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Herlambang Setiadi
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Faculty of Advanced Technology and Multidiscipline, Gedung Kuliah Bersama, Kampus C Mulyorejo, Universitas Airlangga Jl. Dr. Ir. H. Soekarno, Surabaya, East Java 60115, Indonesia
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Journal of Advanced Technology and Multidiscipline (JATM)
Published by Universitas Airlangga
ISSN : -     EISSN : 29646162     DOI : https://doi.org/10.20473/jatm.v1i2.40293
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
Journal of Advanced Technology and Multidiscipline (JATM) aims to explore global knowledge on sciences, information, and advanced technology. JATM provides a place for researchers, engineers, and scientists around the world to build research connections and collaborations as well as sharing knowledge on how addressing solutions to the (real world) problems through discoveries on cutting edge of science and technology. We encourage researchers to submit research in the following fields: ● Power System ● Control Systems ● Renewable Energy Technology ● Advanced Manufacturing ● Optimization & System Engineering ● Human Factors & Ergonomics ● Supply Chain & Logistic Management ● Waste Processing/ Waste Treatment ● Pollutant Removal ● Applied Chemistry ● Nano Medicine ● Sensor ● Artificial Intelligence ● Health Informatics ● Robotics & Mechatronics ● Computer Vision ● Data mining ● Human Computer Interaction ● Software Engineering ● Deep Learning ● Internet Of Things ● Natural Language Processing ● Learning Analytics & technologies ● Machine learning
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)" : 5 Documents clear
Implementation of Artificial Intelligence in Healthcare Fariza Shielda Akzatria
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i2.47091

Abstract

Health is one of the pillars in determining human performance in their daily activities. Someone with good health can work optimally because there are no health problems they have. On the other hand, artificial intelligence is a form of technology that is developing rapidly. This technology has various benefits that can be provided, especially in the health sector to help health workers. The technologies that are often used are expert systems and artificial neural networks because of their ease of operation and accuracy in carrying out the work of health workers. Various other technologies are being developed to facilitate the performance of health workers to lighten their workload, such as robots to help paralyzed patients, automatic operating robots, and other technologies that can help ease the burden on health workers' performance. Keywords”health, artificial intelligence, neural network, expert system
The Study the Relevance of the Development of a Garbage Power Plant to the Large Increase in Waste Volume in Indonesia M. Syaiful Alim; Dwi Lastomo; Nurbaiti Nurbaiti; Donny Yoesgiantoro; Rudy Laksmono
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i2.47839

Abstract

Garbage endangers the community in terms of health, the economy, and the land that is taken up. Indonesia is a country with many waste piles, but there is waste management in terms of recycling, the use of computers, and other things, even from energy sources for power plants. The Waste Power Plant (PLTSa) is an electric power plant that helps add electrical energy for the PLN to be distributed to the community. The source of combustion and the driving point for garbage power plan (PLTSa) is waste; therefore, most of these locations are located in landfills in big cities. This research article aims to strengthen the argument that the development of PLTSa can be accelerated because the increase in waste piles every year will cause unmanaged waste to also increase. The results of studies and literacy studies show that the average managed waste pile is 15,000 tons/year and still leaves 5 million tons/year of waste that is not appropriately managed; however, the PLTSa capacity is still small at 10 MWh/year. It is necessary to increase the quality of waste containers as a source of PLTSa energy to reduce the amount of unmanaged waste.
A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things Juhen Wildan
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i2.51440

Abstract

Abstract”The development of modern technology has brought progress to the agricultural sector. Previously, farming was carried out using traditional methods, resulting in lower crop production. Now the world is faced with various problems, there are challenges such as climate fluctuations and increasing human population. This problem causes food needs to increase drastically, so adopting Industry 4.0 technology in the agricultural sector is necessary. Artificial Intelligence (AI) and Internet of Things (IoT) are part of industrial technology advances 4.0 that can be applied to modern agriculture. This paper reviews several AI technologies used in the agricultural sector, such as Fuzzy Logic (FL), Artificial Neural Network (ANN), Machine Learning (ML), Deep Learning (DL), Genetic Algorithm (GA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Support System (DSS). The application form of integration between AI and IoT is divided into several categories: soil monitoring, agricultural irrigation, fertilizer spraying, pest and plant disease control, harvesting, forecasting, and yield monitoring. This review paper was created to provide a comprehensive overview of modern agriculture integrating AI and IoT. This form of application makes it possible to predict the future of agriculture so that it can manage resources more efficiently and run autonomously. This review aims to analyze and explore the latest developments in integrating AI and IoT in agricultural equipment in the period 2019 to 2023. Thus, it is hoped that this article can provide in-depth insight into future agricultural technology advances. Keywords”Artificial Intelligence (AI), Internet of Things (IoT), Agriculture, Integration of AI and IoT, Smart farming.
Small Signal Stability Analysis of Kalimantan 500 KV Electricity System Akbar Syahbani Agus Sadid; Ismayahya Ridhan Mutiarso; Fauzany Arif; Kemal Iskandar Muda; Fadhil Bintang Prawira
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i2.53379

Abstract

Small signal stability refers to the ability of a system to return to equilibrium after experiencing a small disturbance. In this research, the Kalimantan electricity system will be analyzed for the stability of its small signal. Analysis of the stability of small signals in electrical systems including local disturbances and inter-area disturbances. The Kalimantan system will be analyzed using Power Factory software. System analysis was carried out by evaluating the eigenvalues "‹"‹(real part and imaginary part), oscillation frequency (damped frequency and frequency ratio) produced in the Kalimantan 500 KV electricity analysis. In the analysis results, the Kalimantan system is categorized as stable as indicated by the real part and imaginary part values "‹"‹located on the negative side of the Cartesian coordinate curve. Then, analyzing small signals, there are 117 modes categorized as local mode and 4 modes categorized as inter area mode. 
Optimization of SVC Placement and Capacity in the Electric Power System Transmission Networks using Multi-Objective Improved Sine Cosine Algorithm Muhammad Abdillah; Ferbyansyah Gilang Maulana; Teguh Aryo Nugroho
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM)
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i2.53423

Abstract

Current technological developments are in line with the increasing consumption of electrical energy. There is a value of power losses of the electricity transmission process caused by an increase in the value of power losses, to overcome this, SVC (Static VAR Compensator) of the Flexible AC Transmission System (FACTS) can be used. From previous studies, the optimization of SVC placement in the transmission network has not been carried out to get better power losses. This research uses the Improved-Sine Cosine Algorithm (ISCA) that has a different function of r1 compared to the ordinary SCA, in which the use of the ISCA method is able to overcome the weaknesses of the SCA method. The determination of location and capacity can use more than one objective function. From the result, the optimization of SVC placement and capacity is able to reduce the value of power losses by up to 85%.

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