This study is motivated by the declining revenue of Kaliwungu Printing every month, which is caused by the inconsistency in product promotion. As a result, the lack of regular and comprehensive promotional efforts has led to limited public awareness of Kaliwungu Printing. The purpose of this research is to develop a structured and comprehensive promotional tool to showcase the products offered by Kaliwungu Printing.
This research employs a qualitative method to obtain accurate and relevant data. Data collection techniques include observation, interviews, and questionnaires. The respondents consist of media experts, content experts, and a group of five employees from Kaliwungu Printing. The validity of the data is measured using the rating scale method proposed by Sugiyono. The results of the study show a score of 30 from media experts, 36 from content experts, and an 84% validity rate from the group assessment. Based on the overall evaluation indicators, these results fall into the "very good" category. Thus, the study concludes that the proposed promotional tool is suitable for implementation and can be utilized effectively

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).

Negara yang memiliki pulau terbesar di dunia adalah Indonesia, dengan bangsa yang mewujudkan berbagai seni dan kebudayaan, salah satunya rumah adat. keragaman bentuk dan arsitektur rumah adat memiliki kepentingan, fungsi integritas, dan arti adat kebudayaan di balik corak bangunannya sehingga menjadi satu di antara karakterisasi kebudayaan tertinggi dalam suatu golongan suku atau bangsa. Penelitian ini bertujuan untuk memperkenalkan rumah adat asal Timor Tengah Utara (TTU) dan melestarikannya kepada masyarakat umum melalui teknologi Augmented Reality. Metode yang digunakan Marker Based Tracking, Marker Based Tracking adalah salah satu teknik yang digunakan dalam teknologi Augmented Reality untuk menampilkan objek virtual di dunia nyata. Dalam metode ini, sebuah marker atau penanda fisik digunakan sebagai referensi untuk menampilkan objek virtual. Marker tersebut dapat berupa gambar, simbol, atau pola yang memiliki desain khusus. Pemanfaatan teknologi ini diharapan dapat meningkatkan pemahaman masyarakat terhadap bentuk, fungsi, dan nilai budaya rumah adat asal Timor Tengah Utara (TTU).

Public economic policy plays a crucial role in balancing market efficiency and social justice. This article aims to analyze how optimizing public policy can address major challenges such as negative externalities, limitations in public goods provision, income inequality, and unfair taxation systems. This study employs a qualitative approach through literature review focusing on recent academic works. The findings suggest that data-driven and holistic economic public policies can significantly contribute to enhancing social welfare and promoting inclusive economic growth. Strengthening implementation and fostering cross-sector collaboration are essential to ensure more targeted and sustainable policy outcomes.

Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.

This study aims to evaluate production waste at PT XYZ, a food manufacturing company in Bandung, Indonesia, which experiences inefficiencies caused by overproduction and product defects. A descriptive qualitative method with a case study approach was used, employing data from production reports, field observations, and interviews. The analysis was conducted using the 7 Waste framework, the 5S workplace organization method, and the PDCA (Plan-Do-Check-Act) cycle. The findings show that the main types of waste at PT XYZ are overproduction and defects, which are caused by inaccurate demand forecasting, ineffective layout, and inconsistent quality control. By implementing 5S and PDCA, the company successfully reduced waste, improved workspace organization, and increased process efficiency. This study provides practical insights for manufacturing companies seeking to implement lean strategies for continuous improvement.

This study aims to examine the effectiveness of using UPIN (Uno Pintar) media based on the flipped classroom approach in enhancing the focus and learning motivation of eighth-grade students at SMP Negeri 14 Malang. The primary focus of this research is to describe how the integration of UPIN media with the flipped classroom model can strengthen students’ concentration during the learning process and to evaluate its impact on improving learning motivation. A qualitative approach with a case study design was employed, utilizing observation, interviews, questionnaires, and documentation as data collection techniques. The results indicate that the combination of self-directed learning before class and interactive in-class activities through educational games significantly increases student engagement. Students became more focused, enthusiastic, and motivated in participating in lessons, which positively affected their academic performance. The study concludes that the UPIN learning media innovation, integrated with the flipped classroom approach, is effective in creating an active and enjoyable learning environment that fosters motivation and focus among students in the digital age.

The performance evaluation of student organizations (Ormawa) at Politeknik Negeri Bengkalis is an essential part of the end-of-term management assessment, serving as a form of accountability and a basis for future development. The manual process used so far often takes considerable time and is prone to errors. To address this issue, a web-based decision support system was developed using the Multi-Factor Evaluation Process (MFEP) method, which applies a weighting system in the decision-making process. The system was developed using the Rapid Application Development (RAD) approach, enabling a fast and efficient development process through a linear and sequential methodology. The implementation of the MFEP method in the system achieved an accuracy rate of 88.88%, based on a comparison between field data and system processing results.

Education is the main pillar in the development of quality human resources, especially in developing countries like Indonesia. Madrasah Ibtidaiyah (MI) as a basic education institution has an important role in shaping students' character, knowledge and skills at an early age. However, the challenges faced in education in MI are often related to less innovative teaching methods, which have an impact on students' low learning motivation and academic results. The constructivist approach, which emphasizes the active role of students in the learning process, has been proven effective in improving the quality of learning. However, its application in teacher education in MI is still limited. This study aims to explore the application of the constructivist approach in teacher education in MI and its implications for learning quality. By applying this approach, it is expected that prospective teachers can develop better pedagogical skills, understand the different learning styles of students, and meet the demands of a curriculum that emphasizes 21st century skills. The research also highlights the importance of creating an inclusive learning environment where all students can actively participate. The results of the study are expected to contribute significantly to the development of education in Indonesia, particularly in the context of basic education, as well as providing insights into the integration of constructivist approaches in the teacher education curriculum at Madrasah Ibtidaiyah.

This research was conducted at PT. Mega Prakarsa Engineering Indonesia involving 153 respondents as customer representatives. The purpose of this study was to determine the role of mediation of customer satisfaction on the influence of service quality and social media marketing on customer loyalty. The results showed that service quality has a significant effect on customer satisfaction. Social media marketing has a significant effect on customer satisfaction. Service quality has a significant effect on customer loyalty. Social media marketing has a significant effect on customer loyalty. Customer satisfaction has a significant effect on customer loyalty. Service quality has a significant effect on customer loyalty through customer satisfaction. Social media marketing has a significant effect on customer loyalty through customer satisfaction.