Digital transformation is a strategic solution to address manual recording problems still prevalent among Micro, Small, and Medium Enterprises (MSMEs). This community service activity was conducted at Toko Anugerah, an MSME located in Mangkang Wetan, Semarang City, which previously relied on handwritten transaction records. Through a participatory approach, the service team provided training and assistance in implementing a digital recording system using Google Spreadsheet. The results showed significant improvements in recording efficiency, data accuracy, and ease of information access. In addition, the store owner and staff also demonstrated increased digital literacy. This success confirms that simple and appropriate technology, supported by intensive mentoring, can effectively encourage MSMEs to undertake digital transformation.

This study aims to develop an assessment instrument for critical thinking skills in elementary school science lessons on the topic of Earth's changes, in the form of essay questions that are tested for validity and reliability. The measurement of critical thinking skills uses indicators based on Ennis' framework, which includes providing simple explanations, building basic skills, concluding, providing further explanations, and setting strategies and tactics. The subjects of this study were 28 students from SDN 02 Pekiringanalit, Pekalongan Regency. Data analysis techniques involved content validity testing using Aiken's index. The validity of the assessment instrument was tested using the Rasch Model with the Ministep program to determine validity, calculate reliability, and obtain Cronbach's alpha values. This research is developmental research that applies the 4D development model, simplified into three steps: define, design, and development. The results showed that the critical thinking assessment instrument in the form of essay test questions received an Aiken's content and language validity index of 0.96, which falls into the very valid category. The validity per item obtained an Aiken's index of 0.89, placing it in the high validity category. The item analysis using the Rasch Model yielded a person reliability coefficient of 0.69 and 0.72, which is in the sufficient category. The item reliability was 0.86 and 0.87, which is considered good, and the Cronbach's alpha was 0.77, also considered good.
 

General Elections (Pemilu) are one of the important moments in democratic life in Indonesia. Indonesian elections are a true expression of democracy and a means for the people to declare their sovereignty over the state and government. Elections are based on Pancasila and the 1945 Constitution of the Republic of Indonesia and are held in the Unitary State of the Republic of Indonesia (NKRI) based on the principle of direct, universal, free elections. , confidential, honest and fair Political participation in a democratic country shows that the people fulfill the highest legitimacy of state power (popular sovereignty), this is manifested in the form of participation in democratic political parties (elections) for the welfare of the people. A very interesting group of voters to follow and study Furthermore, novice voters or new voters are voters who are taking part in an election for the first time, especially the millennial generation, who have a crucial role in determining the future direction of the nation. This research aims to explore the understanding and perceptions of novice voters towards the election process, through the film "Kejarlah Janji". This research uses a qualitative approach with data collection techniques through in-depth interviews and analysis of the film "Kejarlah Janji". The research results show that first-time voters tend to have a limited understanding of the election process, but the film "Kejarlah Janji" is able to raise political awareness and motivate them to take part in the election.

This study identifies and analyzes the key factors influencing students’ engagement in science practicum activities within the Open and Distance Higher Education (ODHE) context. 127 students from diverse regions of Indonesia participated in this quantitative study, which assessed perceptions regarding academic challenges, learning interaction, collaborative learning, and the use of technology in online laboratory settings. The results indicate that tutorial modality, employment status, academic achievement (GPA), and residential area significantly influence engagement levels. Students participating in web-based classes and receiving consistent learning feedback reported higher engagement. Conversely, laboratory report writing emerged as a primary challenge, highlighting the urgent need for enhanced scientific writing literacy. The findings are discussed within the Community of Inquiry (CoI) and Self-Determination Theory (SDT) frameworks, emphasizing the importance of teaching presence, social collaboration, and learner autonomy in digital learning environments. Notably, students from rural areas exhibited high motivation despite infrastructural limitations. The study recommends redesigning learning models to include targeted support, structured digital practicum modules, and formative feedback mechanisms. This research contributes to developing inclusive, adaptive, and effective science education practices in ODHE while supporting achieving Sustainable Development Goal (SDG) 4: Quality Education.
 

Emotion recognition in Virtual Reality (VR) has become increasingly relevant for enhancing immersive user experiences and enabling emotionally responsive interactions. Traditional approaches that rely on facial expressions or vocal cues often face limitations in VR environments due to occlusion by head-mounted displays and restricted audio inputs. This study aims to develop an emotion recognition model based on body gestures using a hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The CNN component extracts spatial features from skeletal data, while the LSTM processes the temporal dynamics of the gestures. The proposed model was trained and evaluated using a benchmark VR gesture-emotion dataset annotated with five distinct emotional states: happy, sad, angry, neutral, and surprised. Experimental results show that the CNN-LSTM model achieved an overall accuracy of 89.4%, with precision and recall scores of 88.7% and 87.9%, respectively. These findings demonstrate the model’s ability to generalize across various gesture patterns with high reliability. The integration of spatial and temporal features proves effective in capturing subtle emotional expressions conveyed through movement. The contribution of this research lies in offering a robust and non-intrusive method for emotion detection tailored to immersive VR settings. The model opens potential applications in virtual therapy, training simulations, and affective gaming, where real-time emotional feedback can significantly enhance system adaptiveness and user engagement. Future work will explore real-time implementation, multimodal sensor fusion, and advanced architectures, such as attention mechanisms for further performance improvements

This research is motivated by the problem of communication in economic transactions in the Aru Islands District, which is recruited by a multi-ethnic community with a variety of regional languages. Although Indonesian has great potential as a national communication tool, its use in local economic activities is still limited, thus hampering the smooth interaction between traders and buyers. This communication inefficiency has an impact on the negotiation process and local economic competitiveness. This study aims to instill the role of Indonesian in facilitating economic transactions, identify barriers to use, and develop strategies to improve Indonesian language skills in the community. The approach used is descriptive qualitative with a phenomenological method. Data were collected through observation, in-depth interviews, and document studies, with informants selected using purposive sampling techniques to obtain data relevant to the local economic context. Expected results include an understanding of the contribution of Indonesian in increasing the effectiveness of transactions, revealing communication challenges, and strategies for improving contextual and applicable language literacy. These findings can provide practical contributions to local governments, educational institutions, and business actors in developing language training programs that support economic activities. From a theoretical perspective, this research enriches the study of the relationship between language and economic development, especially in multilingual areas such as the Aru Islands.

Handling natural disaster victims requires a fast, precise, and fair aid distribution process. In this context, expert systems can be utilized as a decision-making tool in determining the type and amount of aid that should be given to victims. This article develops a desktop-based expert system using the Java programming language, which is able to calculate the type of aid based on the condition of the victim, the level of damage, and the number of affected family members. The method used is a rule-based expert system with if-then logic. The results show that this system can assist field officers in accelerating the calculation and distribution of aid.

The rapid evolution of artificial intelligence (AI) has significantly transformed the field of steganography, extending its scope beyond conventional image-based techniques to novel domains such as linguistic and 3D mesh data hiding. This review presents a concise, accessible, and critical examination of recent AI-powered steganography methods, focusing on three distinct modalities: image, linguistic, and 3D mesh. Unlike most surveys focusing solely on one modality, this work highlights some modalities, identifies their unique challenges, and discusses how AI has reshaped embedding mechanisms, evaluation strategies, and security concerns. In image-based steganography, deep models such as GANs and Transformers have improved imperceptibility and extraction accuracy, but face limitations in computational efficiency and extraction consistency. Linguistic steganography, previously hindered by semantic fragility, has been revitalized by large language models (LLMs), enabling context-aware and reversible embedding, though still constrained by metric standardization and synchronization issues. Meanwhile, 3D mesh steganography remains dominated by non-AI methods, offering fertile ground for innovation through geometric deep learning. This review also provides a comparative summary of design principles, performance metrics, and modality-specific trade-offs. The analysis reveals a shift in evaluation paradigms, from numeric fidelity (e.g., PSNR, SSIM) to semantic and perceptual metrics (e.g., LPIPS, BERTScore, Hausdorff Distance). Looking ahead, future directions include cross-modal integration, domain adaptation, lightweight AI models, and the development of unified benchmarks. By presenting recent advances and critical perspectives across underexplored domains, this survey aims to inspire early-stage researchers and practitioners to explore new frontiers of steganography in the AI era.

Penyebab utama turunnya nilai ekonomi dari pohon cendana adalah penyakit. Ada beberapa cara untuk mendeteksi (identifikasi) penyakit pada pohon cendana, salah satunya melalui daun. Salah satu cara melakukan identifikasi adalah dengan mengamati warna dan bentuk daun yang terserang penyakit menggunakan pengolahan citra atau visi komputer. Dalam penelitian ini dilakukan analisis penyakit pada daun Cendana dengan menggabungkan fitur GLCM dengan fitur Color Moment, khususnya nilai mean pada tiap saluran RGB dalam citra daun Cendana. Algoritma ID3 dipilih sebagai metode pembelajaran untuk menganalisis data hasil ekstraksi tekstur dan warna guna menentukan jenis penyakit. Hasil pengujian data menunjukkan tingkat akurasi sebesar 92,31% saat fitur GLCM digabungkan dengan fitur Color Moment. Hal ini menandakan bahwa penggabungan fitur tersebut memberikan hasil yang baik dalam mendeteksi dan mengklasifikasikan jenis penyakit pada daun Cendana.

This activity aims to analyze the results of the study of Science Olympiad content outline on the subject of Quantities, Units and Measurement. This video was made using the PowerPoint application for the content, and the Ice Cream screen recorder to record. The research method uses a descriptive qualitative method with a sample of all viewers, the majority of whom are junior high school students. This activity can be concluded as effective by looking at the number of viewers reaching more than 28 thousand, the number of likes 920 without dislikes and 40 positive comments from viewers who accessed on March 12, 2025 at 21:37.