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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JAIS (Journal of Applied Intelligent System) Journal of Development Research Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Nurhindarto, Aris
Dian Nuswantoro University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Social Sciences

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Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Developing Smart City 5.0 Framework To Produce Competency Indra Gamayanto; Aris Nurhindarto
Journal of Applied Intelligent System Vol 5, No 1 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i1.4228

Abstract

Abstract - Smart cities are essential things that must be applied in the face of globalization and competition. In smart city 5.0, three important things discussed are human resource development, smart marketing, and information technology. These three things cannot be separated from each other because they are related. Furthermore, the smart city 5.0 article is a development from the previous article, namely smart city 1.0-3.0. Smart City 5.0 provides four important formulas for developing a smart city and a framework to guide its implementation. The four formulas and the resulting framework will develop in the next article, namely intelligent intelligence. It will continue to make prototypes and smart city intelligence applications. The result of this article is a framework that is a concept and strategy in developing a smart region that is part of a smart city. This article is still under development and research will continue. Furthermore, the development of this research will certainly require several more stages in reaching the top of the research, namely a big picture of a smart city and performance measurement for each process contained in a smart city. Therefore, it takes the right steps and formulas to produce a smart city 5.0 framework Keywords - Smart city, Human resource, Marketing, Technology, Innovation    
Classification of Toxic Plants on Leaf Patterns Using Gray Level Co-Occurrence Matrix (GLCM) with Neural Network Method Mohammad Faishol Zuhri; S. Kholidah Rahayu Maharani; Affandy Affandy; Aris Nurhindarto; Abdul Syukur; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.202

Abstract

Poisonous plants are plants that must be avoided and not consumed by humans, because the presence of poisonous plants is also often found in the surrounding environment without realizing it. Because of the lack of knowledge to classify poisonous plant species, it will be more difficult to find out. With the help of a computer system, it will be easier to identify the types of poisonous plants. There are 3 types of poisonous plants that will be used in this study, namely cassava, jatropha, and amethyst. There are also 3 types of non-toxic plants with almost the same morphology as a comparison, namely cassava, figs, and eggplant. In this study, researchers tried to classify poisonous plant species using leaf pattern features that would be extracted using shape features and Gray Level Co-occurrence Matrix (GLCM). The value taken from the shape feature is the values ​​of area, width, diameter, perimeter, slender, and round. While the value of contrast, entropy, correlation, energy, and homogeneity for Gray Level Co-occurrence Matrix (GLCM) attributes. To classify data using Neural Network with RapidMiner application. From this study, it is known that from 300 total datasets used, the highest accuracy is 96.13% using the Neural Network method. With an AUC value of 0.986 and is included in the very good category.
Employee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest Aris Nurhindarto; Esa Wahyu Andriansyah; Farrikh Alzami; Purwanto Purwanto; Moch Arief Soeleman; Dwi Puji Prabowo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1345

Abstract

Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%. Thus with those result, we can conclude that we can obtain better prediction using 21 features for employee attrition and performance which help the higher management in making decisions.
Co-Authors Abdul Syukur Affandy Affandy Alzami, Farrikh Dwi Puji Prabowo, Dwi Puji Esa Wahyu Andriansyah Farikh Al Zami Indra Gamayanto Karis W. Moch Arief Soeleman Mohammad Faishol Zuhri Muljono, - Muslih Muslih Purwanto Purwanto Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani