This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.
Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
Digital financial services powered by artificial intelligence (AI) and blockchain technology present transformative opportunities for workforce empowerment, yet vocational workers with limited digital literacy face significant adoption barriers. This study develops and validates an integrated framework combining AI-powered chatbots with blockchain-based smart contracts to enhance fintech accessibility for low-digital-literacy workers in Indonesia. Using sequential explanatory mixed-methods design, we analyzed data from 618 vocational workers across five Indonesian cities through structural equation modeling (SEM) and 12 focus group discussions with 86 participants. Results demonstrate that AI chatbot personalization (β=0.726, p<0.001) and smart contract automation (β=0.742, p<0.001) significantly enhance adoption, with the integrated model explaining 71.4% of adoption variance. AI chatbots reduce onboarding time by 67% through adaptive interfaces and contextual guidance, while smart contracts enable automated salary disbursement (85% efficiency gain), micro-lending decisions (72% approval accuracy), and transparent credential verification (94% accuracy). Individual digital capability (β=0.721), organizational infrastructure (β=0.586), and technical barrier mitigation (β=-0.445) emerged as critical determinants. Qualitative findings reveal three transformation pathways: automated financial processes, personalized learning journeys, and trustless verification systems. The framework provides evidence-based guidance for practitioners and policymakers implementing inclusive fintech systems that combine AI’s adaptive learning capabilities with blockchain’s transparency and automation benefits.
This study aims to implement the Bidirectional Long Short-Term Memory (Bi-LSTM) model for automatic text classification of Mathematics, Natural Sciences (IPA), and Indonesian language questions to support efficient question grouping in digital education systems. The dataset used consists of 2,718 questions, which are evenly distributed across three subject categories. The research stages include text preprocessing, tokenization and padding, splitting the dataset into training and testing sets, designing the Bi-LSTM model architecture, and conducting training and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Bi-LSTM model achieves an accuracy of 97% on the test data, with an average F1-score of 0.97. The confusion matrix analysis indicates that most predictions are correctly classified with a relatively low misclassification rate across categories. Based on these results, it can be concluded that the Bi-LSTM model is effective for automatic text classification of educational questions and has strong potential for further development in technology-based question grouping systems.
The advancement of information technology has transformed product marketing strategies, especially for Micro, Small, and Medium Enterprises (MSMEs). One effective digital promotional medium is the landing page, which can present product information in a structured manner and encourage potential customers to take action, such as making a purchase or contacting the seller. This study aims to develop a landing page for the Koffie Hideung product using the User-Centered Design (UCD) method to ensure that the system is tailored to user needs and experiences. The research stages include identifying user needs, interface design, prototyping, implementation, and evaluation using the System Usability Scale (SUS). The system was developed as a web-based application with a responsive design and integrated with social media and WhatsApp as communication and promotional tools. The evaluation results show that the landing page has a high level of usability, with a System Usability Scale (SUS) score of 82.5, which falls into the Excellent and Acceptable categories. The findings indicate that the application of the UCD method can produce an effective and user-friendly landing page, supporting the enhancement of digital promotion for the Koffie Hideung product through social media
