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Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

Sentiment analysis for Indonesian regional languages faces two persistent challenges: labeled training data is extremely limited for most regional varieties, and transformer models pre-trained on Bahasa Indonesia do not generalize reliably to languages with substantially different morphological structures. Prior work on the NusaX benchmark has primarily relied on direct fine-tuning, treating each regional language independently and without exploiting linguistic proximity between related languages as a transfer signal. This paper proposes Language-Similarity-Guided Transfer (LSGT), a sequential fine-tuning strategy that first adapts a pre-trained model to a pivot language selected using character trigram similarity, followed by fine-tuning on the target language. Four transformer models are evaluated across all 12 NusaX languages using the official train/validation/test splits: IndoBERT, NusaBERT, mBERT, and XLM-R. Performance is evaluated using four metrics: accuracy, macro F1, macro precision, and macro recall. Experimental results show that LSGT improves macro F1 in 44 of 48 model-language combinations, demonstrating that the fine-tuning strategy itself is a major factor in low-resource cross-lingual sentiment classification. XLM-R benefits most strongly from LSGT, achieving an average improvement of +0.137 macro F1 and a peak gain of +0.298 on Madurese. SHAP-based token attribution analysis further reveals that predictions rely heavily on named entities and domain-specific nouns rather than sentiment-bearing vocabulary, indicating a dataset-level bias inherited from the original SmSA corpus and propagated through the NusaX translation pipeline.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

Three-phase induction motors are essential components in industrial cooling systems that require reliable overcurrent protection to maintain operational continuity and prevent equipment damage. In the Cooling Tower Pump panel at PT DongJin Indonesia, a limitation was identified where the terminal of the Electronic Overcurrent Relay (EOCR) could not accommodate the 50 mm² power cable, preventing direct current measurement. This study aims to analyze the effectiveness of integrating a 200:5 A Current Transformer (CT) with the EOCR as an adaptive protection solution that is both safe and efficient. The research employs a case study approach through field observation, motor current measurement using a Fluke 303 Clamp Meter, and descriptive analysis of the recorded data. The results indicate that the three motors operate under stable load conditions, with current values ranging from 236.7 A to 275.7 A, while the secondary current detected by the EOCR ranges from 3.7 A to 3.9 A, consistent with the CT transformation ratio. During the starting phase, the current surged to 600.4 A without causing false tripping, demonstrating that the EOCR effectively distinguishes temporary inrush current from actual fault conditions. Therefore, the integration of CT–EOCR is proven to enhance measurement safety, maintain motor operational stability, and support efficient system maintenance in industrial environments.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs), exploiting their inability to separate instructions from data within application contexts reliably. This paper provides a structured review of current attack vectors, including direct and indirect prompt injection, and highlights the limitations of existing defenses, with particular attention to the fragility of Known-Answer Detection (KAD) against adaptive attacks such as DataFlip. To address these gaps, we propose a novel, hybrid, multi-layered detection framework that operates in real-time. The architecture integrates heuristic pre-filtering for rapid elimination of obvious threats, semantic analysis using fine-tuned transformer embeddings for detecting obfuscated prompts, and behavioral pattern recognition to capture subtle manipulations that evade earlier layers. Our hybrid model achieved an accuracy of 0.974, precision of 1.000, recall of 0.950, and an F1 score of 0.974, indicating strong and balanced detection performance. Unlike prior siloed defenses, the framework proposes coverage across input, semantic, and behavioral dimensions. This layered approach offers a resilient and practical defense, advancing the state of security for LLM-integrated applications.

PLN (Persero) is the state-owned company responsible for supplying electrical energy in Indonesia and plays a crucial role in ensuring the reliability and continuity of power distribution. Therefore, all electrical equipment, particularly power transformers, must be maintained in safe and reliable operating conditions. Transformers function to change voltage levels from high to low or vice versa and are essential components in the electric power distribution system. One important transformer component that requires periodic maintenance is the On Load Tap Changer (OLTC). To maintain transformer performance and extend its service life, routine maintenance activities such as OLTC oil replacement are necessary. PT. PLN (Persero) ULTG Rangkasbitung conducts regular OLTC oil replacement twice a year as part of its preventive maintenance program. Transformer oil serves as both an insulating medium and a cooling agent; therefore, its condition greatly affects transformer reliability and operational safety. Degraded oil quality can cause insulation failure and reduce transformer efficiency. This study aims to describe the procedure and implementation of On Load Tap Changer oil replacement at the transformer substation of PT. PLN (Persero) ULTG Rangkasbitung. The research method used is field research, carried out through direct observation and interviews with maintenance personnel. The results of this study are expected to provide a clear understanding of the OLTC oil replacement process, support proper maintenance practices, and emphasize the importance of transformer maintenance in ensuring the reliability and sustainability of the electric power system.

Distribution transformers play a crucial role in delivering electrical energy from the distribution system to consumers to ensure power quality and supply continuity. However, in practice, overload conditions often occur due to increasing demand and load growth that exceed the transformer’s rated capacity. This situation can lead to reduced efficiency, increased power losses, and accelerated equipment aging. This study aims to analyze the performance of the CMY distribution transformer at PT PLN (Persero) ULP Labuan, which operates beyond its nominal capacity, and to propose an alternative solution through transformer mutation, namely the replacement of the existing unit with a transformer of more appropriate capacity based on load analysis results. The Least Square Method is employed to predict future load growth and determine the projected time when the transformer will again experience overload after the mutation. The results indicate that the existing 100 kVA transformer is overloaded and should be replaced with a 160 kVA unit. After the mutation, the loading percentage decreases significantly, the transformer’s lifespan is extended, and the reliability of the distribution system improves. Furthermore, the Least Square prediction suggests that the new transformer may experience overload again in future years if no further planning is carried out. Therefore, transformer mutation can be considered an effective and medium-term solution to enhance and maintain the reliability of the electrical distribution system within the operational area of PT PLN (Persero) ULP Labuan.

Distribution transformers play a critical role in delivering electrical energy from medium-voltage networks to low-voltage consumers. At ULP Medan Kota, several distribution transformers have been operating with loads exceeding 80% of their nominal capacity, posing risks of overloading, efficiency reduction, and equipment failure. This study aims to analyze the performance of distribution transformers based on actual load data and evaluate mitigation strategies through the implementation of additional parallel transformers (trafo sisip). The methodology includes data collection, load and current calculation, and simulation of load distribution after transformer insertion. The results show that the installation of trafo sisip reduces the load on the main transformer by approximately 50% and significantly lowers the current to safer levels. Moreover, placing the trafo sisip at an optimal position minimizes voltage drop to as low as 0.0745 Volts. Therefore, the addition of trafo sisip is proven to enhance the reliability, efficiency, and operational life of the power distribution system at ULP Medan Kota.