cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Herlambang Setiadi
Contact Email
h.setiadi@ftmm.unair.ac.id
Phone
+62881036000830
Journal Mail Official
jatm@ftmm.unair.ac.id
Editorial Address
Faculty of Advanced Technology and Multidiscipline, Gedung Kuliah Bersama, Kampus C Mulyorejo, Universitas Airlangga Jl. Dr. Ir. H. Soekarno, Surabaya, East Java 60115, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
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 38 Documents
 1   2   3   4 
Synthesis of Cu-Ag core-shell nanoparticles and its electrochemical characterization Laurencia G. Sutanto; Yeremia B. Cristian; Jihan N Adzijah; Imanda Widianti; Prastika Krisma Jiwanti
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i1.43717

Abstract

The Cu-Ag core-shell nanoparticles (Cu@Ag NPs) are prepared by chemical reduction method with polyvinyl pyrrolidone (PVP) was used as a capping agent, ascorbic acid (C6H8O6) and sodium borohydride (NaBH4) was used as a reducing agent. Cu@Ag NPs were synthesized with three variations of (Cu:Ag) 1:3, 1:4, and 1:5.. The uniformity of Cu@Ag NPs samples with three variations was verified by the particle size analyzer test. The sizes of Cu@Ag 1:3, 1:4, and 1:5 was obtained sequentially at the range of 270-280 nm, 300-304 nm, and 690-700 nm respectively. Indications of the successful synthesis of Cu@Ag nanoparticles can be seen from UV-Vis spectra of Cu@Ag 1:3, 1:4, and 1:5 respectively forming AgNP at wavelengths of 434 nm, 450 nm, and 428 nm Furthermore, the stability of Cu@Ag NPs was carried out over a period of 0 days, 4 days, 7 days, 11 days, and 14 days. It can be observed that the variation of 1:5 tends to be more stable as the chart continues to experience significant improvements compared to the variation of 1:3 and 1:4. The electrochemical study was then performed by applying cyclic voltammetry from 0 V to 1.8 V. It is clearly shown that the peak of Cu oxidation is appeared at a potential of 1.2 V while the peak value of Ag oxidation is at a potential of 0.9 V.
In Silico Analysis of Chalcone Derivatives as Potential Antibacterial Agents against DHPS Enzyme Ilma Amalina; Ni Nyoman Tri Puspaningsing; Hery Suwito
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i1.43817

Abstract

Chalcone and its derivatives have been reported to perform as antibacterial agents. With the increasing threat of antibacterial resistance in pharmaceutical sector today, the discovery of new antibacterial agents is essential to accomplish good health and well-being in supporting Sustainable Development Goals (SDGs) point 4. In silico analysis is a method used to evaluate some candidates of active compounds before the synthesis process is conducted. This study aims to investigate three chalcone derivatives as potential antibacterial agents using in silico method of molecular docking. The three chalcone derivatives, 3-(4-methoxyphenyl)1-phenylprop-2-en-1-one (1), 1-(4-aminophenyl)-3-(4- methoxyphenyl) prop-2-en-1-one (2) and 1-(4-bromophenyl)-3-(4-methoxyphenyl)prop-2-en-1-one (3), were designed as pABA competitive inhibitor on DHPS and analyzed against Eschericia coli. This inhibitory mechanism was folate synthesis inhibition as precursor to DNA and RNA synthesis. Molecular docking of three chalcone derivatives with DHPS was generated using Autodock4. The results of this study showed that free energy binding (kcal/mol) of compounds (1), (2) and (3) were -6.27, -5.35 and -5.77, respectively. Besides, the Ki constant for three compounds in order were 25.50 µM, 120.32 µM and 58.84 µM, respectively. In fact, the molecular docking positions illustrated that three chalcone derivatives occupied the active site cleft. Specifically, compound (1) indicated the best outcome among the two other candidates. Meanwhile, sulfadiazine molecular docking as positive control showed lower free binding energy (-0.86 kcal/mol) and Ki constant (233.19 mM) compared to three other candidates. Therefore, three chalcone derivatives analyzed in this study demonstrated a role as potential antibacterial agents.
Image Classification on Fashion Dataset Using Inception V3 Maryamah; Najma Attaqiyah Alya; Muhammad Hanif Sudibyo; Ergidya Liviani; Razim Isyraq Thirafi
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i1.44131

Abstract

Fashion is a form of self-expression that allows us to be able to manifest our personality and identity more confidently. One of the effects of Covid 19 is the economics of the industry, especially the fashion category as the largest category in the e-commerce industry. However, A large number of categories in each fashion brand allows shop owners to be misclassifications about the placement of items that have nearly the same clothing model. The other problem is sellers uploading pictures of products on the platform for the sale and the consequent manual tagging involved. In this paper, we proposed image classification on the fashion dataset using inception V3. The methodology of this paper consists of scrapping data from the official websites of five famous fashion brands, data preprocessing, and classification with the Inception V3 method. The accuracy and F1-Score values obtained using Inception V3 are 92.86% and 92.85%. The proposed method is the highest result of the comparison method and can differentiate between knitted with a scarf that is difficult to classify when compared to other comparison methods.
Human Development Clustering in Indonesia: Using K-Means Method and Based on Human Development Index Categories Indah Fahmiyah; Ratih Ardiati Ningrum
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i1.45070

Abstract

The quality of life for Indonesia's population can be measured from the human development index in each province. People who have a good quality of life indicate a prosperous life. The government has the responsibility to advance the welfare of the nation under the mandate of the constitution. The clustering of the human development index (HDI) in Indonesia is used to determine the distribution of quality of life or the distribution of social welfare. In this study, the K-Means method, which is a popular non-hierarchical clustering method, is used to classify human development in each province based on HDI indicators, namely Expected Years of Schooling, Mean Years of Schooling, Adjusted Per Capita Expenditure, and Life Expectancy at Birth. Provinces in Indonesia are clustered into 4 clusters. These results were also compared with the clustering based on HDI categories determined by Statistics Indonesia based on certain cut-off values. According to the HDI category, provinces in Indonesia fall into the medium, high, and very high categories. The results of the two groupings show that there is a trend toward appropriate characteristics for each group. Thus, K-Means can classify provinces in Indonesia according to the characteristics of the HDI indicators.
Implementation of Artificial Intelligence in Healthcare Akzatria, Fariza Shielda
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 Alim, M. Syaiful; Lastomo, Dwi; Nurbaiti, Nurbaiti; Yoesgiantoro, Donny; Laksmono, Rudy
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 Wildan, Juhen
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 Agus Sadid, Akbar Syahbani; Mutiarso, Ismayahya Ridhan; Arif, Fauzany; Muda, Kemal Iskandar; Prawira, Fadhil Bintang
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 Abdillah, Muhammad; Maulana, Ferbyansyah Gilang; Nugroho, Teguh Aryo
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%.
Student's Behavior Clustering based on Ubiquitous Learning Log Data using Unsupervised Machine Learning Utami, Ika Qutsiati; Hwang, Wu-Yuin; Ningrum, Ratih Ardiati
Journal of Advanced Technology and Multidiscipline Vol. 3 No. 1 (2024): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v3i1.55572

Abstract

Online learning is the source of data generation related to learner's learning behaviors, which is valuable for knowledge discovery. Existing research emphasized more on an understanding of student's performance and achievement from learning log data. In this study, we presented data-driven learning behavior clustering in authentic learning context to understand students' behavior while participating in the learning process. The objective of the study is to distinguish students according to their learning behavior characteristics and identify clusters of students at risk of unsuccessful learning achievement. Learning log data were collected from ubiquitous learning applications before conducting Exploratory Data Analysis (EDA) and cluster analysis. We used partitional clustering using K-means algorithm and hierarchical clustering based on the agglomerative method to improve clustering strategies. The result of this study revealed three different clusters of students supported by data visualization techniques. Cluster 1 comprised more students with active learning behavior based on the total logs, total problems posed, and the total attempts in fraction operation and simplification. Students in clusters 2 and 3 had a higher attempt at problem-solving instead of problem-posing. Both clusters also focused on fraction's conceptual understanding. Knowledge discovery of this study used real data generated from ubiquitous learning application namely U-Fraction. We combined two different types of clustering method for delivering more accurate portrait of a student's hidden learning behaviors. The outcome of this study can be a basis for educational stakeholders to provide preventive learning strategies tailored to a different cluster of students.

Page 2 of 4 | Total Record : 38

 1   2   3   4 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025): Journal of Advanced Technology and Multidiscipline Vol. 4 No. 1 (2025): Journal of Advanced Technology and Multidiscipline Vol. 3 No. 2 (2024): Journal of Advanced Technology and Multidiscipline Vol. 3 No. 1 (2024): Journal of Advanced Technology and Multidiscipline Vol. 2 No. 2 (2023): Journal of Advanced Technology and Multidiscipline (JATM) Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline Vol. 1 No. 2 (2022): Journal of Advanced Technology and Multidiscipline Vol. 1 No. 1 (2022): Journal of Advanced Technology and Multidiscipline