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<![CDATA[Support Vector Machine optimization with fractional gradient descent for data classification ]]>
<![CDATA[Hapsari, Dian Puspita]]>;
<![CDATA[Utoyo, Imam]]>;
<![CDATA[Purnami, Santi Wulan]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1467
<![CDATA[Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.]]>
<![CDATA[Company financial performance improvement through financial literacy and intellectual capital perspective ]]>
<![CDATA[Mollah, Moch. Kalam]]>;
<![CDATA[Suhartini, Suhartini]]>;
<![CDATA[Basuki H M, Gatot]]>;
<![CDATA[Nasrudin Khamimi, Fatkhu]]>;
<![CDATA[Dwi Wahyu Prabowo, Bangkit]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1468
<![CDATA[CV. Sarindofood is one of the companies engaged in manufacturing snacks, one of which is a type of Tost and Stick products made from raw materials for industrial corn. CV. Sarindofood uses considerable capital resources, so it needs analysis, especially in the financial sector. This study aims to determine the effect of intellectual capital on corporate financial performance, financial literacy as an understanding of finance. The independent variables used in this study are human capital, structural capital, value-added capital employed, intellectual capital (VAIC). In contrast, the dependent variables include the performance of ROA, ROI, and ROE. The data in this study are the company's financial statement data CV. Sarindofood consists of a balance sheet report and cash flow for five years from 2019 to 2018. The method used in this study uses Multiple Linear Regression, VAIC, and partial t-test, and a simultaneous F-test is used as analysis tools to see each variable's effect. The results of this study indicate that Capital Employee partially has a significant impact on financial performance. Human capital partly has a significant and positive impact on financial performance. Structural capital partially has a significant and positive effect on financial performance. Whereas the value of the results simultaneously shows that the influence of Intellectual Capital on ROA has a significant effect of 60%, Intellectual Capital on RO1 has a significant effect of 69%, and Intellectual Capital on ROE has a significant effect of 55%.]]>
<![CDATA[Large-Scale Agile Frameworks: A Comparative Review ]]>
<![CDATA[Almeida, Fernando]]>;
<![CDATA[Espinheira, Eduardo]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1832
<![CDATA[This study aims to identify and systematically compare the main large-scale agile frameworks that companies can adopt to manage the work of large-scale and distributed teams. Through this, companies can more consciously perform a better-informed decision on the choice of the framework that best fits the practices and challenges of their organizations. This work employs a qualitative approach supported by an exploratory analysis that identifies and explores the processes of migration to a large-scale agile. In the first phase, fifteen assessment criteria for scaling agile are discussed. In a second phase, these criteria are used to perform a comparative analysis of six large-scale agile frameworks (i.e., DAD, LeSS, Nexus, SAFe, Scrum at Scale, and Spotify). The findings reveal there isn't a dominant large-scale agile framework in all dimensions. However, it is possible to identify frameworks like Nexus and Spotify that target smaller teams and offer low technical complexity. These frameworks easily accommodate changes, while there are other frameworks like SAFe and DAD that offer high levels of scalability but require more demanding and deep efforts in changing work processes in an organization.]]>
<![CDATA[ANALYSIS OF DETERMINATION OF POSITION BASED ON CONTRIBUTION OF TASK AND WORK ACHIEVEMENT ]]>
<![CDATA[Munir, Misbahul]]>;
<![CDATA[Erdianto, Yoniv]]>;
<![CDATA[Suparto, Suparto]]>;
<![CDATA[Suparjo, Suparjo]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1838
<![CDATA[To find the value of employee achievement requires several aspects such as intelligence, aspects of work attitudes, and behavioral. The initial stage of the process is to find the difference between employee and position profile that has been determined by the company where it is called GAP. The next step is to determine the weight of each gap value in three aspects. We multiply the value of 60% for the value of the main factor and 40% for additional factors then add up the two factors to determine the total value. The three factors are assessed based on the percentage determined by the company, namely 25% Intelligence Aspect, 35% Behavioral Aspect, and 40% Work Attitude Aspect. The final results are ranked, and then top ranking is achieved until the last employees who occupy the position of Project Supervisor are employees with ID number: kka_0001. For employees who are likely to fill the position of Purchasing & Logistics are employees with ID number: kka_0040. For employees who are likely to occupy the Estimator position are employees with ID number: kka_0036. For employees who fill the Project Manager position are employees with ID number: kka_0055.]]>
<![CDATA[Numerical Study of The Airfoil Pitch Angle on The Darrieus-Savonius Combination Turbine ]]>
<![CDATA[Pranatal, Erifive]]>;
<![CDATA[Harsi, Renanda Bayu]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.2113
<![CDATA[AbstractThe Darrieus turbine is used as an ocean current converter to generate renewable electrical energy. One way to improve turbine performance is to determine the optimal pitch angle. Therefore, the purpose of this study was to determine the optimal pitch angle of the NACA 0018 airfoil with angle variations of -6°, -2°, 0° and 2°. Numerical method with the help of Ansy Fluent software was carried out for the construction of the turbine. The result of this research is that the angle of 2° produces the largest coefficient of power and torque compared to other angles. This happens because the direction of the lifting force on the forward airfoil is in accordance with the rotation of the turbine.Keywords: Ocean Current, Turbine, Pitch Angle]]>
<![CDATA[Large-Scale Agile Frameworks: A Comparative Review ]]>
<![CDATA[Fernando Almeida]]>;
<![CDATA[Eduardo Espinheira]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1832
<![CDATA[This study aims to identify and systematically compare the main large-scale agile frameworks that companies can adopt to manage the work of large-scale and distributed teams. Through this, companies can more consciously perform a better-informed decision on the choice of the framework that best fits the practices and challenges of their organizations. This work employs a qualitative approach supported by an exploratory analysis that identifies and explores the processes of migration to a large-scale agile. In the first phase, fifteen assessment criteria for scaling agile are discussed. In a second phase, these criteria are used to perform a comparative analysis of six large-scale agile frameworks (i.e., DAD, LeSS, Nexus, SAFe, Scrum at Scale, and Spotify). The findings reveal there isn't a dominant large-scale agile framework in all dimensions. However, it is possible to identify frameworks like Nexus and Spotify that target smaller teams and offer low technical complexity. These frameworks easily accommodate changes, while there are other frameworks like SAFe and DAD that offer high levels of scalability but require more demanding and deep efforts in changing work processes in an organization.]]>
<![CDATA[ANALYSIS OF DETERMINATION OF POSITION BASED ON CONTRIBUTION OF TASK AND WORK ACHIEVEMENT ]]>
<![CDATA[Misbahul Munir]]>;
<![CDATA[Yoniv Erdianto]]>;
<![CDATA[Suparto Suparto]]>;
<![CDATA[Suparjo Suparjo]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1838
<![CDATA[To find the value of employee achievement requires several aspects such as intelligence, aspects of work attitudes, and behavioral. The initial stage of the process is to find the difference between employee and position profile that has been determined by the company where it is called GAP. The next step is to determine the weight of each gap value in three aspects. We multiply the value of 60% for the value of the main factor and 40% for additional factors then add up the two factors to determine the total value. The three factors are assessed based on the percentage determined by the company, namely 25% Intelligence Aspect, 35% Behavioral Aspect, and 40% Work Attitude Aspect. The final results are ranked, and then top ranking is achieved until the last employees who occupy the position of Project Supervisor are employees with ID number: kka_0001. For employees who are likely to fill the position of Purchasing Logistics are employees with ID number: kka_0040. For employees who are likely to occupy the Estimator position are employees with ID number: kka_0036. For employees who fill the Project Manager position are employees with ID number: kka_0055.]]>
<![CDATA[Numerical Study of The Airfoil Pitch Angle on The Darrieus-Savonius Combination Turbine ]]>
<![CDATA[Erifive Pranatal]]>;
<![CDATA[Renanda Bayu Harsi]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
Show Abstract
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Original Source
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DOI: 10.31284/j.jasmet.2021.v2i1.2113
<![CDATA[AbstractThe Darrieus turbine is used as an ocean current converter to generate renewable electrical energy. One way to improve turbine performance is to determine the optimal pitch angle. Therefore, the purpose of this study was to determine the optimal pitch angle of the NACA 0018 airfoil with angle variations of -6°, -2°, 0° and 2°. Numerical method with the help of Ansy Fluent software was carried out for the construction of the turbine. The result of this research is that the angle of 2° produces the largest coefficient of power and torque compared to other angles. This happens because the direction of the lifting force on the forward airfoil is in accordance with the rotation of the turbine.Keywords: Ocean Current, Turbine, Pitch Angle]]>
<![CDATA[Support Vector Machine optimization with fractional gradient descent for data classification ]]>
<![CDATA[Dian Puspita Hapsari]]>;
<![CDATA[Imam Utoyo]]>;
<![CDATA[Santi Wulan Purnami]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.jasmet.2021.v2i1.1467
<![CDATA[Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.]]>
<![CDATA[Company financial performance improvement through financial literacy and intellectual capital perspective ]]>
<![CDATA[Moch. Kalam Mollah]]>;
<![CDATA[Suhartini Suhartini]]>;
<![CDATA[Gatot Basuki H M]]>;
<![CDATA[Fatkhu Nasrudin Khamimi]]>;
<![CDATA[Bangkit Dwi Wahyu Prabowo]]>
<![CDATA[ Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
Show Abstract
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DOI: 10.31284/j.jasmet.2021.v2i1.1468
<![CDATA[CV. Sarindofood is one of the companies engaged in manufacturing snacks, one of which is a type of Tost and Stick products made from raw materials for industrial corn. CV. Sarindofood uses considerable capital resources, so it needs analysis, especially in the financial sector. This study aims to determine the effect of intellectual capital on corporate financial performance, financial literacy as an understanding of finance. The independent variables used in this study are human capital, structural capital, value-added capital employed, intellectual capital (VAIC). In contrast, the dependent variables include the performance of ROA, ROI, and ROE. The data in this study are the company's financial statement data CV. Sarindofood consists of a balance sheet report and cash flow for five years from 2019 to 2018. The method used in this study uses Multiple Linear Regression, VAIC, and partial t-test, and a simultaneous F-test is used as analysis tools to see each variable's effect. The results of this study indicate that Capital Employee partially has a significant impact on financial performance. Human capital partly has a significant and positive impact on financial performance. Structural capital partially has a significant and positive effect on financial performance. Whereas the value of the results simultaneously shows that the influence of Intellectual Capital on ROA has a significant effect of 60%, Intellectual Capital on RO1 has a significant effect of 69%, and Intellectual Capital on ROE has a significant effect of 55%.]]>
