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All Journal Teknofisika Indonesian Journal of Artificial Intelligence and Data Mining Communications in Science and Technology Journal of Robotics and Control (JRC) Indonesian Journal of Teaching in Science
Dwi Joko Suroso
King Mongkut’s Institute of Technology Ladkrabang
Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Earthquake Mitigation: An Application of Wireless Sensor Networks Suroso, Dwi Joko; Cherntanomwong, Panarat; Sunarno, Sunarno
Teknofisika Vol 1, No 1 (2012)
Publisher : Jurusan Teknik Fisika, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (19.34 KB)

Abstract

The high number of victim as the result of earthquake disaster needs some intentions in the technology application. The mitigation scheme to reduce the destruction caused by earthquake is developed. This paper describes the theoretical and empirical concepts of wireless sensor networks for earthquake mitigation applications. First, the existing researches about earthquake mitigation using wireless sensor networks are explored, and a review of each technique and system is provided. Then, the influencing factors which affect the result for each technique are explained. The comparison of the efficient system for earthquake mitigation for each technique and system is emphasized.
Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks Suroso, Dwi Joko; Arifin, Muhammad; Cherntanomwong, Panarat
Journal of Robotics and Control (JRC) Vol 1, No 6 (2020): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.1638

Abstract

Wireless sensor networks (WSNs) have a vital role in indoor localization development. As today, there are more demands in location-based service (LBS), mainly indoor environments, which put the researches on indoor localization massive attention. As the global-positioning-system (GPS) is unreliable indoor, some methods in WSNs-based indoor localization have been developed. Path loss model-based can be useful for providing the power-distance relationship the distance-based indoor localization. Received signal strength indicator (RSSI) has been commonly utilized and proven to be a reliable yet straightforward metric in the distance-based method. We face issues related to the complexity of indoor localization to be deployed in a real situation. Hence, it motivates us to propose a simple yet having acceptable accuracy results. In this research, we applied the standard distance-based methods, which are is trilateration and min-max or bounding box algorithm. We used the RSSI values as the localization parameter from the ZigBee standard. We utilized the general path loss model to estimate the traveling distance between the transmitter (TX) and receiver (RX) based on the RSSI values. We conducted measurements in a simple indoor lobby environment to validate the performance of our proposed localization system. The results show that the min-max algorithm performs better accuracy compared to the trilateration, which yields an error distance of up to 3m.  By these results, we conclude that the distance-based method using ZigBee standard working on 2.4 GHz center frequency can be reliable in the range of 1-3m. This small range is affected by the existence of interference objects (IOs) lead to signal multipath, causing the unreliability of RSSI values. These results can be the first step for building the indoor localization system, which low-cost, low-complexity, and can be applied in many fields, especially indoor robots and small devices in internet-of-things (IoT) world’s today.
Indonesian Public Response to Online Learnings During the Covid-19 Pandemic: An Analysis of Social Media Prabowo, Thoriq Tri; Suroso, Dwi Joko
Indonesian Journal of Teaching in Science Vol 2, No 2 (2022): IJOTIS: September 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijotis.v2i2.50226

Abstract

Online Learnings that were implemented during the Covid-19 pandemic invited pros and cons, especially on social media, that could be directly observed by the public. When analyzed properly, the pros and cons contain valuable information that the authorities can use to evaluate Online Learning policies. This study describes the narrative developed on social media Twitter related to Online Learning during the Covid-19 pandemic. This research includes the social network analysis (SNA) research with a descriptive qualitative approach. The platform used to collect data is Drone Emprit Academic (DEA), an SNA tool. The data analyzed relates to the number of tweets, influential actors, narrative sentiments, and robots that tweet the topic. This research does not contribute justification for right and wrong but attempts to present reliable data exposure. Based on Online Learning tweets collected on July 22-29, 2020, there were 2,903 tweets. The tweets are classified as organic because the influential actors come from personal accounts, not accounts of famous figures. Criticism of Online Learning administration is the dominant response because there is still an economic and digital divide among the public. The government needs to evaluate Online Learning policies and reconsider just education during the Covid-19 pandemic.
Spatial aliasing effects on beamforming performance in large-spacing antenna array Suroso, Dwi Joko; Gautam, Deepak; Sunarno, Sunarno
Communications in Science and Technology Vol 4 No 1 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (631.043 KB) | DOI: 10.21924/cst.4.1.2019.109

Abstract

In the next wireless communication generation, 5G, it is obvious to employ the half-spacing antenna elements as high-resolution antenna array. However, to compensate the lower aperture from short-spacing elements, the number of antennas should be grown larger. It will be costly and increase complexity in terms of antenna array analysis. In this paper, the aliasing effects on beamforming of antenna array geometry utilizes inter-element spacing more than half-lambda. The antenna geometry of linear, circular and planar will be explored in this paper and the center frequency for simulation is 60 GHz. It is also due the fact that many researchers on 5G believe 60 GHz will be employed as 5G frequency band. 60 GHz is truly higher than today Long-term-evolution (LTE) working frequency and it is really challenging to its signal model due to small wavelength and its effective signal working distance as effect of rain attenuation, etc. As our preliminary results, linear array, which only considers the azimuthal, the spatial aliasing appears in the inter-element distance more than 1-lambda. The circular and planar consider the azimuth and elevation properties of incoming signals. In circular array, the power angular of a signal can be detected accurately applying the 3-sector antenna pattern. When the inter-element distance grows more than 1.5 lambda, the spatial aliasing which appear to be side lobe with similar power angular dominate the incoming signal detection. The result shows us that employing the 2-lambda distance or more will be useless. Planar array which actually a 2-axis linear array give unexpected results, most of detections are inaccurate and power angular also low. This concludes that spatial aliasing effects will degrade the beamforming performance due to confusion between real signal and fake signal resulting from similar values of array factor.
Fish Detection and Classification using YOLOv8 for Automated Sorting Systems Kuswantori, Ari; Suroso, Dwi Joko
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.30967

Abstract

Automation plays a crucial role in scaling up freshwater fish cultivation to address the future threat of food scarcity and meet growing nutrition needs. The fish industry, in particular, develops automation in the sorting and selection processes. However, research on this technology's development is still very limited. In this work, we propose an approach for detecting and classifying fish running on conveyors. We use YOLOv8, which is the most popular and newest deep learning model for object detection and classification. We conducted our test using the KMITLFish dataset, a moving conveyor belt recording that encompasses common cultivated freshwater fish in Thailand along with some endemic species. As a result, our proposed method was able to accurately detect and classify eight types of fish at a conveyor speed of 505.08 m/h. Moreover, we developed this work using a ready-to-use AI platform, intending to directly contribute to the advancement of automatic fish sorting system technology in the fish industry.
Co-Authors Gautam, Deepak Kuswantori, Ari Panarat Cherntanomwong Sunarno Sunarno Sunarno Sunarno Tamaulina Br Sembiring Thoriq Tri Prabowo