On the one hand, amid the digital transformation of the financial sector, opportunities have increased for Micro, Small, and Medium Enterprises (MSMEs); on the other hand, challenges remain in technology adaptation and sustainable financial management, especially in a post-crisis era. Thus, given the ongoing push toward strengthening economic resilience, an urgent need arises to examine how far digital financial intelligence may act as a key for the survival and growth of MSMEs. The focus of this study is to examine the impact of digital financial intelligence-the financial literacy, attitude toward financial technology, and capability to use digital financial applications-on MSME economic resilience. Data were collected for the survey using a quantitative approach, which consisted of an online questionnaire administered to 220 MSME actors in Indonesia, who were digital financial services users for a minimum of one year. Participants were selected based on purposive sampling, while data analysis was performed using Structural Equation Modeling (SEM) based on Partial Least Squares (SmartPLS) supported by SPSS, Tableau, and MonkeyLearn. The results of the analysis reveal that all dimensions of digital financial intelligence have a significant relationship with economic resilience, particularly in the tactile domains of crisis adaptation, recovery, and strategic decision-making. Overall, these results now show life documents the need for greater emphasis on literacy and digital readiness as the bedrock of MSME sustainability. This academic contribution is theoretically developing an integrated model of technology and finance while practically implementing the policy implications that would strengthen an inclusive and adaptive MSME digital ecosystem.

To achieve progress and development based on the standards that are used as references, the education process is carried out. Education can help people grow into people with character and are productive every day. But in the world of education, a problem arises such as bullying that can threaten students in education. This study aims to reveal the negative impacts of bullying and find a way out of the problem in order to prepare the golden Indonesian nation. The method of implementing anti-bullying socialization is carried out in three stages: planning, implementation, and evaluation. At the planning stage, several things have been determined, including the location and date of the activity, namely at SDN 1 Kumpulrejo, Kumpulrejo Village, Patebon District, Kendal Regency. In addition, a one-day schedule has been made for socialization, material on physical violence to the presentation of material on the language of the impact of bullying. At the implementation stage, socialization is carried out by showing anti-bullying animation videos, presenting material on bullying behavior, verbal violence, and physical. The results of this study indicate that bullying is an act of intimidating and forcing a weaker person or group to do something against their will with the intention of causing physical or mental harm through delivery and attack. Bullying itself has a negative impact on students because it can make victims lose their confidence and feel traumatized. So that with the existence of anti-bullying socialization, bullying behavior in the world of education can be overcome.

Efforts to protect consumers' personal data on e-commerce platforms can be carried out by conducting a Data Protection Impact Assessment. This article discusses the indicators of the execution of Data Protection Impact Assessment by pivoting on the rights of personal data subjects based on Law Number 27 of 2022.  This research uses a library research method by focusing on legal materials so that it can be said to be library based. The results show that Data Protection Impact Assessment is explained in Article 34 paragraph (1) of Law Number 27 of 2022 concerning Personal Data Protection which is then further regulated through Government Regulations as mandated by Article 34 paragraph (3). However, the Government Regulation relating to this matter has not yet been ratified, so it still refers to the mandate of Law Number 27 of 2022. This Data Protection Impact Assessment indicator can refer to Article 16 paragraph (2), Article 34 paragraph (1) and (2), Article 35, Article 20 paragraph (2), and Article 27 of Law Number 27 Year 2022. In terms of Data Protection Impact Assessment indicators as the protection of consumer personal data on e-commerce platforms, it can refer to the mandate that explains the rights of personal data subjects and their limitations in a separate article and the form of personal data processing as mandated by Article 34 paragraph (2) of Law No. 27 of 2022.

Breast cancer remains one of the leading causes of death among women worldwide, primarily due to late detection and diagnosis. Early and accurate prediction is essential to improve survival rates. Machine learning (ML) techniques have proven effective in supporting early diagnosis. This study aims to evaluate and compare the performance of three different approaches: traditional ML, ensemble ML, and deep learning (DL) for early prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The methodology includes data collection, preprocessing, and the design of predictive models. Traditional ML algorithms used include Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT). Ensemble ML techniques comprise Random Forest (RF), XGBoost, and AdaBoost, while DL models include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The models were evaluated using precision, recall, F1-score, and accuracy. The results indicate that XGBoost achieved the highest accuracy (0.99), with strong recall (0.98) and F1-score (0.986), outperforming all other ensemble and traditional ML methods. CNN achieved 0.99 in all evaluation metrics, slightly outperforming RNN, which attained 0.98 accuracy and 0.985 F1-score. These findings confirm that ensemble ML techniques outperform traditional models, while CNN leads among DL models. Furthermore, the proposed models demonstrated superior prediction performance compared to existing studies, particularly in minimizing false negatives, which is critical for healthcare applications.

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection.
Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP).
Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean.
Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.

Unauthorized use of personal data can cause harm to personal data subjects. In its mandate, Decision Number 78/Pid.Sus/2024/PN Tng provides a general outline of the potential harm that may occur to personal data subjects. However, the decision does not specifically explain the forms of loss and its impact on personal data subjects. This research critically discusses the forms of crimes causing harm and their impact on personal data subjects' damage due to unauthorized use of personal data in Decision Number 78/Pid.Sus/2024/PN Tng. The research employs the Library Research method, also known as Legal Research, which focuses on analyzing primary and secondary legal materials based on existing literature. The findings indicate that several general regulations in Indonesia address the crime of unauthorized use of personal data, including Law Number 24 of 2013, Law Number 1 of 2024, Law Number 14 of 2008, and Law Number 27 of 2022. Meanwhile, Decision Number 78/Pid.Sus/2024/PN Tng outlines the damage to personal data subjects resulting from unauthorized use of personal data, with this study revealing various impacts of damage both materially and non-materially.

This paper presents a novel ingredient-based recipe recommendation system that suggests dishes using only available ingredients, eliminating reliance on user preference data. As online culinary platforms host millions of recipes, users struggle to find dishes that match their pantry constraints, dietary needs, and culinary preferences. Unlike traditional methods like TF-IDF, which lack semantic depth, or existing methods such as graph neural networks (GNNs), which incur high computational costs and struggle with cold-start scenarios, our self-supervised contrastive learning approach maps ingredients and recipes into a 64-dimensional semantic space, capturing complex culinary relationships for precise, scalable recommendations. The system enhances representation quality without user history by leveraging dual-modal Word2Vec embeddings that incorporate ingredients and cooking instructions and optimizing them using triplet loss. Using a dataset of 122,265 standardized recipes, our model achieves a 79.11% ingredient match ratio, 70% recommendation diversity (vs. 50% for baselines), and sub-100ms query times. It outperforms TF-IDF and KNN by 14% in relevance. The implementation addresses ingredient normalization, rare ingredient handling, and diverse cuisine representation, promoting sustainable cooking by maximizing ingredient use and reducing food waste. This work advances the methodology of recommendation systems by addressing the cold-start problem and ensuring privacy through user-data-free modeling. It supports practical culinary applications, contributing to sustainable cooking practices and culinary informatics.

This article focused on examining the political ethics of digital citizenship in Indonesia in the digital era at a time of technological and scientific progress, specifically in the context of the 2024 election. Changes supported by advances in information technology have also shaped the political character of each individual in the digital era. There are many negative as well as positive things that arise, making it an interesting discourse to research. This descriptive qualitative research explored data through in-depth interviews and reviewing literature sourced from books and articles and analyzed the content and conversations of digital citizens on social media Instagram ahead of the 2024 elections. The research results showed a shift in the political ethics of Indonesian citizens in the digital realm towards a better direction. Even though there was still a lot of conflict, disputes, commotion in the digital world in the 2024 election. Sharp discourse in the digital world in the 2024 Election, which was accompanied by data-based campaigns, was in line with the concept of worldly ethics initiated by Hannah Arendt. This concept can also be a solution to overcome the negative side of digital politics, including in the 2024 elections. Moreover, the existing dynamics of digital politics were based on non-primordial political ethics, that is, they are not accompanied by narratives of identity politics among citizens.

This community service research aims to describe hygiene care activities with the community located in the Al-Musta'in Mushollah, Ngentak Hamlet, Sumberjo Village, Plandaan District, Jombang Regency. The author used descriptive qualitative research with observation and documentation data collection techniques at the research location. The research method used before the activity was Particaptory Action Research (PAR). The results of the research show that the residents of Sumberjo Village have a high level of concern for cleanliness around the Al-Musta'in Musholla area in Ngentak Hamlet. These findings illustrate that there is a high sense of togetherness and solidarity among residents, as well as showing the existence of strong values of friendship and mutual respect between fellow residents. This reflects the culture of mutual cooperation that is still strong in society, where they not only care about the cleanliness of the environment individually, but also actively participate in maintaining cleanliness together, making the Al-Musta'in Musholla a focal point for uniting in efforts environmental maintenance and positive social values.

This study aims to analyze the community’s interest in the digitalization design to support the agricultural potential of Sukaharja Village, Bogor Regency. The village has significant agricultural potential but faces marketing challenges due to limited access to broader markets and dependence on middlemen. The research employed qualitative methods through in-depth interviews and questionnaires distributed to 30 farmers managing commodities such as taro, pineapple, and ornamental plants. The findings indicate that while farmers show high enthusiasm for digital solutions like e-commerce, their digital literacy and technological access remain limited. To address these issues, the team developed a digital platform named “Suka Belanja di Harja,” designed to improve product marketing, provide educational content, and empower local farmers. The initiative had a positive impact by increasing farmers’ motivation and awareness about digital marketing. However, ongoing support, infrastructure improvement, and tailored training are necessary to ensure successful digital transformation in rural agriculture.