Pedagogical competence is one of the important competencies for teachers to have. As one of the LPTK that produces prospective teachers, XYZ University has a role in ensuring that its students achieve pedagogical competence by organizing the Clinical Placement Practicum program. This research is an evaluation study using the Context Input Process Product (CIPP) model. Data collection was conducted through interviews, questionnaires, and document studies. The validity of the data is ensured through the process of source triangulation. Data analysis is conducted through the stages of data reduction, presentation, and conclusion drawing. Here are the evaluation results: a) Context, it appears that the program's objectives align with the achievement of the Faculty's graduate profile, particularly in the aspect of competence, specifically in pedagogical competence; b) Input, the resources used have not fully supported the effectiveness of the PPK program implementation, especially in supporting the pedagogical competence of students placed in PAUD; c) Process, the DPL and Mentor Teachers have made efforts with various mentoring actions. However, the guidance provided needs to be further improved; d) Product, students are able to provide emotional support to children, listening skills, skills in interacting with early childhood children, their ability to group early childhood children according to their developmental aspects, and they have consistently applied rules and procedures. However, there is still a need to improve the ability to plan appropriate methods and teaching aids, provide scaffolding, reinforce the child's potential, maintain a reflective and evaluative attitude personally, and use appropriate methods and teaching aids.

Tujuan penelitian yang dilakukan adalah menganalisis tingkat kemampuan siswa melalui penerapan asesmen diagnostik kognitif pada mata pelajaran matematika. Matematika masih menjadi momok bagi sebagian besar siswa termasuk di SD Kanisius Duwet. Padahal matematika memiliki banyak kegunaan bagi pengembangan ilmu pengetahuan lainnya. Oleh karena itu guru harus mampu merancang pembelajaran matematika yang menarik dan menyenangkan sesuai dengan karakteristik siswa melalui hasil analisis tingkat kemampuan siswa pada asesmen diagnostik kognitif. Penelitian ini menggunakan metode eksperimen dengan pendekatan kuantitatif. Teknik pengumpulan data yang digunakan adalah angket, dan wawancara. Subjek penelitian adalah siswa kelas VI A dengan jumlah 30 siswa. Analisis data dilakukan melalui reduksi data, penyajian data dan penarikan kesimpulan. Hasil analisis perencanaan diperoleh data bahwa siswa kelas VI A SD Kanisius Duwet memiliki tingkat kemampuan yang berbeda-beda dibuktikan dengan hasil analisis diperoleh 20% siswa berada pada tingkat kemampuan tinggi 33,3% siswa dengan tingkat kemampuan sedang, dan 46,6% siswa yang berada pada tingkat kemampuan rendah.

Kurangnya variasi model dan media pembelajaran membuat siswa sulit fokus, tidak tertantang untuk berpikir kritis, dan kurang termotivasi pada kegiatan pembelajaran IPA. Penelitian ini dilakukan untuk mengetahui apakah model PBL dengan aplikasi Qreatif efektif meningkatkan kemampuan berpikir kritis dan semangat belajar siswa kelas IV pada pelajaran IPA di SD Pesisir. Penelitian ini menggunakan metode kuantitatif dengan desain kuasi eksperimen kelompok kontrol. Hasil analisis menggunakan uji t-test pada keterampilan berpikir kritis dan motivasi belajar menunjukkan nilai signifikansi <.001 (p < 0.05), yang mengindikasikan adanya perbedaan yang sangat signifikan antara kelas eksperimen dan kelas kontrol. Dengan demikian, penerapan model PBL berbantuan aplikasi Qreatif terbukti lebih efektif dibandingkan model konvensional dalam meningkatkan keterampilan berpikir kritis dan motivasi belajar siswa pada pembelajaran IPA di SDN Trajeng 1 Kota Pasuruan.

This study aims to explore the extent to which promotion and service quality affect GrabFood customer satisfaction among Generation Z who live in Boja Village. This study uses a quantitative method with a survey approach, where data is obtained from 100 respondents who are active users of GrabFood through the distribution of questionnaires. To test the proposed hypothesis, multiple linear regression analysis was used. The findings of the study showed that promotion (with a regression coefficient of 0.45) and service quality (regression coefficient of 0.55) both had a positive and significant impact on the level of consumer satisfaction and the Rvalue of 2 was 0.6 that 60% of the consumer satisfaction variable could be explained by the variables of promotion and service quality, The validity of the regression model used was confirmed through a classical assumption test. These results highlight the importance of synergy between the implementation of effective promotional strategies and service quality improvement to create an optimal customer experience. The practical implications of this research can be used by GrabFood management in designing more efficient marketing strategies to increase customer satisfaction and loyalty. In conclusion, the synergy between effective promotional strategies and improving service quality is the key to creating an optimal customer experience and increasing the satisfaction and loyalty of GrabFood users in the region

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.

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.