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All Journal JURIKOM (Jurnal Riset Komputer) Journal of Computer System and Informatics (JoSYC)
Raditya Galih Whendasmoro
Universitas Bung Karno, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN Raditya Galih Whendasmoro; Joseph Joseph
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4526

Abstract

The large volume of information in the data causes a lot of data to be stored in the dataset. The dataset consists of various attributes and attribute values which contain information stored in the dataset. Data mining is a process that can be used to search for information on datasets. However, the problems encountered in the dataset are often found to have abnormal data such as the range of values that are too far and different between dataset attributes. The value range that is too far causes the results of the information obtained to be not optimal, in data mining itself the process or results are good based on the quality of the data stored in the dataset. Data normalization is a preprocessing stage, where data normalization is scaled back to the range of values in the attribute. Z-Score Normalization is a statistical technique that can be used in data mining to preprocess data by performing data transformations. Z-Score Normalization can be combined with data mining classification techniques, where the role of Z-Score Normalization is to normalize data which is useful for improving the performance of data mining classification algorithms, especially the K-NN algorithm in this study. The results of the study show that Z-Score Normalization is useful for improving performance than the K-NN algorithm. This can be seen from the increase in the accuracy value obtained from the K-NN process before normalizing the dataset and after normalizing the dataset. The accuracy values respectively before normalizing the dataset were 95.13%, 95.83%, 96.11%, 95.77% and 95.81% after normalizing the dataset there was an increase in the accuracy value, namely 97.87%, 98, 57%, 98.77%, 97.23% and 98.11%.
Sistem Pendukung Keputusan Untuk Menentukan Duta Pelajar pada Sekolah Menengah Pertama Menerapkan Metode MOORA Raditya Galih Whendasmoro; Sharyanto Sharyanto; Fifto Nugroho
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.v4i1.2540

Abstract

Student ambassadors are students who are chosen as the right hand of the teachers to lead other students, where these student ambassadors must have advantages that are far from other students, where the selection is through a selection process. With this student ambassador, it is expected that the teacher's task is to direct and secure other students both in terms of order and obedience to school rules. The large number of participants made the committee feel overwhelmed in choosing the ambassadors. To get a solution to the problem of selecting the student ambassador, a decision system is needed that can help find a solution to the selection of student ambassadors. Decision Support System (DSS) is a stage in finding a solution to a problem where the process is carried out using a solution according to how the computer works. DSS can work optimally if using the method. In this study, the authors chose the Multi-Objective Optimization Method on The Basic of Ratio Analysis (MOORA) which is a method that can be used in the completion and completion process of the DSS. This method has a working procedure that uses mathematical calculations. The results of this study obtained that the best Student Ambassador was alternative A5 on behalf of Ucok with a value of 0.4054 as the first rank.
Co-Authors Fifto Nugroho Joseph Joseph Sharyanto Sharyanto