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All Journal Jurnal Jaringan Telekomunikasi
Ahmad Wilda Yulianto
State Polytechnic of Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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COVID-19 Disease Diagnosis Expert System with Certainty Factor Method using iOS-Based App Supriatna Dwi Atmaja Suprayitno; M. Nanak Zakaria; Ahmad Wilda Yulianto
Jurnal Jaringan Telekomunikasi Vol 12 No 3 (2022): Vol. 12 No. 03 (2022) : September 2022
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jartel.v12i3.336

Abstract

The drop in COVID-19 patients in Indonesia from January to February 2022 made many companies prepare policies to no longer enforce work from home. At the office, we can interact and meet other people directly and it is possible to be exposed to Covid-19 that could potentially become a new wave of COVID-19. This effect poses a serious risk to all people who come into contact with COVID-19-infected individuals or are close to them. The major course of action that may be performed when someone has COVID-19 is self-isolation and tracking anyone who is around or has a health condition associated to COVID-19. We require an iOS-based COVID-19 diagnosis expert system application to track the health status of everyone around us because we are unable to know the health status of everyone. The application uses artificial intelligence technology in the form of an expert system to check health conditions. The expert system replaces the role of the expert with the certainty factor method. This app should be used every time before entering a potentially crowded place to clarify tracking by using maps feature. In addition to COVID-19, this expert system can also diagnose diseases that have the same symptoms as Typhoid Fever and Pneumonia. The results of the expert system are in the form of diagnosing the user's health condition based on the symptoms given with a confidence level of up to 0.9999952130944 or 99.99952130944% for COVID-19, 0.9676 or 96.76% for Typhoid Fever, and 97% for Pneumonia.
Indoor Positioning and Navigating System Application Using Wi-Fi with Fingerprinting Method and Weighted K-Nearest Neighbor Algorithm: English Arya Putra Hadi Yulianto; M. Nanak Zakaria; Ahmad WIlda Yulianto
Jurnal Jaringan Telekomunikasi Vol 12 No 3 (2022): Vol. 12 No. 03 (2022) : September 2022
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jartel.v12i3.493

Abstract

The need for accurate indoor location determination, object tracking, digital maps and indoor travel routes is increasing along with the construction of buildings that have complex and spacious layouts. The current Global Positioning System navigation system is only effective for outdoor use. However, when used indoors it becomes inaccurate due to factors such as signal attenuation and multipath caused by wall obstructions in the building. This study designed an application of Indoor Positioning and Navigating System Using Wi-Fi with Fingerprinting method and Weighted K-Nearest Neighbor algorithm. In the design process, it is necessary to create a fingerprinting database by considering the number of Access points and environmental conditions. Based on the results of the study, the location results of the application show that from floors 1,2, and 3. Floor 1 has a room accuracy result of 89% and a point accuracy of 86% with an average deviation of 1.42 px or 0.9 m, floor 2 has room accuracy results. of 65% and a point accuracy of 70% with an average deviation of 2.43 px or 1.7 m, and the 3rd floor has a room accuracy of 86% and a point accuracy of 68% with an average deviation of 2.27 or 1.5 m. Based on the data above, this application is proven to be able to detect the position of someone in the room with a success percentage on the 1st floor by 90%, the 2nd floor by 55%, and the 3rd floor by 80%.
Navigation and Guidance for Autonomous Quadcopter Drones Using Deep Learning on Indoor Corridors Ahmad Wilda Yulianto; Dhandi Yudhit Yuniar; Yoyok Heru Prasetyo
Jurnal Jaringan Telekomunikasi Vol 12 No 4 (2022): Vol. 12 No. 04 (2022) : December 2022
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jartel.v12i4.422

Abstract

Autonomous drones require accurate navigation and localization algorithms to carry out their duties. Outdoors drones can utilize GPS for navigation and localization systems. However, GPS is often unreliable or not available at all indoors. Therefore, in this research, an autonomous indoor drone navigation model was created using a deep learning algorithm, to assist drone navigation automatically, especially in indoor corridor areas. In this research, only the Caddx Ratel 2 FPV camera mounted on the drone was used as an input for the deep learning model to navigate the drone forward without a collision with the wall in the corridor. This research produces two deep learning models, namely, a rotational model to overcome a drone's orientation deviations with a loss of 0.0010 and a mean squared error of 0.0009, and a translation model to overcome a drone's translation deviation with a loss of 0.0140 and a mean squared error of 0.011. The implementation of the two models on autonomous drones reaches an NCR value of 0.2. The conclusion from the results obtained in this research is that the difference in resolution and FOV value in the actual image captured by the FPV camera on the drone with the image used for training the deep learning model results in a discrepancy in the output value during the implementation of the deep learning model on autonomous drones and produces low NCR implementation values.
Co-Authors Arya Putra Hadi Yulianto Dhandi Yudhit Yuniar M. Nanak Zakaria Supriatna Dwi Atmaja Suprayitno Yoyok Heru Prasetyo