Sinkron : jurnal dan penelitian teknik informatika
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A Systematic Review of Multimodal Sentiment Analysis Based on Text-Image Fusion: Trends, Models, and Research Gaps
No full textSentiment analysis has evolved from text-based approaches to multimodal sentiment analysis (MSA), which integrates textual and visual data to enhance the accuracy of emotional understanding, especially in visually rich social media contexts. This study presents a systematic literature review (SLR) focusing on recent developments in text-image-based MSA, aiming to identify prevailing methods, fusion strategies, and major research gaps. Following the PRISMA protocol, a total of 20 key articles published between 2019 and 2024 were selected and analyzed. The results indicate that deep learning models such as LXMERT, ViLBERT, and ERNIE-ViL outperform traditional architectures, achieving accuracies above 80% on datasets like MVSA and Twitter. Attention mechanisms and advanced feature fusion techniques significantly contribute to improving both accuracy and interpretability. However, challenges remain in terms of annotation quality, semantic alignment across modalities, and real-time implementation constraints. This study contributes by mapping the state-of-the-art in multimodal sentiment analysis, highlighting underexplored research gaps, and offering directions for future work toward more adaptive and context-aware sentiment system
Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry
No full textIn today’s highly competitive retail landscape, businesses face increasing challenges in retaining customer loyalty and achieving sustainable growth. A common issue, particularly among small and medium-sized enterprises (SMEs), is the absence of a structured method for identifying and categorizing customers based on their value and behavior. This study addresses the challenge by implementing a data-driven customer segmentation approach using Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means clustering algorithm. The research utilized 2,353 transaction records from 369 unique customers collected over three years from a local retail business. After preprocessing and normalizing the RFM values using Min-Max scaling, the Elbow Method was applied to determine the optimal number of clusters, resulting in four distinct customer segments. Cluster 3, labeled “Loyal Customers,” consisted of customers with high purchase frequency and very high spending; Cluster 1 (“Potential Loyalists”) included those with moderate activity; Cluster 0 represented “At-Risk Customers,” and Cluster 2 comprised “One-Time Buyers.” This segmentation framework supports the development of targeted Customer Relationship Management (CRM) strategies, such as loyalty programs and re-engagement campaigns. However, the approach also has limitations, including potential data bias due to the use of static transaction records and the challenge of interpreting clusters without qualitative customer feedback. Despite these constraints, the study demonstrates the practical utility of combining RFM analysis with clustering techniques to extract actionable insights in environments with limited technical infrastructure
A Comparative Study between Logistic Regression and SVM for Resource Management in Network Slicing
No full textNetwork slicing is an essential component of 5G and subsequent networks. It enables administrators to partition shared physical infrastructure into several virtual segments, each with distinct Quality of Service (QoS) requirements. Effective and adaptable real-time resource management is essential for optimal performance in dynamic situations, characterized by low latency and high throughput. Despite the increasing body of literature on machine learning in communication networks, there is a paucity of direct comparisons between Logistic Regression (LR) and Support Vector Machines (SVM) concerning network slicing resource management. Prior comparisons have predominantly concentrated on sectors such as education, healthcare, and the Internet of Things (IoT), resulting in minimal exploration of slicing prospects. This study rectifies this gap by doing a comparative analysis of Logistic Regression and Support Vector Machine models utilizing the CICIDS2017 dataset in a network slicing simulation environment. Both models were utilized independently, employing class balancing and feature selection to forecast overload. We evaluated their performance for accuracy, ROC AUC, latency, jitter, and throughput across network slices. Results indicate that SVM exhibited somewhat superior classification accuracy; however, LR consistently surpassed SVM in critical network-level parameters, including reduced delay, enhanced throughput, and improved jitter stability. These results indicate that LR is an effective option for the real-time management of network slicing resources due to its practicality and comprehensibility. In conclusion, LR is a dependable primary option for scholars and professionals pursuing effective, low-latency solutions, improving the superior classification accuracy of SVM with enhanced overall network performance
Blockchain Model for Tracking Plastic Waste Using Smart Contracts to Reduce Emissions
No full textThis research focuses on the design and development of a blockchain-based plastic waste tracking system aimed at enhancing transparency, efficiency, and accountability in plastic waste management. The system utilizes Hyperledger Fabric as a permissioned blockchain platform and integrates smart contracts to manage transactions between organizations, including waste generators, collectors, sorting warehouses, and final processing warehouses. This system records each stage of the plastic waste journey, from creation to final processing, in a permanent, transparent, and immutable manner. The testing results demonstrate that the system can accurately record the status and history of waste, manage transfers between organizations, and process plastic waste into recycled products. Moreover, the system shows a significant potential for carbon emission reduction, with an estimated reduction of up to 50% compared to traditional plastic waste management methods, such as incineration or landfilling. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. The blockchain-based system also provides real-time monitoring, ensuring that each transaction is verified and recorded immediately, contributing to more effective management. The implementation of smart contracts further guarantees that waste-related activities are executed automatically when predefined conditions are met, reducing administrative overhead. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. Ultimately, this system presents a scalable solution that could be adopted in various regions to improve global waste management strategies
Attention Augmented Deep Learning Model for Enhanced Feature Extraction in Cacao Disease Recognition
No full textAccurate cacao disease recognition is critical for safeguarding yields and reducing losses. Prior cacao studies primarily rely on handcrafted descriptors (eg, Color Histogram, LBP, GLCM) or standard CNN/transfer-learning pipelines, often limited to ≤ 3 classes and a single plant organ; explicit channel-spatial attention and comprehensive multiclass evaluation remain uncommon. To the best of our knowledge, no prior work integrates Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) on a ResNeXt50 backbone for six-class cacao disease classification, accompanied by a standardized ablation study and t-SNE-based interpretability. We propose a six-class classifier (five diseases + healthy) built on ResNeXt-50 enhanced with SE (channel recalibration) and CBAM (channel-spatial emphasis) to highlight lesion-relevant cues. The dataset comprises labeled leaf and pod images from public sources collected under field-like conditions; preprocessing includes resizing to 224x224, normalization, and augmentation (flips, small rotations, color jitter, random resized crops). Trained with Adam and early stopping, ResNeXt50+SE+CBAM attains 97% test accuracy and 0.97 macro-F1, surpassing a ResNeXt50 baseline of 94% and 0.95 and SE-only/CBAM-only variants. Confusion matrix and t-SNE analyses show fewer mix-ups among visual classes and clearer separability, while the ablation validates complementary benefits of SE and CBAM. On a desktop-hosted, web-based setup, batch-1 inference at 224x224 is 7.46 ms/image (134 FPS), demonstrating real-time capability. The findings support deployment as browser-based decision-support tools for farmers and integration into continuous field-monitoring systems
Smart CRM Application Development Using Artificial Intelligence and Extreme Programming Method
No full textCustomer Relationship Management (CRM) is an important strategy for companies to understand customer behavior, increase loyalty, and reduce churn rates. However, the challenge that is often faced is how to manage increasingly complex customer transaction data and turn it into useful information for decision-making. This research aims to develop an artificial intelligence-based smart CRM application by integrating the K-Means algorithm for customer segmentation and XGBoost for retention prediction, as well as using the Extreme Programming (XP) methodology in the development process. The XP methodology was chosen because it is able to provide a fast, adaptive, and user-oriented iterative cycle, so that applications can be developed according to user needs. The results showed that K-Means can group customers into segments that are relevant to marketing strategies, while XGBoost provides retention prediction results with good accuracy. In addition, the application was tested using Blackbox Testing to ensure that the functionality runs according to specifications, as well as the System Usability Scale (SUS) which resulted in an average score of 89 and was included in the excellent usability category. This confirms that the system built is not only technically feasible, but also well received by users. This research contributes to presenting a smart CRM application that combines AI with modern software development methodologies, as well as opening up opportunities for advanced research at a larger data scale and integration with digital marketing systems
Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE
No full textThe timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies
A Hybrid Three-Term Conjugate Gradient Algorithm for Solving Unconstrained Optimization Problems
No full textIn this paper, we introduce a novel hybrid three-term conjugate gradient algorithm referred to as THREER, designed to address unconstrained optimization problems. The proposed approach integrates the -parameter introduced by Al-Neami with an additional third component derived from a rate-based vector , resulting in a search direction that preserves and enhances key characteristics of traditional conjugate gradient methods. A rigorous theoretical investigation establishes that the algorithm satisfies the sufficient descent condition regardless of the line search technique employed. Furthermore, the global convergence of the method is guaranteed under commonly accepted assumptions. Extensive numerical experiments conducted on large-scale benchmark problems reveal that THREER achieves superior performance when compared with several classical algorithms, particularly in terms of iteration count and function evaluations. These results highlight the algorithm’s robustness, efficiency, and potential for solving high-dimensional optimization tasks
Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods
No full textThe teacher's role is very important in improving the national learning system. Many honorary teachers are empowered in curriculum development in a number of schools who want to collaborate in improving the quality of their students. The purpose of this research is to provide rewards to honorary teachers who have long served for the progress of the nation in the world of education to be appointed as permanent teachers. The selection method was carried out through a criteria weighting technique with the MCDM-AHP method which was integrated with the WASPAS method. The technique of developing the MCDM-AHP method as an eigenvector measurement concept with proof of optimization through mathematical algebra matrices that is correlated with the Expert Choice application to get optimal values. The result optimization value is integrated with the WASPAS method as a determinant of the ranking system for permanent teacher candidates. This method is a unification of the concepts of the weight product model and weight sum model methods, so that it has special stages to support decision making with the WASPAS method. The results of selecting twelve honorary teachers for appointment as permanent teachers can be seen from the acquisition of the Qi optimization value as a ranking. The results of support for decision making for permanent teacher appointments with the highest optimization value were given to TC04 with a weight of 0.878; followed by a significant difference in the next rank. The findings of this study provide evidence that the integration of the MCDM-AHP and WASPAS methods provides continuous optimization results for decision-making support
Revolution in Image Data Collection: CycleGAN as a Dataset Generator
No full textComputer vision, deep learning, and pattern recognition are just a few fields where image data collection has become crucial. The Cycle Generative Adversarial Network has become one of the most effective instruments in the recent revolution in image data collection. This research aims to comprehend the impact of CycleGAN on the collection of image datasets. CycleGAN, a variant of the Generative Adversarial Network model, has enabled the unprecedented generation of image datasets. CycleGAN can transform images from one domain to another without manual annotation by employing adversarial learning between the generator and discriminator. This means generating image datasets quickly and efficiently for various purposes, from object recognition to data augmentation. One of the most fascinating features of CycleGAN is its capacity to alter an image's style and characteristics. Using CycleGAN to generate unique and diverse datasets assists deep learning models in overcoming visual style differences. This is a significant development in understanding how machine learning models can comprehend visual art concepts. CycleGAN's use as a data set generator has altered the landscape of image data collection. CycleGAN has opened new doors in technological innovation and data science with its proficiency in generating diverse and unique datasets. This research will investigate in greater detail how CycleGAN revolutionized the collection of image datasets and inspired previously unconceived applications