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Muhammad Ikhsan
Program Studi Ilmu Komputer, Universitas Islam Negeri Sumatera Utara
Computer Science & IT

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ANALYSIS OF WLAN NETWORK HANDOVER PERFORMANCE USING RSSI AND THRESHOLD ON MOBILE DEVICES Mhd Ikhsan Rifki; Muhammad Ikhsan; Rini Halila Nasution; Dysa Handira
INFOKUM Vol. 10 No. 4 (2022): October, computer, information and engineering
Publisher : Sean Institute

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Abstract

Random user mobility on a WLAN network allows users to move away from the access point (AP) that serves them so it has an impact on the possibility of users losing connectivity to the communication network. This study aims to simulate handover performance at the Faculty of Science and Technology UIN North Sumatra to maintain network QoS by using the comparison method of RSSI AP1 and AP2 values ​​and comparing RSSI values ​​with Threshold values. The computational results on the RSSI comparison method with the threshold are carried out at a distance of 15 meters, and the comparison of the RSSI AP1 values ​​has dropped below the RSSI AP2 values ​​with values ​​of -64.35 dBm and -62.75 dBm respectively. Meanwhile, in the threshold method, the RSSI AP1 value is below the threshold so that the user connectivity service will be transferred to AP2. This research is expected so that the Faculty of Science and Technology can obtain information on the quality of WLAN network services through mobile devices.
IDENTIFICATION OF AUTHENTICITY BASED ON DIGITAL IMAGES USING LOCAL BINARY PATTERN AND SUPPORT VECTOR MACHINE METHODS DIAN NIKITA SARI; Muhammad Ikhsan; Armansyah
INFOKUM Vol. 10 No. 4 (2022): October, computer, information and engineering
Publisher : Sean Institute

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Abstract

Technological progress has an influence on development. Where the influence that arises can be in the form of positive or negative influences related to community behavior in utilizing and applying existing information technology. Paper money is a basic need in society where its use is as a means of payment, either cash or electronically. Counterfeit money is currency whose production is carried out without any legal approval from the state or government on the official website of Bank Indonesia. Counterfeit money in Indonesia from 2014 to 2018 has decreased and increased on a national scale. In this study the authors want to make a study to be able to distinguish counterfeit money or real money with the concept of Computer Science in the field of image processing using extraction and classification techniques. In this study, the extraction technique used is texture extraction with LBP and Classification using SVM to be able to recognize the authenticity of banknotes using the MATLAB application as a means to complete the analysis process
ANALYSIS OF CONTENT-BASED IMAGE RETRIEVAL (CBIR) AND DECISION TREE METHODS FOR IDENTIFICATION TYPES OF COFFEE SEEDS Rizki Saidah Srg Marahasian; Muhammad Ikhsan; Mhd Furqan
INFOKUM Vol. 10 No. 4 (2022): October, computer, information and engineering
Publisher : Sean Institute

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Abstract

Identification of the type oficoffee by nakedleye is difficult to distinguish so that special skills are needed, one method that can be usedlto identifylthe typeiof coffee is to use digital image processing such as Content Based ImageiRetrieval (CBIR) which aims to identify the characteristics or features of the object. This method is usedito carry out the feature identification process, onelof which is the texture feature possessed byiseveral types oflcoffee. By using the Decision Tree classification method, it aims to break down the decision-making process from complicated or complex to simpler so that it can be easier to make decisions. The concept used by the decision tree is to convert the data into a decision treeiand decision rules (rules). The texture feature extraction used is by using first-order parameters, the total dataset used is 50 images in 6 types of Arabica coffee. With the CBIR and Decision Tree systems, it is ablelto identifyithe type of coffee beans. Keywords: Content Based Image Retrieval (CBIR), Decision Tree, first orders parameters, Coffee Beans.
Co-Authors Armansyah DIAN NIKITA SARI Dysa Handira Mhd Furqan Mhd Ikhsan Rifki Rini Halila Nasution Rizki Saidah Srg Marahasian