Publication Search

The condition and situation of education after the Covid 19 Pandemic caused "Learning Loss" and "Learning Outcomes Loss". It takes a learning experience by providing students' reasoning insights and intellectual competence through a portfolio. The presence of educators with the ability to transform learning is needed as a learning conditioning for the ability of students' portfolios.  Quantitative research using a Likert scale questionnaire instrument constructed and developed from the teacher assessment system and David M. Johnson's portfolio. The population is 80 teachers and 80 students of grade VI elementary school in the Kebon Jeruk area, West Jakarta. The findings of the study provide information that the ability of students' portfolios can be constructed and optimized through learning transformation with three dimensions in the form of active participation and success of students, assessment and feedback on the progress of learning experiences and management of students and learning materials.

Visual impairment is a general term used to describe partial or complete loss of vision function. This research uses a qualitative approach. Observation results show that by implementing appropriate teaching methods, blind children can learn effectively. Good interaction between teachers and students, as well as the use of braille and audio visual aids, greatly contribute to a positive learning process.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

Motivation to learn is certainly one of the important things in determining the success of learning. However, students often feel a loss of motivation to learn so that students experience a decrease in learning motivation which will affect student learning outcomes. Therefore, it is important for teachers to create strategies to increase students' learning motivation. Strategies that can be implemented by teachers include using interactive learning media and can increase students' active participation during learning. The aim of this research is to determine students' learning motivation after implementing science learning using diorama learning media. This research uses descriptive qualitative methodology, examining the process and influence of implementing diorama media on student learning motivation. The data collected for this research took the form of direct observation, student interviews and analysis of several documents used to collect data, which was then analyzed using descriptive analysis methods, the form of this research is Classroom Action Research (PTK). The results obtained from this research are that the application of the Diorama of the Nature of Light learning media in science and science learning in class V at SDN Karangrejo 2 succeeded in increasing student motivation in learning. Students who were previously passive and did not play much of a role in the learning process after implementing learning using the Nature of Light Diorama showed significant changes in their activity in learning.

Technology has undoubtedly revolutionized the way we learn and acquire knowledge. In recent years, there has been a significant shift towards integrating technology into educational practices, with the aim of enhancing the learning experience for students. This transformation has sparked a debate among educators and researchers about the implications of this shift on the traditional methods of teaching and learning. Some argue that technology has the potential to democratize education and provide equal opportunities for all learners, while others express concerns about the impact of digital distractions and the loss of face-to-face interaction. Through the lens of post-positivism, criticality, and constructivism theory, this critical analysis aims to explore the complexities of the transformation of learning with technology. By examining the underlying assumptions and implications of integrating technology into education, we can gain a deeper understanding of how it may shape the future of learning. This analysis will delve into the various perspectives and theories surrounding the use of technology in education, considering both the benefits and drawbacks. Through a balanced examination of the evidence, we hope to uncover the key factors that will determine the success of technology in shaping the future of learning. Ultimately, the goal is to provide insights that will guide educators and policymakers in making informed decisions about integrating technology into educational practices.

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  

Palembang Religious Education and Training Center as an education and training institution utilizes information technology, especially wifi internet networks to support various learning and training activities. To support these needs, a quality internet network is needed, so an evaluation and measurement of Quality of Service (QoS) is needed, because QoS is able to measure various important parameters in the network, such as throughput, delay, jitter, and packet loss, all of which play an important role in ensuring the network functions properly.  The standard used is TIPHON. By using the Action Research method, this research will produce real internet quality data in accordance with actual conditions. After testing, the average results on the 4 parameters tested obtained an index value of 3. So that the overall quality of the internet network at the Palembang Religious Education and Training Center has good network quality.

After the COVID-19 pandemic, the phenomenon of loss learning forced curriculum changes to become an unavoidable necessity so that the government launched an independent curriculum. The curriculum was developed following the times. The research method used in this research is the library research method or literature study. The author concludes that the independent curriculum is expected to be able to provide concrete and meaningful and effective learning experiences in developing the skills and expertise of students as lifelong learners with Pancasila character. The independent curriculum in social studies learning that focuses on the integration of values and competencies not only encourages intellectual intelligence, but also emotional and social intelligence, such as empathy, social awareness, and effective communication skills. An active and innovative social studies curriculum in independent learning is expected to not only focus on academic achievement, but also on developing students' characters and competencies as socially responsible individuals, while supporting post-pandemic learning recovery.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

This study presents the implementation of Transfer learning using the ResNet-18 architecture for classifying 10 musical instrument categories based on visual representations of audio signals. The audio waveform is transformed into image-like inputs appropriate for CNN processing, accompanied by data augmentation and ImageNet-standard normalization. ResNet-18 is utilized due to its efficient feature extraction capability enabled by residual blocks, which help overcome vanishing gradient issues. The model was trained for 10 Epochs using the AdamW optimizer and Cross-Entropy Loss. Experimental results show that the model achieved a maximum validation accuracy of 77.35%, with a stable downward trend in training loss, indicating effective feature learning. However, several misclassification cases were observed, particularly among instruments with similar spectral characteristics, such as drum–violin and tabla–sitar. These findings demonstrate that while ResNet-18 performs reliably for musical instrument classification, further improvements remain possible through deeper architectures like ResNet-50, more comprehensive hyperparameter optimization, and the use of richer audio representations such as Mel-Spectrograms. This research provides an essential foundation for developing automated music analysis systems powered by Deep Learning.

This research aims to analyze the role of social support on learning motivation among migrant students at the Faculty of Economics, Jakarta State University class of 2022. Migrant students often face challenges in the form of adapting to a new environment, which can influence their learning motivation. Social support, whether from family, friends, or the campus environment, is an important factor in helping this adaptation process. This research identifies types of social support such as emotional, instrumental, informational and self-esteem support, and their influence on learning motivation. Based on existing literature, social support can increase students' self-confidence, which has an impact on higher enthusiasm and motivation to learn. On the other hand, a lack of social support has the potential to cause stress, low self-confidence, and loss of motivation to learn. Using the case study method, this research examines the relationship between social support and the learning motivation of migrant students. The research results show that social support has a positive correlation with learning motivation. External factors, such as the campus environment and lecturers' teaching methods, as well as internal factors, such as personal interests and suitability of the field of study, also influence student learning motivation. This research is expected to provide new insights in increasing the learning motivation of migrant students through strengthening social support.

Landslide disasters can cause environmental damage, property loss, and cause deaths, disappearances, injuries, and displacement with various health problems in refugee camps such as infectious diseases and nutritional disorders. The level of disaster risk is determined by the student's potential and preparedness which can be known from the student's interpretation of landslide disaster management. Health education is a learning process that can change students' preparedness to be able to prepare action plans to reduce the impact of landslides. The aim of this research is to determine the effect of health education on landslide disaster management at SDN 1 Selo Boyolali. This type of research uses pre-experimental methods with a quantitative approach and a one group pretest-posttest research design. The sample consisted of 29 respondents using purposive sampling technique. Measuring student preparedness uses a preparedness questionnaire sheet with 25 questions using a Likert scale. The data analysis technique uses the Wilcoxon test with the research results showing a p-value of 0.000 (p<0.05), so that it can be concluded that there is an influence of landslide disaster management health education on student preparedness at SDN 1 Selo Boyolali.

Faculty The Faculty of HOLS Hospitality School faces challenges in manual administration, such as accumulating registration data, difficulty in data retrieval, risk of data loss, administrative inaccuracies, and time-consuming registration and attendance processes, increasing the risk of administrative errors. To address these issues, researchers designed a web-based information system using the waterfall model and PHP programming language. This system allows students to mark attendance using a QR code within a 5-meter radius, mentors to reschedule classes quickly and easily, and administrators to control administrative data accurately and efficiently. The result of this research is an information system product that facilitates academic administration management at HOLS Hospitality School, supports e-learning facilities, and simplifies coordination between students, mentors, and administrators.

The process of manually identifying fruits to determine ripe and unripe can affect the production and quality of the food and beverages themselves. The CNN method is able to group images and analyze images based on objects. Therefore, it is necessary to conduct research using the CNN method on the ripeness of strawberries. This study aims to determine the level of maturity of strawberries during harvest time. The accuracy graph shows that the model is not only capable of learning the training data well but can also generalize well to the validation data. In contrast, the validation accuracy graph starts from 0.825 in the 0th epoch and rises consistently until it reaches 0.975 in the 30th epoch. Both charts remained stable above those values throughout the training period. Overall, the development of the CNN model for the detection of strawberry ripeness resulted in excellent performance. The model achieved the lowest loss of 0.0383 and an accuracy as high as 98% on the validation data, demonstrating a strong ability to accurately predict between ripe and unripe strawberries.

This research discusses the implementation and evaluation of the Convolutional Neural Network (CNN) convolutional neural network model for classification of fruit types, specifically to differentiate between Banana and Papaya. The CNN model used consists of several convolutional, pooling, and fully connected (dense) layers designed to extract features and perform binary classification. Data augmentation is applied to the training set to increase data variation and prevent overfitting. The image data used is normalized to speed up training convergence. The model was trained using the Adam optimizer and the binary crossentropy loss function for 20 epochs. Performance evaluation was carried out using the validation set. The results show that the model is able to effectively classify fruit images with a high level of accuracy. Predictions are made by uploading images, resizing them, and normalizing them before using the model for predictions. The classification threshold was set at 0.4, where a predicted probability greater than or equal to 0.4 was classified as Banana and a probability less than 0.4 was classified as Papaya. This research shows that the CNN model can be used effectively for binary image classification tasks and can be extended to classify more types of fruit with appropriate data adjustments and model architecture.

Parental divorce significantly impacts early childhood development, especially in the digital age. Children may experience low self-esteem, difficulties in building relationships and socializing, aggressive behavior, learning difficulties, value confusion, deviant behavior, and loss of trust.Addressing these impacts requires digital parenting, open communication, moral education, and collaboration with educational institutions. Parents and stakeholders must understand the effects of divorce to provide appropriate support for children's healthy and happy growth. A qualitative case study at TK Negeri Bung Karno involved interviews with PAUD teachers and parents, as well as classroom observations. The findings showed that children tend to imitate negative parental behaviors. Through gradual guidance, children learned to distinguish between good and bad behavior, showing progress in emotional control, social interactions, and setting positive examples for peers.

Chronic diseases in humans are very difficult to detect visually, for example glaucoma, hypertension, diabetes, and others. So it takes a lot of time for further medical examination by visiting a health center or hospital. Therefore, this research aims to find a solution combining medical and computer science to classify quickly and precisely. Classifying eye images requires good features and characteristics so that disease images can be classified. This research uses the Deep Learning method, namely Convolutional Neural Network with MobileNet-V3 architecture which can extract features from large resolution images very well. This research resulted in accurate classification of images of chronic diseases Normal, Diabetes, Glucoma, Cataract, Age related macular degeneration, Hypertension, Pathalogical Myopia. uses the MobileNet-V3 architecture, with transfer learning reaching 81%, and loss only 0.4913.

This research aims to develop and apply a Convolutional Neural Network (CNN) algorithm to detect handwritten Roman numerals. Handwriting recognition is a classic challenge in the fields of image processing and machine learning, especially for less common characters such as Roman numerals. In this research, we use data augmentation techniques to increase the diversity and number of datasets used in model training, which is expected to increase model accuracy and generalization. The dataset used consists of 1,120 images for testing and 280 images for validation, each of which is divided into 14 classes of Roman numerals I, II, III, IV, V, VI, VII, VIII, IX, X, L, C, D , and M. Image data was created directly using simple software, namely Paint version 6.3. This research uses the Python programming language and Google Colab as a computing platform. Model training was carried out for 300 epochs and showed significant accuracy in the 150th to 300th iteration. The results at the 300th epoch show an accuracy of 0.9607 and a loss of 0.1162. The implementation of this algorithm shows significant potential in practical applications, such as in the fields of education and historical documentation. The conclusion of this research is that data augmentation is an effective technique to improve the performance of CNN models in detecting handwritten Roman numerals.

Bullying refers to deviant behavior that is often carried out consciously or unconsciously. Bullying is repeated aggressive and negative behavior towards someone with the aim of hurting him. Bullying is defined as negative actions carried out by a stronger party against a weaker party through the use or non-use of tools with the aim of creating physical and emotional pressure on the weaker party. The increase in bullying cases in schools shows how worrying the current condition of education in Indonesia is. The aim of this research is to find out whether bullying behavior has a significant influence on students' learning motivation and enthusiasm for learning. We hope this can help schools know how big the impact of bullying is at school and help victims overcome the problems and risks of bullying. The method used is the Systematic Literature Review or SRL method, namely selecting each journal or previous research and getting results in the form of references for models/ideas that will be developed in the future. The results of this research show that bullying has a big influence on students' learning motivation, this is characterized by not having the courage to participate actively in the learning process and showing low interest in lessons. Bullying behavior significantly affects elementary school students' learning motivation. Students who experience bullying tend to show lower levels of motivation, loss of interest in learning, and lack of confidence in their academic abilities.