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Independent Nursing Practice (INP) is a form of healthcare service that provides direct care to the community, including Homecare services. Public interest in these services is influenced by various factors; however, the dominant factors affecting interest in the Tambah Rejo area have not been clearly identified. This study aimed to identify and analyze factors associated with interest in Homecare services at independent nursing practices and to determine the most influential factor. This study employed a quantitative approach using an analytical survey design with a cross-sectional method. A total of 153 respondents were selected through purposive sampling. Data were collected using questionnaires and analyzed using the Chi-square test and multiple logistic regression. The results showed significant relationships between economic factors (p = 0.023; OR = 2.187), distance (p = 0.003; OR = 0.361), comfort (p < 0.001; OR = 49.67), service quality (p = 0.009; OR = 0.404), family support (p < 0.001; OR = 52.62), patient satisfaction (p < 0.001; OR = 0.212), and interest in Homecare services. Multiple logistic regression analysis revealed that comfort was the most dominant factor influencing interest in Homecare services (p < 0.001; OR = 0.124). Comfort was identified as the strongest determinant affecting community interest in Homecare services at independent nursing practices. Therefore, improving comfort in service delivery should be a primary priority in the development of independent nursing practices.

Independent Nursing Practice (INP) is a form of healthcare service that provides direct care to the community, including Homecare services. Public interest in these services is influenced by various factors; however, the dominant factors affecting interest in the Tambah Rejo area have not been clearly identified. This study aimed to identify and analyze factors associated with interest in Homecare services at independent nursing practices and to determine the most influential factor. This study employed a quantitative approach using an analytical survey design with a cross-sectional method. A total of 153 respondents were selected through purposive sampling. Data were collected using questionnaires and analyzed using the Chi-square test and multiple logistic regression. The results showed significant relationships between economic factors (p = 0.023; OR = 2.187), distance (p = 0.003; OR = 0.361), comfort (p < 0.001; OR = 49.67), service quality (p = 0.009; OR = 0.404), family support (p < 0.001; OR = 52.62), patient satisfaction (p < 0.001; OR = 0.212), and interest in Homecare services. Multiple logistic regression analysis revealed that comfort was the most dominant factor influencing interest in Homecare services (p < 0.001; OR = 0.124). Comfort was identified as the strongest determinant affecting community interest in Homecare services at independent nursing practices. Therefore, improving comfort in service delivery should be a primary priority in the development of independent nursing practices.

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.

Digitalization in the fitness industry is increasingly essential to improve efficiency and service quality. However, many fitness centers still manage data manually, including KRESNA GYM, which faces challenges in member registration, workout scheduling, and facility information delivery. This study aims to design a web-based e-fitness application using the Laravel framework to support digital service management. The development follows the Waterfall model through stages of requirement analysis, design, implementation, testing, and evaluation. The application includes features such as a landing page, admin/member login and registration, management dashboards, workout schedules, equipment guides, and supplement listings. Black-box testing confirms that all features function as expected. This system simplifies administrative processes and enhances member experience, making it effective in supporting digital transformation at KRESNA GYM.

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.

Transportation plays an important role in supporting community mobility and enhancing socio-economic activities in urban areas. The increasing population growth and community activities in Sorong City have led to a higher demand for transportation modes. This study aims to identify the characteristics of transportation users in Kota Sorong and to analyze the probability of selecting different transportation modes using the Multinomial Logit (MNL) method. A quantitative approach was employed in this research. Questionnaires were distributed to 225 transportation users in Sorong City. The variables analyzed included gender, age, occupation, income, trip purpose, travel frequency, and reasons for choosing a transportation mode. The data were analyzed using multinomial logistic regression with SPSS software. The results indicate that transportation mode choice is influenced not only by socio-economic factors, but also by showed that income, trip purpose, travel frequency, and reasons for choosing the mode significantly influenced transportation mode choice, while gender, age, and occupation did not have significant effects. The probability analysis indicated that private vehicles had the highest probability of selection at 89.63%, followed by online transportation at 9.96%, and conventional motorcycle taxis at 0.41%. These findings imply that socio-economic and travel behavior factors strongly influence transportation mode preferences in Sorong City and may serve as a reference for future urban transportation planning and policy development.

This study aims to analyze the forms of tasybīh ḍimnī in Dīwān al-Imām al-Syāfi‘ī by Imam al-Shafi‘i, identify its elements (musyabbah, musyabbah bih, and wajh al-syabah), and examine the Islamic educational values contained within it. This research employs a descriptive qualitative approach using the content analysis method. The data consist of poetic verses containing tasybīh ḍimnī, focusing on 10 representative stanzas selected from 279 poems. The results show that tasybīh ḍimnī is an implicit form of simile that does not explicitly employ comparative particles, but is instead understood through contextual meaning relations. From the perspective of balaghah, this form is characterized by its implicit and argumentative nature, as well as its ability to construct meaning through logical analogy. The elements of tasybīh can still be identified through meaning reconstruction, even though they are not directly stated. Furthermore, the study reveals Islamic educational values encompassing the dimensions of īmān, islām, and ihsān. The value of īmān is reflected in dependence upon Allah, the value of islām in social life practices, and the value of ihsān in the cultivation of moral virtues such as patience and qanā‘ah (contentment). Thus, tasybīh ḍimnī functions not only as an aesthetic element, but also as an integrative educational medium.

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.

Inclusive education aims to provide equal learning opportunities for all students, including those with special needs, within regular educational settings. However, mathematics learning in inclusive classrooms remains challenging because mathematical concepts are often abstract and require logical reasoning that may not be easily accessible to learners with diverse cognitive characteristics. Ethnomathematics has emerged as an alternative approach by integrating cultural practices, local wisdom, and students’ daily experiences into mathematics instruction, creating more meaningful and accessible learning environments. This study aims to analyze the development, implementation patterns, opportunities, and research gaps related to ethnomathematics in inclusive mathematics learning. A literature review method was employed by examining scientific publications from 2020–2025 obtained from Google Scholar, Scopus, ERIC, Springer, and ProQuest databases. Data were analyzed through content analysis involving reduction, classification, interpretation, and synthesis. The findings indicate that ethnomathematics has been implemented through cultural artifacts, digital teaching materials, and project-based contextual learning. The approach supports inclusive learning through multi-representational access, instructional adaptations, scaffolding strategies, and collaborative teaching practices aligned with Universal Design for Learning principles. Furthermore, ethnomathematics enhances students’ motivation, conceptual understanding, mathematical literacy, and cultural identity. Nevertheless, studies focusing on disability-specific adaptations and long-term learning outcomes remain limited and require further investigation.

Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

This study investigates the decoupling phenomenon between ESG (Environmental, Social, and Governance) sustainability reporting and communal Tri Hita Karana (THK) sustainability practices in a Rural Bank in Bali. Through Ethnographic Content Analysis (ECA) of official documents from BPR Luhur Damai covering 2023–2025, this study identifies that the Sustainability Report (SR), prepared strictly according to Financial Services Authority Regulation (POJK) 51/2017, does not incorporate substantial THK practices, namely banten (ceremonial offerings) Rp131.6 million, dana punia (religious donations) Rp8.5 million, and monthly banjar (communal community unit) contributions, producing a Hindu religious expenditure to formal Social and Environmental Responsibility (SER) ratio of 10:1. Drawing on the Institutional Logics perspective, this study identifies four decoupling mechanisms: (1) cognitive, namely THK as taken-for-granted, not perceived as “sustainability”; (2) administrative, namely departmental silos between Compliance and General Affairs; (3) template, namely POJK 51/2017 provides no space for local wisdom; and (4) capacity, namely limited Human Resources (HR) and institutional capacity. These findings lead to the concept of “invisible sustainability,” that is, real sustainability contributions that are invisible to conventional reporting frameworks, and “cultural accounting gap,” that is, the absence of accounting categories for local cultural-religious contributions. The theoretical contribution is demonstrating that decoupling in Global South contexts is not merely symbolic compliance but results from structural misalignment between transnational and communal logics that renders local sustainability contributions institutionally invisible.

This research analyzes the interconnection between energy geopolitics and global security through the case study of Venezuela, the country with the world's largest proven oil reserves. The background highlights that energy has become a strategic commodity driving geopolitical tensions in the international system. The objective is to answer three fundamental questions: why energy is critically needed, how the distribution of the world's oil reserves creates strategic inequality, and how the Venezuela case illustrates resource grabbing mechanisms. The method used is qualitative with a single case study approach, employing neorealism and dependency theory as analytical frameworks, with data sourced from official reports of international organizations (OPEC, IEA, IMF, EIA), news media, and academic literature. The findings reveal that the United States' escalating pressure on Venezuela from economic sanctions, maritime blockades, to military threats reflects a systematic pattern of resource imperialism disguised as counter-narcotics efforts. The impact on Venezuela includes GDP contraction of 4 percent, hyperinflation exceeding 530 percent, and 8 million people displaced. The implications underscore that the current global energy order remains dominated by zero-sum logic, requiring reform of international energy governance to be more just and sustainable.

The rapid digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

This study aims to analyze the role of the Regional Disaster Management Agency (BPBD) of East Flores Regency in managing the eruption disaster of Mount Lewotobi Laki-Laki during the pre-disaster, emergency response, and post-disaster phases. The study employed a descriptive qualitative method involving 18 informants from BPBD and affected communities in Wulanggitang and Ile Bura Sub-districts. Data were collected through observation, interviews, and documentation. The findings show that during the pre-disaster phase, BPBD implemented preparedness measures through disaster-prone area mapping, the establishment of Disaster Preparedness Teams and Disaster-Resilient Villages, public outreach, and the use of local wisdom. However, outdated contingency plans, limited early warning systems, weak relocation efforts, and low public awareness contributed to panic and casualties during the eruption. In the emergency response phase, BPBD successfully coordinated evacuation efforts, established emergency posts, and distributed logistics effectively. Nevertheless, displaced communities experienced discomfort due to prolonged emergency conditions and limited fulfillment of basic needs in temporary shelters. In the post-disaster phase, BPBD conducted rehabilitation and reconstruction through transportation recovery, trauma healing programs, and permanent housing development based on the Rehabilitation and Reconstruction Plan (R3P). However, limited funding and delays in reconstruction processes remained major challenges. Therefore, improving coordination, institutional capacity, disaster communication, and community participation is essential for effective disaster management.

This study presents an innovation design for an Internet of Things (IoT)-based watering and liquid fertilizer control system for chili plants using a NodeMCU ESP32. The main problem addressed is the manual watering and fertilizing process, which makes it difficult for farmers to monitor soil moisture, temperature, air humidity, and light intensity in real time. The recommended method used in this draft is Research and Development (R&D) with a prototyping approach because the study focuses on designing, building, integrating, and testing an IoT device through iterative stages. The system is designed using a soil moisture sensor, DHT11, LDR sensor, two-channel relay, two 12 V DC pumps, 16x2 I2C LCD, and the Blynk Mobile application for remote monitoring and control. Sensor data are transmitted to Blynk as percentage values and plant condition statuses, while the water and fertilizer pumps can be controlled using virtual buttons. The control logic defines the optimal condition for chili plants based on soil moisture of 60-80%, temperature of 25-30°C, air humidity of 60-80%, and light intensity of 50-90%. Prototype documentation and functional testing data will be completed in the next stage.

Trucking peirformancei is onei of thei important factors in supporting thei smoothneiss of thei clinkeir loading proceiss at PT. Varia Usaha Bahari, Maspion Port, Greisik. In practicei, seiveiral obstacleis arei still eincounteireid, such as deilays in truck arrivals, leiss optimal veihiclei conditions, and opeirational coordination that has not beiein fully maximizeid. Theisei conditions may affeict thei smoothneiss of thei clinkeir loading proceiss. This study aims to deiteirminei thei condition of trucking peirformancei in thei clinkeir loading proceiss baseid on deiscriptivei analysis and to deiteirminei thei eiffeict of trucking peirformancei on thei clinkeir loading proceiss baseid on simplei lineiar reigreission analysis. This study useid a quantitativei meithod with deiscriptivei analysis and simplei lineiar reigreission. Thei data weirei obtaineid through queistionnaireis distributeid to reispondeints involveid in clinkeir loading activitieis. Thei reisults showeid that trucking peirformancei was in thei good cateigory, with aveiragei indicator scoreis ranging from 3.7 to 3.9. Thei higheist indicator was opeirational coordination and communication at 3.9, whilei thei loweist indicator was loading punctuality at 3.7. Thei reisults of thei simplei lineiar reigreission showeid thei eiquation Y = 249.374 + 34.930X, which meians that trucking peirformancei has a positivei eiffeict on thei clinkeir loading proceiss. Thei significancei valuei of 0.000 < 0.05 indicateis that thei eiffeict is significant. Theireiforei, thei beitteir thei trucking peirformancei, thei beitteir thei clinkeir loading proceiss.

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.

This study aims to analyze the implementation of public policies in improving community welfare through the Direct Cash Assistance (BLT) Program, the Subsidized Direct Cash Assistance (BLTS), and rice distribution managed by the State Logistics Agency (BULOG) in Cipete Village. The research method used is a descriptive qualitative approach. Data collection techniques were carried out through interviews with policy implementers and beneficiary communities, direct observation of the aid distribution process, and review of relevant official documents and data. The results show that the BLT and BLTS programs play a significant role in helping low-income communities meet their basic needs and increasing household purchasing power, especially during unstable economic conditions. Meanwhile, rice distribution by BULOG supports community food security by providing access to staple foods at prices lower than market rates. Despite the positive impacts, the implementation of these three programs still faces various obstacles, including inaccurate beneficiary targeting due to imprecise and rarely updated data, delays in the distribution process, lack of information transparency, and suboptimal inter-agency coordination. Based on these findings, improvements are required, such as developing an integrated and technology-based data system, increasing information disclosure to the public, strengthening monitoring and periodic evaluation systems, and enhancing cooperation among related institutions. Optimizing the implementation of these social assistance programs is expected to create sustainable, equitable, and targeted improvements in community welfare.

Student dropout in higher education remains a persistent socioeconomic challenge, yet many predictive models reported in the literature are methodologically compromised by randomized cross-validation schemes that introduce temporal data leakage and artificially inflate predictive performance. This study proposes a longitudinal prescriptive learning analytics framework integrating three complementary methodological components: a Leave-One-Cohort-Out (LOCO) temporal validation protocol, a hybrid SMOTE-ENN class balancing strategy, and temporal velocity feature engineering derived from Learning Management System (LMS) behavioral trajectories. The framework was evaluated on a longitudinal dataset comprising 464,739 enrollment records and 77 features. Five predictive algorithms—XGBoost, LightGBM, CatBoost, Random Forest, and Logistic Regression—were comparatively assessed on a strictly isolated blind holdout cohort (2022), with CatBoost emerging as the champion estimator, achieving a PR-AUC of 0.8859, a Macro F1-Score of 0.9143, and the lowest Brier Score (0.0221), thereby demonstrating superior calibration and discriminative capability under severe class imbalance (93:7 ratio). Comprehensive ablation analysis revealed that temporal velocity features function not merely as additive predictors, but as a structural prerequisite enabling Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) to generate high-quality synthetic boundary instances; removing these features reduced minority-class precision from 0.8302 to 0.6721. To operationalize predictive outputs into actionable intervention pathways, Diverse Counterfactual Explanations (DiCE) were implemented under a three-tier causal constraint architecture on 96 borderline high-risk students, generating 384 feasible intervention scenarios exclusively targeting forward-looking behavioral velocity metrics without constraint violations. Collectively, these findings advance the paradigm of prescriptive learning analytics by providing educational institutions with interpretable risk diagnostics and operationally feasible intervention guidance grounded in empirically validated behavioral and temporal dynamics.

Penelitian ini menguji indikasi kecurangan pelaporan keuangan pada entitas Badan Usaha Milik Negara (BUMN) yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 2022-2024 dengan mengadopsi kerangka Fraud Heptagon. Target keuangan, frekuensi rapat komite audit, opini audit, masa jabatan serta rangkap jabatan direktur utama, pelatihan tata kelola perusahaan yang baik (GCG), dan remunerasi dewan direksi digunakan sebagai proksi variabel penelitian. Melalui pendekatan kuantitatif asosiatif terhadap 45 sampel observasi dengan analisis regresi logistik, hasil empiris merepresentasikan bahwa seluruh variabel independen tersebut tidak memengaruhi kecurangan pelaporan keuangan. Temuan ini mengindikasikan adanya keterbatasan kapasitas eksplanatori model Heptagon ketika diterapkan pada konteks BUMN yang berkarakteristik kelembagaan, regulasi, dan pengelolaan publik yang spesifik. Hal ini menyebabkan hubungan kausal yang diasumsikan dalam konseptualisasi awal model tersebut tidak sepenuhnya terkonfirmasi secara empiris. Secara teoretis, hasil ini menekankan pentingnya memahami perilaku kecurangan secara kontekstual, sementara secara praktis, penelitian ini mendorong perlunya evaluasi terintegrasi antara indikator keuangan dan mekanisme tata kelola dalam upaya deteksi dini kecurangan pelaporan keuangan pada entitas negara.