This article examines the important role of law in promoting economic development and ensuring the well-being of people in Indonesia. It analyzes the legal framework that governs economic activities, focusing on principles and regulations that facilitate economic growth, protect rights, and promote social justice. This article explores the evolution of Indonesia's legal system in response to economic challenges and opportunities, highlighting key laws and policies that have shaped the nation's economic landscape. This includes an analysis of the constitutional foundations of Indonesia's economic system, with a special emphasis on Article 33 of the 1945 Constitution, which mandates a "people's economy" based on the principles of kinship, cooperative ownership, and state control over vital resources. Furthermore, this article discusses the challenges and prospects of using the law as a tool to achieve sustainable and inclusive economic development. It emphasizes the importance of legal certainty, regulatory efficiency, and the protection of public interests in fostering a conducive investment climate and promoting equitable economic growth. The article also discusses the role of law in addressing important issues such as environmental protection, labor rights, and consumer protection, highlighting the linkages between economic development and social welfare. By examining Indonesia's experience, this article contributes to a deeper understanding of the complex relationship between law and economic development in the context of developing countries.

This study evaluates a training program designed to enhance the competence of religious educators, known as "penyuluh agama" in Indonesia, in utilizing ICT-based media for religious education. The program, conducted in Bangkalan Regency, aimed to equip participants with the knowledge and skills to develop and manage effective religious education media in the digital age. A needs assessment was conducted to inform the curriculum development, which covered fundamental aspects of ICT, introduction to ICT-based media, technical skills, and effective communication strategies. The training employed interactive workshops and provided ongoing support to participants. Evaluation results indicated significant improvement in participants' understanding and utilization of ICT-based media for religious education, as well as fostering a strong sense of community among them. This study highlights the effectiveness of ICT-based media training in empowering religious educators to adapt to the evolving needs of the community and expand the reach of religious education in the digital age.

Pancasila education in Indonesia is claimed to be the main foundation for the formation of national character. This education aims to teach basic values which include diversity, justice, unity, democracy and humanity, which are the foundation for the characteristics and morality of the Indonesian nation.This article reviews the importance of Pancasila education in forming national character values. Students are taught to respect differences of opinion, respect human rights, and practice democratic principles in everyday life. This all aims to create citizens who are fair, just and have a democratic culture. Apart from that, Pancasila education also develops a sense of love for the country and humanism. Through this education, students are taught to appreciate Indonesia's cultural and natural riches, and have the enlightenment to contribute to preserving the environment and overcoming social problems among the people.This, Pancasila education is not only about theoretical knowledge, but also about the practice of values in everyday life.

The era of the digital economy in Indonesia has made buying and selling transactions easier and faster to carry out. By using social media, consumers can channel their opinions, criticism and suggestions regarding the products/services they consume to business actors in various forms. One of them is in the form of a review (overview) of products/services expressed through photos or videos. However, this activity has reaped pros and cons in society, because it is considered that this activity actually tarnishes a good name. This research aims to examine the form of legal protection for the provisions for reviewing products on social media. The sources of legal materials used in this writing are firstly primary legal material sources which have binding force, secondly secondary legal material sources which are legal materials originating from the results of a literature review by reading books, legal journals, and articles that are in accordance with the law. the issue being faced. Data were analyzed using qualitative methods. This research uses a normative research type using a deductive thinking approach. The data that has been analyzed shows that legal protection for the provisions for reviewing products on social media is stated in Law Number 12 of 2005 article 23 paragraph (2) concerning Human Rights, Article 44 paragraph (3) UUPK, and criminal sanctions for perpetrators are stipulated in the UUPK which can be sentenced to prison and a fine.

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

Twitter is one of the media used by the Indonesian people to express their opinions regarding the 2024 Presidential Election. However, there is no scientific calculation that can determine the tone of public opinion regarding the 2024 presidential election. In this study, sentiment analysis was carried out on the tweets of the Indonesian people related to the 2024 Presidential Election (Pilpres 2024). The purpose of this study is to find out the opinions of Indonesian Twitter users regarding the 2024 Presidential Election using Natural Language Processing (NLP) Technology and Long Short Term Memory (LSTM) algorithms. NLP techniques are used to understand natural language and extract meaning from tweet copy, and LSTM is used to analyze the accuracy and accuracy of classification. The data used in this study was 1,004 tweets with the topic "Presidential Election", this data researchers obtained through the process of crawling using the tweet harvest library. In this study, 53.2% had positive emotions, 3.5% had neutral emotions, and 43.3% had negative emotions. 78% accuracy, 67% precision, and 67% recall.

Geographically and demographically, Indonesia has natural conditions that have the potential for floods disaster. There are at least 16,771 islands and 65,017 rivers that fill the archipelago. Unfortunately, the ever-increasing urban population accompanied by a lack of awareness and preparation for protecting the environment has resulted in a higher risk of flooding in urban areas. This study utilizes digital imagery to detect flood conditions in urban areas. In terms of access, digital images are available in urban CCTV monitoring systems as well as office areas, housing, and from people who have smartphones. The detection method used in this study is the VGG19 model which consists of 16 convolution layers and 3 standard classification layers. All convolution layers are divided into 5 blocks followed by a MaxPooling layer for each block to reduce the resolution of the input image. In the last layer, SoftMax layer is used to estimate the probability between flood labels and normal conditions. There are 4 parameters that were optimized during the VGG19 model training process, namely Batch Size, Learning Rate, Dropout and Epoch (training repetition). To test the proposed model, public datasets are used, namely the Roadway Flooding Image Dataset and Road Vehicle Images Dataset. The best flood detection results (or normal conditions) achieve the accuracy of 98.78%. As for the other three performance metrics, namely precision, recall and F1-score, they reach 99%. These results are generated by the VGG19 model with a Batch Size parameter of 20, a Learning Rate of 1e-5 (0.00001), 50% Dropout and 100 Epoch. The achievement values of the four metrics can be considered quite good, so that the VGG19 model has the opportunity to be developed for flood detection applications in order to monitor urban flood conditions.