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All Journal Jurnal Teknoinfo Building of Informatics, Technology and Science
Umbar Riyanto
Universitas Muhammadiyah Tangerang
Computer Science & IT

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DECISION SUPPORT SYSTEM USING THE COMPOSITE PERFORMANCE INDEX (CPI) FOR WIRELESS REPEATER SELECTION Joko Trianto; Mohammad Imam Shalahudin; Umbar Riyanto
Jurnal Teknoinfo Vol 17, No 1 (2023): Vol 17, No 1 (2023): JANUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i1.2352

Abstract

To connect to the internet, a device must have an internet connection via an internet network, one of which is through a wireless network or what is called Wireless Fidelity (Wi-Fi). On a Wi-Fi network there is a Wi-Fi router that is used to provide a connection to the internet network, but the signal from the WI-FI router is limited. Then the Wi-Fi signal needs to be strengthened with a signal booster known as a Wi-Fi Repeater or Wi-Fi Extender. Wi-Fi Repeaters are important devices for individuals, agencies and companies so that the desired areas get internet connection. So, to make a purchase of this product, foresight is needed so that the device chosen is right and in accordance with the needs. The large selection of Wi-Fi Repeater products on the market results in a person having to find information in advance regarding the specifications of the Wi-Fi Repeater product to be purchased. This has an impact on the length of the process in making decisions. The purpose of this research is to implement the Composite Performance Index (CPI) method on a decision support system for choosing Wi-Fi Repeaters, so that it can make it easier for users to determine alternatives quickly and precisely. The CPI method is used to solve decision problems with a number of alternatives through a combined index to rank alternatives from several criteria. The results of the calculations in the case study produced the highest combined index value, namely the Asus N300 Range Extender (A2) getting a score of 125, then followed by Mercusys WiFi Extender (A1) getting a score of 92.5, Comfast WiFi Extender (A3) getting a score of 100 and Xiaomi Mi AC1200 (A4) get a value of 100. The calculation results of the CPI method obtained by the system obtain the same value as manual calculations, so the developed system is declared valid. In addition, based on tests using black-box testing, it shows that the system built can function as it should.
Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm Nuke L Chusna; Nurhasan Nugroho; Umbar Riyanto; Ahmad Ari Aldino
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4104

Abstract

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Co-Authors Ahmad Ari Aldino Chusna, Nuke L Joko Trianto Mohammad Imam Shalahudin Nurhasan Nugroho