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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal of Animation & Games Studies Jurnal Infomedia Manutech : Jurnal Teknologi Manufaktur Jurnal Vokasi Internet of Things and Artificial Intelligence Journal eProceeding of TIK Jurnal Teknologi Rekayasa Informasi dan Komputer
Atthariq, Atthariq
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Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences

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Journal : Internet of Things and Artificial Intelligence Journal

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Monitoring leakage in water pipe installation using a water flow sensor Prima, Delta Ardy; Atthariq, Atthariq; Parenreng, Jumadi Mabe
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i4.861

Abstract

The need for water for humans is the main thing and cannot be replaced with anything else. Water will always be needed every time for consumption and daily activities. Therefore, the need for water is always considered in the distribution process so that every community can meet its water needs. Especially residential areas where water needs are important. However, there are obstacles to pipe leaks that cause this water distribution to be hampered. The development of the Internet of Things is the main solution in terms of monitoring water flow and water pressure in pipes. This makes it easier for pipe repair parties to increase the effectiveness of their supervision of the condition of their housing pipes. Based on the results of trials and evaluations that have been carried out, it gives the conclusion that the existence of this system can help pipe repair parties in knowing the state of water flow in pipe installations in their residential areas. This system is also supported by using the calculation of physical formulas such as the Bernoulli equation and the principle of continuity where the formula P = 1/2ρV² has been used in the calculation of calibration on the water flow sensor and to test the validation of the results issued by the water flow sensor, the Absolute Relative Error calculation is also used where the formula used is Absolute Relative Error = | (estimated - actual) / actual | * 100. Through the calculation processes carried out, it can increase the chances of reading data that is more accurate than usual and the results will be maximized in detecting any anomalies that occur during monitoring. Therefore, the opportunity to implement a residential pipe monitoring system is a convincing thing to do in improving services that are more responsive to residents in the housing complex.
Implementation of the Convolutional Neural Network Method in Highway Traffic Monitoring Systems Atthariq, Atthariq; Azhar, Azhar; Hendarawaty, Hendarawaty; Parenreng, Jumadi Mabe
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i1.862

Abstract

Traffic Flow Calculation is one of the first steps in urban planning and road infrastructure management, for monitoring traffic flow on a road is very important. To do traffic planning, the Department of Transportation must count every passing vehicle where later the data will be used as material for analysis. Currently, the Department of Transportation calculates vehicles that pass on a road by calculating it with manual tools, so it requires large operational costs and takes a long time. Based on the problems faced, the researcher offers a solution for an intelligent traffic flow monitoring system that can count the number of vehicles that pass by using a closed circuit television camera (CCTV) installed at every city traffic light. Here we propose a high-performance algorithm model You Only Look Once (YOLO), which is based on the TensorFlow framework, to improve real-time vehicle monitoring. From the results of testing the system was built using the Python programming language using the YOLOv4 method, the Tensorflow library, and the PyQT5 library. The accuracy of reading the number of passing vehicles is 97%.
Co-Authors Aan Febriansyah Afdilla, Muhammad Aidil, Rahmat Amin, Haris Al Amri Amri Anwar Anwar Aswandi Aswandi, Aswandi Atthariq, Husaini Azhar Azhar Barri, Rafsan Devnanza, Rahul Dwina, Novira Fachrina, Itsna Fajrirrahman, Faiz Aiman Fata, Akmalul Firdaus, Muhammad Raja Gumilang, Muhammad Imam Guntur Syahputra, Guntur Haviza, Nadya Hendarawaty, Hendarawaty Hendrawaty Hendrawaty, Hendrawaty Hidayat, Hari Toha Husaini Husaini Indrawati*, Indrawati Jumadi Mabe Parenreng Khomeini, Ayatullah Rohullah Maulidi Maulidi MUHAMMAD HARIS Muhammad Ilham Muhammad Nasir Muhammad Zaini Mukhramin, Ahlul Mursyidah Mursyidah Muttaqin Muttaqin Nurhayati Nurhayati Prima, Delta Ardy Rafsanjani, Muhammad Ryzka Rahmalia, Rahmalia Ramadhana, Muhammad Afrinza Rasya, Muhammad RUDI, FACHRI YANUAR Salfikar, Inzar Satriananda, Satriananda Suhazli, Abdi Taufik Taufik