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The incidence of labor complications is one of the main challenges in maternal health. With the rapid development of digital technology, many health facilities have begun to integrate digital systems into health services. According to data from the Ministry of Health, the number of labor complications is still high, especially in areas with limited access to quality health facilities. Various factors, such as maternal age, nutritional status, education, and access to health services, contribute to the risk of complications that can endanger the health of mothers and babies.Objective: This study aims to analyze the factors that influence the incidence of childbirth complications in the era of health digitalization.Method: This type of research uses a quantitative approach with the Cross-Sectional Survey method. The population in this study were mothers who gave birth in health facilities in Malang Regency. The research sample was taken using a purposive sampling technique, which included pregnant women with a history of childbirth complications and the use of digital health services in the period 2023-2024. Sample: A total of 50 pregnant women were taken. The results of this study indicate a significant relationship between sociodemographic factors and the incidence of childbirth complications in Malang Regency, with a p value = 0.030 (p <0.05). This finding is in line with various previous studies that identified education, maternal age, age of marriage, and parity as important determinants of the incidence of childbirth complications. These findings confirm that although digital technology is increasingly developing in health services, fundamental factors such as maternal sociodemographic characteristics remain important determinants of the incidence of childbirth complications.

The incidence of labor complications is one of the main challenges in maternal health. With the rapid development of digital technology, many health facilities have begun to integrate digital systems into health services. According to data from the Ministry of Health, the number of labor complications is still high, especially in areas with limited access to quality health facilities. Various factors, such as maternal age, nutritional status, education, and access to health services, contribute to the risk of complications that can endanger the health of mothers and babies.Objective: This study aims to analyze the factors that influence the incidence of childbirth complications in the era of health digitalization.Method: This type of research uses a quantitative approach with the Cross-Sectional Survey method. The population in this study were mothers who gave birth in health facilities in Malang Regency. The research sample was taken using a purposive sampling technique, which included pregnant women with a history of childbirth complications and the use of digital health services in the period 2023-2024. Sample: A total of 50 pregnant women were taken. The results of this study indicate a significant relationship between sociodemographic factors and the incidence of childbirth complications in Malang Regency, with a p value = 0.030 (p <0.05). This finding is in line with various previous studies that identified education, maternal age, age of marriage, and parity as important determinants of the incidence of childbirth complications. These findings confirm that although digital technology is increasingly developing in health services, fundamental factors such as maternal sociodemographic characteristics remain important determinants of the incidence of childbirth complications.

In the digital era, communication technology enhances interaction quality across various sectors, including healthcare. Communication between doctors and patients can be improved through innovative digital media platforms. However, there is a knowledge gap regarding the effectiveness and implementation of digital media in doctor-patient interactions. This study aims to evaluate the impact of digital media platforms on doctor-patient communication and identify factors influencing the effectiveness of such communication. This research employs a qualitative method with a case study approach over six months, with inclusion criteria comprising doctors and patients actively using digital media. Subjects were selected through purposive sampling, involving 20 doctors and 50 patients. Data were analyzed using thematic analysis to identify critical patterns and themes. The findings indicate that digital media usage enhances the accessibility and responsiveness of communication, enables real-time health monitoring, and increases patient satisfaction through more personal and interactive communication. Factors such as doctors' digital skills, patients' preferences, and institutional support play significant roles in communication effectiveness. Digital media also reduce communication errors and improve the efficiency of health information delivery. The implications of this study suggest that integrating digital technology in medical communication enhances interaction quality and accelerates digital transformation in the healthcare sector.

In the digital era, communication technology enhances interaction quality across various sectors, including healthcare. Communication between doctors and patients can be improved through innovative digital media platforms. However, there is a knowledge gap regarding the effectiveness and implementation of digital media in doctor-patient interactions. This study aims to evaluate the impact of digital media platforms on doctor-patient communication and identify factors influencing the effectiveness of such communication. This research employs a qualitative method with a case study approach over six months, with inclusion criteria comprising doctors and patients actively using digital media. Subjects were selected through purposive sampling, involving 20 doctors and 50 patients. Data were analyzed using thematic analysis to identify critical patterns and themes. The findings indicate that digital media usage enhances the accessibility and responsiveness of communication, enables real-time health monitoring, and increases patient satisfaction through more personal and interactive communication. Factors such as doctors' digital skills, patients' preferences, and institutional support play significant roles in communication effectiveness. Digital media also reduce communication errors and improve the efficiency of health information delivery. The implications of this study suggest that integrating digital technology in medical communication enhances interaction quality and accelerates digital transformation in the healthcare sector.

The reconstruction of the digital learning space for ealy childhood education an important role in learning the Indonesian. One of them in childhood literacy learning. Aside from being a source of learning for the learning community, childhood literacy is also the basis for childhood competing in the revolutionary 4.0 era, ranging from infrastructure development, expansion and learning performance which will become an asset of Indonesia. Simultaneous collaboration efforts with multi-effect players are needed to improve the public literacy index. One of the efforts is through the Reconstruction of digital library learning for early childhood education. The analysis technique in this research is the content analysis.

The reconstruction of the digital learning space for ealy childhood education an important role in learning the Indonesian. One of them in childhood literacy learning. Aside from being a source of learning for the learning community, childhood literacy is also the basis for childhood competing in the revolutionary 4.0 era, ranging from infrastructure development, expansion and learning performance which will become an asset of Indonesia. Simultaneous collaboration efforts with multi-effect players are needed to improve the public literacy index. One of the efforts is through the Reconstruction of digital library learning for early childhood education. The analysis technique in this research is the content analysis.

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.

Objectives: to find out the percentage of reading interest of STMA TRISAKTI students, especially in terms of: 1) interest in reading text books and insurance documents in English (print and digital), 2) mastery of the contents of text books and documents - insurance documents in English (printed and digital), 3) mastery of reading methods: Speed Reading and SQ3R, mastery of Skimming and Scanning reading techniques in English (print and digital), 4) understanding of Structure Sentences, Style, Vocabulary, and Insurance Terminology in English (print and digital). Research methods: qualitative, comparative, and simple quantitative, data collection techniques: questionnaires. The results: 1) interest in reading text books by 26% (print) and by 36% (digital), while documents by 74% (print) and by 84% (digital), 2) mastery of the content of reading books text by 10% (print and digital), while documents by 40% (print and digital), 3) Mastery of Speed Reading method by 12%, SQ3R by 6%, Skimming by 16%, Scanning by 52%, 4) Understanding Sentence Structure of textbooks by 60% (print and digital), Sentence Structure of Documents by 42% (print and digital), Language Style of textbooks by 34% (print and digital), Language Style of documents by 42% (print and digital), Vocabulary by 52% (print and digital), Terminology by 44% (print and digital).

Objectives: to find out the percentage of reading interest of STMA TRISAKTI students, especially in terms of: 1) interest in reading text books and insurance documents in English (print and digital), 2) mastery of the contents of text books and documents - insurance documents in English (printed and digital), 3) mastery of reading methods: Speed Reading and SQ3R, mastery of Skimming and Scanning reading techniques in English (print and digital), 4) understanding of Structure Sentences, Style, Vocabulary, and Insurance Terminology in English (print and digital). Research methods: qualitative, comparative, and simple quantitative, data collection techniques: questionnaires. The results: 1) interest in reading text books by 26% (print) and by 36% (digital), while documents by 74% (print) and by 84% (digital), 2) mastery of the content of reading books text by 10% (print and digital), while documents by 40% (print and digital), 3) Mastery of Speed Reading method by 12%, SQ3R by 6%, Skimming by 16%, Scanning by 52%, 4) Understanding Sentence Structure of textbooks by 60% (print and digital), Sentence Structure of Documents by 42% (print and digital), Language Style of textbooks by 34% (print and digital), Language Style of documents by 42% (print and digital), Vocabulary by 52% (print and digital), Terminology by 44% (print and digital).