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This study aims to analyze the values of qana’ah (contentment and gratitude) in the novel Sepatu Dahlan by Khrisna Pabichara. This research employs a qualitative approach with a library research design. Data were collected through documentation techniques by analyzing the novel text and supported by relevant literature on Islamic values, character education, and literary studies. The data were analyzed using descriptive qualitative methods to identify and interpret the representation of qana’ah values portrayed in the main character. The results show that the value of qana’ah is reflected in Dahlan through acceptance of reality with sincerity, self-control over desires, patience in facing hardship, absence of envy, and constant gratitude despite economic limitations. These values are not only formed individually but are also strongly influenced by family education through parental role modeling, moral guidance, and everyday life experiences. Furthermore, the value of qana’ah presented in this novel is highly relevant for adolescents in the digital era, particularly in dealing with FOMO, materialistic tendencies, and consumerist lifestyles. Therefore, Sepatu Dahlan can serve as an effective medium for character education in fostering qana’ah, gratitude, resilience, and moral awareness among the younger generation.

Religious values play an essential role in shaping students' character and behavior through the practice of faith, worship, and moral conduct in everyday life. This study aims to analyze the efforts of Islamic Religious Education teachers in improving students' religious values at Nurul Ilmi Islamic Elementary School, Pengabuan Village, Abab District, Penukal Abab Lematang Ilir Regency (PALI), as well as to identify the supporting and inhibiting factors encountered during the process. The study employed a qualitative approach with a descriptive method. Data were collected through observations, interviews, and documentation, while data analysis followed three stages: data reduction, data presentation, and conclusion drawing. The findings reveal that teachers enhance students' religious values through several strategies, including habituation of worship practices, providing religious advice, serving as role models, utilizing school literacy media, and implementing the 5S culture (Smile, Greeting, Salutation, Politeness, and Courtesy). Supporting factors include the consistent implementation of religious programs, effective collaboration among teachers, parents, and the community, as well as adequate educational facilities. Meanwhile, the main obstacles include students' limited awareness of religious values, unfavorable social environments, insufficient cooperation between schools and families, the negative influence of technology and social media, and peer pressure. The study concludes that strengthening students' religious values requires continuous collaboration between schools, families, and communities, supported by consistent educational programs and exemplary teacher behavior to foster students' spiritual and moral development.

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

Education plays a fundamental role in shaping the quality of human resources and determining national progress. However, many young people still lack awareness and motivation regarding the importance of education due to limited access to information, socioeconomic challenges, and insufficient understanding of its long-term benefits. This Community Service Program aims to increase young people’s awareness of the importance of education through collaboration among multiple universities. The program involved lecturers and students from various higher education institutions who conducted educational outreach activities, motivational seminars, interactive discussions, and mentoring sessions. Methods used included educational counseling, inspirational presentations, question-and-answer sessions, and participant evaluations. The results show an improvement in participants’ understanding, motivation, and awareness of education as an investment in the future. In addition, inter-university collaboration expanded the program’s reach, enriched perspectives shared with participants, and strengthened higher education institutions’ commitment to community engagement. This initiative demonstrates that cross-university synergy can serve as an effective and sustainable model for fostering educational awareness and supporting the development of future generations in diverse communities.

The rapid advancement of digital technology has significantly transformed various aspects of human life, including the education sector. In the Society 5.0 era, educational institutions are expected to integrate digital technologies into learning processes to develop adaptive, innovative, and globally competitive human resources. This study aims to analyze digital technology-based learning innovations in improving educational quality, identify their potential benefits, and examine the challenges and strategies for optimizing their implementation. This research employed a qualitative descriptive approach through a literature review by analyzing relevant academic sources, including scholarly journals, books, conference proceedings, and other scientific publications. The findings indicate that digital learning innovations are implemented through the utilization of Learning Management Systems (LMS), Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), interactive learning media, blended learning, and flipped classroom models. These technologies contribute to enhancing learning effectiveness, expanding educational accessibility, increasing student motivation and engagement, and fostering essential twenty-first-century competencies. Nevertheless, several challenges remain, including digital inequality, inadequate technological infrastructure, limited digital competencies among educators, and concerns related to digital ethics and cybersecurity. Therefore, effective implementation requires comprehensive strategies, such as strengthening teachers' digital competencies, improving educational infrastructure, developing adaptive curricula, integrating innovative learning media, and promoting digital literacy and ethical awareness. These efforts are expected to support the development of a more adaptive, inclusive, and high-quality education system that aligns with the demands of the Society 5.0 era.

Evaluating food portions and types in the Free Nutritious Meal (MBG) program is generally still performed manually, which is time-consuming and potentially subjective. This study aims to develop an automated deep learning-based system to efficiently detect food types and estimate their nutritional value. The method used is a quantitative experiment integrating the YOLOv11m architecture for real-time object detection and the Google Gemini 2.5 Flash Large Language Model (LLM) for contextual nutritional estimation reasoning. The model training utilized a dataset of 2,630 food tray images categorized into five classes (fruit, side dish, staple food, vegetable, milk) that had undergone an augmentation process. The results showed that the YOLOv11m model achieved excellent performance with a mean Average Precision (mAP@0.5) of 0.9727 and the highest F1-score of 0.9522 at a confidence threshold of 0.1. Furthermore, validation of the LLM integration demonstrated a high prediction agreement rate of 85%. In conclusion, the combination of the YOLOv11m algorithm and LLM reasoning is capable of detecting and validating nutritional classification quickly and precisely, showing strong potential as an objective nutritional evaluation monitoring solution for large-scale MBG program implementation.

The development of digital technology has transformed public relations practices from conventional communication models to digital-based communication. Digital Public Relations (Digital PR) enables organizations to build more interactive, rapid, and transparent relationships with their publics through social media, websites, and various digital platforms. This study aims to analyze the role of Digital Public Relations in building organizational image in the social media era. The research employs a literature review method by examining various national journal articles related to Digital PR and Cyber Public Relations. The findings indicate that Digital PR significantly contributes to enhancing organizational image through two-way communication, online reputation management, increased audience engagement, and more effective information dissemination. Social media serves as the primary instrument of Digital PR implementation because it facilitates participatory relationships between organizations and their publics. However, challenges such as digital reputation crises, the spread of negative information, and the demand for rapid responses remain critical issues that must be strategically managed by public relations practitioners. Therefore, effective Digital PR strategies are essential to maintain organizational reputation and strengthen public trust in the digital era.

The purpose of this study is to examine the impact of financial constraints on workforce management practices in agricultural companies listed on the Indonesia Stock Exchange between 2022 and 2025. Earnings management was measured using the DeAngelo Discretionary Accrual model, while financial distress was measured using the Altman Z-Score. A purposive sample yielded 32 observations, and the data were analyzed quantitatively using simple linear regression in SPSS. The analysis results showed that Financial Distress had no significant effect on Earnings Management, with a significance value of 0.718 (>0.05). The coefficient of determination (R²) of 0.004 indicates that only 0.4% of the variation in earnings management practices can be explained by financial distress, while the remaining 99.6% is influenced by factors outside this model. This finding suggests that the adoption of earnings management strategies in pharmaceutical companies is not primarily driven by financial distress. Therefore, it is recommended that future research include additional variables that could potentially influence earnings management.

Marriage at an early age remains widespread, especially among communities with limited education and financial resources. This study aims to analyze the impact of early marriage on harmony in household life. The research model we employed was a library research design. The approach used was descriptive qualitative through literature review, referring to scholarly sources and secondary data from reputable journals published in the last five years. Findings indicate that the mental unreadiness of both parties (husband and wife) who marry at an early age can lead to instability in household life and increase the risk of marital failure or reduced family harmony. Young couples commonly face challenges in maintaining healthy communication, making joint decisions, and coping with financial pressures, all of which contribute to domestic conflict. In addition, emotional immaturity and limited life experience are also primary causes of disharmony within the family. Therefore, there is a need to improve education and public awareness about the importance of age maturity and mental readiness before marriage in order to create stable and harmonious households. Thus, it is necessary to increase education and public awareness about the importance of age maturity and mental readiness before marriage, in order to create a stable and harmonious household.

Land-use changes dominated by impervious surfaces in residential areas reduce the natural infiltration capacity of the soil and increase surface runoff, which may lead to flooding and waterlogging. This study aimed to design a rainwater infiltration well system as an effort to reduce surface runoff in Samudera Afroza 3 Housing Estate, Jambi City. A quantitative approach was employed through field surveys, hydrological analysis, soil permeability testing, GIS-based land-use analysis, and infiltration well design in accordance with SNI 8456:2017 and SNI 03-2453-2002 standards. Rainfall analysis was conducted using maximum daily rainfall data from 2014–2023, applying several probability distributions, with the Gumbel distribution identified as the most representative model. The results revealed that the residential area covers 8,104 m², with 91.84% of the land occupied by built-up surfaces, resulting in limited natural infiltration capacity. The average soil permeability value of 0.00024 m/s indicates favorable conditions for the implementation of infiltration wells. Based on the design calculations, 41 Type III infiltration wells are required, providing a total storage capacity of 289.665 m³ and an infiltration discharge of 0.0919 m³/s. The proposed infiltration well system is capable of reducing runoff volumes by 15.044%, 12.486%, and 11.223% for 2-year, 5-year, and 10-year return periods, respectively. Therefore, infiltration wells can be considered an effective alternative for water conservation and runoff management in residential areas.

This study aims to implement the Cooperative Integrated Reading and Composition (CIRC) learning model to improve collaboration skills and learning outcomes among phase E students in class X-3 at SMA Katolik 2 Kabanjahe in Catholic Religious Education. This research employed Classroom Action Research conducted in two cycles, consisting of planning, implementation, observation, and reflection stages. Data were collected through observation sheets, learning outcome tests, and documentation, then analyzed using descriptive qualitative analysis supported by process and learning achievement indicators. The findings showed that the implementation of the CIRC learning model improved across learning stages, including orientation from 87% to 95%, organization from 90% to 97%, concept introduction from 87% to 92%, publication from 93% to 98%, and reflection and reinforcement from 87% to 98%. Students’ collaboration skills also increased in each indicator, namely working productively from 75% to 88%, actively participating in problem solving from 82% to 88%, maintaining a balance between listening and speaking from 76% to 86%, appreciating group members’ contributions from 73% to 87%, and demonstrating responsibility as group members from 77% to 93%. Learning outcomes also improved, as shown by the increase in the proficient category from 27% to 77%, while the basic and developing categories decreased to 0% in cycle II. These results indicate that CIRC effectively enhances students’ active participation, collaboration, responsibility, and learning achievement. Therefore, CIRC is recommended as an alternative collaborative learning model for Catholic Religious Education, particularly in strengthening cooperative interaction and meaningful understanding of religious learning materials in classroom practice.

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

Hypertension is one of the leading non-communicable diseases contributing significantly to cardiovascular morbidity and mortality worldwide. Despite the availability of extensive electronic medical record data in healthcare institutions, these data are often utilized only for administrative reporting rather than predictive analysis. Consequently, opportunities to identify age groups with a higher probability of developing hypertension remain underutilized. This study aims to implement the Naïve Bayes classification algorithm to analyze age distribution and classify the risk of hypertension among patients using healthcare data. The research adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Patient medical record data consisting of demographic and clinical attributes, including age, systolic blood pressure, diastolic blood pressure, body weight, gender, and hypertension status, were processed using the Naïve Bayes algorithm. Model performance was evaluated using a confusion matrix by measuring accuracy, precision, recall, specificity, and balanced accuracy. The implementation demonstrates that the Naïve Bayes algorithm is capable of classifying hypertension risk efficiently while providing probabilistic information regarding age groups with a higher tendency to experience hypertension. The resulting classification model offers an effective decision-support tool for healthcare providers in conducting targeted screening, preventive interventions, and evidence-based health planning. The findings also indicate that data mining techniques can transform routinely collected medical records into valuable clinical knowledge for early hypertension prevention and healthcare decision-making.

This study aims to determine the effect of assertive leadership and self-actualization on the performance of nurses at Dr. Soekardjo Regional General Hospital in Tasikmalaya. The research design used in this study is a quantitative approach using a survey method. Research subjects: All 40 nurses working in the Melati ward were included as respondents. Research data were collected through the distribution of questionnaires to all respondents. The collected data were analyzed using a series of multiple linear regression tests, along with instrument validation and hypothesis testing. The results of the data analysis yielded the regression equation Y = 0.743 + 0.482X1 + 0.521X2 + e. Furthermore, the analysis indicated that assertive leadership positively contributes to improved nursing performance. Additionally, self-actualization also plays a role in supporting performance improvement. Simultaneously, both variables exert a significant influence on nursing performance. The coefficient of determination of 68.2% indicates that 68.2% of the variation in nurses’ performance can be explained by the two independent variables—assertive leadership and self-actualization—while the remainder is influenced by other factors outside the research model. These findings suggest that strengthening a more assertive and clear leadership style and fostering nurses’ self-actualization can serve as strategies for improving the quality of nursing care.

Tuberkulosis (TB) merupakan penyakit menular yang masih menjadi masalah kesehatan global dan terus meningkat setiap tahunnya. Penyakit ini tersebar luas dan menjadi salah satu penyebab utama kematian akibat infeksi. Pemberian Terapi Pencegahan Tuberkulosis (TPT) merupakan salah satu strategi penting dalam upaya penanggulangan TB, khususnya pada kelompok berisiko seperti kontak serumah pasien TB, pelaksanaan program TPT di Kota Kendari masih belum optimal dan tergolong rendah dan Puskesmas Poasia merupakan puskesmas dengan jumlah kasus TB tertinggi dalam tiga tahun terakhir. Penelitian ini bertujuan untuk mengetahui gambaran penerimaan Terapi Pencegahan Tuberkulosis pada kontak serumah pasien TB dengan pendekatan teori Health Belief Model (HBM) di wilayah kerja Puskesmas Poasia Kota Kendari tahun 2025. Jenis penelitian adalah deskriptif kuantitatif yang dilaksanakan pada Maret 2026. Jumlah sampel sebanyak 80 responden yang dipilih menggunakan teknik simple random sampling. Analisis data yang digunakan adalah analisis univariat untuk menggambarkan distribusi frekuensi dan persentase setiap variabel. Hasil penelitian menunjukkan bahwa sebagian besar responden tidak menerima TPT yaitu sebanyak 71 responden (88,8%), sedangkan yang menerima hanya 9 responden (11,2%). Mayoritas responden memiliki persepsi kerentanan dan keseriusan yang rendah, persepsi hambatan yang tinggi, serta efikasi diri yang rendah, sedangkan persepsi manfaat menunjukkan hasil yang cukup berimbang. Hal ini menunjukkan bahwa faktor-faktor dalam Health Belief Model berperan dalam memengaruhi penerimaan TPT. Diperlukan peningkatan edukasi dan dukungan tenaga kesehatan untuk meningkatkan kesadaran serta penerimaan TPT.

This study examines plant water requirement management in urban farming using an automatic drip irrigation system. The system consists of 20 reused 1.5-liter bottles as water reservoirs and 20 emitters as drip control valves. The experiment was conducted on two planting beds, namely basil and mustard greens, with drip settings of 60, 125, and 180 drops. Before operation, calibration and correction factors were applied to ensure that the design discharge matched field conditions. The objective of this study was to determine the drip irrigation layout model and plant water requirements over 37 days. The layout used a parallel arrangement of three rows of plants with two rows of irrigation devices placed between them. Water demand was calculated using the radiation method based on daily evapotranspiration (ETo), considering latitude, altitude, temperature, wind speed, and relative humidity data. The results showed that ETo ranged from 8.51 to 8.84 mm/day, equivalent to 38.937 to 40.094 liters/day. After applying correction factors for each drip setting, the actual water requirement indicated surplus water stored in the soil and used for plant growth. The study concludes that drip irrigation management must account for residual soil water so that evapotranspiration demand during sunlight exposure is continuously met

This study aims to design and implement a simple solar panel-based battery charging system equipped with protection and voltage limiting circuits. The study was conducted to address the limitations of a direct solar panel-to-battery connection, which may cause short-circuit risk, reverse current, unstable charging voltage, and excessive battery charging. This research used a design-based experimental approach. The system was developed by preparing a block diagram, dividing it into several functional sub-blocks, and implementing the design into an electronic circuit. The main circuit sections consisted of solar panel protection, voltage limiter, interlocking circuit breaker, power regulator, and voltage divider. The assembled circuit was tested through real-time measurement by observing solar panel output voltage under different time and sky conditions and measuring battery charging time from different initial voltage levels. The results show that the solar panel output voltage varied according to time and weather condition. The lowest measured voltage was 15.0 V under cloudy conditions, while the highest measured voltage was 25.2 V under clear sky conditions around midday. The battery charging test showed that lower initial battery voltage required longer charging time, while voltage closer to the target level required shorter charging time. The evaluation was limited to voltage measurement and charging time; charging current, battery temperature, solar irradiance, and charging efficiency were not measured in detail. The proposed system can be applied for small-scale solar charging, educational practice, and basic renewable energy experiments. The study offers a practical circuit model that integrates protection, interlocking, voltage limiting, and regulation functions using simple and accessible electronic components.

This study contributes to the advancement of Islamic social finance discourse by developing a conceptual framework for a digitally integrated zakat governance model within the agricultural sector. The study addresses a critical gap between productive zakat practices and the emerging landscape of digital agricultural transformation. While previous research has largely focused on the redistributive function of zakat as a mechanism for poverty alleviation, limited scholarly attention has been directed toward its potential transformation into a technology-enabled and productivity-oriented instrument that supports sustainable development objectives. Employing a qualitative research approach through content analysis, this study synthesizes interdisciplinary perspectives from Islamic economics, zakat governance, agricultural technology innovation, and digital sustainability studies. The study proposes a Digital Farming Zakat Platform framework consisting of five interconnected dimensions: geospatial-based beneficiary identification, smart farming empowerment, sharia-compliant financial mechanisms, agricultural market integration, and data-driven monitoring systems.

The drainage channel located on Jalan Ketintang, Gayungan District, Surabaya, East Java, is influenced by geographical conditions, vulnerable to damage due to landslides and flooding. Along with the presence of quite high rainfall during the wet months (October - January), the problem of inundation in the form of flooding always arises in the area. To minimize flooding, it is necessary to repair the channel (capacity) periodically at certain periods. In the period of August - December 2022, channel repairs were carried out by installing a fabricated rectangular channel using U/DITCH 150/150. The purpose of this thesis is to determine the value of the design flood discharge (Q) and obtain the value of the channel dimensions that can accommodate the design flood discharge at a return period of 5 years and 10 years. The analysis in this thesis is in the form of obtaining rainfall data at 3 (three) rainfall stations, namely Perak 1, Perak 2 and Juanda rainfall stations, rainfall data using 2008 – 2022 (15 years) data, calculation of planned discharge using the Log Pearson Type III method, rainfall data testing using the Smirnov Kolmogorof, rainfall intensity using the Mononobe model, and calculation of designed flood discharge using the Nakayasu Synthetic Unit Hydrograph. The results of the analysis show that the design flood discharge obtained is 17.1 m3⁄s (10-year return period) and the dimensions of the planned channel are a square channel measuring 1,325 m x 1,325 m. This is in accordance with the capacity of the 150/150 U/Ditch channel installed along 945 meters which can minimize the occurrence of inundation/flooding/overflow of channels in the area because the channel has been repaired and the capacity is enlarged

Employee performance is essential to organizational success, particularly in the service industry, where service quality depends heavily on employees’ capabilities and workplace conditions. This study examines the effects of a learning organization culture and the work environment on employee performance, with job satisfaction as a mediating variable among service sector employees in West Jakarta. A quantitative causal approach was employed using an online questionnaire distributed through purposive sampling, resulting in 210 valid respondents. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The findings reveal that both a learning organization culture and a positive work environment have significant positive effects on job satisfaction. In addition, learning organization culture, work environment, and job satisfaction each positively and significantly influence employee performance. Job satisfaction also mediates the relationships between learning organization culture, work environment, and employee performance. These results suggest that organizations can improve employee performance by strengthening continuous learning practices, creating supportive working conditions, and enhancing employee satisfaction. The study contributes to the human resource management literature and provides practical guidance for organizations seeking sustainable performance through employee-centered management strategies.