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International Journal of Smart Systems
Published by Etunas Sukses Sistem
ISSN : -     EISSN : 29865263     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
International Journal of Smart Systems with eISSN: 2986-5263 is a peer-reviewed journal as a media for publishing research results that support the development of cities, villages, sectors, and other systems. The International Journal of Smart Systems is published by Etunas Suskes Sistem and is published every three months (February, May, August, and November). This journal is expected to be a forum for the publication of research results from practitioners, academics, and related interested parties. The scope of the system discussed is attached but not limited; Smart System System engineering Artificial Intelligence (AI) Technology Machine Learning & Deep Learning Internet of Things Big data Computer Vision Natural Language Processing Smart city security Smart infrastructure Smart Health Smart Education Robots process automation (RPA) etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2023): May" : 5 Documents clear
Numerical Analysis of Mathematical Modeling with the Bisection Method in Finding the Roots of Complex Functions Nurul Auliya, Silvana; Atikah Al Kudsiya
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.8

Abstract

Mathematical modeling using the bisection method for finding complex function roots is a significant topic in numerical analysis. This research has a significant background as it focuses on solving complex function root problems, which play a crucial role in various scientific and technological applications. The objective of this research is to develop an efficient and accurate bisection algorithm to address the challenges in finding complex function roots. The research methodology includes mathematical modeling, numerical analysis, and implementation using the Python programming language. The research results demonstrate that the bisection method can effectively and efficiently discover complex function roots. We also present a Python implementation that can serve as a practical tool in real-world applications. In conclusion, this research finds that the bisection method is highly valuable for discovering complex function roots, providing accurate results and good convergence properties. The contribution of this research to the field of science is the development of an algorithm that can be applied across various domains, including simulation techniques, data analysis, and modeling complex systems.
Population Data Collection System in the Digital Era by Utilizing the Advantages of the Web to Improve Data Quality Nabila Zahrani; Siti Amelia
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.9

Abstract

In the digital era, population data collection plays a vital role in effectively managing and utilizing demographic information. This paper presents a system for population data collection that leverages the advantages of web technology to enhance data quality. The system utilizes web-based platforms, specifically harnessing the power of the web and its associated technologies, such as PHP and MySQL. The system allows for real-time data collection, ensuring that demographic information is up-to-date and accurate. The advantages of web-based data collection methods include increased accessibility, enhanced efficiency, and the potential for real-time data updates. By leveraging the web, population data can be collected through online surveys, registration systems, and interactive data portals. These platforms allow for convenient data collection, ensuring broader participation and reducing geographical barriers. Through user-friendly web interfaces, individuals can easily input their personal data, eliminating the need for manual paper-based processes. This not only improves data accuracy but also improves efficiency in data collection. Furthermore, the system incorporates data validation mechanisms to ensure the integrity of the collected information. It employs automated validation processes to minimize errors and inconsistencies in the data. Additionally, the system includes security measures to protect the privacy of individuals' data, safeguarding against unauthorized access or data breaches. The use of web technology, particularly PHP and MySQL, in the population data collection system offers several benefits. These include improved data accuracy, real-time data updates, efficient data analysis, and enhanced accessibility for both data input and retrieval. Moreover, the system facilitates seamless integration with other data management systems, enabling better collaboration and information sharing. Overall, the implementation of a web-based population data collection system demonstrates the potential to leverage the advantages of web technology in enhancing data quality. By embracing digital solutions, organizations and institutions can streamline their data collection processes, ensuring more accurate and reliable demographic information for effective decision-making and policy formulation.
A Comparative Study of Explainable AI Models in High-Stakes Decision-Making Systems Gupta, Aarav Sharma; Desai, Meera
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.72

Abstract

High-stakes decision-making systems such as those used in healthcare, finance, and criminal justicedemand not only high predictive accuracy but also transparency to ensure trust, accountability, and ethical compliance. Explainable Artificial Intelligence (XAI) has emerged as a pivotal approach to address the black-box nature of complex machine learning models, offering interpretable insights into model predictions. This study presents a comparative analysis of leading XAI techniques, including SHAP, LIME, Counterfactual Explanations, and Rule-based Surrogates, across three real-world high-stakes domains. Using standardized evaluation metrics—fidelity, stability, usability, and computational efficiency—we examine the trade-offs between explanation quality and system performance. The results reveal that while SHAP consistently provides the highest fidelity explanations, it suffers from higher computational costs, whereas LIME offers faster, though sometimes less stable, explanations. Counterfactual methods excel in user interpretability but face challenges in generating plausible scenarios for complex datasets. Our findings highlight that no single XAI method is universally optimal; rather, the selection should align with domain-specific requirements and the criticality of the decisions involved. This comparative study contributes to the discourse on responsible AI deployment by providing actionable insights for practitioners, policymakers, and researchers seeking to integrate XAI into high-stakes environments.
Adaptive Federated Learning for Privacy-Preserving Smart Applications Dominguez, Beatrice Lorraine; Emmanuel, Richard; Montemayor, Angelica Faye
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.73

Abstract

The rapid growth of smart applications across domains—ranging from healthcare and finance to personalized education—has intensified concerns about data privacy and model scalability. Federated Learning (FL) offers a promising framework by enabling distributed model training without sharing raw data, yet conventional FL approaches struggle with challenges such as heterogeneous data distributions, limited device resources, and dynamic network conditions. This paper introduces an Adaptive Federated Learning (AFL) framework designed to address these limitations while preserving user privacy. The proposed AFL dynamically adjusts aggregation strategies, learning rates, and participation levels based on client performance metrics and resource availability. We integrate differential privacy mechanisms and secure aggregation to ensure robust privacy guarantees without compromising model accuracy. Experimental evaluations on benchmark smart application datasets—including IoT sensor data and mobile user behavior logs—demonstrate that AFL achieves up to 15–20% improvement in convergence speed and notable reductions in communication overhead compared to standard FL methods. Our findings highlight AFL’s potential as a scalable and privacy-preserving solution for next-generation smart applications, paving the way for more secure and adaptive AI ecosystems.
Interpretable Deep Learning for Industrial Fault Detection Syarif, Ahmet Yılmaz; Demir, Elif; Kaya, Mehmet
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.74

Abstract

The integration of deep learning into industrial fault detection systems has significantly enhanced predictive accuracy and operational efficiency. However, the lack of model interpretability poses a critical barrier to its widespread adoption in safety-critical environments. This study proposes an interpretable deep learning framework that combines Convolutional Neural Networks (CNNs) with attention mechanisms and Layer-wise Relevance Propagation (LRP) to enable transparent fault diagnosis in complex machinery. Using a benchmark dataset from a rotating machinery system, the model achieves high classification performance while providing intuitive visual and quantitative explanations for its predictions. The attention module highlights critical temporal and spatial features, while LRP decomposes prediction scores to reveal feature-level contributions. Experimental results demonstrate that the proposed model not only maintains high accuracy (above 95%) but also delivers interpretable outputs that align with domain expert reasoning. Additionally, the model supports root cause analysis and facilitates trust in automated systems, which is essential for industrial stakeholders. This research bridges the gap between black-box deep learning models and real-world industrial applications by promoting transparency, accountability, and actionable insights. The proposed framework serves as a practical step toward deploying explainable AI in industrial settings, supporting both real-time monitoring and decision-making processes.

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