Publication Search

This qualitative study, using a phenomenological approach, aims to uncover, study, and deeply understand students' perspectives regarding the curriculum transformation for 21st-century learning they experience in elementary schools. The phenomenological approach was used to explore students' direct experiences as the primary subjects in the learning process, thus gaining a comprehensive understanding of the meaning of curriculum change from their perspectives. Data collection was conducted through in-depth interviews with upper-grade students in elementary schools in Jambi City. The results indicate that the curriculum transformation has a significant impact on students' learning styles, particularly through the implementation of project-based learning that integrates the use of digital technology. This learning model encourages students to be more active, creative, and collaborative in completing learning tasks. Furthermore, student-centered learning makes them the primary actors in the learning process, while teachers act as facilitators. This curriculum transformation also helps develop 21st-century skills, such as critical thinking, communication, collaboration, and digital literacy in elementary school students.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

India faces a persistent civic deficit reflected in weak civic sense, low compliance with public norms, and a structural imbalance between rights and duties. Although ancient Indian traditions emphasized collective responsibility, colonial legacies, institutional weaknesses, and growing socioeconomic disparities have eroded civic culture. This study employs qualitative content analysis using historical texts, constitutional provisions, judicial interpretations, comparative international case studies, and interdisciplinary scholarly literature. Thematic coding is used to analyze the socio-legal, political, and educational roots of India’s civic deficit. Findings reveal that India’s civic deficit stems from interconnected structural factors: weak enforcement of laws, lack of experiential civic education, limited trust in institutions, political interference, and socioeconomic inequality. Comparative insights from Japan, Nordic countries, Singapore, and Brazil demonstrate that civic sense improves when experiential education, strong institutional integrity, and participatory governance coexist. Addressing India’s civic deficit requires systemic reforms: reorienting civic education toward experiential learning, strengthening judicial independence, enforcing rules consistently, expanding participatory mechanisms, and promoting community-level ownership. Comparative models show that long-term cultural change requires both structural reforms and citizen-centered engagement.

This article explores the concept of a “House of Faith” as a holistic educational framework designed to build students’ character based on religious values. The fundamental idea of the House of Faith is likened to a school environment that provides a comfortable and safe space for students to develop their faith and noble character through learning activities connected to religious teachings. A religious school environment is created through everyday atmospheres that encourage the practice of worship, the presence of religious symbols, and community activities that strengthen students’ spirituality. Programs to reinforce religious spirit include activities such as communal prayers, religious studies, and social projects designed to gradually enhance students’ spiritual awareness. Teachers and educational staff play a highly effective role, acting as role models and facilitators in the learning process. Their approach to instilling faith and moral values involves interactive methods such as group discussions, inspirational stories, and personal reflection, all adapted to students’ developmental stages. Collaboration between the school and parents becomes a key factor in ensuring consistent support. This is carried out through regular meetings, joint workshops, and open communication that reinforces family values. The impact of religious spirit on students can be seen in increased discipline, social empathy, emotional resilience, and stronger academic motivation. However, implementation faces challenges such as limited curriculum time, diversity of student backgrounds, and cultural resistance. These challenges are addressed through solutions, including regular teacher training, program evaluation, and the integration of technology for distance learning. Overall, the article emphasizes that a school functioning as a House of Faith can shape young generations who are not only intellectually capable but also strong in faith and morals, contributing to a more harmonious and meaningful society.

Pertumbuhan eksponensial Internet of Things (IoT) menghasilkan tantangan keamanan jaringan yang signifikan, terutama intrusi jaringan, di mana metode deteksi tradisional gagal melawan serangan adaptif. Kajian ini menyajikan Systematic Literature Review (SLR) berpedoman PRISMA untuk memahami pemanfaatan Artificial Intelligence (AI) dalam deteksi anomali pada IoT. Tujuannya adalah mengidentifikasi algoritma AI yang dominan, mengevaluasi performa, dan menilai pertimbangan efisiensi energi dalam penelitian terkini. SLR menganalisis 24 studi primer dari basis data Scopus yang diterbitkan antara tahun 2021 hingga 2025. Temuan utama menunjukkan dominasi algoritma deep learning: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), dan model hibrida/ensemble. Pendekatan ini terbukti sangat efektif, dengan akurasi deteksi seringkali melebihi 99% pada dataset benchmark. Selain itu, efisiensi sumber daya diidentifikasi sebagai isu sentral. Solusi yang diimplementasikan meliputi optimalisasi model ringan, kompresi Quantized Autoencoder (QAE), dan seleksi fitur, yang secara signifikan mengurangi konsumsi energi dan beban komputasi. Penelitian ini memberikan gambaran komprehensif mengenai state-of-the-art deteksi anomali IoT berbasis AI, menegaskan perlunya keseimbangan antara akurasi tinggi dan efisiensi sumber daya. Implikasinya, riset mendatang disarankan untuk memprioritaskan pengujian di dunia nyata, mengintegrasikan Explainable AI (XAI), dan mengembangkan metrik efisiensi yang terstandarisasi demi solusi keamanan IoT yang lebih praktis dan terpercaya.

Penyakit jantung tetap menjadi penyebab utama kematian di seluruh dunia, sehingga menekankan perlunya deteksi dini dan langkah pencegahan aktif melalui pendekatan yang didasarkan pada analisis data. Penelitian ini bertujuan untuk memprediksi kemungkinan terjadinya penyakit jantung dengan memanfaatkan berbagai metode pembelajaran mesin, yang bergantung pada indikator kesehatan seperti nilai tekanan darah, tingkat kolesterol, indeks massa tubuh, serta pengukuran denyut nadi. Penelitian ini menerapkan teknik pembelajaran terawasi, meliputi Regresi Logistik, Pohon Keputusan, Hutan Acak, dan Mesin Vektor Pendukung, untuk mengevaluasi efektivitas masing-masing model dalam menentukan kondisi kesehatan pasien. Data yang digunakan berasal dari repositori kesehatan yang dapat diakses secara gratis, mencakup 303 catatan medis pasien, di mana setiap catatan ditandai oleh 14 atribut kesehatan yang berbeda. Untuk memastikan keandalan data, beberapa langkah pra-pemrosesan diterapkan, seperti normalisasi, seleksi fitur, dan penanganan data yang hilang. Temuan eksperimen mengungkapkan bahwa algoritma Hutan Acak menunjukkan akurasi tertinggi sebesar 92,3%, mengungguli model lainnya dalam aspek presisi, recall, dan skor F1. Hal ini menandakan bahwa metode Hutan Acak unggul dalam mendeteksi pola dan faktor yang memperbesar risiko penyakit jantung. Penelitian ini berkontribusi pada pengembangan sistem diagnostik cerdas, yang dapat mendukung tenaga medis dalam pengambilan keputusan awal yang didasarkan pada bukti kuat. Kesimpulan dari studi ini menyoroti pentingnya memasukkan teknologi pembelajaran mesin ke dalam ekosistem layanan kesehatan digital guna meningkatkan ketepatan diagnosis dan memperbaiki hasil kesehatan pasien.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

This study aims to explore the effectiveness of an integrated Islamic learning model in optimizing intellectual (IQ), emotional (EQ), and spiritual (SQ) intelligence in early childhood at an integrated Islamic Early Childhood Education (PAUD) in Kotabumi. The study used a qualitative method with a case study approach. Subjects included teachers, parents, and children aged 4–6 years, while data were collected through participant observation, in-depth interviews, and learning documentation. The results indicate that the integrated Islamic learning model positively influences the development of children's IQ, EQ, and SQ. Activities such as reading the Hijaiyah alphabet, Islamic logic games, and Quranic stories enhance children's cognitive skills. Activities based on Islamic values, such as moral stories, empathy exercises, sharing, and group work, strengthen emotional intelligence, while prayer, Surah memorization, and the Prophet's example foster spiritual intelligence. The holistic integration of these three dimensions of intelligence creates intelligent, emotionally mature, and spiritually strong children.

Transformasi digital yang pesat dalam pendidikan tinggi menuntut pengalaman pengguna yang optimal pada sistem e-learning. Sistem Informasi Akademik (SIAKAD) Universitas STEKOM yang berlaku saat ini menghadapi masalah UX mendasar, terutama karena fitur pembelajaran bercampur dengan fungsi administratif yang kompleks. Hal ini menciptakan Extraneous Cognitive Load (ECL) tinggi dan alur pengguna (user flow) yang panjang saat mengakses kelas, yang pada akhirnya menurunkan kemudahan penggunaan dan motivasi mahasiswa. Penelitian ini bertujuan untuk mengoptimalkan pengalaman pengguna sistem informasi perkuliahan Universitas STEKOM melalui perancangan dan pengembangan prototipe berbasis arsitektur Single Page Application (SPA) menggunakan framework React. Metodologi yang digunakan adalah Research and Development (R&D) dengan pendekatan kuantitatif. Arsitektur sistem memanfaatkan desain berbasis komponen React untuk memisahkan secara jelas modul pembelajaran dari tugas administratif. Optimasi performa dilakukan melalui implementasi lazy loading, code splitting, dan DOM virtual untuk memastikan responsivitas sistem yang optimal. Efektivitas sistem yang dikembangkan akan dievaluasi secara kuantitatif menggunakan User Experience Questionnaire (UEQ) (mengukur enam dimensi, termasuk efficiency dan attractiveness) dan System Usability Scale (SUS). Target keberhasilan adalah pencapaian skor SUS minimal 75 (di atas rata-rata industri) dan minimal pengurangan 40% pada Time-on-Task untuk aktivitas pembelajaran inti. Penelitian ini berkontribusi sebagai model rujukan praktis untuk adopsi modern web technologies (SPA React) di institusi pendidikan tinggi lainnya dan meningkatkan efektivitas pembelajaran dengan meminimalkan beban kognitif yang tidak perlu (ECL).

This community engagement program aims to strengthen the economic capacity of community groups in Azerbaijan through an integrated model combining artificial intelligence (AI)–supported learning, digital skills enhancement, and practical economic training. Although Azerbaijan has demonstrated rapid digital progress, micro-entrepreneurs still face challenges related to limited digital literacy, insufficient financial planning capabilities, and the absence of structured mentorship that integrates emerging technologies. This program utilizes a participatory development approach involving baseline assessment, AI-driven training modules, intensive workshops, and mentoring over two months. A total of 45 participants from three community clusters were included. Quantitative findings reveal a 63% increase in digital literacy scores and a 67% adoption rate of AI-supported tools for marketing and financial planning. Qualitative insights show improved confidence in business decision-making, better understanding of market dynamics, and enhanced practical skills in budgeting and content creation. Participants also reported early indicators of improved sales performance and customer engagement. The integration of AI tools into community-based capacity-building initiatives demonstrates high effectiveness in accelerating digital readiness and entrepreneurial resilience. The program concludes that structured AI-based interventions can significantly empower grassroots communities in emerging economies. Limitations include the small sample size and short mentoring duration. Future research should explore larger populations and long-term economic outcomes.

Polusi udara partikulat halus (PM2,5) merupakan ancaman serius bagi kesehatan masyarakat di Kabupaten Brebes, Jawa Tengah. Faktor penyumbang utamanya adalah emisi kendaraan di jalur Pantura, aktivitas industri perikanan, serta konsentrasi tinggi selama musim kemarau (Juni–November). Tidak adanya model peramalan sub-jam yang akurat menghambat pengembangan sistem peringatan dini yang efektif. Penelitian ini mengembangkan dan mengevaluasi model deep learning berbasis Transformer untuk memprediksi konsentrasi PM2,5 dengan resolusi waktu 15 menit. Data yang digunakan berasal dari NASA GEOS-CF (band PM25_RH35_GCC) yang diakses melalui Google Earth Engine menggunakan API Python. Dataset mencakup periode 1 Januari hingga 22 November 2025, menghasilkan 7.813 observasi per jam, yang kemudian diinterpolasi linear menjadi 31.249 titik data dengan resolusi 15 menit. Arsitektur Transformer terdiri dari 3 lapis enkoder, 4 kepala perhatian multi-head, dimensi embedding 128, dimensi feed-forward 256, panjang sekuen 60 timestep, dan augmentasi fitur menggunakan rerata bergulir (*rolling mean*, jendela = 3) dan beda pertama (*first difference*). Pelatihan dilakukan dengan TensorFlow-Keras, pengoptimal Adam, penjadwal peluruhan kosinus (*cosine decay scheduler*), dan fungsi kerugian Huber. Pembagian data dilakukan secara kronologis: 70% pelatihan, 30% validasi. Evaluasi pada set uji independen (16 Agustus–21 November 2025, 9.357 observasi atau 97 hari 11 jam 15 menit) menghasilkan MAE 0,7691 µg/m³, RMSE 1,2052 µg/m³, R² 0,9945, dan *Explained Variance Score* 0,9948. Model ini mampu menggambarkan variasi diurnal dan anomali musiman secara akurat, jauh melampaui model LSTM dan GTWR konvensional. Penelitian ini memberikan kontribusi signifikan di bidang Teknologi Informasi melalui kerangka kerja pengolahan *big data* satelit untuk aplikasi lingkungan.

This research focused on improving fine motor development in early childhood through creative learning activities that utilize recycled materials, designed and implemented using the ADDIE instructional model. The study involved children from class B2 at TK Prawidya Darma and was conducted systematically through five stages, namely analysis, design, development, implementation, and evaluation. Throughout the learning process, children were engaged in a variety of hands-on craft activities, including cutting, folding, gluing, and assembling recycled objects, which were intentionally structured to train hand–eye coordination, finger flexibility, and concentration. The results demonstrate that learning activities based on recycled materials offer rich and meaningful experiences for young learners. Children showed noticeable improvements in accuracy, independence, and creative expression while completing tasks. In addition, the use of recycled materials helped cultivate environmental awareness from an early age, as children learned the value of reusing everyday objects. The classroom atmosphere became more interactive and enjoyable, with students actively participating and showing enthusiasm during the activities. Overall, the findings indicate that creative recycling-based activities are effective in supporting fine motor skill development through enjoyable, practical, and environmentally friendly learning experiences. This study concludes that integrating recycled materials into creative activities can be an innovative, sustainable, and pedagogically valuable approach for early childhood education programs.

This study examines how equity-driven curriculum design can catalyze transformative leadership within multicultural school systems. As global classrooms become increasingly diverse, traditional curricula often fail to address disparities experienced by marginalized learners. Through qualitative library research and content analysis, this study synthesizes theoretical, empirical, and policy-oriented perspectives to explore how culturally responsive curricula promote inclusive learning ecosystems. The findings highlight that equity-driven curriculum design requires not only the integration of diverse cultural perspectives but also the dismantling of hidden power structures embedded in instructional practices. Transformative leadership emerges as a crucial driver, demanding cultural competence, collaborative decision-making, and critical pedagogical reflection from school leaders. The study emphasizes that effective equity implementation depends on teacher empowerment, distributed leadership models, and coherent policy frameworks that align curriculum, assessment, and institutional values. Moreover, technology-enhanced learning, accessibility principles, and Universal Design for Learning strengthen equity outcomes in increasingly digital environments. Overall, this research underscores the interconnectedness of curriculum, leadership, and policy in fostering democratic, socially just educational systems that affirm students’ cultural identities, reduce achievement gaps, and empower communities. The study concludes that achieving equity requires systemic transformation grounded in cultural responsiveness, shared accountability, and continuous professional development.

Higher education institutions are experiencing accelerated transformation driven by global competition, technological advancements, and shifting learner expectations. In this context, Internal Quality Assurance Cells (IQACs) serve a strategic role in enhancing teaching quality, strengthening learning processes, and ensuring curriculum relevance through the implementation of Outcome-Based Education (OBE) and pedagogical innovation. This study employs qualitative content analysis by synthesizing theoretical frameworks and recent empirical literature to develop an integrated model of IQAC-led academic transformation. The analysis highlights three key mechanisms: curriculum intelligence systems, pedagogical innovation ecosystems, and comprehensive faculty empowerment. Findings indicate that effective IQACs function not merely as compliance bodies but as catalysts of academic excellence by aligning curriculum with industry needs, fostering continuous faculty development, and promoting technology-enhanced learning and learning analytics. Moreover, phased OBE implementation—comprising capacity building, curriculum redesign, instructional support, and continuous evaluation—significantly strengthens learning outcomes and cultivates reflective academic culture. The proposed framework underscores the interconnected role of curriculum, pedagogy, and faculty capabilities as foundations for institutional excellence. Ultimately, the study affirms that IQACs’ strategic leadership in embedding OBE, leveraging educational technologies, and empowering faculty positions them as pivotal agents in driving sustainable educational transformation.

The purpose of this study is to discuss the introduction of the Pendet Dance in the Markandeya–Walailak cultural exchange program and to evaluate its effectiveness as a cross-cultural character education tool. To investigate program design, execution, and results, the study used a descriptive qualitative approach backed by the CIPP (Context, Input, Process, Product) evaluation model. During the cultural exchange activities, information was gathered through participant reflection, documentation, and observation. The results show that the program effectively combined the principles of character education with conventional performing arts. Thai students showed greater appreciation, tolerance, respect, and empathy for cultural diversity as well as a deeper comprehension of the Pendet Dance's meaning, symbolism, and cultural value. In addition, Indonesian students who served as facilitators saw improvements in their discipline, self-assurance, cultural adaptability, and communication abilities. The exercise emphasizes how traditional performing arts can be a powerful tool for international engagement and cultural diplomacy. All things considered, the program shows that cultural exchange programs can make a significant contribution to global character education, and it is advised that comparable projects be maintained and developed in subsequent international education partnerships.

The diversity of Indonesian society, which consists of various ethnic groups, religions, cultures, and races, represents a great potential for the creation of a harmonious social life. However, the reality shows that intolerance and narrow fanaticism still often arise and even begin to influence students in the educational environment. This has the potential to create social conflicts and damage the nation's character in the future. This study aims to examine the role of education in shaping students to avoid fanatic behavior and social conflicts. The research method used is descriptive qualitative with data collection techniques through relevant literature studies. The results of the study show that schools have a strategic role in instilling values of tolerance, inclusive attitudes, and religious moderation through learning, teacher role models, and a school culture that respects diversity. In addition, synergy between schools, families, and communities is needed to create an educational environment that is conducive to the development of students' character. Through the implementation of multicultural education and the strengthening of national values on a sustainable basis, students are expected to become a generation with strong character, tolerant, and able to live peacefully alongside others in a pluralistic society.

This study looks at how the Contextual Teaching and Learning (CTL) model is used in Christian Religious Education (CRE) and checks how it helps students develop their critical thinking abilities. In today’s education system, students are not just expected to remember Bible stories, but also to understand what they mean, think about the moral lessons, and use Christian values wisely in their everyday lives. However, past observations show that CRE classes often use a lot of lectures and memorization, which doesn’t give students much chance to think deeply or link what they learn to real-life situations. The CTL model provides a better way for learning by using seven key parts: constructivism, inquiry, questioning, learning community, modeling, reflection, and real-world assessmen. A descriptive qualitative design was employed involving CRE teachers and students selected purposively from classes that have integrated CTL in online and face-to-face settings. Data were collected through classroom observations, semi-structured interviews, and instructional documents. The analysis followed Miles and Huberman’s stages of data reduction, display, and conclusion drawing, supported by triangulation to strengthen credibility. The findings reveal that CTL enhances student engagement, promotes higher-order reasoning, and supports the internalization of Christian values in real-life contexts. Supporting factors include a positive school climate, parental involvement, and teacher readiness, whereas obstacles emerge from limited facilities, large class sizes, and insufficient teacher understanding of CTL. The study underscores the need for context-based and character-oriented instructional designs in CRE.

This study develops an architectural design framework integrating fun and playful spatial characteristics with futuristic, science-based approaches to enhance children’s engagement with science and technology learning. Traditional learning environments are often rigid and fail to stimulate curiosity, especially among children aged 7–11. Utilizing a qualitative conceptual analysis, this research synthesizes architectural theory, neuroscience learning principles, children’s spatial psychology, and science-based design methodologies. The results show that playful spatial elements—dynamic forms, color contrasts, sensory interactions, and imaginative environments—significantly improve motivation, exploration, and conceptual understanding. Futuristic design components further support scientific literacy by projecting technological possibilities and integrating interactive digital tools. The study produces a conceptual architectural model linking playfulness, scientific experience, and future-oriented visualization. It concludes that learning environments must be intentionally designed to merge emotional engagement, cognitive stimulation, and technological representation. The implications extend to the design of children’s museums, science centers, and early STEM learning facilities.

This study explores the major challenges faced by moral education in the modern era, particularly within Madrasah Ibtidaiyah, where rapid technological advancement, globalization, and shifting patterns of student interaction have significantly influenced the traditional process of moral development. The purpose of this research is to analyze teachers’ strategies in revitalizing moral education, identify the supporting and inhibiting factors in its implementation, and evaluate its impact on students’ discipline. Employing a qualitative approach with a case study design, data were collected through in-depth interviews with the school principal, Islamic ethics teachers, and students, as well as observations of daily moral habituation practices within the school environment. The findings indicate that effective revitalization of moral education occurs through an integration of teacher role modeling, consistent habituation of moral practices, and value internalization supported by interactive learning methods. These strategies contribute to improved student discipline, greater independence, and more stable behavioral patterns. However, the effectiveness of these efforts is still influenced by external factors such as excessive gadget use and limited continuity of moral guidance at home. The study provides an integrative and adaptive model of moral education that aligns with the characteristics of the digital generation and emphasizes the essential collaboration between teachers, schools, and families. The results highlight the importance of a responsive, contextually relevant approach to moral development that meets the evolving needs and challenges of 21st-century learners.

This study conceptually examines a self-supervised multi-scale fusion framework designed to enhance accuracy and computational efficiency in medical image segmentation, a domain where data scarcity and annotation cost remain major challenges. Traditional supervised approaches are constrained by their reliance on extensive labeled datasets, limiting applicability in real-world clinical environments. Self-supervised learning (SSL) mitigates this issue by extracting supervisory signals directly from unlabeled data, enabling the model to learn rich feature representations without human annotation. Simultaneously, multi-scale fusion architectures integrate global contextual information with fine-grained local features, supporting robust segmentation across varying anatomical structures and image resolutions. Through a qualitative methodology involving library research and content analysis, this study synthesizes state-of-the-art SSL-driven segmentation techniques and highlights how adaptive multi-scale fusion mechanisms address limitations of existing convolutional and transformer-based architectures. The analysis indicates that combining SSL and multi-scale strategies leads to more generalizable, scalable, and computationally efficient segmentation pipelines suitable for diverse medical imaging modalities. The proposed framework represents a promising direction for developing next-generation diagnostic tools capable of handling sparse labels, complex textures, and real-time deployment constraints.