The integration of augmented reality (AR) technology in education is advancing, as seen in a project at Atisa Dipamkara School. This project developed a chemistry learning application using Unity, Android XR Plugin, and ARCore to address laboratory limitations. The app uses interactive 3D visualizations to help students grasp complex chemistry concepts. Blackbox testing showed that all main modules work well, except for a bug in the compound reset module. The app effectively improves students' understanding, interest, and motivation in chemistry, making abstract concepts more tangible. This application is both a learning aid and an educational innovation, promoting a more interactive and enjoyable learning experience.

In the digital age, public road lighting (PJU) monitoring efficiency is becoming essential for effective infrastructure management. The research developed a PJU monitoring system based on LoRa technology and an Arduino microcontroller to monitor the operating conditions of PJU solar panels in real time. The system uses the LoRa 433 MHz module for remote data communication and is equipped with a voltage sensor to monitor batteries and solar panels as well as an ACS712 current sensor to measure current consumption on LED lights. The data is displayed on an I2C 16x2 LCD screen, making monitoring easy. LoRa technology offers the advantages of broad communication range and low power consumption. The development method used is prototyping, including needs analysis, system design, implementation, testing, and maintenance. Test results show that the system works well, with sensors providing adequate accuracy and LoRa communication enabling remote data access. The system improves the efficiency and accuracy of PJU monitoring, as well as reduces time and effort in the monitoring process. Overall, the system is an effective solution for PJU management in the digital age.

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.

This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning process were also identified. Support and training are needed for lecturers to overcome these challenges. These findings provide valuable insights for the development of problem-based teaching practices in the context of digital image processing.

Rohingya in Indonesia has become trending conversation on social media. Sentiment analysis can get public responds. Big data makes the problem time efficiency labeling process, therefore the lexicon dictionary is needed for the labeling process. Data is growing and circulating very rapidly so it takes a fast and efficient time. Although it is fast and makes it easier to solve problems, it is still necessary to question the accuracy produced when using the lexicon labeling. A comparison of the labeling process between the InSet lexicon and the VADER lexicon was conducted to determine the accuracy of the labeling. It was done by combining lexicon with machine learning method of support vector machine and TF-IDF weighting and accuracy result calculated using confusion marix. Data from social media X as many as 9117 lines and labeled with InSet lexicon result 5241 negative sentiments, 1369 positive, and 521 neutral. Then the labeling results with VADER produced 2749 positive, 2523 negative, and 1881 neutral. After labeled, processed SVM and calculated accuracy with results of InSet lexicon accuracy having an average of 85.8% while the VADER SVM lexicon has an average of 82.65%.
 

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

Education is an effective way to improve people's quality of life. The quality of education depends on the quality of the teachers who teach. Teachers have an important role in the growth and development of students at school. SDN Langenharjo 01 evaluates teacher performance in teaching students by selecting the best teachers as a form of appreciation for their performance and also as a form of teacher motivation to improve the quality of teaching students at school. The school still carries out manual calculation processes and does not yet use the system. The aim of this research is to develop a decision support system for determining the best teachers at SDN Langenharjo 01 using the web-based Simple Additive Weighting (SAW) method. This research uses the Agile System Development method, starting with initial planning, then continuing with the analysis and design stages, and ending with implementation and testing. The SAW method calculation shows that Raharja S.Pd is the best teacher with a preference value of 0.9913. Testing of system functionality shows that all functions in this decision support system work as expected

Abstract: The New Student Admission Information System (SIPMB) at STMIK Kaputama aims to enhance efficiency and transparency in the student admission process. This study conducted an information system audit using the COBIT 5 framework to evaluate system maturity and provide improvement recommendations. The audit results indicate that the system has not met the expected targets in all domains, with an average maturity level of 1.35 or 135%, classified as Fully Achieved (F). However, according to the capability model mapping condition index, the value is at level 1 (Performed Process). STMIK Kaputama needs to increase openness to innovation inputs and clear communication with prospective students to avoid errors in the admission process, as well as improve service quality for prospective students.
 

The Binjai Library has developed a technology-based information service system to improve service quality for the community. This study aims to audit the information service system of the Binjai Library using the COBIT 5 framework. Based on the analysis, it was found that the information system has not reached the expected target levels in all domains. The average maturity level calculation shows a score of 1.43 or 143%, which is classified as Fully Achieved. Key findings include the near alignment of human resources and IT capabilities with institutional needs, suboptimal innovation in services, and the need for improvement in HR management and communication. This study concludes that the Binjai Library needs to implement improvements and innovations to enhance the performance of the information system and service quality.
 

This research aims to analyze the influence of AI Chat GPT in completing practicum reports by students of the Informatics Engineering Study Program at Muhammadiyah University of Ponorogo. The background to this research is based on the increasingly rapid development of AI technology, offering various conveniences in various fields, including education. The aim of the research is to analyze the extent to which Chat GPT influences students' ability to prepare practical reports effectively and efficiently. The research method used is a quantitative method by collecting data through a questionnaire using a Google form which was distributed to active students of the Informatics Engineering study program at Muhammadiyah University of Ponorogo. The results of this research show that Chat GPT significantly increases students' understanding of practical material, helps students solve problems encountered in preparing reports, and reduces the time needed to complete reports. The implication of this research is the importance of integrating AI technology in the learning process to increase student academic effectiveness and efficiency. However, appropriate policies are needed to avoid excessive dependence on this technology.