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30,411 Article Results

Deepfake detection using convolutional neural networks: a deep learning approach for digital security

10.11591/ijeecs.v39.i2.pp1092-1099
Fenina Adline Twince Tobing , Adhi Kusnadi , Ivransa Zuhdi Pane , Rangga Winantyo
The development of artificial intelligence technology, especially deep learning, has facilitated the emergence of increasingly sophisticated deepfake technology. Deepfakes utilize generative adversarial networks (GANs) to manipulate images or videos, making it appear as if someone said or did things that never actually happened. As a result, deepfake detection has become a critical challenge, particularly in the context of the spread of false information and digital crime. The purpose of this research is to create a method for detecting deepfakes using a convolutional neural network (CNN) approach, which has been proven effective in visual pattern recognition. Through training with a dataset of original facial images and deepfakes, the CNN model achieved an accuracy of 81.3% in detecting deepfakes. The evaluation results for metrics such as precision, recall, and F1-score indicated good performance overall, although there is still room for improvement. This study is expected to make a significant contribution to enhancing digital security, especially in detecting visual manipulations based on deepfakes.
Volume: 39
Issue: 2
Page: 1092-1099
Publish at: 2025-08-01

Revolutionizing recommendations a survey: a comprehensive exploration of modern recommender systems

10.11591/ijai.v14.i4.pp2579-2589
Prithvi Ram Vinayababu , Pushpa Sothenahalli Krishna Raju
The rapid proliferation of digital information and online services has fundamentally reshaped user interactions with websites, necessitating the evolution of recommender systems. These systems, crucial in domains such as e-commerce, education, and scientific research, serve to enhance user engagement and satisfaction through personalized recommendations. However, it comes up with new challenges, including information overload, prompting the development of recommender systems that can efficiently navigate this vast group to offer more personalized and relevant suggestions. This survey paper explores the dynamic opinion of recommendation systems, addressing the limitations of traditional approaches, the emergence of deep learning models, and the extended potential for additional data. By investigating various recommendation systems and the evolving role of deep learning, this paper illuminates the path toward more accurate, personalized, and effective recommender systems, considering challenges like sparse data and improved context-based recommendations. The study encompasses three primary recommendation approaches: collaborative filtering, content-based filtering, and hybrid systems. It further investigates into the transformation brought about by deep learning models, showcasing how these models intricate user-item interactions. This survey offers a comprehensive exploration of recommendation systems and their advancements in the digital era, providing insights into the future of personalized content delivery.
Volume: 14
Issue: 4
Page: 2579-2589
Publish at: 2025-08-01

A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries

10.11591/ijeecs.v39.i2.pp783-796
Muhammad Aiman Noor Zelan , Muhammad Nabil Hidayat , Nik Hakimi Nik Ali , Muhammad Umair , Muhammad Izzul Mohd Mawardi , Ahmad Sukri Ahmad , Ezmin Abdullah
This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Volume: 39
Issue: 2
Page: 783-796
Publish at: 2025-08-01

Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM

10.11591/ijai.v14.i4.pp3033-3046
Musdalifa Thamrin , Ida Mulyadi , I Dewa Made Widia , Muhammad Faisal , Suardi Hi Baharuddin , Medy Wismu Prihatmono , Nurdiansyah Nurdiansyah , Nasir Usman
Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
Volume: 14
Issue: 4
Page: 3033-3046
Publish at: 2025-08-01

Driving agricultural evolution: implementing agriculture 4.0 with Raspberry Pi and internet of things in Morocco

10.11591/ijai.v14.i4.pp3462-3473
Raja Mouachi , Elbelghiti Youssef , Sanaa El mrini , Mustapha Ezzini , Mustapha Raoufi
The purpose of this project was to investigate the use of embedded system and smartphone technologies in conjunction with Raspberry Pi and NodeMCU to create an intelligent system for smart farming (SF). By means of experiments and comparative analysis carried out in several agricultural contexts, the research evaluated the efficacy of the intelligent system. Results showed that the system was able to handle pertinent agricultural activities and effectively monitor important environmental factors including temperature, humidity, soil moisture, and climatic quality. The system's remote accessibility helped farmers by allowing them to effectively oversee agricultural operations at any time and from any location. As a consequence, SF techniques produced more production, lower costs, and maintained assets.
Volume: 14
Issue: 4
Page: 3462-3473
Publish at: 2025-08-01

Solving k-city multiple travelling salesman using genetic algorithm

10.11591/ijai.v14.i4.pp2741-2752
Alikapati Prakash , Uruturu Balakrishna , Thangaraj Manogaran , Thenepalle Jayanth Kumar
This paper addresses a novel variant of the classical multiple traveling salesman problem (MTSP) i.e. k-city multiple traveling salesman problem (k-MTSP). The problem can describe as follows. Let there are n cities, m salesman positioned at depot city and a predefined positive value k. The distance between each pair of cities is known. The objective of the k-MTSP is to determine a collection of m closed tours for salesman, which covers exactly k (including depot city) of n cities such that the total distance covered is minimum. The k-MTSP can be seen as a combination of both subset selection and permutation characteristics. From the through literature review, it is found that this study on k-MTSP is first of its kind to the best of author’s knowledge. The paper introduces a zero-one integer linear programming (0-1 ILP) formulation alongside an efficient genetic algorithm (GA), designed to address k-MTSP. No comparative studies carried out due to the absence of existing studies on k-MTSP. However, the developed GA is tested over various benchmark test cases from TSPLIB and results are reported, which may potentially serve as basis for further comparative studies. Overall findings demonstrate that the GA consistently produces best solutions within reasonable computational times for relatively smaller and medium test cases, suggesting its robustness and effectiveness in tackling the k-MTSP. However, to enhance consistency and efficiency, particularly for larger datasets, further algorithm improvements are necessary.
Volume: 14
Issue: 4
Page: 2741-2752
Publish at: 2025-08-01

Design of a segmented current steering digital to analog converter using PMOS cascode current source in UMC 65 nm technology

10.11591/ijeecs.v39.i2.pp821-830
Ashok Kumar Adepu , Balaji Narayanam
Digital to analog converters (DAC) are the fundamental data converters used in the digital data transmission. In this paper 8-bit DAC is proposed using current cells with LSB current of 2 µA and full-scale voltage of 420 mV. Current cells mean the current sources designed using the MOSFETs. When it comes to mixed-signal and analog integrated circuits, current cells are the fundamental building blocks that are available. The optimized performance of current source is obtained with the proper biasing circuit. The performance of these current mirrors is evaluated in terms of key parameters such as output impedance, transconductance and linearity. The simulations for testing these parameters are performed using Virtuoso Cadence tool in umc 65 nm technology. After transistor characterization, various types of current sources are designed, and for each current mirror, PVT analysis is carried out for comparison and Monte-Carlo analysis is carried out to find the mismatch in current mirrors. Then different digital blocks are designed, that are D-Latch, Binary-thermo decoder and Row-column decoder which are required for designing of current steering DAC. Creating an 8-bit segmented current steering DAC by combining the ideas of 4-bit unary weighted and 4-bit binary-weighted DACs is the aim of this research. Performance measurements such as signal-to-noise ratio (SNR), effective number of bits (ENOB), spurious-free dynamic range (SFDR), differential non-linearity (DNL), and integral non-linearity (INL) are calculated to assess the proposed 8-bit segmented DAC. The analysis and designing of current mirrors in advanced CMOS technologies are critical for the development of high performance integrated circuits. An 8-bit DAC implemented using ILSB current of 2 µA with an accuracy of ±2%.
Volume: 39
Issue: 2
Page: 821-830
Publish at: 2025-08-01

Analyzing radicalism sentiments in Indonesian da’wah content on website da’wah through text mining techniques

10.11591/ijict.v14i2.pp575-585
Aulia Aziza , Risqiatul Hasanah , Juairiah Juairiah , Munsyi Munsyi
This study investigates the classification of radical content in Indonesian Da’wah websites using text mining techniques. A content search engine application, developed with PHP, processes queries by comparing results against a database of keywords, classifying content into four categories: red, yellow, green, and white. Manual labeling based on data from the Ministry of Communication and Informatics yielded 126 labeled articles, forming the dataset for classification. The K-nearest neighbors (K-NN) algorithm, with an optimal k value of 7, achieved a classification accuracy of 66.37%, demonstrating its reliability compared to manual methods. The “White” class showed the highest precision and recall. System testing revealed efficient performance, with 0.704 seconds per classification task and 884,656 bytes of memory usage. Future enhancements include incorporating synonym identification for Indonesian keywords and exploring machine learning algorithms such as Naive Bayes and neural networks to improve accuracy. This research highlights the potential for text mining in identifying online radical content while emphasizing the need for system adaptability.
Volume: 14
Issue: 2
Page: 575-585
Publish at: 2025-08-01

Prediction of side effects of drug resistant tuberculosis drugs using multi-label random forest

10.11591/ijai.v14.i4.pp2899-2908
Siti Syahidatul Helma , Wisnu Ananta Kusuma , Mushthofa Mushthofa , Diah Handayani
Drug-resistant tuberculosis (DR-TB) has become a concern because anti-tuberculosis drugs (ATD) used to treat it can cause side effects in patients. This study aimed to predict the potential side effects of ATD using a multi-label classification approach with a random forest (RF) algorithm. This study used 660 medical record data, including the 14 ATD treatments prescribed to the patients and the six side effects experienced by patients. The model was trained using the best parameters based on the hyperparameter tuning process. The results show that the RF multi-label algorithm can be an alternative for building ATD side effect prediction models because it produces the most optimal performance value compared to the decision tree (DT) and extreme gradient boosting (XGBoost). The area under the curve (AUC) score of all RF multi-label models is above 0.8, which means that all RF multi-label models are considered acceptable and applicable for ATD side effect prediction. In addition, eight features influenced the models based on the average feature importance score of the RF models. This study is expected to help predict the side effects of ATD used to treat DR-TB based on ATD treatment and determine the most promising tree-based machine learning algorithm for predicting ATD side effects.
Volume: 14
Issue: 4
Page: 2899-2908
Publish at: 2025-08-01

A routine immunization decision support system framework for vaccine demand forecasting in the city health office

10.11591/ijict.v14i2.pp625-635
Mariannie A. Rebortera , Jovito P. Bolacoy Jr. , Jovanne Alejandrino , Mark Ronald S. Manseguiao
“Immunization” has been documented as one of the most flourishing measures for community well-being ever devised. Management of “immunization” information will ensure that children and newborns receive immunization on schedule. However, managing this immunization information is done manually. Customary data processing method are timeintensive, lengthy, slow in progress and susceptible to inaccuracies during encoding, verification, and re-ordering. In this study, a web-based routine immunization decision support system (RIDSS) was conceptualized to address these challenges. The web-based system is an innovative tool designed to streamline vaccine demand forecasting within the city health office (CHO) of Panabo. This system uses time series analysis and machine learning models to output accurate predictions of future vaccination demand. Using historical data on the performance of routine immunization (RI), it allows identification and analysis of actionable signals to facilitate betterinformed decisions with respect to vaccine procurement, distribution and allocation. The system is a substantial improvement of the current basic vaccine supply management, making it possible for Panabo CHO to have an organized program in administering immunization. Key stakeholders identified were presented with the prototype of system to assure effectiveness and utility. An act of major recognition to the system and its relevance in community health.
Volume: 14
Issue: 2
Page: 625-635
Publish at: 2025-08-01

IoT-enabled smart healthcare system with machine learning for real-time vital sign monitoring and anomaly detection

10.11591/ijeecs.v39.i2.pp1155-1163
Sanjay Deshmukh , Shrey Shah , Asim Wahedna , Nimish Sabnis
This paper presents an innovative IoT-enabled smart healthcare system that combines real-time vital sign monitoring with machine learning-based anomaly detection. The system utilizes a MAX30102 photoplethysmography sensor interfaced with an ESP-32 microcontroller to collect heart rate and blood oxygen saturation (SpO2) data. MQTT protocol ensures efficient data transmission to a cloud database. A long short-term memory (LSTM) neural network architecture is employed for time-series prediction of vital signs and anomaly detection. The system demonstrates high accuracy, with mean squared errors of 0.3% in offline testing and over 90% accuracy in real-time prediction. This affordable and scalable solution offers continuous monitoring capabilities, making it viable for widespread adoption in healthcare settings. The integration of IoT and machine learning techniques provides a robust framework for early detection of health anomalies, potentially improving patient care and outcomes in various medical scenarios.
Volume: 39
Issue: 2
Page: 1155-1163
Publish at: 2025-08-01

Bridging generations: a scoping review of teaching technology to the elderly using intergenerational strategies

10.11591/ijict.v14i2.pp529-539
Nahdatul Akma Ahmad , Tengku Shahrom Tengku Shahdan , Norziana Yahya
The proportion of the global population aged 60 and above is projected to nearly double by 2050, emphasizing the urgent need for societies to adapt to the challenges posed by an aging population. As the elderly increasingly face difficulties in navigating digital technologies, which are essential for daily tasks and accessing services, the digital divide often leads to digital exclusion. This scoping review investigates intergenerational strategies used to teach technology to older adults. Seventeen studies from 11 countries were analyzed, highlighting six key intergenerational learning strategies: reverse mentoring, virtual learning, collaborative learning, family intergenerational activities, game play learning, and storytelling. These strategies offer diverse methods for enhancing digital literacy and social engagement, with reverse mentoring showing promise in fostering digital competence, and virtual learning promoting inclusivity across generations. However, barriers such as technological access, ongoing support, and cultural differences complicate implementation. This review underscores the importance of adapting instructional approaches to the needs of the elderly while leveraging intergenerational interactions to bridge the digital literacy gap. It calls for sustained efforts to address user needs, provide technical support, and ensure inclusivity, especially for isolated individuals, to maximize the effectiveness and sustainability of these strategies.
Volume: 14
Issue: 2
Page: 529-539
Publish at: 2025-08-01

A framework for security risk assessment of blockchain-based applications

10.11591/ijeecs.v39.i2.pp952-962
Mohammad Qatawneh
Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.
Volume: 39
Issue: 2
Page: 952-962
Publish at: 2025-08-01

Development of ResNet-18 architecture to lesion identification in breast ultrasound images

10.11591/ijeecs.v39.i2.pp1236-1248
Silfia Andini , Sumijan Sumijan , Iskandar Fitri
Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Volume: 39
Issue: 2
Page: 1236-1248
Publish at: 2025-08-01

Technology in halal certification: a ten-year bibliometric study

10.11591/ijeecs.v39.i2.pp1280-1298
Yan Putra Timur , Sri Abidah Suryaningsih , Clarashinta Canggih , Fira Nurafini , Maryam Bte Badrul Munir , Asiah Binti Ali
This study explores the role of technology in halal certification using bibliometric analysis. Based on 88 articles from the Scopus database (2014–2024), the research employs tools like Publish or Perish (PoP), Microsoft Excel, and VOSviewer to reveal the intellectual framework of relevant literature. The finding indicates a steady increase in manuscript productivity from 2014-2024 despite a declining citation trend. Journal of Islamic Marketing, Mohd Zabiedy Mohd Sulaiman, Malaysia, and the National Defence of Malaysia emerged as most prolific journal, author, country, and institution that produce the most, respectively, in publishing on the topic. The paper that has influenced other research the most is Rejeb et al.’s integrating the IoT in the halal food supply chain: a systematic literature review and research agenda. Five significant keyword clusters that frequently show up in the 88 articles examined in this study are halal supply chain, consumer behavior towards halal foods, the role of blockchain in the halal industry, the role of information technology in halal cosmetics, and halal logo in food products. This study highlights the increasing integration of technology in halal certification, emphasizing the need for continuous innovation, interdisciplinary collaboration, and alignment with industry demands to maintain relevance. Additionally, it underscores Malaysia’s leadership in this field while noting the global expansion of halal research, the impact of emerging technologies like blockchain and IoT, and the need for stronger institutional collaboration to enhance transparency, traceability, and market growth.
Volume: 39
Issue: 2
Page: 1280-1298
Publish at: 2025-08-01
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