This study explores how company size affects environmental costs and carbon emission transparency in Indonesia's metal and mining sector. Despite its economic importance, the industry contributes heavily to environmental harm. Analyzing secondary data from 20 listed companies, most are classified as large firms. Environmental cost reporting varies greatly, from IDR 35 million to IDR 708 billion. Five companies lack valid carbon data or fall short of OJK standards, while PT Trimegah Bangun Persada records the highest emissions. Larger companies tend to report more extensively, though not always comprehensively. The study highlights the need for stricter regulations to ensure environmental reporting transparency.

This study aims to examine the effectiveness of using the Kahoot! application as a tool for student learning evaluation at the elementary school level. Using a literature review method, the researchers analyzed various relevant journals and scientific articles to assess the advantages, limitations, and impacts of Kahoot in the evaluation process. The findings indicate that Kahoot effectively enhances student motivation and participation, improves learning outcomes, and facilitates teachers in conducting interactive and enjoyable assessments. This application also supports the integration of online learning and can be flexibly used across multiple platforms. However, several challenges remain, such as limited internet access and the lack of ICT competence among teachers. Therefore, institutional support in the form of training and infrastructure provision is necessary to optimize the use of Kahoot in the learning process.

Traditional games play an important role in the motor development of elementary school children. However, cultural changes and increasing use of technology have caused these games to be played less and less. This study aims to analyze the effect of traditional games on the motor development of elementary school children. Using a quasi-experimental method with a pretest-posttest control group design, this study involved 20 4th grade elementary school students who were divided into experimental and control groups. The experimental group was given treatment in the form of traditional games such as hopscotch, gobak sodor, and jump rope for 4 weeks. The results showed a significant increase in the gross motor development of children who participated in traditional games compared to the control group. In addition to motor benefits, traditional games also contribute to children's social, cognitive, and character development. Therefore, the integration of traditional games into the physical education curriculum in elementary schools is highly recommended to support children's holistic development.

This study aims to examine the strengthening of collaborative learning strategies in Islamic Religious Education (PAI) to develop students' social intelligence. Collaborative learning is considered an effective approach to improving social skills and interactions among students, which are crucial in character development based on Islamic values. Through a literature review, this research analyzes various references discussing the basic concepts, implementation, and role of collaborative strategies in building social intelligence according to Islamic teachings. The findings indicate that collaborative learning strategies enhance students' communication skills, empathy, and social responsibility, while also reinforcing religious values in social life. However, the success of its implementation is influenced by supporting factors such as teacher support, learning facilities, as well as challenges like disparities in students' abilities and limited time. This research is expected to contribute to the development of collaborative-based learning methods in religious education in Indonesia.

Crude Palm Oil (CPO) contains ?-carotene as source of pro-vitamin A. Alternative adsorbent that can be used to adsorb ?-carotene is Palm Oil Fly Ash (POFA). This study aims to determine the ability of POFA activated with H3PO4 and Cu/Zn impregnation to adsorb ?-carotene from CPO to obtain characteristics data of modified POFA and its ability to adsorb ?-carotene. The main materials used were CPO and POFA. The parameters observed were the characterization and performance test of 9% H3PO4-activated POFA produced at various heating times for 90, 150, and 210 minutes with Cu/Zn impregnation. BET analysis showed the POFA with heating time of 90 minutes had the largest surface area of 19.4785 m2/g, XRD showed the presence of CuO, ZnO, quartz, and SiO2 diffraction patterns, FTIR showed that the POFA contained O-H groups, O-H, Si-H, Al-O, asymetric Si-O, and Si-O-Si bending vibrations, and SEM-EDX results showed that POFA has hollow pores and a rough spherical surface. Based on the results of UV-Vis Spectrophotometry analysis, the most optimal result was obtained in the ?-carotene adsorption process with POFA-modified 2 variations of activation heating time for 150 minutes, which succeeded in adsorbing 86% of ?-carotene.

Kulit terong ungu merupakan salah satu varietas yang berwarna ungu yang mengandung antosianin. Senyawa antosianin berfungsi sebagai antioksidan bagi tubuh yang dapat mencegah stress oksidasi. Kulit terong ungu memiliki potensi sebagai bahan dasar pembuatan minuman olahraga karena kandungan senyawa antosianin. Penelitian ini bertujuan untuk menganalisis karakteristik sensori dan fisikokimia. Penelitian ini menggunakan Rancangan Acak Lengkap dengan dua kali pengulangan. Terdapat 3 formulasi konsentrasi bubuk kulit terong ungu yaitu F1 (22,9 g), F2 (27,9 g), F3 (33,6 g). Minuman disimpan selama 28 hari pada suhu 20C -80C dan dianalisis setiap 7 hari. Parameter yang diamati adalah pH, total padatan terlarut, dan uji sensori (warna, aroma, kekentalan, penampakan fisik). pH dan total padatan terlarut di uji menggunakan Kruskall-Wallis, uji sensori dianalisis dengan teoritis deskriptif. Hasil pengamatan menunjukan tidak ada pengaruh yang signifikan (p > 0,05) antara perbedaan formulasi dengan nilai pH dan total padatan terlarut. Uji sensori menunjukan bahwa perubahan paling nyata terjadi pada aroma. Warna, kekentalan, dan penampakan fisik cenderung stabil.

Handwritten digit recognition is one of the key challenges in the field of digital image processing and artificial intelligence, with significant potential in various applications such as automatic form input systems, handwritten data correction, and attendance systems based on handwriting. This study aims to develop a web-based information system capable of automatically recognizing handwritten digits using the K-Nearest Neighbors (KNN) classification method. The system is designed through several main stages, including image preprocessing (conversion to grayscale, thresholding, and image size normalization), feature extraction using the zoning technique, and classification using the KNN algorithm. This research utilizes the MNIST dataset, which contains thousands of handwritten digit images ranging from 0 to 9. The system is developed using the Google Colab platform, supported by Python libraries such as OpenCV, NumPy, and Scikit-learn. Test results show that the system can achieve an accuracy of over 90% at certain K values, indicating that the KNN method is quite effective and efficient in recognizing handwritten digit patterns. This system is expected to be applicable for various digitization needs of handwritten numbers in education, administration, and information technology sectors

Dalam era modern ini, olahraga virtual atau esports semakin berkembang sebagai bukti kemajuan pada bidang olahraga. Penelitian ini bertujuan untuk mengetahui pengaruh dari konsentrasi, motivasi intrinsik, dan pengalaman bertanding terhadap prestasi atlet esports eFootball Mobile. Penelitian ini menggunakan desain korelasional melalui uji survey dan tes. Tiga puluh atlet berprestasi menjadi sampel pada penelitian ini. Penelitian ini menggunakan instrumen berupa kuesioner, grid concentration test, dan skor prestasi atlet. Hasil analisis pada uji koefisien determinasi menunjukkan jika pengaruh konsentrasi, motivasi intrinsik, dan pengalaman bertanding terhadap prestasi atlet esports efootball mobile ditunjukkan dengan koefisien R square memiliki nilai sebesar 0.670 atau 67.0%, sisanya 33.0% dipengaruhi oleh faktor lain yang tidak termasuk dalam penelitian ini. Sementara pada hasil analisis uji regresi linear berganda dapat disimpulkan bahwa variabel Pengalaman Bertanding memiliki pengaruh terbesar terhadap variabel Prestasi Atlet sebesar 38.7%. Disusul oleh Motivasi Intrinsik dengan pengaruh sebesar 31.0% serta variabel Konsentrasi dengan pengaruh sebesar 30.2%. Variabel konsentrasi, motivasi intrinsik, dan pengalaman bertanding secara simultan memiliki pengaruh signifikan terhadap prestasi atlet esports eFootball Mobile, yang dibuktikan oleh nilai signifikansi sebesar 0.000.

This research aims to explore the effectiveness of Project-Based Learning (PBL) based learning material design that utilizes local wisdom in fostering creative thinking abilities during Society 5.0. This research uses a qualitative method with a case study design. Data was collected through participant observation, in-depth interviews with students and lecturers, document analysis of teaching materials, and documentation studies. The research results show that the design of PBL-based teaching materials with the integration of local wisdom is effective in increasing student involvement in learning and training their creative thinking skills. The project-based learning process encourages collaboration between students and intense interaction with local communities, while the integration of local wisdom makes learning more relevant and contextual for students. The implications of this research show the importance of developing learning design skills for lecturers, active involvement of students in learning, and the need for further research to explore the effectiveness of this approach in a broader educational context. It is hoped that the results of this research can contribute to improving the quality of education in the Society 5.0 era.

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).