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All Journal Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Julius Santony
UPI YPTK Padang
Computer Science & IT Engineering

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Pengelompokan Mahasiswa Potensial Drop Out Menggunakan Metode Clustering K-Means Ieannoal Vhallah; Sumijan Sumijan; Julius Santony
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 2 (2018): Agustus 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (709.253 KB) | DOI: 10.29207/resti.v2i2.308

Abstract

Clustering K Mean is used for grouping. The K-Means method seeks to group the existing data into several unique groups, where data in one group have the same characteristics with each other and have different characteristics than the data exists in the other group. To perform student grouping the potential drop out required attributes. Total Semester Credit System, Comunative Achievement Index, and Total Semester. Clustering process K- Mean is done by determining the nearest initial centroid point in a group of potential drop out students. Clustering results K-Mean by Total Credit System semester, Comunative Achievement Index, and Total Semester. Results Clustering of potential drop out students for class of 2014 is in cluster 0 of 4 students or 30.77% of 13 Samples, class of 2015 is in cluster 1 amounted to 4 students and cluster 2 amounted to 2 students or 66.7% of 9 samples , the force of 2016 is in cluster 0 amounting to 2 students and cluster 1 is 10 students or 50% from 24 samples, and force of 2017 is in cluster 2 strength 4 student or 22,22% from 18 Keywords: Data Mining, Clustering, K-Mean, Potensial Drop Out,,
Tingkat Prediksi Pendaftar Ujian Kompetensi Laboratorium Menggunakan Metode Least Square Gunadi Bin Senitio; Julius Santony; Jufriadif Na’am
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 3 (2018): Desember 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (795.299 KB) | DOI: 10.29207/resti.v2i3.530

Abstract

Services for laboratory competency test participants must be increased for each period. With the number of participants fluctuating, the laboratory must prepare and predict how many facilities will be used to support these activities, such as laboratory rooms, exam questions, and other equipment. To overcome this problem, a method is needed to predict the number of participants in the coming period. In this study, the Least Square method is used to predict participants in the next period. This method managed to get the number of predictions in the coming period with a prediction error rate of 9.99% using the Mean Absolute Percentage Error (MAPE). The empirical results show that this research is very helpful for the laboratory in preparing laboratory competence examination facilities
Co-Authors - Sumijan Eri Haryadi Gunadi Bin Senitio Ieannoal Vhallah Jufriadif Na`am, Jufriadif Sumijan Sumijan