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All Journal Jurnal Optimasi Sistem Industri Journal of Computer Engineering, Electronics and Information Technology Indonesian Journal of Innovation Multidisipliner Research
Mantau, Aprinaldi Jasa
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Four Wheel Omni Trajectory with Gyrodometry Method in the Indonesia 2022 Ash Robot Contest Sutrisno, Imam; Harlinanda, Daffa Alifian; Ahmad, Zindhu Maulana; Priyonggo, Projek; Mantau, Aprinaldi Jasa; Santosa, Ari Wibawa Budi; Khalil, Muhammad; Umasangadji, Fahmi
Indonesian Journal of Innovation Multidisipliner Research Vol. 2 No. 2 (2024): June
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v2i2.130

Abstract

The National Achievement Center (Puspresnas) and the Ministry of Education and Culture of the Republic of Indonesia proudly present the Indonesian Ash Robot Contest (KRAI), a yearly student robotics competition where teams tackle ash-related challenges with their robot creations. At KRAI 2021, it was shown that many robots were controlled using joysticks and resulted in a lack of effectiveness in robotic motion. The use of a joystick makes the movement of the robot take longer. This is because the robot is controlled by humans and the robot has not been able to pinpoint the robot's location. Gyrodometry is a method to map the robot's position using a inner ear and compass of the machine. In this research, Gyrodometry is used to map the position based on the readings of the gyroscope sensor and rotary encoder so that the robot can move automatically. In this research, the KRAI hitter robot can map positions and move in an accurate direction and can move automatically and effectively so that it can save more time and can increase the possibility of strategy in the match. From the results of this study, it was found that without using gyrodometry the error of X reached 11.825 cm and Y reached 35.1325 cm and also the angle error reached 13.7 degrees. and by using gyrodometry error X reaches 4.25 cm and Y reaches 5.775 cm and angle error 2.01 degrees. From these results, it was found that using gyrodometry was better than without using gyrodometry. The addition of waypoints to the robot only produces an average error of 8.2 cm X and Y error of -7.8 cm and the angle error reaches 10.2 degrees.
Forecasting Survival Rates Post-Gastrointestinal Surgery: Integrating The New Japanese Association of Acute Medicine (JAAM Score) and Neural Network Classification Pradana, Ayu Nabila Kusuma; Mantau, Aprinaldi Jasa; Jiea, Guo; Zhang, Shuo; Murata, Masaharu; Narahara, Sayoko; Akahoshia, Tomohiko
Journal of Computer Engineering, Electronics and Information Technology Vol 3, No 1 (2024): COELITE: Volume 3, Issue 1, 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/coelite.v3i1.68500

Abstract

Following gastrointestinal surgery, the incidence of disseminated intravascular coagulation (DIC) has a bad prognosis. Consequently, it is essential to identify the variables that can predict the prognosis of DIC. This study will examine the factors that may affect the outcome of DIC in patients who have had gastrointestinal surgery. From 2003 to 2021, 81 patients were admitted to the intensive care unit at Kyushu University Hospital following gastrointestinal surgery. DIC scores were computed using the new Japanese Association of Acute Medicine (JAAM) score from before and after surgery. Comparisons will be made between DIC values and The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a range of biochemical markers. This study utilized machine learning techniques to determine the prognosis of DIC following gastrointestinal surgery. After gastrointestinal surgery, the results of this study are anticipated to serve as an indicator for determining patient prognosis, hence increasing life expectancy and decreasing mortality rates among DIC patients.
Feature Selection and Performance Evaluation of Buzzer Classification Model Afra, Dian Isnaeni Nurul; Fajri, Radhiyatul; Prafitia, Harnum Annisa; Arief, Ikhwan; Mantau, Aprinaldi Jasa
Jurnal Optimasi Sistem Industri Vol. 23 No. 1 (2024): Published in July 2024
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.919 KB) | DOI: 10.25077/josi.v23.n1.p1-14.2024

Abstract

In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.
Co-Authors Afra, Dian Isnaeni Nurul Ahmad, Zindhu Maulana Akahoshia, Tomohiko Ari Wibawa Budi Santosa C. I. Sutrisno Fajri, Radhiyatul Harlinanda, Daffa Alifian Ikhwan Arief Jiea, Guo Muhammad Khalil, Muhammad Murata, Masaharu Narahara, Sayoko Pradana, Ayu Nabila Kusuma Prafitia, Harnum Annisa Projek Priyonggo Sumangun Lukitadi Umasangadji, Fahmi ZHANG, Shuo