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All Journal The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Journal of Computer System and Informatics (JoSYC)
Rini Nuraini
Universitas Nasional, Jakarta Selatan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Journal : Journal of Computer System and Informatics (JoSYC)

 1 
Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis Rini Nuraini
Journal of Computer System and Informatics (JoSYC) Vol 3 No 3 (2022): May 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i3.1694

Abstract

To learn about electronic devices, one must know about the types of electronic components. Capacitors are one of several important electronic components. Capacitors are part of passive components that are able to store energy or electric charge at a temporary time. Capacitors or commonly called capacitors have many types. However, some people do not know about these types of capacitors. Especially for someone or a student who will learn about electronic components. The purpose of this study is to develop a digital image processing system for the classification of transistor types by applying the K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) methods. PCA serves to reduce and retain most of the relevant information from the original features according to the optimal criteria. Based on the results of feature extraction and data reduction performed by PCA, it is easier for the KNN algorithm to classify. KNN performs a classification based on the data closest to the object being processed. Based on the test results, the developed model is able to produce an average accuracy value of 82.50%. This means that PCA and KNN algorithms can be used in the process of classifying capacitor type images properly
Implementasi Metode Complex Proportional Asessment (COPRAS) Pada Sistem Pendukung Keputusan Pemilihan Bluetooth Audio Transmitter Dedy Alamsyah; Rini Nuraini; Muhammad Bagir
Journal of Computer System and Informatics (JoSYC) Vol 3 No 3 (2022): May 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i3.1695

Abstract

Bluetooth audio transmitter is a device capable of converting wired audio signals into wireless. With a Bluetooth audio transmitter, a TV or PC that doesn't have bluetooth facilities can connect to headphones or speakers without using a cable. However, currently bluetooth audio transmitters have been circulating in the market with various brands and brands that offer different capabilities and specifications. So, it takes foresight in choosing the right bluetooth audio transmitter and as needed. Inaccuracy in choosing a bluetooth audio transmitter results in the device's performance not being maximized and incompatibility with the wishes of the user. This study aims to develop a decision support system for selecting a bluetooth audio transmitter using the Complex Proportional Assessment (COPRAS) approach. The COPRAS method is able to solve election problems through the calculation of the utility level which shows the extent to which an alternative is better or worse than other alternatives through a comparison process. The system built has features such as managing criteria data, determining weights, managing alternatives, assigning a value to each alternative, seeing the results of the COPRAS method calculations and seeing the ranking of the results of the system recommendations. Based on testing through the black-box testing technique, it shows that the system built has been running as it should
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 Alfry Aristo Jansen Sinlae Daniarti, Yeni Dedy Alamsyah Muhammad Bagir Nanang Sadikin Purbha Irwansyah, Irsyad Rhaishudin Jafar Rumandan Setiawansyah Setiawansyah Yuri Rahmanto