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Anxiety before childbirth is a common psychological condition experienced by pregnant women, particularly those who are primigravida. Such anxiety can be influenced by various factors, including lack of knowledge, fear of pain, and uncertainty about the childbirth process. Prenatal classes are educational programs designed to improve pregnant women’s understanding, confidence, and readiness in facing labor. These classes typically provide information about pregnancy, childbirth, postpartum care, and newborn care. However, the extent to which prenatal classes effectively reduce anxiety before childbirth requires further investigation. This study aimed to determine the relationship between the implementation of prenatal classes and labor anxiety levels among pregnant women. A quantitative research design with a cross-sectional approach was used. The sample consisted of 30 pregnant women selected through purposive sampling. Data were collected using standardized anxiety questionnaires administered before and after participants attended prenatal classes. The data were analyzed using the Chi-Square statistical test with a significance level set at p < 0.05. The findings revealed a significant relationship between participation in prenatal classes and a reduction in labor anxiety, with a p-value = 0.000 (p < 0.05). These results indicate that prenatal classes can play an important role in preparing pregnant women mentally and emotionally for the childbirth process. The interactive education and supportive environment provided during these sessions help participants gain confidence and reduce fear. It is recommended that healthcare providers in health facilities continue to optimize the delivery of prenatal classes by incorporating more interactive methods and involving family members. Family support, in combination with structured prenatal education, can further enhance the effectiveness of these programs in reducing childbirth-related anxiety.

Anxiety before childbirth is a common psychological condition experienced by pregnant women, particularly those who are primigravida. Such anxiety can be influenced by various factors, including lack of knowledge, fear of pain, and uncertainty about the childbirth process. Prenatal classes are educational programs designed to improve pregnant women’s understanding, confidence, and readiness in facing labor. These classes typically provide information about pregnancy, childbirth, postpartum care, and newborn care. However, the extent to which prenatal classes effectively reduce anxiety before childbirth requires further investigation. This study aimed to determine the relationship between the implementation of prenatal classes and labor anxiety levels among pregnant women. A quantitative research design with a cross-sectional approach was used. The sample consisted of 30 pregnant women selected through purposive sampling. Data were collected using standardized anxiety questionnaires administered before and after participants attended prenatal classes. The data were analyzed using the Chi-Square statistical test with a significance level set at p < 0.05. The findings revealed a significant relationship between participation in prenatal classes and a reduction in labor anxiety, with a p-value = 0.000 (p < 0.05). These results indicate that prenatal classes can play an important role in preparing pregnant women mentally and emotionally for the childbirth process. The interactive education and supportive environment provided during these sessions help participants gain confidence and reduce fear. It is recommended that healthcare providers in health facilities continue to optimize the delivery of prenatal classes by incorporating more interactive methods and involving family members. Family support, in combination with structured prenatal education, can further enhance the effectiveness of these programs in reducing childbirth-related anxiety.

Chronic kidney disease (CKD) is an escalating public health issue, particularly among the elderly. High salt intake is a modifiable dietary risk factor suspected to accelerate CKD progression. However, large-scale evidence from Indonesia remains scarce. This study investigates the association between salty food consumption and CKD among older adults in Indonesia. Using data from the 2023 Indonesia Health Survey (Survei Kesehatan Indonesia/SKI 2023), we analyzed 97,339 individuals aged 60 and above. Descriptive statistics outlined participant characteristics, while chi-square tests and binary logistic regression assessed associations and adjusted effects. Statistical analyses were conducted using STATA 14.1, with significance set at p < 0.05. Among participants, 89.3% reported consuming salty foods, and CKD prevalence was 0.5%. Bivariate analysis showed significant associations between CKD and sex, education, and salty food intake (p < 0.001). Multivariate results indicated that elderly individuals who did not consume salty food had significantly reduced odds of CKD (OR = 0.70; 95% CI: 0.54–0.90; p = 0.006), suggesting a protective effect. These findings highlight a strong association between salty food consumption and CKD risk in Indonesia’s aging population. Reducing dietary salt intake may serve as an effective, low-cost intervention for CKD prevention. Urgent public health strategies focusing on dietary behavior change and nutrition education for the elderly are needed to curb the rising burden of kidney disease.

Chronic kidney disease (CKD) is an escalating public health issue, particularly among the elderly. High salt intake is a modifiable dietary risk factor suspected to accelerate CKD progression. However, large-scale evidence from Indonesia remains scarce. This study investigates the association between salty food consumption and CKD among older adults in Indonesia. Using data from the 2023 Indonesia Health Survey (Survei Kesehatan Indonesia/SKI 2023), we analyzed 97,339 individuals aged 60 and above. Descriptive statistics outlined participant characteristics, while chi-square tests and binary logistic regression assessed associations and adjusted effects. Statistical analyses were conducted using STATA 14.1, with significance set at p < 0.05. Among participants, 89.3% reported consuming salty foods, and CKD prevalence was 0.5%. Bivariate analysis showed significant associations between CKD and sex, education, and salty food intake (p < 0.001). Multivariate results indicated that elderly individuals who did not consume salty food had significantly reduced odds of CKD (OR = 0.70; 95% CI: 0.54–0.90; p = 0.006), suggesting a protective effect. These findings highlight a strong association between salty food consumption and CKD risk in Indonesia’s aging population. Reducing dietary salt intake may serve as an effective, low-cost intervention for CKD prevention. Urgent public health strategies focusing on dietary behavior change and nutrition education for the elderly are needed to curb the rising burden of kidney disease.

The purpose of this study is to analyze the types and factors of speech errors that uttered by character Dory in “Finding Dory” movie. This study used descriptive qualitative approach. The source of the data is “Finding Dory” movie.  Furthermore, the data was collected through watched and transcribed the utterances of Dory that indicate the speech errors. In total there are 90 data of speech errors that identified and classified based on Clark & Clark’s (1977) theory into eight types. Those are predominantly categorized as repeats with total 30 data, followed by silent pauses with 25 data, filled pause with 12 data, interjection with 9 data, unretraced false starts 5 data, retraced false starts 4 data, stutters 3 data, and correction with 2 data. The speech error in Dory’s utterances is mostly influenced by her cognitive cognition.

The purpose of this study is to analyze the types and factors of speech errors that uttered by character Dory in “Finding Dory” movie. This study used descriptive qualitative approach. The source of the data is “Finding Dory” movie.  Furthermore, the data was collected through watched and transcribed the utterances of Dory that indicate the speech errors. In total there are 90 data of speech errors that identified and classified based on Clark & Clark’s (1977) theory into eight types. Those are predominantly categorized as repeats with total 30 data, followed by silent pauses with 25 data, filled pause with 12 data, interjection with 9 data, unretraced false starts 5 data, retraced false starts 4 data, stutters 3 data, and correction with 2 data. The speech error in Dory’s utterances is mostly influenced by her cognitive cognition.

Traditional medicine has been believed for generations to be effective in treating various diseases and is recognized for its safety. One reason people opt for herbal medicine is its affordability compared to chemical drugs. Additionally, herbal medicine is required to be free of medicinal chemicals (BKO). However, a public warning issued between 2020 and 2025 indicated that some herbal preparations still contained substances like sildenafil citrate and caffeine. The aim of this research is to establish a validated analytical method to detect the presence of sildenafil citrate and caffeine in stamina-enhancing herbal medicines using Thin Layer Chromatography (TLC) with densitometry. This method was selected due to its cost-effectiveness and its ability to separate two compounds. In this study, category II validation was conducted to identify impurities in the preparations, focusing on test parameters such as selectivity and Limit of Detection (LOD). The selected mobile phase for this study was a mixture of chloroform and ethanol (9.5:0.5 v/v). The TLC plate was analyzed with a densitometer at a wavelength of 254 nm. The Rf value for sildenafil citrate was found to be 0.32, while the Rf value for caffeine was 0.52, resulting in a separation (Rs) value of 2. The LOD for sildenafil citrate was determined to be 8.5418 μl/ml (equivalent to 0.0854 mg in a 600 mg sample), whereas the LOD for caffeine was 4.7987 μl/ml (or 0.0959 mg in a 600 mg sample).

Traditional medicine has been believed for generations to be effective in treating various diseases and is recognized for its safety. One reason people opt for herbal medicine is its affordability compared to chemical drugs. Additionally, herbal medicine is required to be free of medicinal chemicals (BKO). However, a public warning issued between 2020 and 2025 indicated that some herbal preparations still contained substances like sildenafil citrate and caffeine. The aim of this research is to establish a validated analytical method to detect the presence of sildenafil citrate and caffeine in stamina-enhancing herbal medicines using Thin Layer Chromatography (TLC) with densitometry. This method was selected due to its cost-effectiveness and its ability to separate two compounds. In this study, category II validation was conducted to identify impurities in the preparations, focusing on test parameters such as selectivity and Limit of Detection (LOD). The selected mobile phase for this study was a mixture of chloroform and ethanol (9.5:0.5 v/v). The TLC plate was analyzed with a densitometer at a wavelength of 254 nm. The Rf value for sildenafil citrate was found to be 0.32, while the Rf value for caffeine was 0.52, resulting in a separation (Rs) value of 2. The LOD for sildenafil citrate was determined to be 8.5418 μl/ml (equivalent to 0.0854 mg in a 600 mg sample), whereas the LOD for caffeine was 4.7987 μl/ml (or 0.0959 mg in a 600 mg sample).

Since the launch of the “Bilingual Policy for all schools supported by Yayasan Pendidikan Katolik (YPK) Don Bosco KAM in 2030,” schools have debated its impact. Teachers of non-English subject express concern about adapting, while parents fear their children may struggle with bilingual instruction. This study investigates how students, teachers, parents, and staff at Private Senior High School of Santo Yoseph Medan perceive the Bilingual education policy decided by YPK Don Bosco KAM in 2030. The research explores key concerns: the necessity of the policy, its effect on local languages, feasibility by 2030, budget sufficiency, availability of qualified teachers, and whether non-English subject teachers can teach bilingually. A questionnaire with seven questions was distributed to 40 participants in Indonesian and later translated into English. Results show 60% believe bilingual education is essential, 10% disagree, 25% are uncertain, and 5% have no opinion. While over half support the policy, challenges remain, including limited funding, a shortage of trained bilingual teachers, and the need for more English classes and learning materials. Successful implementation requires the Don Bosco Catholic Education Foundation to increase funding and address these challenges to ensure smooth integration of bilingual education by 2030.

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.

Stroke is one of the leading causes of death globally and is particularly prevalent in Indonesia. Early prediction of stroke is critical to reducing the risk of long-term disability and mortality. This study aims to build a stroke prediction model using the Support Vector Machine (SVM) classification method. The dataset used is sourced from Kaggle, containing 5,110 records with class imbalance. To address the imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. The study evaluates model performance across multiple data splits (70:30, 80:20, 90:10) and k-fold cross-validation values (k=5, 7, 10). The SVM was tested with various kernel types—linear, polynomial, and radial basis function (RBF)—along with parameter tuning for C, gamma, and degree. The results show that the polynomial kernel yielded the highest prediction accuracy of 92%. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics.

The digital revolution in global health presents great opportunities and challenges through the integration of artificial intelligence (AI), big data and telemedicine. However, the absence of a transcendent ethical foundation in the development of medical technology risks undermining human values and justice. This study aims to critically examine the direction of digital health development by integrating the ethical and spiritual values of the Qur'an. The method used is a critical qualitative approach based on library research, with normative analysis of verses related to health principles, information ethics, and maqāṣid al-syarī'ah. The results of the study show that in QS. Al-Isrā': 70 emphasizes human dignity as the basis for medical data protection; likewise in QS. Al-Syu'arā': 80 emphasizes that healing is divine and should not be separated from spiritual values; while in QS. Al-Baqarah: 286 provides guidance on the limits of responsibility and human capabilities in the utilization of technology. The critical discussion underlines the importance of building Qur'anic-based digital ethics that emphasize justice ('adālah), trustworthiness, and compassion (raḥmah) in designing equitable and inclusive artificial intelligence (AI) systems and remote health services. It is hoped that this study will provide recommendations for the development of Qur'anic Digital Ethics as a future normative framework for global digital health innovation.

The digital revolution in global health presents great opportunities and challenges through the integration of artificial intelligence (AI), big data and telemedicine. However, the absence of a transcendent ethical foundation in the development of medical technology risks undermining human values and justice. This study aims to critically examine the direction of digital health development by integrating the ethical and spiritual values of the Qur'an. The method used is a critical qualitative approach based on library research, with normative analysis of verses related to health principles, information ethics, and maqāṣid al-syarī'ah. The results of the study show that in QS. Al-Isrā': 70 emphasizes human dignity as the basis for medical data protection; likewise in QS. Al-Syu'arā': 80 emphasizes that healing is divine and should not be separated from spiritual values; while in QS. Al-Baqarah: 286 provides guidance on the limits of responsibility and human capabilities in the utilization of technology. The critical discussion underlines the importance of building Qur'anic-based digital ethics that emphasize justice ('adālah), trustworthiness, and compassion (raḥmah) in designing equitable and inclusive artificial intelligence (AI) systems and remote health services. It is hoped that this study will provide recommendations for the development of Qur'anic Digital Ethics as a future normative framework for global digital health innovation.

Stunting remains one of the major public health issues at the national level in Indonesia. As an archipelagic country, Indonesia faces unique challenges in tackling stunting, particularly in regions with limited access to healthcare services. Advances in information technology offer new opportunities to support stunting prevention efforts, including through the utilization of health data. Such data can be used to detect stunting risks early and to monitor children's nutritional status more effectively. The use of health data applications or systems by Posyandu cadres is influenced by various factors, including availability of time, cost, level of trust, and perceptions of ease of use and usefulness. This study aims to identify the determinants of health data utilization by Posyandu cadres for toddlers as a stunting prevention effort in Geneng Subdistrict, Ngawi Regency. This research is an analytical quantitative study with a cross-sectional approach. A sample of 80 Posyandu cadres for toddlers in Geneng Subdistrict was selected using purposive sampling. Data were collected through questionnaires and analyzed using univariate, bivariate, and multivariate logistic regression tests to identify the factors influencing the use of health data in stunting prevention. The results show that the significant determinants include the age of the Posyandu cadre, their education level, and the amount of time they dedicate to Posyandu activities. The determinants of age, education level, and time significantly influence the utilization of health data and thereby affect the optimization of stunting prevention. Strengthening cadre capacity in these aspects is necessary to support more effective stunting prevention.

Since the launch of the “Bilingual Policy for all schools supported by Yayasan Pendidikan Katolik (YPK) Don Bosco KAM in 2030,” schools have debated its impact. Teachers of non-English subject express concern about adapting, while parents fear their children may struggle with bilingual instruction. This study investigates how students, teachers, parents, and staff at Private Senior High School of Santo Yoseph Medan perceive the Bilingual education policy decided by YPK Don Bosco KAM in 2030. The research explores key concerns: the necessity of the policy, its effect on local languages, feasibility by 2030, budget sufficiency, availability of qualified teachers, and whether non-English subject teachers can teach bilingually. A questionnaire with seven questions was distributed to 40 participants in Indonesian and later translated into English. Results show 60% believe bilingual education is essential, 10% disagree, 25% are uncertain, and 5% have no opinion. While over half support the policy, challenges remain, including limited funding, a shortage of trained bilingual teachers, and the need for more English classes and learning materials. Successful implementation requires the Don Bosco Catholic Education Foundation to increase funding and address these challenges to ensure smooth integration of bilingual education by 2030.

Stunting remains one of the major public health issues at the national level in Indonesia. As an archipelagic country, Indonesia faces unique challenges in tackling stunting, particularly in regions with limited access to healthcare services. Advances in information technology offer new opportunities to support stunting prevention efforts, including through the utilization of health data. Such data can be used to detect stunting risks early and to monitor children's nutritional status more effectively. The use of health data applications or systems by Posyandu cadres is influenced by various factors, including availability of time, cost, level of trust, and perceptions of ease of use and usefulness. This study aims to identify the determinants of health data utilization by Posyandu cadres for toddlers as a stunting prevention effort in Geneng Subdistrict, Ngawi Regency. This research is an analytical quantitative study with a cross-sectional approach. A sample of 80 Posyandu cadres for toddlers in Geneng Subdistrict was selected using purposive sampling. Data were collected through questionnaires and analyzed using univariate, bivariate, and multivariate logistic regression tests to identify the factors influencing the use of health data in stunting prevention. The results show that the significant determinants include the age of the Posyandu cadre, their education level, and the amount of time they dedicate to Posyandu activities. The determinants of age, education level, and time significantly influence the utilization of health data and thereby affect the optimization of stunting prevention. Strengthening cadre capacity in these aspects is necessary to support more effective stunting prevention.

The global healthcare landscape is undergoing a digital transformation, driven by the integration of Artificial Intelligence (AI), Big Data, and Telemedicine. These technologies have become essential not only in addressing immediate clinical demands but also in shaping long-term system-wide reforms. However, digital health adoption remains uneven across countries, influenced by variations in infrastructure, policy frameworks, and socioeconomic conditions. This study aims to explore how the integration of AI, Big Data, and Telemedicine contributes to healthcare system transformation through a comparative qualitative-descriptive analysis of four countries: the United States, India, Indonesia, and Rwanda. Secondary data were collected from peer-reviewed journals, government reports, and institutional publications from 2019 to 2024. Thematic analysis focused on policy direction, infrastructure readiness, implementation models, and observed outcomes. Findings reveal that while the U.S. leads with private-sector-driven innovation, India emphasizes national-scale integration, Indonesia navigates digital transformation in a geographically dispersed setting, and Rwanda demonstrates scalable solutions in low-resource environments. Despite different contexts, common challenges include interoperability gaps, ethical data governance, and disparities in digital literacy. The study synthesizes strategic insights and highlights the importance of inclusive, adaptable, and well-regulated digital ecosystems. It concludes that successful digital health implementation depends not only on technology, but also on governance, investment, and equity-focused design.

The global healthcare landscape is undergoing a digital transformation, driven by the integration of Artificial Intelligence (AI), Big Data, and Telemedicine. These technologies have become essential not only in addressing immediate clinical demands but also in shaping long-term system-wide reforms. However, digital health adoption remains uneven across countries, influenced by variations in infrastructure, policy frameworks, and socioeconomic conditions. This study aims to explore how the integration of AI, Big Data, and Telemedicine contributes to healthcare system transformation through a comparative qualitative-descriptive analysis of four countries: the United States, India, Indonesia, and Rwanda. Secondary data were collected from peer-reviewed journals, government reports, and institutional publications from 2019 to 2024. Thematic analysis focused on policy direction, infrastructure readiness, implementation models, and observed outcomes. Findings reveal that while the U.S. leads with private-sector-driven innovation, India emphasizes national-scale integration, Indonesia navigates digital transformation in a geographically dispersed setting, and Rwanda demonstrates scalable solutions in low-resource environments. Despite different contexts, common challenges include interoperability gaps, ethical data governance, and disparities in digital literacy. The study synthesizes strategic insights and highlights the importance of inclusive, adaptable, and well-regulated digital ecosystems. It concludes that successful digital health implementation depends not only on technology, but also on governance, investment, and equity-focused design.

This paper examines the implementation of translanguaging pedagogies within digital learning environments and their effectiveness in developing multilingual competence among Generation Z learners. Through a mixed-methods study involving 186 students and 14 educators across six higher education institutions, this research investigates how strategic integration of translanguaging practices in digital spaces supports linguistic flexibility, metalinguistic awareness, and cross-cultural competence. Findings reveal that digitally-mediated translanguaging approaches resulted in significant improvements in learners' willingness to communicate across languages (p<.01), metalinguistic awareness (p<.001), and cross-cultural communication efficacy (p<.01) compared to monolingual instructional approaches. The research further identifies five key digital translanguaging strategies that effectively leverage Generation Z's technological aptitude and multimodal literacy. This study contributes to the growing field of multilingual digital pedagogy by demonstrating how translanguaging can be systematically integrated into digital learning environments to foster linguistic repertoire expansion that aligns with the communicative needs of increasingly globalized contexts. The pedagogical framework presented offers practical guidance for educators seeking to implement translanguaging approaches in various digital learning modalities while accommodating Generation Z's learning preferences and multilingual development.

This study examines strategies for enhancing community participation in regional development planning by implementing bottom-up approaches. As democratic governance evolves, the need for meaningful citizen engagement in development planning becomes increasingly critical for ensuring sustainable and equitable outcomes. This research analyzes the effectiveness of participatory planning mechanisms, identifies barriers to community engagement, and proposes innovative strategies for strengthening bottom-up development planning processes. Using a comparative case study methodology across six regional governments, this study evaluates various participatory instruments including citizen forums, participatory budgeting, community mapping, and digital engagement platforms. The findings reveal that successful bottom-up planning requires institutional commitment, capacity building, cultural sensitivity, and sustained dialogue between government and communities. The study provides a framework for designing and implementing effective participatory planning systems that genuinely incorporate community voices in regional development decisions.