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Strategic Planning Technology Validity Turing Tests Using Artificial Intelligence Understanding Wan Ahmad, Wan Fatimah; Saputra, Yogi
JESII: Journal of Elektronik Sistem InformasI Vol. 2 No. 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3415

Turing test has been used as an artificial intelligence (AI) countermeasure for decades. This test measures a machine's capacity for intelligent behavior that is comparable to human capacity. However, Turing's trial has come under fire due to its emphasis on linguistic behavior and potential anthropological bias. The primary goal of this study was to determine whether the Turing Test's validity was connected with comprehension and disclosure of artificial intelligence's aim. The Likert scale was utilized for data gathering, statistical analysis using linear regression, and theoretical analysis. Both qualitative and quantitative research approaches will be used in this investigation. Reviews of the literature and conversations with authorities on the Turing Test and artificial intelligence will be used to gather qualitative data. This resulted in significant advancements in our knowledge of the variables influencing the Turing Test's validity, particularly with regard to comprehending and illuminating the function of artificial intelligence. It is anticipated that this research will yield a technical planning method that will help numerous fields and increase the validity of the Turing Test. This strategy will be grounded in the most recent knowledge about the operation of AI.

Pond Water Quality Monitoring in Consumption Fish Farming Industry Based on Internet of Things Subrata, Arsyad Cahya; Sulisworo, Dwi; Fitrianawati, Meita; Shafee Kalid, Khairul; Wan Ahmad, Wan Fatimah; Hamdi Batubara, Zul; Ramadhani, Muhammad
Rekayasa Vol 17, No 3: Desember, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i3.25428

The rapid increase in population in Indonesia has increased the demand for animal protein. As a source of animal protein, fish has excellent potential to be developed in Indonesia. However, care for water quality, a basic need, is often ignored. Meanwhile, increasing fish production can be done by ensuring that water quality is always in good condition. This research conducted aims to monitor water quality continuously. Integrating water quality monitoring systems using the Internet of Things (IoT) offers convenience in real-time monitoring and does not have to be present on-site. The parameters determining fish water quality are pH, electrical conductivity (EC), dissolved oxygen (DO), turbidity, and water temperature. The data obtained is then displayed on the Water Monitoring dashboard as graphs, indicators, and raw data the user can download. Overall, the system can measure, monitor in real-time, and store data on the results of measuring the quality of freshwater fish ponds on smartphones/laptops. The developed system also provides information on whether the water quality is “normal” or in conditions less and more than the threshold. Therefore, the developed system helps farmers monitor the quality of their fish ponds to increase the productivity of fish farming.

A Preliminary Study on Promoting Contextual Teaching and Learning Using Smart Water Quality Sensors Sulisworo, Dwi; Fitrianawati, Meita; Subrata, Arsyad Cahya; Kalid, Khairul Shafee; Wan Ahmad, Wan Fatimah
Indonesian Review of Physics Vol. 6 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v6i1.8115

Building awareness among students on the issues of natural environmental phenomena has always been a challenge due to the difference in location between the student and the observed phenomena. The issues of the natural environment have been a part of the curriculum in elementary schools. One of the lessons taught on the natural environment in elementary schools is related to the water conditions in various areas filled with water, such as ponds, rivers, lakes, etc. Currently, learning in the natural environment is based on text, images, and videos, and learning activities using real-time data are still rare. This study presents the development of an IoT-based Smart Water Quality application prototype. The prototype consists of conductivity, pH, oxygen levels, salinity, and turbidity sensors. The IoT prototype can also be used to automatically monitor fish, shrimp, and other species in aquaculture ponds. Using the IoT-based Smart Water Quality application prototype, teachers can enhance students' higher-order thinking skills by designing learning activities using real-time data to identify, compare, and classify various concepts or phenomena.

Psychometric Analysis of an Instrument Evaluating Students’ Acceptance of Online Platform to Support Online English Learning Sulthonah, Fathia Amalia; Mulyono, Herri; Wan Ahmad, Wan Fatimah
Jurnal Pendidikan Indonesia Vol 11 No 4 (2022): Desember
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Acceptance of technology takes as a crucial role in context of the technology adoption. Before encouraging the maximal use of technology, it is important that users have to first acknowledge its use and that they have to also accept it. The current study was proposed in aims to examine the validity and the reliability of the Indonesian version of instrument investigating TAM constructs such as perceived usefulness, perceived ease of use, and behavioural intention. The questionnaire was adopted to elaborate the undergraduate students’ technology acceptance of WhatsApp, Google Classroom, and Microsoft Teams as online platform to support online English learning based on the Technology Acceptance Model (TAM). Data were collected from 370 undergraduate students from different universities in Indonesia and the study applied Rasch analysis technique to address the Rasch assumptions such as items dimensionality, person and item reliability, person and item mapping, rating scale, and differential items functioning measure. The findings of the study suggested that the adopted and translated to Indonesian questionnaire was found to be sufficient in context of psychometric characteristics and was considered eligible to measure the technology acceptance of online platforms used for English online learning.

Strategic Planning Technology Validity Turing Tests Using Artificial Intelligence Understanding Wan Ahmad, Wan Fatimah; Saputra, Yogi
JESII: Journal of Elektronik Sistem InformasI Vol 2 No 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3415

Turing test has been used as an artificial intelligence (AI) countermeasure for decades. This test measures a machine's capacity for intelligent behavior that is comparable to human capacity. However, Turing's trial has come under fire due to its emphasis on linguistic behavior and potential anthropological bias. The primary goal of this study was to determine whether the Turing Test's validity was connected with comprehension and disclosure of artificial intelligence's aim. The Likert scale was utilized for data gathering, statistical analysis using linear regression, and theoretical analysis. Both qualitative and quantitative research approaches will be used in this investigation. Reviews of the literature and conversations with authorities on the Turing Test and artificial intelligence will be used to gather qualitative data. This resulted in significant advancements in our knowledge of the variables influencing the Turing Test's validity, particularly with regard to comprehending and illuminating the function of artificial intelligence. It is anticipated that this research will yield a technical planning method that will help numerous fields and increase the validity of the Turing Test. This strategy will be grounded in the most recent knowledge about the operation of AI.

The best machine learning model for fraud detection on e-platforms: a systematic literature review Yussiff, Alimatu-Saadia; Frank Prikutse, Lemdi; Asuah, Georgina; Yussiff, Abdul-Lateef; Dortey Tetteh, Emmanuel; Ibrahim, Norshahila; Wan Ahmad, Wan Fatimah
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p195-204

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on eplatforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

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