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ROSY DASMITA
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Penerapan Metode Ahp Untuk Penentuan Penerima Beasiswa (Study Kasus AMIK Depati Parbo Kerinci) ROSY DASMITA
JURNAL ILMU PENGETAHUAN & SISTEM INFORMASI (JIPSI) Vol 4 No November (2017): JIPSI, Edisi November 2017
Publisher : DEPATI PARBO PRESS

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Abstract

ABSTRACT in accordance with regulations prescribed by the Kopertis to obtain a scholarship, then the required criteria to determine who will be selected to receive scholarships. Scholarships awarded to students to help someone less capable or accomplished during his studies to take. To assist in determining a person's determination would be eligible for a scholarship is needed a support system keputusan.Salah one method that can be used for Decision Support System is to use AHP (Analytical Hierarky Proces). This research will be appointed a case of finding the best alternative Based on criteria that have been determined by use traditional AHP method in that case. This method was chosen because it is able to select the best alternative from a number of alternatives, in this case meant that alternatives are eligible to receive scholarships based on specified criteria. Research carried out by finding the weights for each attribute, then do the ranking that will determine the optimal alternative, which is the best student. Keywords: Decision Support Systems, Applications and Software AHP Method Expert Choice
Jaringan Syaraf Tiruan Untuk Akreditasi Sekolah Menengah Atas/Madrasah Aliyah DARIYO DARIYO; ROSY DASMITA
JURNAL ILMU PENGETAHUAN & SISTEM INFORMASI (JIPSI) Vol 1 No Maret (2018): JIPSI, Edisi Maret 2018
Publisher : DEPATI PARBO PRESS

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ABSTRACT Education Unit Senior High School / Madrasah Aliyah must have a school accreditation ratings, as listed in the Regulation of the Minister of National Education Republic of Indonesia No. 52 of 2008. Obtaining accreditation ratings from the implementation of school education units to eight National Education Standards. The algorithm used the Backpropagation with Momentum. Neural network used has one input layer consisting of 8 nodes, one hidden layer with n nodes are randomly determined by the activation function, and an output layer with 3 nodes form a prediction rank accreditation, accreditation B and accreditation C with the function of accreditation purelin activation. The data is separated into two parts, ie data to be trained and the data that will be tested. Training and testing was conducted using the software Matlab 6.1 with the tested network architecture that is 8-2-1, 8-5-1, 8-9-1, and 8-12-1, which concludes the result of processing with matlab 21 for data sharing training and 21 data for testing with the architectural pattern of 8-5-1 is the best pattern with the percentage of 100% truth. Keywords: Artificial neural network, backpropagation, accreditation, school.
Jaringan Syaraf Tiruan Untuk Menentukan Jurusan Di Sekolah Menengah Atas (SMA) (Studi Kasus : SMA Negeri 1 Sungai Penuh) SI ARDIZAL; ROSY DASMITA
JURNAL ILMU PENGETAHUAN & SISTEM INFORMASI (JIPSI) Vol 1 No Maret (2018): JIPSI, Edisi Maret 2018
Publisher : DEPATI PARBO PRESS

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Abstract

ABSTRACT Students in the Education Unit High School (SMA) must determine when the department will increase from class X to class XI, as noted in the Director General SK Mendikdasmen No.12/C/Kep/TU/2008 dated 12 February 2008 on the Consult Report Preparation learning (LHB) of Students SMA (SBC). Determination of majors students from nine criteria specified. The algorithm used is the Backpropagation with Momentum. Neural network used has an input layer consisting of nine pieces of nodes, one hidden layer with n nodes are randomly determined by the activation function, and an output layer with 2 pieces of input nodes majoring in science and social studies majors with purelin activation function. Data is separated into two parts, ie the data to be trained and the data that will be tested. Training and testing was conducted using the software Matlab 6.1 with the tested network architecture that is 9-2-1, 9-4-1, 9-6-1, and 9-9-1, where the results of processing with matlab menyimpulkam sharing of data 120 for training and 48 data for testing the architectural pattern of 9-9-1 is the best pattern to the percentage of 100% truth. Key words : artificial neural networks, backpropagation, schools, and schools
Co-Authors DARIYO DARIYO SI ARDIZAL