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All Journal JURIKOM (Jurnal Riset Komputer) Journal of Computer System and Informatics (JoSYC)
Nanang Sadikin
Sekolah Tinggi Teknologi Informasi NIIT, Jakarta Selatan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Sistem Pendukung Keputusan Dalam Penilaian Kinerja Karyawan Terbaik dengan Algoritma Simple Additive Weighting (SAW) Arisantoso Arisantoso; Nanang Sadikin; Ahmad Fatih; Mochamad Sanwasih
JURIKOM (Jurnal Riset Komputer) Vol 8, No 4 (2021): Agustus 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v8i4.3600

Abstract

The goal of this study is to implement the best employee selection decision support system (DSS) using simple additive weighting (SAW) methods. The advantage of a simple additive weighting method over other decision models is that SAW can choose the best alternative among several alternatives, also allowing for more accurate estimation because it is based on predetermined criteria and preference weights.  The decision support system for the best performance performance assessment of employees is designed using an object-oriented approach that usecase diagrams. So that it makes it easier to design the system created, and implemented by using the PHP programming language with MySql database so that the calculation process can be stored computerized using the database. System testing results using blackbox testing get 100% results in accordance with system functionality testing, and Technology Acceptence Model test results get 85.41% results then Technology Acceptence Model prototype Is Excellent for decision support systems for the selection of the best employees
Klasifikasi Citra Jenis Daun Berkhasiat Obat Menggunakan Algoritma Jaringan Syaraf Tiruan Extreme Learning Machine Rhaishudin Jafar Rumandan; Rini Nuraini; Nanang Sadikin; Yuri Rahmanto
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2586

Abstract

Leaves are one part of a plant that has benefits for humans, especially for the health of the body. Leaves can be used as herbal medicine which can be an alternative that can help in increasing immunity and body resistance. However, not all leaves have medicinal properties, therefore knowledge of the types of medicinal leaves is important. The aim of this research is to develop a classification system for the image of medicinal leaves using the Extreme Learning Machine (ELM) artificial neural network model. To support the ELM algorithm, morphological feature extraction is used which can provide information about the shape characteristics of existing objects. Extreme Learning Machine (ELM) is also known as an artificial neural network approach that uses one hidden layer. At the classification stage, the Extreme Learning Machine (ELM) algorithm can determine the weight value between the input neurons and the hidden layer randomly so that the learning pattern becomes faster. Based on the results of the precision, recall and accuracy tests, the precision value is 90.67%, the recall value is 89.47% and accuracy of 90%. So, based on these results it can be said that the ELM model that was built can classify images of leaf types with medicinal properties well.
Co-Authors Ahmad Fatih Arisantoso Arisantoso Mochamad Sanwasih Rhaishudin Jafar Rumandan Rini Nuraini Yuri Rahmanto