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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA The IJICS (International Journal of Informatics and Computer Science)
Mohammad Imam Shalahudin
Sekolah Tinggi Teknologi Informasi NIIT, Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Multi-Criteria Decision Making Using the WASPAS Method in Webcam Selection Decision Support Systems Arisantoso Arisantoso; Mochammad Hasymi Somaida; Mochamad Sanwasih; Mohammad Imam Shalahudin
The IJICS (International Journal of Informatics and Computer Science) Vol 7, No 1 (2023): March 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v7i1.6001

Abstract

To carry out all virtual or online activities, you need hardware that can support it, one of which is a webcam. Many webcam products issued by various electronics companies are compatible for laptops and computers. However, to make a webcam selection the user must know one by one the specifications of each webcam. This of course takes a long time to determine the right webcam. This study aims to implement the Multi-Criteria Decision Making (MCDM) approach with Aggregated Sum Product Assessment (WASPAS) on a webcam selection decision support system, in order to get the best, right and fast alternative. The WASPAS method can determine the best alternative through prioritization that is relevant to the weighting used. Based on the case studies conducted, the WASPAS method was able to determine the best webcam with the best alternative results, namely NYK Nemesis A96 with a value of 0.7053, followed by Aukey PC-LM7 with a value of 0.6826, JETE W2 with a value of 0.6799, Logitech C922 with a value of 0.6499 and Ausdom AF660 with a value 0.6271. Because the findings are identical to manual calculations, the created system generates legitimate WASPAS method calculations. Based on the tests carried out using the black-box testing approach, it shows that all the functions in the system can run as they should
Classification of Medicinal Wild Plant Leaf Types Using a Combination of ELM and PCA Algorithms Dedy Alamsyah; Farli Rossi; Ri Sabti Septarini; Mohammad Imam Shalahudin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6817

Abstract

Despite their detrimental nature, it turns out that wild plants have many benefits for human health. Wild plants with a form of herbaceous vegetation contain ingredients that can be used as medicine, especially in their leaves. However, because the information is very similar and the form is similar, people don't know about it. For this reason, the aim of this research is to implement an artificial neural network algorithm using Extreme Learning Machine (ELM) and the Principal Component Analysis (PCA) algorithm to classify images of wild plant leaves with medicinal properties, especially in herbaceous vegetation. The feature extraction used in this research involves morphological features by considering the shape of the object. The PCA algorithm will reduce data complexity and identify hidden patterns in the data by changing the original feature space to a new and more concise feature space. Next, the ELM algorithm is used to recognize class grouping patterns when solving classification problems. Accuracy test results show a value of 90.667%.
Co-Authors Arisantoso Arisantoso Dedy Alamsyah Farli Rossi Mochamad Sanwasih Mochammad Hasymi Somaida Ri Sabti Septarini