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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 3 Documents
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Search results for , issue "Vol. 6 No. 4 (2025): December 2025" : 3 Documents clear
Web based IoT monitoring system for ultrasonic water flow measurement using ESP32-S3 and cloud database Nugroho, Waluyo; Arifianto, Mada Jimmy Fonda; Afianto, Afianto; Wicaksono, Andreadie; Nursim, Nursim
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.625

Abstract

Efficient water management is crucial for ensuring sustainable resource utilization and reducing water losses in both industrial and domestic applications. This study presents the design and implementation of a smart water monitoring system based on an ultrasonic flow meter, which enables accurate, real-time measurement of water flow without physical contact with the medium. The proposed system integrates ultrasonic sensors with a microcontroller-based data acquisition unit and wireless communication to transmit flow rate, volume, and consumption data to a cloud-based monitoring platform. The system was tested in various flow conditions to evaluate accuracy, stability, and energy efficiency. Experimental results demonstrate that the ultrasonic flow meter achieved a measurement accuracy of ±1% compared to a reference turbine flow meter, while maintaining minimal power consumption. Furthermore, the integration of Internet of Things (IoT) capabilities allows remote monitoring, anomaly detection, and data logging for long-term analysis. The results indicate that this ultrasonic-based monitoring system provides a reliable and non-invasive solution for smart water management, offering potential applications in household metering, agricultural irrigation, and industrial fluid monitoring.
Application of the TAM model for assesing the acceptance of IoT technology in a residential security application Ghaniy, Rajib; Wicaksana, Binanda; Arnes, Fahmi; Melati, Laras; Septiana, Helena
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.629

Abstract

Residential crime continues to be a significant concern, and traditional security systems relying oHousing is an area vulnerable to crime, especially if it is not supported by an adequate security system. Many housing complexes still rely on conventional security systems that only involve officers without technological support. Therefore, the application of technology, especially the Internet of Things (IoT), is needed to improve housing security systems. The success of a system is largely determined by the level of user acceptance, which can be measured using the Technology Acceptance Model (TAM). This study aims to measure user acceptance of an IoT-based housing security system using the TAM model. Data were obtained from 100 respondents and analyzed using the PLS-SEM method to test the research hypotheses. The results showed that four hypotheses had a significant relationship, namely the relationship between Subjective Norm (SN) and Perceived Usefulness (POU), Perceived Ease of Use (PEU) and POU, PEU and Attitude Toward Use (ATU), and POU and Behavioral Intention (BEI). Meanwhile, the other four hypotheses did not show a significant relationship.n manual monitoring are often insufficient in addressing modern security challenges. With the rapid development of Internet of Things (IoT) technology, digital security solutions offer new opportunities for improving surveillance and access control within housing environments. This study aims to assess user acceptance of an IoT-based residential security application by applying the Technology Acceptance Model (TAM). A quantitative survey method was used, involving 100 respondents who evaluated the prototype after testing it directly. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Perceived Ease of Use significantly affects both Perceived Usefulness and Attitude Toward Using, while Perceived Usefulness strongly influences Behavioral Intention. However, Attitude Toward Using shows a marginal impact on Behavioral Intention, and Behavioral Intention does not significantly predict Actual Use. These findings reveal the dominant factors influencing acceptance and highlight areas for improvement in IoT-based security applications.
Topic modelling analysis of public policy narratives on prabowo-gibran in national news Hakim, Lukmanul; Aditya, Anggi Yudistira; Lubis, Muharman
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.632

Abstract

The rapid acceleration of digital transition has become an inevitable reality of the modern era. The proliferation of online communication platforms, news portals, and heterogeneous data formats has substantially increased big data volumes, leading to large-scale collections of unstructured data. This study aims to analyze dominant public policy–related topics concerning the Prabowo–Gibran administration by applying topic modeling techniques to national online news media. Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) were employed as unsupervised learning approaches to extract latent semantic structure from a corpus of 200 credible news articles collected through URL fetching using Python 3. Data preprocessing included text cleaning, tokenization, bigram and trigram construction, and the development of a dictionary and corpus. Model performance was evaluated using topic coherence metrics, yielding scores of 0.3709 for LDA and 0.68 for NMF. To examine temporal dynamics, the dataset was divided based on the official inauguration date of the president and vice president, enabling a comparative analysis of dominant topics before and after the inauguration. Topic similarity across both periods was measured using cosine similarity, with the highest similarity score of 0.663 observed between Topic 4 in the pre-inauguration period and Topic 1 in the post-inauguration period. The findings provide insights into evolving media discourse and policy-related topic trends across the two periods, demonstrating the potentials of topic modeling in analyzing large-scale unstructured news data for diverse purposes to bridge computational science and empirical evidence of social science.

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