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Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

This study aims to analyze public concerns regarding fast food consumption from the perspective of risk society, particularly among university students. The increasing consumption of fast food reflects shifts in consumption patterns influenced by globalization, practicality, and time efficiency. This research employs a qualitative approach through a literature review supported by interview data to strengthen the analysis. The findings reveal that fast food consumption is not solely driven by biological needs but also by practical, emotional, and social factors. Although students demonstrate a relatively high awareness of health risks associated with fast food, their consumption behavior persists as an adaptive response to structural conditions such as time constraints, accessibility, and academic pressures. From the perspective of risk society, this phenomenon illustrates a contradiction between risk awareness and everyday consumption practices. Risks produced by modernity are not entirely avoided but are negotiated and normalized in daily life. Therefore, fast food consumption cannot be understood merely as an individual choice but as a result of the interaction between structural factors, consumer culture, and social construction within modern society.

This study examines the lexical and grammatical meanings of news headlines from the November 2025 edition of detik.com, posted on Instagram. The shift from a news site to a social media platform often necessitates adjustments to headlines, which can lead to shifts in meaning. To collect relevant headlines, this study employed a qualitative descriptive method with a listening and note-taking technique. The analysis shows that lexical meaning in headlines is evident through the use of words with basic dictionary meanings, such as names of people, places, objects, and actions. Grammatical meaning is discovered through affixation and reduplication, which can change or add to the meaning of basic words according to the context. These two types of meaning play a crucial role in creating concise, clear headlines that are appropriate for the way news is presented on Instagram. This study concludes that the formation of meaning in headlines on detik.com is influenced by a combination of lexical and grammatical meanings tailored to the communication needs of social media platforms.

Online gambling has become an important phenomenon in today's society, influencing various aspects of social and cultural life. This research aims to explore the impact of online gambling on the formation of global identity and the changes occurring within modern society. Using a descriptive qualitative method, data were collected through interviews with five students in Tanjung Pinang. The findings indicate that online gambling not only alters the way people interact but also leads to shifts in social values, evident in the rise of consumerist and individualistic culture. While there are negative effects, such as addiction and a decline in the quality of social relationships, online gambling also presents opportunities in the digital economy. This study recommends the need for better regulation and educational programs to raise public awareness about the risks associated with online gambling, as well as to leverage its potential benefits. Thus, this research is expected to provide a deeper understanding of the role of online gambling in shaping global identity and the dynamics of modern society.

Human Trafficking (TPPO) has undergone a significant transformation in its modus operandi alongside the massive use of information technology, giving rise to the phenomenon of cyber-recruitment within the online scam industry. This study aims to analyze the juridical construction of the criminal elements in the cyber-recruitment modus operandi based on Law Number 21 of 2007, and to evaluate the potential disharmony of norms between the TPPO regulation and the Electronic Information and Transactions Law (UU ITE) regarding the regulation of digital evidence. The research method employed is normative legal research with a statutory approach and a conceptual approach. The results indicate that, dogmatically, the elements of TPPO—comprising the process of virtual recruitment, the means of fraudulent job promises, and the purpose of digital forced labor exploitation—under Law No. 21 of 2007 remain relevant for prosecuting cyber-recruitment perpetrators, although it demands a broader legal interpretation of cyberspace realities. However, legal certainty issues were found due to the lack of synchronicity in the regulation of digital evidence, where differences in procedures and qualifications exist between electronic evidence in the TPPO Law and the evidentiary standards in the UU ITE, which are volatile in nature. The implications of this research conclude the need for regulatory synchronization through the establishment of technical guidelines for handling TPPO-specific electronic evidence to provide a solid legal foundation for law enforcement agencies. This step is crucial to overcome cross-border jurisdictional obstacles and to ensure substantive justice and comprehensive protection of victims' rights in the digital era.

Blasting is a critical method for overburden removal in open-pit coal mines, where fragmentation quality directly impacts loading efficiency and operational costs. This study aims to analyze the actual fragmentation resulting from overburden blasting at Pit 4 Middle of PT. Victor Dua Tiga Mega, Central Kalimantan, to predict fragmentation using the Kuz-Ram model, and to evaluate the conformity of both results against the company standard (boulder size ≤144.6 cm or ≥50 cm for analysis). The research employed a quantitative comparative method. Primary data included blasting geometry and photographs of muck piles from 10 blasting events, which were analyzed using WipFrag software to obtain actual fragmentation distribution. Secondary data comprised rock characteristics and explosive properties for Kuz-Ram prediction input. The results showed significant variation in actual boulder percentage (≥50 cm), ranging from 6.19% to 32.91% with an average of 16.05% (medium category). Statistical analysis revealed a very weak negative correlation (r = -0.21) between powder factor (PF) and boulder percentage, indicating that PF is not the dominant factor within the consistent application range (0.21-0.23 kg/bcm). Comparison with Kuz-Ram predictions showed that the model consistently over-predicted coarse material, with an average difference of +25.21%, suggesting the need for rock factor (A) recalibration. It is concluded that the blasting results are inconsistent, strongly influenced by uncontrollable factors such as geological conditions. Recommendations include geometri evaluation, particularly burden and spacing, and calibration of the Kuz-Ram model for more accurate future predictions.

This study aims to analyze the influence of Qirâat (variant Qur’anic readings) on the interpretation of legal verses in Adhwâ’ al-Bayân by Muhammad al-Amîn al-Syinqîthi. Employing a qualitative library research approach, this study examines how both mutawâtir and syâdz Qirâat contribute to the process of Islamic legal deduction (istinbâth). The findings reveal that Qirâat function not merely as linguistic variations but also have significant implications for expanding, reinforcing, and providing flexibility in legal meanings across various domains, including worship (ibadah), family law (munâkahât), transactions (mu‘âmalah), and jihad. Al-Syinqîthi integrates linguistic analysis, textual evidence, and the objectives of Islamic law (maqâṣid al-syarî‘ah) in a comprehensive manner, enabling him to accommodate different Qirâat without generating legal contradictions. Mutawâtir Qirâat serve as the primary foundation for legal rulings, while syâdz Qirâat function as complementary and supportive arguments. This study concludes that the diversity of Qirâat constitutes an integral component of Qur’anic exegesis and Islamic jurisprudence, contributing to the development of a flexible, contextual, and relevant Islamic legal framework.

The phenomenon of emotional healing has become increasingly prominent in popular culture as the younger generation experiences increasing loss, emotional exhaustion, and numbness. Popular music serves as a medium of expression, representing these emotional dynamics through poetic language and symbolic structures. This study aims to analyze the emotional representation of healing in Umay Shahab's song "Perayaan Mati Rasa" using Roland Barthes's semiotic approach. The research method used is descriptive qualitative, analyzing three layers of meaning: denotation, connotation, and myth, through breaking down the lyrics into several lexicons and reading signs. The results show that (1) denotatively, the lyrics construct an emotional arc from inner tension to self-contemplation; (2) connotatively, the use of diction and imagery represents vulnerability, resilience, and emotional reflection; and (3) at the mythical level, the song constructs release as a form of emotional maturity and loss as a process of identity formation. Thus, this song functions as a cultural representation in interpreting the process of emotional healing in popular culture.

This study aims to analyze e-commerce strategies in selecting applicable law and jurisdiction to avoid conflicts of jurisdiction in cross-border transactions. This study employs a normative legal method using legislative, conceptual, and comparative approaches to analyze the selection of applicable law and jurisdiction in cross-border e-commerce transactions. The data used consists of primary and secondary legal materials collected through a literature review. The analysis was conducted qualitatively using descriptive-analytical and legal interpretation methods, as well as deductive reasoning to formulate strategies for minimizing jurisdictional conflicts. The research findings indicate that the application of the principle of freedom of contract, combined with international instruments and digital technology, can serve as an effective strategy for e-commerce actors to minimize jurisdictional conflicts and litigation risks. This strategy not only provides legal protection, particularly for SMEs, but also enhances the trust of global partners and competitiveness in the international market through the use of clear contract clauses, hybrid forums, and technological support such as blockchain and ODR.

This research aims to examine and analyze the phenomenon of speeding and illegal racing, which poses a significant threat to traffic safety within the legal jurisdiction of the Buleleng Resort Police (Polres Buleleng). The study employs empirical legal research methods, gathering primary data through observations and interviews with traffic police officers, alongside secondary data from legal literature and previous studies. The findings indicate that the speeding phenomenon in Buleleng is predominantly carried out by teenagers and young adults, often motivated by social media trends, peer pressure, and the search for identity. This reckless behavior leads to a high rate of traffic accidents, frequently resulting in severe injuries or fatalities. Furthermore, law enforcement efforts by Polres Buleleng face several challenges, including the high mobility of the perpetrators, lack of personnel during specific hours, and low legal awareness among the public. Preventive measures such as counseling in schools and repressive measures like electronic traffic law enforcement (E-TLE) and routine patrols need to be optimized. This study concludes that an integrated approach involving the police, educational institutions, and parents is crucial to mitigating the speeding phenomenon and ensuring traffic safety.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

This article examines honor killing in North Africa as a form of gender-based violence rooted in family honor, patriarchal control, and social change that has not yet produced a stable new order. The research employs normative legal methods through statutory, case, and comparative criminal law approaches across three representative jurisdictions, namely Egypt, Morocco, and Tunisia. The primary legal materials include criminal provisions on homicide, adultery, mitigating excuses, and the protection of women, with particular attention to Article 237 of the Egyptian Penal Code, Articles 418 and 491 of the Moroccan Penal Code, the repeal history of Article 207 of the Tunisian Penal Code, Article 236 of the Tunisian Penal Code, and Law No. 58 of 2017 on Eliminating Violence Against Women. The findings show that honor in this region operates as a social mechanism for regulating women’s bodies, sexuality, mobility, and life choices, reinforced by community pressure, reputational stigma, and family-based moral legitimacy. Social transition does not remove this logic; instead, it reshapes conflict and diversifies forms of control, ranging from threats, confinement, coercion, and the criminalization of sexual morality to homicide itself. On the legal plane, Egypt and Morocco still retain norms that soften criminal responses in certain situations linked to adultery, whereas Tunisia has moved in a more progressive direction by abolishing explicit mitigation and strengthening protection for women, even though the regulation of sexual morality has not been fully abandoned.

This study aims to comprehensively evaluate the potential pharmacological activities and safety profiles of seven secondary metabolite compounds (Caffeic Acid, Syringic Acid, Quercetin, Luteolin, p-Coumaric Acid, Ferulic Acid, and Epicatechin) identified in the Bajakah plant (Spatholobus littoralis Hassk.). The research approach integrates in silico analysis using the PubChem database, biological activity prediction via PASS Online, oral toxicity assessment through ProTox-II, and pharmacokinetic evaluation using pkCSM, which were subsequently validated through an empirical literature review. The results indicate that these compounds exhibit significant activity probabilities, particularly as antimutagenic, antiseptic, and antioxidant agents. Luteolin demonstrated the highest antimutagenic potential, while Quercetin showed dominant antioxidant activity. Toxicity profiling revealed that Luteolin and Caffeic Acid possess the highest safety levels (Class 5), whereas Quercetin requires special attention (Class 3). These computational findings strongly correlate with empirical evidence demonstrating that Bajakah extract exhibits broad-spectrum antibacterial activity against Staphylococcus aureus, antifungal activity against Candida albicans, as well as high antioxidant and anti-inflammatory capacities. This study provides a strong molecular foundation for the development of Bajakah as a safe and effective phytopharmaceutical candidate.

This study aims to analyze the in silico profile of the compound orientin derived from the water hyacinth plant (Eichhornia crassipes) as a potential antioxidant candidate. Orientin was selected based on its chemical structure data registered in PubChem, which provides complete information regarding molecular identity, physicochemical properties, and 2D and 3D structural representations. The prediction of biological activity was conducted using PASS Online, which indicated that orientin possesses a promising likelihood of exhibiting antioxidant activity according to relevant probability values. Furthermore, the safety assessment of the compound was carried out through ProTox-II to identify potential toxicity, including toxicity class, possible hepatotoxic effects, and other predicted safety parameters. To determine its pharmacokinetic profile, pkCSM was employed to predict ADMET characteristics such as absorption, distribution, metabolism, excretion, and additional toxicity risks. The results of these analyses show that orientin demonstrates favorable potential as an antioxidant candidate, supported by predicted pharmacological properties and relatively low toxicity levels according to in silico evaluations. Therefore, orientin has promising potential for further development in subsequent in vitro and in vivo studies.

This study aims to analyze the phytochemical profile of Sambung Nyawa (Gynura procumbens) leaves as a potential herbal candidate for mild hypertension therapy using in silico methods. Plant samples were examined to identify active compounds documented in the PubChem database. The identified compounds were further analyzed using PASS Online to predict their pharmacological activities, ProTox-II to evaluate toxicity levels, and pkCSM to assess ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. The findings reveal that several bioactive compounds present in Sambung Nyawa leaves demonstrate strong predicted anti-hypertensive activity accompanied by minimal toxicological risk. PASS Online analysis indicates potential mechanisms of action, including vascular receptor modulation and mild diuretic properties that may support blood pressure regulation. ProTox-II classification places most compounds in the low-toxicity category, while pkCSM predictions confirm acceptable bioavailability and favorable pharmacokinetic properties. Overall, these results provide a preliminary scientific foundation for the development of Gynura procumbens as an alternative herbal therapy for mild hypertension and support the need for further validation through in vitro and in vivo experimental studies.

This study aims to analyze the fluctuations in chili production in Kabanjahe District, Karo Regency, which affect market price instability and uncertain supply. One approach applied in this study is the Single Exponential Smoothing (SES) method to forecast chili production. SES was chosen for its simplicity, ease of implementation, and its ability to generate accurate predictions even when the data lacks significant seasonal patterns. The data used is secondary data on chili production obtained from official publications by the Karo Regency BPS for the period of 2020–2024. The analysis results show that a smoothing parameter (α) of 0.8 produced the lowest Mean Absolute Percentage Error (MAPE) of 3.08%. These findings indicate that applying a higher α makes the model more responsive to recent data changes, thus yielding more accurate forecasts. This study demonstrates the effectiveness of the SES method in forecasting chili production in areas with significant seasonal fluctuations.

This study aims to analyze the potential of eupatorin found in Cat’s Whiskers (Orthosiphon stamineus) as an anticancer agent using in silico methods. Eupatorin was selected due to its promising biological activity reported in previous literature. The research employed compound structure data registered in PubChem, analyzed through PASS Online for pharmacological activity prediction, ProTox-II for toxicity evaluation, and pkCSM for ADMET parameters. The results indicated that eupatorin has a high probability of inhibiting cancer cell proliferation with significant pharmacological activity values. Toxicity predictions showed a safe profile with an LD50 supporting its potential therapeutic use. ADMET analysis demonstrated that eupatorin possesses good bioavailability, adequate absorption and distribution, and minimal metabolic interactions, supporting its efficacy as an anticancer compound. These findings suggest that eupatorin could be an important candidate for herbalbased drug development, particularly for cancer therapy, and provide opportunities for further research through in vitro and in vivo experiments to validate anticancer activity comprehensively.