support Vector Machine hanya parameter kernel dalam satu parameter C yang memberikan pinalti pada titik data yang di klasifikasikan secara acak. Dalam Support Vector Machine bobot (w) dan bias (b) merupakan solusi global optium dari quadratic programming sehingga cukup dengan sekali running akan menghasilkan solusi yang akan selalu sama untuk pilihan kernel dan parameter yang sama. Melalui penerapan support vector machine diharapakan untuk mendapatkan parameter Support Vector Machine yang digunakan tepat untuk memperoleh margin terbaik dalam memprediksi mahasiswa drop out.Kata Kunci— prediksi drop out, kernel, support vector machine, unified modeling language
Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu Christin Nandari Dengen; Kusrini Kusrini; Emha Taufiq Luthfi
SISFOTENIKA Vol 10, No 1 (2020): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.866 KB) | DOI: 10.30700/jst.v10i1.484

Abstract

Students who are accepted every year are increasing, but not all students can graduate on time. In achieving graduation, of course, there are stages or processes that must be passed by each student such as following a number of courses, conducting fieldwork practices, real work lectures and final assignment seminars. These processes are carried out within a period of time determined by the University. For this reason, a prediction system for student graduation is needed in order to minimize students who graduate not on time. In predicting student graduation on time using 50 sample data for the 2013 graduation year with gender, IPK, graduation and toefl attributes. This study carried out the application of the CRISP-DM method with the C4.5 algorithm in predicting student graduation. The use of the C4.5 algorithm is supported by simulations carried out using the WeKa application and gets an accuracy value of 60%. With the existence of this research, it is expected to be able to help the Informatics Engineering Program at Universita Mulawarman so that students can graduate on time.
Co-Authors Abdul Rokhim Andi Sunyoto Arief Setyanto Astika Wulansari Bambang Soedijono Wiraatmadja Christin Nandari Dengen Elik Hari Muktafin Krisnawati Krisnawati Kusrini Kusrini Lili Kartikawati M Suyanto Patmawati Patmawati, Patmawati Pendi Ventri Hendika Ripto Sudiyarno Siti Alvi Sholikhatin Siti Nurhayati Supriatin Supriatin