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The development of Artificial Intelligence (AI) has driven significant transformation in hospital management, particularly in operational efficiency, service quality, and patient safety. This study aims to analyze the implementation of AI in hospital management based on recent scientific evidence from 2020 to 2026. The method used was a systematic review guided by the PRISMA 2020 framework. Literature was retrieved from PubMed, ScienceDirect, SpringerLink, Google Scholar, and ProQuest. From 360 identified articles, a stepwise selection process was conducted, resulting in 15 articles that met the inclusion criteria. The findings indicate that AI contributes to improved operational efficiency through patient flow optimization, operating room management, workforce scheduling, and electronic medical record management. AI also enhances service quality through predictive data analytics and supports patient safety through risk detection and early warning systems. In conclusion, AI has strong strategic potential to support modern hospital management. However, its implementation still faces several challenges, including human resource readiness, data security, algorithmic bias, system interoperability, and investment requirements. Therefore, AI implementation should be carried out in a planned, ethical manner and evaluated from a health economics perspective.

Identity politics has become one of the most debated concepts in contemporary democratic discourse, yet its conceptual boundaries remain poorly defined in both academic and public conversations. This paper argues that three commonly conflated terms political identity, identity politics, and the politicization of identity carry fundamentally distinct meanings with divergent normative and practical consequences. Through a conceptual literature review, this study synthesizes theoretical frameworks and empirical findings to demonstrate that while political identity refers to an individual's self-positioning within a political community, and identity politics denotes collective action by marginalized groups seeking recognition, the politicization of identity is an elite-driven strategy of exploiting social group markers for political gain. This distinction matters because the conflation of these terms obscures how digital platforms  through filter bubble and echo chamber mechanisms  amplify identity-based polarization in Indonesia and beyond. The study finds that social media algorithms do not merely reflect pre-existing social divisions but actively intensify affective polarization by constructing homogeneous information environments that deepen inter-group animosity. The paper concludes with implications for digital citizenship and democratic resilience in Indonesia.

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

The rapid growth of TikTok has transformed digital communication practices and created new opportunities for content creators to establish stronger relationships with their audiences. From the perspective of Digital Public Relations, communication style plays a crucial role in influencing follower engagement and enhancing the effectiveness of online interactions. This study aims to examine the communication styles employed by TikTok content creators to build and strengthen follower engagement. The research adopts a qualitative approach using the Systematic Literature Review (SLR) method by analyzing relevant scholarly articles published between 2021 and 2026. The findings reveal that communication styles emphasizing authenticity, interactivity, storytelling, content consistency, and emotional connection significantly contribute to higher audience engagement, as reflected in the number of likes, comments, shares, and active participation. Furthermore, the effective use of trending content, TikTok's algorithmic features, and adaptive communication strategies strengthens relationships between content creators and followers while enhancing credibility and digital presence. The review also identifies opportunities for future research on the influence of audience characteristics, digital culture, and evolving social media algorithms on the development of sustainable engagement in digital communication.

This study aims to identify the pragmatic failures of Large Language Models (LLMs) and the biases of Anglophone-based AI moderation algorithms in detecting Indonesian hate speech expressed through sarcasm, satire, euphemism, and local cultural metaphors. It also examines the extent to which AI systems understand and interpret the pragmatic meanings within the corpus. This study employs a qualitative descriptive approach with a comparative design. Data were collected through the documentation of hate speech expressions on social media containing elements of local cultural hatred. The data were analyzed using qualitative descriptive methods with pragmatic and thematic approaches. The findings show that all corpus data contain political satire and indirect hate expressed through irony, sarcasm, absurd metaphors, and popular culture wordplay. Testing with Claude AI showed that the system was capable of identifying the data as implicit criticism and recognizing the pragmatic functions of emoticons and contextual meanings in the utterances. However, the analysis also demonstrated limitations in understanding local sociocultural contexts, particularly the metaphors “daun nangka” and “daun sawit,” which were interpreted merely as absurd humor. These findings indicate that AI detection accuracy does not necessarily reflect a deep pragmatic and cultural understanding within the Indonesian context.

Stunting is a multifactorial public health problem influenced by various risk factors that may emerge during the prenatal period. Early identification of stunting risk during pregnancy is important to support preventive interventions. This study aimed to develop a stunting risk prediction model based on maternal prenatal factors using the Random Forest algorithm. Secondary data from 172 pregnant women, consisting of 83 stunting cases and 89 non-stunting cases, were analyzed. The predictor variables included maternal age during pregnancy, height, hemoglobin level, mid-upper arm circumference (MUAC), smoking history, hypertension, asthma, and diabetes mellitus. The research stages consisted of data preprocessing, model training using Stratified 5-Fold Cross Validation, performance evaluation, external testing, and feature importance analysis. Internal evaluation results showed an accuracy of 60%, precision of 60.6%, recall of 57.3%, F1-score of 58.9%, and AUC of 0.6688. External testing yielded an accuracy of 70% and an AUC of 0.6167. Feature importance analysis identified maternal age during pregnancy as the most influential variable in the prediction process. The findings indicate that maternal prenatal factors have potential for early stunting risk identification, although the predictive performance remains moderate. This approach may serve as a foundation for developing early screening tools to support targeted interventions among high-risk pregnancies.

This study explores how Generation Z in Indonesia produces and spreads narratives of micro-interfaith harmony through the TikTok platform. Amid growing social polarization in digital spaces, casual and personal tolerance content created by Gen Z offers a new approach to building social cohesion. Using a qualitative netnography method, this research observes 20 viral videos with over 10,000 views during the 2024-2025 Ramadan period, including the War Takjil trend and the #LoginLintasIman campaign, as forms of affective digital citizenship. The findings reveal that TikTok’s algorithmic logic, driven by emotional engagement, allows grassroots narratives of tolerance to reach broad audiences organically. Within this ecosystem, values of pluralism and solidarity are not shaped by formal institutions but emerge from the participatory dynamics and digital habitus of Gen Z. This study concludes that a new form of digital interfaith citizenship is emerging, termed algorithmic harmony, where tolerance is fostered through affective interactions, viral distribution, and the everyday media practices of youth. The findings provide new insights for media studies, diversity education, and digital tolerance discourse.

This article examines the impact of dehumanization in the era of Artificial Intelligence (AI) on the formation of the personality of future priests in light of the teachings of Pastores Dabo Vobis, particular number 43-44, as well as from Philosophical and theological perspectives. The development of AI brings significant changes in the way human think, relate, and build their self-identity. On the one hand, AI offers great opportunities to support intellectual formation through access to information, pastoral simulations, and digital learning. However, on the other hand, the dominance of technology has the potential to cause dehumanization, namely a reduction in the quality of interpersonal relationships, the depth of reflection and sensitivity. This study employs a literature review method, examining relevant theological, philosophical, and scientific literature. The findings indicate that excessive reliance on AI can disrupt the development of the human dimension in seminarians, particularly regarding affective maturity, relational competence, and the integrity of personal identity. From a philosophical perspective, humans are understood as free, rational, and relational beings; thus, reducing humans to mere components of an algorithmic system contradicts their very nature. Meanwhile, theology affirms human dignity as the Imago Dei, which cannot be replaced by technology. Therefore, a critical and prudent formation approach is required, one that positions AI as an aid, not a substitute for human relationships.

This study designs and implements an up-down counter system based on an AT89C2051 microcontroller programmed in assembly using the MC-51 application. The system modifies an existing digital clock board by mapping the display selector pins, seven-segment segment pins, pushbuttons, and buzzer to the microcontroller ports. The research method consists of literature review, hardware identification, algorithm design, assembly programming, program downloading, and functional testing using a 5 V DC supply. The implementation uses a four-digit common-cathode seven-segment display and a multiplexing routine to show the counter value in real time. The functional test shows that the system can display the initial value, increase the value through the up button, and decrease the value through the down button. The display is readable during operation, and the program can run on the target circuit after being downloaded to the AT89C2051. This study confirms that assembly programming on MC-51 can be applied to implement a simple counter system on a reused digital clock circuit. The main limitations are the absence of explicit button debouncing, overflow and underflow protection, quantitative response-time measurement, and non-volatile data retention.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

The development of digital media has transformed virtual public spaces into major arenas for shaping public opinion on religious issues, including Islamic law and sharia in Indonesia. Discussions surrounding sharia on social media are frequently accompanied by stigma and misperceptions influenced by media framing, digital algorithms, and identity polarization. This study aims to analyze the construction of stigma toward Islamic law in the digital era, identify forms of sharia misperception in the Indonesian public sphere, and explain factors contributing to the reproduction of such stigma. This research employs a qualitative approach based on an integrative literature review combined with digital media discourse analysis. Data were collected through scientific literature reviews, social media observations, and analysis of digital content related to sharia discourse. The findings reveal that sharia is often associated with violence, anti-democracy, restrictions on women’s rights, and opposition to modernity due to media simplification and emotionally driven digital content. In addition, low levels of religious digital literacy and the prevalence of echo chambers reinforce the spread of stigma toward Islamic law in virtual public spaces. This study emphasizes the importance of strengthening religious digital literacy and promoting moderate and inclusive Islamic narratives in contemporary digital society.

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)

This study aims to explore the phenomenon of using a second Instagram account among Generation Z as a strategy for impression management and privacy protection. Amidst the dominance of a culture of show-off and hegemonic aesthetic standards on primary accounts, Generation Z tends to experience aesthetic fatigue and social pressure due to scrutiny from diverse audiences (context collapse). Using Erving Goffman's Dramaturgy theory, this study examines how individuals construct distinct identities on the front stage and back stage. The research method used is descriptive qualitative. Data were collected through in-depth interviews with five Generation Z informants who have multiple accounts, as well as passive participant observation of their digital activities. The sampling technique used was purposive sampling, while data analysis followed the Miles and Huberman model, which includes data reduction, data presentation, and drawing conclusions. The results show that the primary account functions as a highly curated front stage to maintain professional and social reputations. Conversely, the second account functions as a back stage that allows Generation Z to engage in emotional catharsis, honest self-disclosure, and identity experimentation through unique names (pseudonyms). The use of a second account is a strategic response to reclaim personal authority over their life narratives from the pressure of algorithms and public judgment. The study concludes that second accounts are not just a technological trend, but rather a self-defense mechanism for Generation Z to maintain authentic space and maintain mental health amidst massive digital transparency.

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.