Krida Wacana Christian University

Program Makan Bergizi Gratis (MBG) adalah kebijakan strategis pemerintah yang diperkenalkan di bawah Presiden Prabowo Subianto Indonesia, yang bertujuan untuk meningkatkan status gizi anak, mengurangi stunting, dan memperkuat kualitas sumber daya manusia. Meskipun manfaat program pemberian makan di sekolah telah terbukti secara global, studi komprehensif tentang efektivitas program MBG dalam konteks kapasitas regulasi dan kelembagaan Indonesia masih terbatas. Artikel ini menganalisis program MBG menggunakan pendekatan kebijakan publik berbasis bukti dan lensa ekonomi politik, berdasarkan tinjauan artikel jurnal internasional yang terindeks Scopus, buku-buku akademis, dan kerangka regulasi nasional. Analisis menunjukkan bahwa program pemberian makan di sekolah memiliki efek positif pada status gizi anak, kehadiran di sekolah, dan hasil belajar; namun, efektivitasnya sangat dipengaruhi oleh kualitas tata kelola, keamanan pangan, koordinasi lintas sektoral, dan keberlanjutan fiskal. Berdasarkan temuan ini, artikel ini berpendapat bahwa program MBG memiliki potensi yang cukup besar sebagai kebijakan transformatif. Meskipun demikian, tanpa desain fiskal yang baik, penguatan kapasitas kelembagaan, dan mekanisme akuntabilitas publik yang kuat, kebijakan ini berisiko menjadi intervensi populis yang mahal dan tidak berkelanjutan.

The Free Nutritious Meal (Makan Bergizi Gratis [MBG]) program is a strategic government policy introduced under President Prabowo Subianto of Indonesia, aimed at improving children's nutritional status, reducing stunting, and strengthening human resource quality. Although the benefits of school feeding programs are well established globally, comprehensive studies on the effectiveness of the MBG program within Indonesia's regulatory and institutional capacity context remain limited. This article analyzes the MBG program using an evidence-based public policy approach and a political economy lens, drawing on a review of Scopus-indexed international journal articles, academic books, and the national regulatory framework. The analysis indicates that school feeding programs have positive effects on children's nutritional status, school attendance, and learning outcomes; however, their effectiveness is strongly influenced by governance quality, food safety, cross-sectoral coordination, and fiscal sustainability. In light of these findings, this article argues that the MBG program holds considerable potential as a transformational policy. Nonetheless, without sound fiscal design, strengthened institutional capacity, and robust public accountability mechanisms, the policy risks becoming a costly and unsustainable populist intervention.

Remaja merupakan kelompok usia yang rentan mengalami masalah kesehatan mental, salah satunya depresi. Salah satu faktor yang diduga berhubungan dengan depresi adalah kualitas tidur. Selain itu penggunaan gawai juga sering dikaitkan dengan gangguan tidur dan kondisi psikologis remaja. Penelitian ini bertujuan untuk mengetahui hubungan antara kualitas tidur dan tingkat depresi pada remaja berusia 15-18 tahun. Penelitian ini menggunakan desain kuantitatif dengan pendekatan potong lintang (cross-sectional). Penelitian ini melibatkan 190 orang dengan menggunakan seluruh sampel yang terjangkau (total sampling) yang sudah memenuhi kriteria inklusi dan eksklusi. Penelitian menggunakan instrumen Patient Health Questionnaire-9 dan Pittsburgh Sleep Quality Index sebagai alat ukur. Hasil penelitian menunjukan bahwa terdapat hubungan yang signifikan dengan korelasi positif lemah antara kualitas tidur dengan tingkat depresi diukur dengan menggunakan uji Spearman (r= 0,274 p= 0,000), signifikan pada uji Chi-Square (c2= 10,128 p= 0,038) dan signifikan pada regresi linear (b= 0,657 p= 0,000). Hasil regresi dengan melibatkan variabel perancu penggunaan gawai yang diteliti secara bersamaan juga menunjukan hasil yang signifikan (p=). Dapat disimpulkan bahwa kualitas tidur dan pola penggunaan gawai, terutama sebelum tidur, berperan dalam tingkat resiko depresi pada remaja. Oleh karena itu, penting bagi remaja untuk menjaga kualitas tidur guna mendukung kesehatan mental.

Purpose: This study examines the effect of risk perception and electronic Word of Mouth (e-WOM) on investment decisions in cryptocurrency assets, mediated by trust, among members of the Pepe Coin Indonesia Community Telegram group. It focuses on how psychological and social factors such as risk perception and online community information influence investment decisions in the digital investment environment. Research Methodology: A quantitative approach was employed, using purposive sampling to select 100 active members of the Telegram group. Data were collected through an online questionnaire and analysed using PLS-SEM version 4 to test the hypotheses and the mediation model. Results: The results indicate that both risk perception and e-WOM significantly influence trust, with an R-square of 0.548, indicating a moderate effect. Furthermore, trust significantly mediates the relationship between perceived risk, e-WOM, and investment decisions, with an R-square of 0.583, also categorized as moderate. All relationships were statistically significant and positive. Conclusions: The study concludes that trust plays a critical role in investment decisions within cryptocurrency communities, acting as a mediator between risk perception, e-WOM, and investment decisions. Limitations: The study is limited by its sample size and cross-sectional design, which restricts generalizability and causality. Additionally, the focus on a single Telegram group may not fully represent the broader cryptocurrency investor population, and the use of a self-reported questionnaire may introduce response biases. Contributions: This study contributes to the literature on digital investment behaviour, emphasizing the importance of trust in mitigating risks and influencing investment decisions within crypto communities.

Convolutional Neural Networks (CNNs) continue to face several challenges in cataract classification using image data, particularly due to limitations in dataset size and variability in color, shape, and position. These constraints arise because CNNs primarily process local features within each layer. The research uses Vision Transformer (ViT) to capture global spatial relationships in front-eye images. ViT can detect cataracts more accurately than CNN-based owing to its capability of modeling long-range dependencies. The research aims to evaluate ViT’s performance on four publicly available cataract datasets using training time, accuracy, precision, recall, and F1-score. To assess the model’s reliability when working with a small number of training datasets, ViT’s performance is also be compared with ResNet-50 and EfficientNet-B7. The results indicate that ViT outperforms ResNet-50 in accuracy by 10%–31% and exceeds EfficientNet-B7 by 30%–41%. However, ViT requires approximately 16 seconds longer to train than ResNet-50 and EfficientNet-B7 due to its deeper architecture. Although ViT’s accuracy is 2.47% lower than previous studies using hybrid deep learning approaches, its parameter structure is simpler. Overall, the findings indicate that, compared with CNN-based models, ViT performs more effectively when trained on smaller datasets. Future research may incorporate segmentation techniques to further validate cataract detection in anterior eye images.