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This study analyzes the short story Cinta Semata Wayang by Nuryana Asmaudi SA using a literary psychology approach grounded in Sigmund Freud’s psychoanalytic theory. The research applies a literature study with a descriptive qualitative method. Data were collected through close reading to identify structural elements and the psychological dynamics of the characters. The findings reveal marital conflict symbolized through wayang figures, particularly Rama and Sinta. Structurally, the story presents a progressive plot, symbolic and metaphorical language, and central themes of love and responsibility. The psychological analysis highlights the interaction of the id, ego, and superego within the main characters: Rama experiences tension between personal desires and moral norms, while Sinta demonstrates superego dominance through loyalty and emotional endurance. The short story emphasizes that true love is not solely based on emotion but also requires responsibility, commitment, and moral awareness as the foundation of a harmonious and sustainable relationship. This research contributes to literary psychology studies and deepens understanding of moral values in literature.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

The development of information technology has driven digital transformation in various sectors, including the food and beverage (F&B) industry. However, many small to medium-scale F&B businesses still rely on manual ordering systems, resulting in long queues, order recording errors, limited menu information, and suboptimal user experience. This study aims to design the user interface (UI) and user experience (UX) of a web-based Smart Ordering System that provides convenience, efficiency, and comfort in the food ordering process. The research method used is the Design Thinking approach, which includes empathize, define, ideate, prototype, and testing stages. The design process involves user needs analysis, user flow development, wireframe creation, and high-fidelity prototype development using Figma. Usability testing is conducted using the Single Ease Question (SEQ) method to evaluate ease of use and user satisfaction. The results indicate that the proposed UI/UX design provides a clear ordering flow, intuitive interface, and easy-to-understand user experience. Based on the SEQ results, most users experienced no difficulty in using the system, indicating that the design meets usability criteria with a very good category and is suitable for implementation in the F&B industry.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The skill mismatch between university graduates and technology industry requirements remains a significant challenge in Indonesia. PT Vinix Seven Aurum requires an assessment tool to objectively identify the initial competencies of MBKM and internship program participants. This research aims to design a web-based self-assessment platform that helps students measure their skill gaps against industry standards through radar chart visualization and personalized learning recommendations. The UI/UX design applies the Design Thinking method with empathize, define, ideate, prototype, and test phases, utilizing Figma for wireframe and high-fidelity prototype development. Data collection was conducted through observation, interviews, literature studies, and usability testing with 10 respondents. The results demonstrate good usability with a 100% completion rate across all features. The VINIX Skill Radar platform provides five assessment categories, a 1-10 rating scale system, radar chart visualization, gap analysis, and learning recommendations. This system enhances students' self-awareness of their competencies and supports effective mapping of training program participants' capabilities.

Stunting and wasting are nutritional problems in toddlers that remain a double burden of malnutrition in Indonesia and have an impact on the quality of health and future human resource development. Monitoring the nutritional status of toddlers is generally carried out using anthropometric indicators, but the use of this data is still limited to descriptive analysis. This study aims to apply the K-Means algorithm in clustering infants vulnerable to stunting and wasting based on anthropometric indicators, so that groups of infants with different levels of nutritional vulnerability can be identified. The dataset used consists of infant data with variables of gender, age (months), height (cm), and weight (kg). The research stages included data preprocessing, encoding categorical variables, data normalization, determining the optimal number of clusters using the Elbow and Silhouette Score methods, and analyzing the characteristics of each cluster. The evaluation results showed that the optimal number of clusters was four. Each cluster has different anthropometric characteristics and distributions of stunting and wasting status, ranging from groups with relatively normal nutritional conditions, groups with a tendency toward overnutrition, to groups that are vulnerable to acute and chronic malnutrition. These clustering results provide a more comprehensive and segmented mapping of toddlers, which can be used as a basis for formulating more targeted and data-driven nutrition policies and interventions.

Conventional management of sports services often leads to inefficiency and limited public access to experts and facilities. Reliance on manual systems poses a high risk of scheduling conflicts or human error. This study aims to develop the User Interface (UI) and User Experience (UX) design for the Movement and Athletic Talent Hub (MATCH) application as an integrative digital solution. The approach employed is the Design Thinking method, encompassing the stages of empathize, define, ideate, prototype, and testing. The design process resulted in an interactive prototype featuring key functions such as facility booking, trainer search, and a digital payment system. Evaluation was conducted using the System Usability Scale (SUS) method involving target users. The test results yielded an average score of 79.5, categorizing the MATCH application within the Good rating and Acceptable status. These findings indicate that the design is effective in meeting user needs and is viable for further development as a digital sports ecosystem.

Student selection for the National Student Sports Olympiad (O2SN), particularly in the karate category, is still largely conducted manually, which may lead to subjectivity and inconsistency in the assessment process. This study aims to design and develop a web-based Decision Support System (DSS) to assist schools in selecting the best students for the O2SN karate competition by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at SMA Negeri 2 Muaro Jambi. The system was developed using the waterfall model, which consists of requirement analysis, system design, implementation, and testing stages. The student evaluation process is based on criteria referring to the official O2SN karate standards, including stance accuracy, techniques, movement transitions, timing and harmony, breathing control, focus (kime), conformity (consistency with ryu-ha/style), strength, speed, and balance. The developed system processes assessment data to generate preference values and student rankings, which are separated by male and female categories. The results indicate that the application of the TOPSIS method is able to support more objective decision-making and improve transparency and efficiency in the O2SN student selection process within the school environment.

The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.

Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.

This study examines Japanese advertising and product-information texts on Shiseido Japan’s official website (www.brand.shiseido.co.jp) that grammatically prevent readers from construing statements as universal claims (“always” or “true for everyone”). It addresses two problems: how universal readings are blocked through grammatical construction in this register, and how the main blocking mechanisms differ in limiting generalisation and managing scope. The data consist of sentence-level usage, precautionary, and quality-related statements that plausibly invite broad general interpretations. Seven analytically representative tokens are used as illustrative evidence, covering wake-negation, baai-based case framing, and event/occasion packaging with V-ru koto ga aru, including rare-event calibration with mare ni and layered conditional framing. The study employs qualitative, theory-driven grammatical analysis focusing on clause structure, embedding, nominalisation, connective relations, and the compositional contribution of key markers. The results identify recurring templates with distinct structural signatures. Wake-negation blocks over-strong uptake by denying a candidate inference (…to iu wake de wa arimasen). Case framing with baai shifts categorical commitments into situation-restricted possibility (…baai ga arimasu), including complex variants that add causal linkage, avoidance marking, and directive closure. Event/occasion packaging with koto plus existential predication (…koto ga arimasu) presents anomalies as contingent occurrences, and it can be triggered by causal conditions (e.g., temperature change) or conditional frames (…to). Rare-event marking with mare ni further calibrates frequency and often co-occurs with contrastive reassurance about quality. Overall, universal-blocking emerges as a set of non-redundant grammatical routes that constrain inference, situational domain, and event profiling in a compact public informational genre.

This study aims to analyze the effect of the use of kahoot game media on the cognitive learning outcomes of moral beliefs in grade VII C MTS negeri 1 Tuban students. The background of this research is the low enthusiasm and learning outcomes of students in learning moral beliefs which are still dominated by conventional methods while the characteristics of agency students demand a more interactive and adaptive learning approach to technology. This study uses a quantitative approach with a quasi-experimental design of one group pretest design involving 34 students as a sample. This research instrument is in the form of a multiple-choice cognitive learning outcome test of 20 questions covering 4 levels of bloom taxonomy, namely remembering, understanding, applying, and analyzing. The data was analyzed using descriptive statistics and normalistic calculations, the results of the study showed a significant increase in students' cognitive learning outcomes where the average class score increased from 66.03 in the pre-test to 80.29 in the post-test with an engine value of 0.42 which was in the medium category. These findings identify that kahood media has a positive impact on improving students' cognitive learning outcomes.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

Financial inclusion has become a key driver in promoting sustainable development, especially in the era of Industry 4.0, which is characterized by rapid digitalization, technological innovation, and the transformation of financial services. Although academic interest in this topic continues to grow, research in this field has not been systematically mapped, resulting in limited understanding of global trends and thematic evolution. This study uses bibliometric analysis (BA) to explore developments, intellectual structures, and key research focuses in financial inclusion research. Data were collected from the Scopus database for the period 2015–2025, using keywords related to financial inclusion, thus ensuring a comprehensive dataset for analysis. Bibliometric methods were applied using analytical tools such as VOSviewer and R Studio to support the assessment. The results of the analysis show a consistent increase in the number of publications over the last decade, reflecting growing academic attention. The main contributions came from India, China, and the United States, with increasing participation from universities in Africa and Southeast Asia through international collaboration. The main research focus has shifted from microfinance and poverty alleviation to more digital-oriented themes, including fintech, digital finance, blockchain, and green finance. This study contributes by mapping the structure and trends of financial inclusion research and providing insights for policymakers and academics in developing inclusive financial systems that support national strategies such as the SNKI, MSME digitalization, and financial literacy programs in Indonesia to achieve sustainable development goals.

Public service reform in Indonesia increasingly emphasizes inter-agency collaboration as a mechanism to enhance administrative efficiency, service integration, and responsiveness to citizens’ needs; however, in many regions, including West Kotawaringin Regency, collaborative practices remain fragmented due to uneven human resource (HR) capacities, inconsistencies in standard operating procedures (SOPs), and limited technological integration across institutions. This study aims to examine the current state of HR collaboration, identify key barriers and enabling factors, and propose an integrated and adaptive HR collaboration model to strengthen public service delivery. Using a qualitative multi-case study approach, data were collected through semi-structured interviews with leaders and operational staff from multiple regional government agencies and analyzed using NVivo 15 to generate thematic coding and visual tools such as word clouds, hierarchy charts, and project maps. The findings indicate that current collaboration is largely transactional and administrative, exemplified by the physical co-location of agencies at the Public Service Mall (MPP) without effective systemic interoperability. Three critical themes emerged: gaps in HR distribution, competencies, and role clarity; structural and behavioral constraints including sectoral ego, SOP discrepancies, and limited digital integration; and opportunities to enhance collaboration through shared digital dashboards, inter-agency forums, and cross-functional HR mobility. Based on these results, the study proposes a three-pillar Integrated and Adaptive HR Collaboration Model comprising comprehensive digital integration, flexible HR competency sharing, and inclusive service co-creation involving community stakeholders, offering both theoretical contributions to collaborative governance and practical guidance for regional governments seeking to improve public service effectiveness.

The development of digital banks and the operational losses still experienced by PT Bank Aladin Syariah Tbk necessitate a financial health analysis to assess the potential for financial distress. This study aims to assess the potential bankruptcy level of Bank Aladin for the period 2021–2024 using the Modified Altman Z-Score model. The research method is descriptive quantitative with secondary data from annual financial reports and OJK publications, which are analyzed through four main ratios, namely working capital, retained earnings, earnings before taxes, and equity value to total debt. The results show that the Z-Score values are well above the safety threshold, with the highest value of 17.764 in 2021 and the lowest of 9.422 in 2022, mainly driven by high liquidity and equity strength. Thus, it can be concluded that PT Bank Aladin Syariah Tbk is in the Safe Zone category and does not show any potential for bankruptcy during the research period, although an increase in profitability is still needed.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Non-formal institutions, such as farmer groups, play a strategic role in increasing farmer capacity and strengthening agricultural production systems at the village level. This study aims to analyze the role, function, and institutional dynamics of the Bina Satu Farmer Group in supporting tomato farmers in Parabubu Village, Mego District. A descriptive qualitative approach was used, with data collection techniques including observation, in-depth interviews, and documentation. The results indicate that the Bina Satu Farmer Group serves as a learning platform, a collaborative unit, and a liaison between farmers and external institutions. This institution functions in disseminating information on tomato cultivation technology, strengthening access to production inputs, and enhancing farmers' bargaining power in marketing. However, several weaknesses were identified, such as low member participation in routine meetings and limited managerial skills among administrators. Overall, the existence of this farmer group has significantly contributed to increasing the knowledge, productivity, and independence of tomato farmers in Parabubu Village.