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CANONICAL CORRELATION ANALYSIS OF ECONOMIC GROWTH AND UNEMPLOYMENT RATE Purwadi, Joko; Gumelar, Bagus; Widiantoro, Tri; Ningsih, Zhilvia Noviana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1273-1282

Abstract

The paper discusses the relationship between economic growth and the unemployment rate in Indonesia in each province in 2021. Both variables are considered as the dependent variable and there are 5 independent variables used in this research such as human index development, wage minimum region, poor citizens percentage, investment, and farmer rate value in each province. The method used to analyze is canonical correlation analysis, which is one of the dependent methods that are used for multivariate analysis. This method was used to determine which variable had the most significant relationship between dependent and independent variables, the data was taken from the Center of Statistics Bureau Indonesia in 2021. The result shows that among independent variables the human index development had the strongest relation it had 79%, while the correlation between the dependent and independent variable the unemployment rate gives the strongest influence it is 68%.
Comparison Testing Functional and Usability System Mapping Land Agriculture On Platform Web and Mobile Chrismanto, Antonius Rachmat; Purwadi, Joko; Wibowo, Argo; Santoso, Halim Budi; Delima, Rosa; Balisa, Delfia
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 2 No 2 (2021): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v2i2.401

Abstract

The Agricultural Land Mapping System (SPLP) is indispensable in an agricultural country where part of the population is farmers. This system has been developed by the research team since 2019 and has resulted in web and mobile based systems. The Dutatani SPLP system was developed using the Rapid Application Development (RAD) method. Before this system is further implemented in the community, this system needs to be tested in terms of functionality and usability. This research article aims to compare the functionality and reusability testing of web and mobile-based SPLP. The test was carried out using ISO / IEC 9126-4 usability metrics that focus on effectiveness and efficiency, and involve farmers and farmer groups from Gilang Harjo Village, Bantul, Yogyakarta. The results of testing the web-based and mobile-based SPLP system show that overall respondents can do all the tasks given, but it takes a long time to complete. This is influenced by internal factors of the respondents, namely the respondent's lack of experience in using mobile phones for other activities besides telephone and short messages. So that when testing, respondents need more time to adapt to the system. However, based on time on task, mobile-based SPLP testing is faster than web-based ones.
Support Vector Machine for Classification: A Mathematical and Scientific Approach in Data Analysis Restiani, Yulia; Purwadi, Joko
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.8122

Abstract

In this research, SVM will be used to differentiate between plain nail art designs (class 0), 3D nail art designs (class 1), and hand painting nail art designs (class 2). The dataset used consists of images of nail designs that have been collected and analyzed previously. First, the dataset is divided into three different classes based on the type of nail design. The first class (class 0) includes plain nail art designs, then the second class (class 1) is 3D nail art designs, and the third class (class 2) is hand painting nail art designs. This process is carried out to allow SVM to learn the feature differences between the two types of designs. The data used will be divided into training and testing data and divided into three data division schemes, namely 60/40, 70/30, and 80/20. Based on the results of the research discussed, it can be concluded that classification using the Linear SVM model on three data sharing schemes provides the best level of accuracy on the 80/20 scheme, namely 81.25%. Meanwhile, classification using the non-linear SVM model achieved the highest level of accuracy of 95% in the 80/20 scheme with the RBF Kernel. Thus, the SVM model that is suitable for classifying nail art designs is a non-linear SVM model with the 80/20 scheme. The accuracy results obtained from this research also show that SVM provides good performance in classifying nail art designs.