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All Journal Sistemasi: Jurnal Sistem Informasi Journal Sensi: Strategic of Education in Information System
Milla Rochmawati
Universitas Singaperbangsa Karawang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Prediksi Penerimaan Mahasiswa Baru Universitas Singaperbangsa Karawang dengan Naive Bayes Ganes Wisnu Cahya Bagaskara; Milla Rochmawati; Ismai Adhiya Adha; Mayland Trifena
Journal Sensi: Strategic of Education in Information System Vol 9 No 2 (2023): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v9i2.2913

Abstract

Sistem pendukung keputusan merupakan alat penting dalam proses pengambilan keputusan yang efektif dan efisien. Dalam konteks pendidikan, prediksi penerimaan mahasiswa baru menjadi salah satu aspek penting dalam pengelolaan sebuah universitas. Penelitian ini bertujuan merancang sebuah sistem pendukung dalam mengambil keputusan dengan metode Naive Bayes dalam memprediksi penerimaan mahasiswa baru di Universitas Singaperbangsa Karawang.Kriteria yang telah ditetapkan untuk diterima di universitas ini meliputi asal sekolah calon mahasiswa, nilai akhir ujian, daya tampung universitas, peluang penerimaan, dan profil calon mahasiswa. Penyelesaian kriteria tersebut dilakukan melalui teknik Data Mining dengan metode Naive Bayes. Dataset yang digunakan dalam penelitian ini berupa Data Set dengan jumlah sebanyak 816 data. Berdasarkan uji coba menggunakan data tes SNBT, metode Naive Bayes berhasil mengklasifikasikan 4 dari 816 data yang diuji, dengan akurasi prediksi mencapai 97,79%, persentase tertinggi dalam memprediksi penerimaan mahasiswa baru. Dengan memanfaatkan sistem pendukung keputusan ini, universitas dapat melakukan prediksi penerimaan mahasiswa baru dengan akurasi tinggi, mempermudah pengambilan keputusan terkait penerimaan mahasiswa baru, meningkatkan efisiensi proses seleksi, dan mengurangi kesalahan dalam proses tersebut.
Implementation of the K-Means Algorithm in Sales Clustering at a Company using the KDD Methodology Milla Rochmawati; Ganes Wisnu Cahya Bagaskara; Ismail Adhiya Adha; Yuyun Umaidah; Apriade Voutama
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3074

Abstract

This research aims to implement K-Means algorithm in sales clustering at PT Sila Tirta Gemilang using Knowledge Discovery in Databases (KDD) methodology. PT Sila Tirta Gemilang is a company operating in the bottled drinking water industry sector. This research was conducted using a KDD approach that involves collecting historical sales data and has the main objective of improving the company's understanding of their product sales patterns. K-Means Clustering algorithm is used to classify products based on similar sales characteristics. In the K-Means method, the optimal cluster center point is determined to group products with comparable sales performance. By applying clustering using K-Means algorithm and KDD method, clustering of water types that are in significant demand at PT Sila Tirta Gemilang was conducted. As a result, three clusters were found, each containing water types with different characteristics. Cluster 0 has 1 water type with a high level of interest, while Cluster 1 has 3 water types with a low level of interest. Finally, Cluster 2 consists of 2 water types with a medium level of interest. From the results that have been obtained, companies can take more appropriate steps to increase profits and optimize their sales performance.
Co-Authors Apriade Voutama Ganes Wisnu Cahya Bagaskara Ismai Adhiya Adha Ismail Adhiya Adha Mayland Trifena Yuyun Umaidah