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Histology practicum, as one of the compulsory practicum courses, offers a great opportunity to practice various aspects of science process skills through microscopic tissue observation activities, identification of cell and tissue structures, and analysis of tissue functions in organisms. This study aims to analyze the science process skills biology education students through histology practicum activities. This study uses a quantitative descriptive approach with a survey method to analyze the science process skills of biology education students through histology practicum activities. Data collection in this study used three main techniques, namely science process skills tests, direct observation, and analysis of practicum report documents. Furthermore, the data were analyzed descriptively quantitatively using descriptive statistics to describe the profile of students' science process skills. Analysis per aspect revealed that students had good skills in the aspects of observing (78.50%), classifying (76.33%), interpreting data (71.67%), applying concepts (73.25%), and communicating results (73.40%), which indicates that histology practicum is effective in developing basic science process skills. However, the aspects of formulating hypotheses (58.17%) and planning experiments (54.83%) were still in the sufficient category, indicating that students experienced difficulties in integrated science process skills that require higher-order thinking skills. The findings this study provide important implications for the development of more comprehensive histology practicum learning strategies, including the integration of inquiry approaches, the use of continuous formative assessment, the application of differentiated learning, and strengthening the connection between theory and practice to optimize the development of all aspects of science process skills of prospective biology teachers.  

Low student achievement in mathematics, particularly in arithmetic sequences and series, has led to decreased learning motivation and participation. Therefore, innovative and engaging instructional strategies are required, one of which is the implementation of ice-breaking activities to reduce boredom and enhance students’ learning engagement. This study aimed to examine the effect of implementing ice breaking on the mathematics learning outcomes of tenth-grade students at SMK Kesehatan Samarinda in the topic of arithmetic sequences and series. This research employed an experimental method using a quasi-experimental design, involving two groups: an experimental group that received instruction incorporating ice breaking and a control group that did not receive such treatment. The total sample consisted of 60 students selected through purposive sampling. The research instrument was a learning achievement test administered as a pretest and posttest. Data were analyzed using a paired sample t-test to determine differences in learning outcomes between the two groups. The results revealed a significance value of 0.000 (< 0.05), indicating a significant difference in learning outcomes between the experimental and control groups. Therefore, it can be concluded that the implementation of ice breaking has a significant effect on improving students’ mathematics learning outcomes. This strategy creates a more active, interactive, and enjoyable learning environment, thereby facilitating students’ understanding of mathematical concepts, particularly arithmetic sequences and series.

Research. Elementary School Teacher Education Study Program, STKIP PGRI Papua. The purpose of this study is to examine the Influence of Using Kahoot as an Interactive Learning Media to Increase the Learning Interest of Sixth-Grade Students in Civic Education at SD YPK Bethania Mariadei. This type of research is Experimental research. The population in this study consists of students at SD Bethania Mariadei, while the research sample is 21 students in the sixth grade. Data collection was conducted through questionnaires and documentation. The collected data was analyzed using descriptive statistics and inferential statistics techniques. Based on the hypothesis testing results, the calculated t-value is 2.926 with a significance of 0.009. The significance value shows 0.009 < 0.05, so H0 is rejected and H1 is accepted, which leads to the conclusion that there is a significant and positive effect of using Kahoot media as an interactive learning tool to increase the learning interest of sixth-grade students in Civics subjects at SD YPK Bethania Mariadei. The Kahoot Interactive Learning Media can be applied for Civics learning in elementary schools. Keywords: Kahoot, Learning Media, Learning Interest, Civics.  

This research is motivated by the persistent difficulty elementary school-aged children have in controlling their emotions and behavior in social situations, particularly within the context of church community development. The study aimed to examine the effect of Christian values-based emotion management training on improving children's self-control. The study used a quantitative approach with a quasi-experimental one-group pretest-posttest design on 30 children aged 7–12 years at the GPM Wonreli–Kisar Congregation. The intervention was implemented over four days through activities involving emotion recognition, value reflection, emotion regulation training, and social simulations. Data were collected using a self-control scale and analyzed using paired-difference tests and effect size calculations. The results showed a significant increase in self-control scores after the training, with the largest changes in rule compliance and impulse control, and a more moderate increase in resilience to frustration. The effect size indicates a strong practical impact of the changes. These findings indicate that training that integrates social experiences and value reflection can facilitate the shift in behavioral control from external control to internal awareness. Practically, this research provides a basis for developing a community-based child development program that emphasizes not only discipline but also the understanding of values ​​in character formation and self-regulation.

This quasi-experimental study examines the effectiveness of Artificial Intelligence (AI)-assisted learning in enhancing critical thinking skills among undergraduate history students. The study involved 60 students divided into experimental and control groups. The experimental group received AI-supported instruction integrating adaptive learning modules, scaffolded source-analysis prompts, and guided argumentative discussions facilitated by conversational AI tools, while the control group followed conventional lecture-based instruction. Data were collected using a validated critical thinking test, classroom observation protocols, and semi-structured interviews. Quantitative data were analyzed using paired and independent sample t-tests, while qualitative data were examined through Miles and Huberman’s interactive analysis model. Results indicate statistically significant improvements in critical thinking scores in the experimental group compared to the control group. Thematic findings reveal enhanced sourcing, contextualization, corroboration, and evidence-based argumentation skills. However, minor risks of over-reliance on AI highlight the need for instructional scaffolding and ethical guidance. The findings suggest that AI can function as a cognitive scaffold that strengthens historical thinking and metacognitive awareness when implemented within a structured pedagogical framework.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Dysmenorrhea, which is common in adolescent girls, is a painful menstrual cycle that frequently interferes with daily activities, both in the classroom and in social situations. Dysmenorrhea is a common condition that needs particular care as a reproductive health concern, notably among young people. Both pharmacological and non-pharmacological approaches can be used to treat dysmenorrhea. The use of warm water compresses is a simple, inexpensive, and effective non-pharmacological approach. By increasing blood vessel dilatation, enhancing blood circulation, lowering muscular tension, and lowering pain perceptions, warm water compresses aid in alleviating dysmenorrhea symptoms. The objective of this study is to assess the impact of warm compresses on the severity of dysmenorrhea pain scales in teenage girls at the Buntalo Community Health Center. The design of this trial was pre-experimental, using a one-group pretest-posttest technique. Using a purposive sampling method, a sample of 20 respondents was chosen. The Wilcoxon test was used to conduct the data analysis. According to the data, there was a statistically significant reduction in pain intensity, with a Z value of -4.065 and a p-value of 0.000 (p < 0.05). The average pain scale decreased from 2.95 to 1.80. Based on these findings, it is reasonable to conclude that warm water compresses are helpful in alleviating dysmenorrhea pain in young women. One of the suggestions for non-pharmacological treatments to help alleviate dysmenorrhea symptoms in adolescents is anticipated to be the outcome of this research.

Pain is a prevalent issue frequently seen by pregnant women in their third trimester, attributable to physiological, biomechanical, and psychological alterations during gestation. Non-pharmacological pain management is essential as a secure alternative to enhance the comfort of pregnant women. Perinatal Yoga and Relaxation Techniques are two commonly endorsed non-pharmacological therapies; yet, comparative analyses of their efficacy remain scarce, particularly within primary health care settings. This study is to evaluate the efficacy of Perinatal Yoga against Relaxation Techniques in alleviating pain among pregnant women in their third trimester at the Bongo II Community Health Centre. The research employed a quasi-experimental design with a two-group pretest-posttest methodology. The sample comprised 38 pregnant women in their third trimester, categorized into two groups: 19 participants in the Perinatal Yoga group and 19 participants in the Relaxation Techniques group. The Numeric Rating Scale (NRS) was utilized to assess pain severity prior to and following the intervention. A paired t-test was utilized for intra-group differences, whereas an independent t-test was employed to assess inter-group efficacy.The findings indicated that both Perinatal Yoga and Relaxation Techniques markedly diminished pain intensity in pregnant women throughout their third trimester (p<0.001). The decrease in pain in the Perinatal Yoga group was statistically significant compared to the Relaxation Technique group (p<0.05). This study concludes that Perinatal Yoga is superior than Relaxation Techniques in alleviating pain in pregnant women during their third trimester. Perinatal Yoga is indicated as an effective and practical non-pharmacological technique in antenatal treatment inside primary health facilities.

The quality of complementary feeding (MP-ASI) for infants aged 6–23 months is a key factor in supporting growth and preventing early nutritional problems. However, MP-ASI practices that do not comply with recommendations are still common, especially in areas with limited access to nutrition information and education. One of the promotive-preventive efforts developed in primary health care is the implementation of toddler classes. This study aims to analyze the effectiveness of toddler classes in improving the nutritional quality of infants receiving MP-ASI in the working area of the Popayato Timur Community Health Center. This study used a quasi-experimental design with a one-group pretest–posttest approach. The study sample consisted of 33 infants aged 6–23 months selected using a total sampling technique. Data were collected through infant anthropometric measurements to assess nutritional status based on indicators of weight for age (BW/A) and weight for length/height (BW/H), as well as assessing the quality of MP-ASI using quality scores before and after the intervention. Data analysis was carried out descriptively and inferentially using paired statistical tests. The results showed an increase in the quality of complementary feeding (MP-ASI) after the implementation of toddler classes, accompanied by improvements in infant nutritional status based on indicators of weight for age and weight for height, with a statistically significant difference between conditions before and after the intervention. In conclusion, toddler classes are effective in improving the quality of complementary feeding and infant nutritional status, thus potentially being an applicable educational strategy in efforts to improve infant nutrition in primary health care.

While TLS 1.3 is the latest standard, TLS 1.2 remains widely implemented in many cloud infrastructures. The selection of cipher suites in TLS 1.2, particularly between AES-128-GCM and AES-256-GCM, presents a trade-off between cryptographic strength and system performance. This research aims to analyze the performance comparison of these two algorithms on an Nginx server to determine the optimal configuration for cloud storage services. The study uses a quantitative experimental method by benchmarking two scenarios: (A) Strict (AES-256-GCM), and (B) Balanced (AES-128-GCM). Performance metrics measured include Requests Per Second (RPS), Latency, and Throughput. The results show that handshake performance (RPS and Latency) is nearly identical across all scenarios. However, in large file transfer tests, the AES-128-GCM algorithm (Scenario B) achieved a throughput of 32.4 MB/s, which is 12.5% faster than AES-256-GCM (28.8 MB/s). This study concludes that AES-128-GCM provides the best balance of security and efficiency for data-intensive environments.

Syphilis is one of the reproductive health problems that remains a challenge in Indonesia, with a trend of increasing cases, while the level of adolescent knowledge about this disease is still low. Educational efforts are needed to improve adolescent understanding, one of which is through video media which is considered more interesting because it combines visual and audio elements. This study aims to evaluate the effectiveness of video as an educational medium in improving adolescent knowledge about syphilis at SMKS Al Hikmah 2 Sirampog, Brebes Regency. This type of research is a pre-experimental study with a one group pretest-posttest design. The study sample consisted of 47 students selected using a purposive sampling technique. The research instrument was a syphilis knowledge questionnaire administered before and after the educational video intervention. Data analysis used the Wilcoxon Signed Rank Test and N-Gain calculation. The study showed that Results Before the intervention, the level of knowledge of respondents was in the good category for 24 students (51.1%), sufficient for 11 students (25.5%), and poor for 12 students (23.4%). After being given the educational video intervention, there was a significant increase with all respondents (100%) being in the good category. The Wilcoxon Signed Rank Test showed a significant difference between pretest and posttest scores (p-value = 0.000). N-Gain analysis categorized the increase in knowledge as high. The study concluded that video media has proven effective as an educational tool in increasing adolescent knowledge about syphilis, making it a viable alternative learning medium in reproductive health programs.

Language barriers represent one of the most significant obstacles to educational equity and access worldwide. This study investigates the application of Natural Language Processing (NLP) technologies in multilingual educational contexts to facilitate cross-linguistic learning and improve educational outcomes for linguistically diverse student populations. We implemented and evaluated a comprehensive NLP-powered multilingual learning platform across 47 educational institutions in 12 countries, serving 8,450 students speaking 23 different languages. Our experimental framework integrated machine translation, speech recognition, multilingual content generation, and adaptive language learning algorithms. Results demonstrate that NLP-enhanced multilingual education improved student comprehension by 43.6% (p<0.001), increased participation rates by 67.8%, and reduced achievement gaps between native and non-native speakers by 52.4%. Students using NLP-assisted learning tools achieved test scores averaging 78.3% compared to 54.7% for control groups. However, challenges persist regarding cultural context preservation, idiomatic expression handling, and equitable performance across language families. This research provides evidence that NLP technologies can effectively democratize education across linguistic boundaries while identifying critical areas requiring continued development.

Effective communication is a key competency for medical professionals, but traditional classroom-based methods in Indonesia face challenges such as limited practice, geographical barriers, and insufficient exposure to diverse patient scenarios. Mobile-assisted learning (MAL) offers a promising solution to enhance medical communication training outside traditional settings. This study investigates the effectiveness of MAL interventions in developing communication skills among Indonesian medical students and healthcare professionals. A mixed-methods approach was used, involving a quasi-experimental design with pre- and post-assessments of communication competence among 180 participants from three Indonesian medical schools. The MAL intervention included a mobile app with video demonstrations, interactive case scenarios, peer feedback, and microlearning modules. Quantitative data showed a significant improvement in communication competence (mean increase of 23.4%, p<0.001), with notable gains in information gathering (28%), relationship building (26%), and patient education (21%). The mobile platform saw high engagement (average of 4.3 sessions/week) and 87% module completion. Qualitative data revealed increased confidence in consultations, improved cultural sensitivity, and better time management. Challenges included inconsistent internet access, varying digital literacy, and resistance from traditional educators. MAL shows potential for improving medical communication in Indonesia, offering flexible, accessible training. Successful implementation requires addressing infrastructure issues, integrating MAL into existing curricula, and training faculty. This study adds to the growing evidence supporting technology-enhanced medical education in resource-limited settings.

The development of Artificial Intelligence (AI) technology has provided new opportunities to enhance the quality of learning, particularly in elementary science education. One form of AI utilization is chatbots, which function as interactive learning media capable of encouraging students’ active participation. In science learning, questioning behavior is an important indicator of students’ cognitive engagement; however, many elementary school students tend to be passive in asking questions. This study aimed to examine the effect of using AI chatbots in science learning on students’ questioning behavior in Grade V of SDN Plalangan 03 Gunungpati, Semarang City. This study employed a quantitative approach with a quasi-experimental design. Data were collected through observation, questionnaires, and documentation. The data were analyzed to identify changes in students’ questioning behavior before and after the implementation of AI chatbots in science learning. The results indicated that the use of AI chatbots improved students’ questioning behavior in terms of frequency, confidence, and activeness in asking questions. AI chatbots provided a comfortable and flexible interaction space for students, which helped reduce psychological barriers in questioning. It can be concluded that the use of AI chatbots has a positive effect on students’ questioning behavior in elementary school science learning.

This study aims to analyze the effect of the implementation of the Intelligent Tutoring Sistem (ITS) based on Artificial Intelligence (AI) on improving the understanding of mathematical concepts, especially in fractional and basic geometry materials, in Class V students of SD Negeri 2 Badran, Temanggung Regency. The research method used was a quasiexperimental experiment with a Non-equivalent Control Group Design. The research sample consisted of 48 students who were divided into two groups, namely the experimental group (n=24) who received learning with the help of AI-based ITS, and the control group (n=24) who received conventional learning with lecture methods and practice questions. The research instrument is in the form of a test of understanding of mathematical concepts that has been validated by experts and tested for reliability. Data were analyzed using parametric statistical tests of the Independent Sample t-test and N-Gain Score to measure the improvement. The results showed that there was a significant difference in understanding of mathematical concepts between the experimental group and the control group. The average post-test score of the experimental group (82.45) was significantly higher than that of the control group (70.12) with a p< value of 0.05. N-Gain analysis showed that the improvement in conceptual understanding in the experimental group was in the "moderate" category (g=0.56), while the control group was in the "low" category (g=0.32). These findings indicate that AI-based ITS is effective in improving students' understanding of mathematical concepts. The advantages of the system lie in its ability to provide instant feedback, personalize materials according to learning pace, and present interactive materials, thus helping to better construct students' conceptual understanding. It is recommended that schools consider the integration of ITS technology as a supplementary tool in mathematics learning at the elementary level.

Improving service quality is a primary priority in hospital management. Service quality can be improved by reducing the percentage of non-value added activities through the Lean Hospital approach. This study aims to identify activities and analyze the root causes of critical waste in the drug distribution and utilization processes at the Outpatient Pharmacy Installation of Karanganyar Regency Hospital. This study is a non-experimental research with a qualitative descriptive design. Critical waste was obtained through the distribution of a waste weighting questionnaire assessed by all personnel involved in the drug distribution and utilization processes. The results show that the Value Stream Mapping calculation for the drug distribution process obtained a lead time of 147.41 minutes and a VAR value of 36%, while in the drug utilization process, compounded prescription service obtained a lead time of 128.53 minutes and a VAR of 24%, and non-compounded prescription service obtained a lead time of 75.8 minutes and a VAR of 26%. The critical waste questionnaire calculation using the Borda method in the drug distribution process showed overproduction 60%, inventory 53.33%, and waiting 43.33%, while in the drug utilization process, waiting 43.75%, overproduction 42.85%, and defect 39.70%. The 5S method, a Lean method, was used to eliminate waste in the service process at the Outpatient Pharmacy Installation of Karanganyar Regency Hospital.

The incidence of degenerative diseases, which are part of Non-Communicable Diseases (NCDs), remains a major cause of death worldwide and in many regions of Indonesia. Meanwhile, efforts to prevent degenerative diseases affecting the adolescent age group are still rare, such as the establishment of a Youth Posyandu, including in Sungai Duren Village. There is no health post that provides youth care services in Sungai Duren Village, while there are many teenagers living there (students under 21 years old) with various lifestyles that are at risk of degenerative diseases. This study aims to analyze the increase in adolescent knowledge about CERDIK between before and after the establishment of the Youth Posyandu in order to prevent degenerative diseases early. This study is a quasi-experimental study involving 30 adolescents by measuring the dependent variable of knowledge. The research instrument used is a questionnaire. Computerized data processing and analysis were conducted through descriptive statistical analysis and T-test analysis. The results showed a significant difference in respondents' knowledge of CERDIK before and after the establishment of the Youth Posyandu in Sungai Duren Village (p=0.000). This study indicates that structured health education can improve adolescents' understanding of CERDIK and can be used as an effective promotive and preventive strategy.

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations