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The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.

This study explores the influence of childhood memories, particularly the experience of "unresolved revenge" in the form of a failed tricycle ownership, on the process of creating works of art. This phenomenon is analyzed through the lens of psychoanalysis and consumerism criticism, where childhood fantasies transform into an obsession with collecting in adulthood, as an outlet for powerful passionate sensations. Qualitatively, works of art are positioned not only as manifestations of personal expression, but also as sustainable and critical practices. The articulation of art becomes a way to resolve consumerist trauma in a non-physical way, transforming the urge for unsustainable material accumulation (the infatuation of collecting) into artistic output that reflects and critiques the symbolic prestige value of commodities in the 1980s. This study concludes that personal memory-based art practices offer a sustainable framework for psychological recovery and social critique of the endless cycle of material desires.

Pindang tetel is a traditional Pekalongan culinary specialty born from the creativity of the community in utilizing limited food ingredients, especially beef offal, which in the past was synonymous with lower-class consumption. This culinary dish not only serves as a daily meal, but also represents historical, social, and symbolic values ​​that reflect how Javanese people respond to their economic conditions and the dynamics of their lives. This study aims to examine the cultural meaning, local identity values, and the transformation process of pindang tetel in a modern context, especially when the dish is marketed in the tourist area of ​​Lake Al Kautsar, Kayugeritan. The study used a qualitative approach with a mini-ethnography method through direct observation, in-depth interviews with sellers, and informal conversations with consumers. The results show that pindang tetel has undergone adaptations in its presentation without losing its basic character, so it remains recognized as a traditional culinary dish. In the context of local tourism, pindang tetel serves as a representation of Pekalongan's cultural identity and a medium for cultural introduction for tourists. These findings confirm that traditional culinary plays an important role in shaping collective memory, strengthening local identity, and providing sustainable symbolic and social value in addition to economic value.

The integration of language and culture plays a crucial role in shaping the complexity of human cognitive processes. Language acts not only as a means of conveying messages but also as a conceptual system that influences how individuals construct mental representations, process information, and reason. Culture, on the other hand, provides a set of values, norms, and thought patterns that shape attention, interpretation of experiences, and strategies for understanding the social world. A review of literature from the fields of linguistic relativity, cultural psychology, neurolinguistics, and cross-cultural cognition found that differences in language structure, bilingual experience, and cultural value orientations result in variations in perception, memory, and executive function. Neurocognitive findings indicate that the influence of language and culture is evident down to the level of brain activation. Furthermore, the sociocultural approach emphasizes the role of language as a mediating tool in the process of internalizing values ​​and developing higher-order cognitive abilities. Thus, human cognition is the result of a dynamic interaction between language, culture, and social experience.

Language processing disorders are disorders that arise when someone is under mental stress or experiencing deep emotional trauma. This study aims to analyze the various types of language processing disorders experienced by the character Tari in the film "Bolehkah Sekali Saja Aku Menangis" (May I Cry Once) and to determine the relationship between the character's emotional state and the linguistic symptoms that appear. This study uses a qualitative approach with a naturalistic approach and analytical descriptive methods, where data are collected through observation and recording of dialogues in the film that show signs of language disorders. Samples were taken purposively based on statements that indicate irregularities in sentence structure, long pauses, repetitions, or inappropriate word choices. This analysis compares the results of the study with existing psycholinguistic theories regarding how language processing works and the influence of trauma on speech production. The findings of this study indicate four main types of language processing disorders experienced by the character Tari: unfinished sentences, long pauses, repetitions of words or phrases, and inaccuracies in word choice. These types of disorders are more apparent when the character experiences high emotional stress and when interacting with family members related to her trauma.  The results of this study support the theory that emotional trauma can disrupt verbal working memory function and speech planning processes, ultimately impeding communication skills.

This study aims to analyze in depth the role of drumband extracurricular activities in developing motor and cognitive abilities of early childhood at Aisyiyah Bustanul Athfal 50 Kindergarten, Surabaya. This study uses a qualitative approach with data collection methods through direct observation, in-depth interviews, and the distribution of questionnaires involving teachers, parents, and students as research subjects. The results show that drumband activities provide a positive contribution to the development of gross and fine motor skills of children, especially through rhythmic movement exercises, hand-eye coordination, and body control when playing musical instruments. In addition to motor aspects, drumband activities also play a role in improving children's cognitive abilities, such as concentration, memory, the ability to follow instructions, and understanding musical patterns and rhythms. Children's active participation in drumband activities also helps shape discipline, the ability to work together in groups, and increases children's self-confidence. Thus, drumband extracurricular activities can be used as an effective and enjoyable learning medium to support the motor and cognitive development of early childhood holistically and sustainably.

The use of Islamic music, especially nasyid, as a creative learning strategy in Islamic Religious Education (PAI) plays an important role in increasing students' motivation, understanding, and appreciation of religious values. This study uses a qualitative descriptive method to describe in depth how nasyid can be used as an innovative and effective learning medium to enrich the IRE teaching process. The findings show that the application of nasyid in the teaching and learning process can create a more interesting, interactive, and enjoyable learning atmosphere. This condition has been proven to encourage active student participation, making learning activities more meaningful and memorable. In addition, nasyid effectively helps students improve their memory and ability to memorize religious material in an easier and more enjoyable way. The positive impact is not only seen in cognitive aspects but also in the overall formation of students' religious character. Through nasyid, moral and spiritual values can be instilled more deeply, naturally, and continuously in everyday life. However, this study also found several obstacles, such as limited supporting facilities and the need to develop teachers' competencies in optimally applying nasyid as a learning medium. Therefore, the use of nasyid in PAI learning can be seen as a strategic innovation that has the potential to improve the quality of religious education holistically and have a positive impact on the development of students in a dynamic and challenging modern era.  

This researcher aims to improve student learning activity in history subjects of class X SMK Arnoldus Betun through the application of the memory board game method. The formulation of the problem raised in this study is how to apply the memory board game method to improve student learning activity in history subjects and what is the impact of applying the memory board game method to improve student learning activity in history subjects. This researcher uses a classroom action research (CAR) method consisting of three cycles. Each cycle includes the stages of planning, implementation, observation, and reflection. The data collection instruments used are observation sheets and student learning interest questionnaires. The results of the study showed a significant increase in student learning interest after the application of the memory board game method. In the second cycle, the average student learning activity was at 60%, and increased gradually to reach 86% in the third cycle. The discussion of these results shows that the memory board game method is not only able to increase student learning activity, but also facilitates collaboration, active participation, and student creativity in the history learning process. Thus, it can be concluded that the memory board game method is effective in increasing the active learning of history among 10th-grade students at Arnoldus Betun Vocational School.

The implementation of the core tax administration sistem (Core Tax Administration Sistem or Coretax) in early 2025, which was expected to modernise tax services, instead sparked a wave of public criticism due to various fatal technical obstacles. The dysfunction of this sistem not only hampered administration but also gave rise to discourse on distrust of state budget management. This study aims to analyse in depth how the failure to implement Coretax has eroded public trust and damaged the fiscal social contract between taxpayers and the state. The research method uses a qualitative approach with a Critical Discourse Analysis perspective based on Norman Fairclough's model. Data was collected through netnography from public comments and interactions on social media, then analysed through three dimensions: text (micro), discourse practice (meso), and social practice (macro). The findings show that public discourse is not merely technical complaints, but a form of symbolic resistance. At the micro level, sistem dysfunction is interpreted as evidence of incompetence and alleged budget irregularities. At the meso level, the public mobilises collective memory related to past government project failures to validate their distrust. At the macro level, this signifies a violation of the principle of reciprocity, whereby the state is perceived as demanding tax compliance without providing adequate services, thereby triggering a crisis of legitimacy. The implications of this study emphasise that digital transformation of the public sector requires transparency and accountability; failure to respond to this crisis has the potential to significantly reduce voluntary tax compliance.

Fasting as a spiritual and health practice has been practiced for thousands of years in various religious and cultural traditions around the world. In the last decade, researchers have shown increasing interest in the health aspects of fasting, not only from a physiological but also a psychological perspective. This phenomenon is interesting to study further, especially in the context of modern society which often faces stress, anxiety, and various mental disorders. The approach used in this study was a qualitative approach with a literature study method that analyzed in depth various reference sources related to the mental impact of fasting. The results of the review of several studies show that fasting has been proven to have an effect on mental health because it has a significant impact on overall mood improvements, with reduced anxiety levels and increased feelings of calm. Furthermore, another important aspect of mental health is cognitive function, which includes attention, concentration, memory, and executive function. It was found that cognitive alertness is generally maintained during short- to medium-term fasting, but begins to decline after longer fasting durations. Fasting also has an impact on extraordinary psychological resilience and good mental health despite advanced age. Researchers attribute this extraordinary mental resilience to the consistent practice of long-term fasting, which may have induced significant neuroplastic and psychological adaptations.

This study aims to develop a web-based Virtual Assistant to improve the efficiency of academic information services at SMA Negeri 1 Parittiga. The research was motivated by the delays and inaccuracies in information delivery caused by the manual system still used in the school. The system development was carried out using the Research and Development approach with the Waterfall model, which includes the stages of needs analysis, design, implementation, and evaluation. The main technologies used are Natural Language Processing (NLP) and the Long Short-Term Memory (LSTM) machine learning algorithm, which allow the assistant to understand and respond to user questions in natural language in a contextual way. The system architecture uses Flask as the backend, Vue.js as the frontend, and Laravel for administrative data management. The testing results show that the system has an accuracy level of 88.4% in providing correct answers and a user satisfaction level of 92%, surpassing the target success rate of 80%. These findings prove that integrating NLP and LSTM can enhance the system's ability to understand conversational context and speed up the distribution of academic information. The study concludes that a web-based Virtual Assistant is an effective solution for the digitalization of school information services and has the potential to support the implementation of artificial intelligence technology in secondary education in Indonesia.

In today’s era, we are very familiar with operating systems that function to manage the work of hardware and software so they can be used properly. One of the most widely used operating systems is Windows, because it has an easy-to-understand interface and is capable of running multiple applications simultaneously. To support this, Windows requires a process management mechanism, namely a way for the system to organize running programs so they do not interfere with each other and remain stable. In process management, there is an important component called the Process Control Block (PCB). The PCB can be likened to an identity card or a complete record of a process, as it contains information such as the process ID, status, CPU usage, memory, and files being used. This study aims to analyze the role of the process control block, focusing on how the Process Control Block stores important information regarding the status and activity of each process, ensuring smooth, efficient, and non-conflicting application execution. In this research, experiments were conducted to measure CPU usage, memory, and execution time by various processes with different priorities to observe the information of running processes. The analysis results show that each application has its own Process ID and PCB, which records status, CPU registers, memory allocation, and I/O resources used. The PCB enables multiple applications to perform multitasking effectively.

The limitation of physical memory (RAM) is a primary constraint hindering optimal performance in modern operating systems, especially when running large applications or performing intensive multitasking, often resulting in crashes and high latency. This research aims to quantitatively analyze the effectiveness of Virtual Memory (VM) implementation as a solution to this RAM constraint on the Windows 10 operating system, focusing on VM’s impact on CPU performance, GPU performance, and multitasking response. The methodology employed is a controlled experiment using industry-standard benchmarks: Cinebench R20 (CPU), Unigine Heaven (GPU), and response time measurements in intensive multitasking scenarios. Experimental results demonstrate that VM activation improves CPU/GPU performance by up to 5% and accelerates multitasking response time by up to 15%, confirming VM's effectiveness in mitigating memory bottlenecks. Nevertheless, this study also identifies potential performance overhead stemming from excessive paging and swapping processes, which trigger the phenomenon of Thrashing. Therefore, the research recommends a dual optimization strategy to achieve maximum and stable performance: software optimization via the Least Recently Used (LRU) algorithm to suppress page faults, supported by hardware optimization including the use of an SSD for the swap file and increased RAM capacity.

The researcher used a qualitative descriptive research type at Robbaniyyin Middle School. The data sources were all 24 students in grade IX of Robbaniyyin Plus Middle School, Islamic Religious Education teachers and the Principal. Data collection instruments were observation, interviews and documentation studies. The results of this study indicate that there is student learning activity in the Islamic Religious Education learning process in grade IX of Robbaniyyin Middle School, namely students are active in writing, reading, speaking, listening, moving, discussing and asking and answering questions. Meanwhile, the factors that influence students in increasing their learning activity are from within the students, namely: healthy physique, having strong attention, perception and memory. From outside the students, namely: a place far from the sound of vehicles, facilities namely personal stationery, a clean room and a clean classroom whiteboard, then the teacher's varied learning methods such as the lecture method with the material of faith in Allah SWT, the practice method with the material of reading and writing the Quran and the method of presentation with the material of congregational prayer.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

The proliferation of fake news across digital platforms has raised critical concerns about information reliability. A notable example is the viral rumour falsely claiming that the Nigerian Minister of the Federal Capital Territory, Nyesom Wike, had collapsed at an event and was rushed to an undisclosed hospital an entirely fabricated claim that caused public confusion. While both traditional machine learning and deep learning approaches have been explored for automated fake news detection, many existing models have been limited to topic-specific datasets and often suffer from overfitting, especially on smaller datasets like ISOT. This study addresses these challenges by proposing a standalone Bidirectional Long Short-Term Memory (BiLSTM) model for fake news classification using the ISOT dataset. Unlike multi-modal frameworks such as the MM-FND model by state-of-the-art model, which achieved 96.3% accuracy, the proposed BiLSTM model achieved superior results with 98.98% accuracy, 98.22% precision, 99.65% recall, and a 98.93% F1-score. The model demonstrated balanced classification across both fake and real news and exhibited strong generalization capabilities. However, training and validation performance plots revealed signs of overfitting after epoch 2, suggesting the need for regularization in future work. This study contributes to the growing body of research on fake news detection by showcasing the efficacy of a focused, sequential deep learning model over more complex architectures, offering a practical, scalable, and robust solution to misinformation detection