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All Journal Brilliance: Research of Artificial Intelligence SIGMA: Information Technology Journal
Widodo , Edy
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Penerapan Sistem Pendukung Keputusan Penerima Beasiswa Di SMKN 1 Cikarang Utara Dengan Metode Analitycal Hierarchy Process Naya, Candra; Widodo , Edy; Sanudin; Sadikin, Mumin
Jurnal SIGMA Vol 14 No 3 (2023): September 2023
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Proses seleksi dalam menentukan penerima beasiswa masih mengalami kendala. Di lapangan masih ditemukan kurang tepatnya penyaluran beasiswa yang diakibatkan oleh sistem yang masih konvensional atau manual. Selain itu pendukung keputusan tidak dapat melihat kriteria-kriteria dalam beasiswa secara bersama-sama. Dalam ilmu komputer terdapat suatu sistem yang dapat membantu pendukung keputusan untuk mengatasi masalah yang sifatnya semi struktur ataupun tidak terstruktur yaitu sistem pendukung keputusan. Dalam Sistem Pendukung Keputusan terdapat berbagai metode salah satunya yaitu metode Analytical Hierarchy Process (AHP) yang ditemukan oleh Thomas L.Saaty. AHP sendiri dapat membantu dalam menentukan prioritas dari beberapa kriteria dengan melakukan analisa perbandingan berpasangan dari masing-masing kriteria yang sudah ditemukan. Dengan melihat masalah yang ada dalam pendukung keputusan dalam pemilihan penerima beasiswa, sistem pendukung keputusan dengan menggunakan metode AHP dirasa tepat untuk digunakan dalam membantu pendukung keputusan untuk menentukan penerima beasiswa. Diharapkan hasil dalam penelitian ini dapat membantu pendukung keputusan dalam menentukan penerima beasiswa. Kata Kunci: Ilmu Komputer, Sistem Pendukung Keputusan, Metode AHP
Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality Susilo, Arif; Widodo , Edy; Rilvani, Elkin; Suryana, Syahro
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4096

Abstract

Industry is increasingly developing rapidly and has an impact on the emergence of competition between companies, both private and state, both companies engaged in manufacturing and service companies. Linear Regression is used to find out how the dependent/criterion variable can be predicted through independent variables or predictor variables, individually. Based on the results of the tests that have been carried out, the variables or attributes used in this research (minute and thinkness results) have a significant effect on this research. It is proven that using the linear regression algorithm is able to provide good results with a Root Mean Squared Error value of 0.273 +/- 0.000. This is because there is a correlation or functional relationship (cause - effect) between one variable (dependent or criterion) and another variable (independent or predictor). This testing process is carried out to identify stock needs using a linear regression algorithm
Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality Susilo, Arif; Widodo , Edy; Rilvani, Elkin; Suryana, Syahro
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4096

Abstract

Industry is increasingly developing rapidly and has an impact on the emergence of competition between companies, both private and state, both companies engaged in manufacturing and service companies. Linear Regression is used to find out how the dependent/criterion variable can be predicted through independent variables or predictor variables, individually. Based on the results of the tests that have been carried out, the variables or attributes used in this research (minute and thinkness results) have a significant effect on this research. It is proven that using the linear regression algorithm is able to provide good results with a Root Mean Squared Error value of 0.273 +/- 0.000. This is because there is a correlation or functional relationship (cause - effect) between one variable (dependent or criterion) and another variable (independent or predictor). This testing process is carried out to identify stock needs using a linear regression algorithm
Co-Authors ARIF SUSILO Elkin Rilvani Naya, Candra Sadikin, Mumin Sanudin Suryana, Syahro