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Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in Indonesia's economic development, particularly in developing products based on local wisdom. Toko Jayabaru is an MSME that markets Maumere's signature souvenirs made from traditional ikat woven fabric, facing various challenges and opportunities amid business and tourism dynamics. This study aims to identify internal and external factors affecting Toko Jayabaru's performance and formulate appropriate business development strategies through SWOT analysis. The research employs a qualitative descriptive approach with data collection through in-depth interviews with the owner and management of Toko Jayabaru. Data analysis uses the SWOT framework (Strengths, Weaknesses, Opportunities, Threats) to identify the business's strategic position and formulate alternative development strategies. Toko Jayabaru's main strengths lie in strong local cultural identity, authentic product quality, and adequate business experience. Weaknesses include limited digital marketing, restricted production capacity, and lack of derivative product variations. Opportunities encompass Flores tourism sector growth, increasing interest in local products, and potential strategic collaborations. Main threats are competition from similar products, influx of imitation products, and dependence on tourism sector fluctuations. Recommended development strategies include strengthening branding through storytelling, diversifying woven derivative products, optimizing digital marketing, strategic collaboration with tourism stakeholders, enhancing human resource capacity, and implementing customer loyalty programs. Implementation of these strategies is expected to increase MSME competitiveness while preserving Maumere's local culture.

Local governments in Indonesia rely significantly on local tax revenues to strengthen fiscal independence and sustain regional development. Among these revenue sources, the Motor Vehicle Tax (PKB) and the Motor Vehicle Transfer Fee (BBNKB) play a vital role in shaping regional fiscal capacity. This study explores how both taxes contribute to the Regional Own-Source Revenue (PAD) of Central Java Province during the 2020–2024 period. Drawing on quantitative analysis and secondary data from the Central Java Regional Revenue Agency (Bapenda), the research assesses the effectiveness and contribution of PKB and BBNKB in supporting local fiscal performance. The results show that PKB consistently exerts a positive, meaningful influence on PAD, underscoring its central role in sustaining the province’s fiscal strength. In contrast, BBNKB demonstrates a weaker, less stable impact, primarily driven by changes in vehicle ownership trends and administrative challenges. When considered together, both tax instruments contribute to enhancing local fiscal capacity, although the reliance on PKB remains predominant. These findings underscore the importance of modernizing regional tax administration, particularly through digital innovation, transparent reporting systems, and community-based compliance strategies. Strengthening these mechanisms is essential to building a more resilient and autonomous fiscal framework, especially in the context of post-pandemic economic recovery and long-term regional development planning

This study aims to analyze the effect of sustainability report disclosure, audit committee meeting frequency, liquidity, leverage, and total asset turnover on profitability in mining companies listed on the Indonesia Stock Exchange (IDX) during the 2021–2023 period. Profitability is measured using Return on Equity (ROE). This research adopts a quantitative approach using secondary data obtained from annual financial statements and sustainability reports. The sample was selected using purposive sampling, yielding 34 mining companies with 102 observations in total. Multiple linear regression analysis was employed after fulfilling classical assumption tests. The results indicate that sustainability report disclosure, audit committee meetings, liquidity, leverage, and total asset turnover simultaneously have a significant effect on profitability. However, partially, total asset turnover has a positive and significant impact on profitability. Meanwhile, sustainability report disclosure, audit committee meeting frequency, liquidity, and leverage do not significantly affect profitability. These findings suggest that asset utilization efficiency plays a crucial role in improving profitability in the mining sector. This study is expected to provide insights for companies, investors, and regulators to understand the determinants of profitability better and to support improved corporate governance and financial decision-making in mining companies.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

This paper explores the epistemological dimensions of the digital transformation occurring in traditional game development through the integration of machine learning systems. By examining how knowledge creation, validation, and application have evolved in this domain, we identify fundamental shifts in the epistemological frameworks governing game development practices. The research investigates how machine learning has redefined creative processes, technical implementation, and experiential design while challenging traditional notions of authorship, expertise, and knowledge transmission. Through analysis of industry case studies, technological capabilities, and theoretical frameworks, this paper contributes to understanding how machine learning systems are not merely tools but epistemological agents that fundamentally transform how knowledge is generated, validated, and utilized in game development ecosystems.

Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Laskar Buah is a modern retail chain specializing in the sale of fresh fruit. At present, the company operates one hundred branches across ten regencies. One of its outlets, Laskar Buah Ngumpakdalem, ranks among the top three branches in terms of transaction volume; however, it has received a considerable number of customer complaints regarding the quality of service provided. This situation has prompted management to conduct a thorough evaluation of the store’s service quality.This study was conducted with the aim of analyzing and evaluating service quality using the Importance–Performance Analysis (IPA) method based on the Retail Service Quality Scale (RSQS). The results of the IPA analysis were subsequently used as a foundation for determining priority areas for service improvement.The findings reveal that four service attributes fall within Quadrant B, indicating that they should be prioritized for immediate improvement. These attributes include the cleanliness of the shopping area, store layout, employee product knowledge, and product quality. Additionally, twelve attributes fall under Quadrant C, where performance should be maintained due to their already strong results. On the other hand, eleven attributes fall into Quadrant A, meaning they are considered lower  priority, while one attribute is located in Quadrant D, suggesting that Laskar Buah Ngumpakdalem is providing excessive performance in that particular aspect.

This research examines inter-ethnic marriages between Acehnese and Bugis-Makassar communities in Makassar City from 1990 to 2004, which emerged as a consequence of social mobility, cross-cultural interaction, and compatibility of religious values. The purpose of this study is to explain the factors that trigger inter-ethnic marriages, the underlying customary processes, and the socio-cultural dynamics that occur within family life. This research uses historical methods, including the heuristic stage, source criticism, interpretation, and historiography, with data obtained thru in-depth interviews with four families and related documentary sources. The research findings indicate that holding a wedding requires cultural negotiation between the two groups, with some families adopting traditional Acehnese ceremonial customs, while others implement Bugis-Makassar traditions. In family life, couples face differences in values regarding communication, gender role division, and religious practices, which are then overcome thru a dialogue-based process of adaptation and compromise. This process results in the formation of hybrid cultural identities, the expansion of social networks, and the strengthening of inter-ethnic integration. Overall, intertribal marriages serve as a medium for cultural reconciliation and a means of building more inclusive social relationships in a multicultural society.

This research examines inter-ethnic marriages between Acehnese and Bugis-Makassar communities in Makassar City from 1990 to 2004, which emerged as a consequence of social mobility, cross-cultural interaction, and compatibility of religious values. The purpose of this study is to explain the factors that trigger inter-ethnic marriages, the underlying customary processes, and the socio-cultural dynamics that occur within family life. This research uses historical methods, including the heuristic stage, source criticism, interpretation, and historiography, with data obtained thru in-depth interviews with four families and related documentary sources. The research findings indicate that holding a wedding requires cultural negotiation between the two groups, with some families adopting traditional Acehnese ceremonial customs, while others implement Bugis-Makassar traditions. In family life, couples face differences in values regarding communication, gender role division, and religious practices, which are then overcome thru a dialogue-based process of adaptation and compromise. This process results in the formation of hybrid cultural identities, the expansion of social networks, and the strengthening of inter-ethnic integration. Overall, intertribal marriages serve as a medium for cultural reconciliation and a means of building more inclusive social relationships in a multicultural society.

Children living in orphanages face various psychological pressures, such as the loss of attachment figures, unstable social dynamics, and difficulty expressing emotions adaptively. These conditions require appropriate coping strategies to build emotional resilience. This community service activity utilizes a Service Learning (SL) approach, which integrates academic learning with community service. It aims to provide psychoeducation on emotions and coping strategies, and implements expressive writing as a means of emotional processing. The activity methods included interactive lectures, a pretest and posttest to measure understanding, an expressive writing therapy session, and a reflective interview at the end of the activity. Results showed an increase in understanding of emotions and coping strategies, as evidenced by improved posttest scores. Furthermore, expressive writing helped children express previously suppressed emotions, reduced psychological tension, and fostered a sense of relief, calm, and insight into personal problems. Overall, expressive writing has proven effective as a coping strategy in building emotional resilience in orphanage children, and the Service Learning approach has the potential for sustainable application in psychosocial support programs.

This study aims to describe the implementation of environmental literacy through the One Student One Plant Movement program at MI Muhammadiyah Gawang. The research method uses a descriptive qualitative approach. Data collection techniques are carried out using questionnaires and documentation. This program is designed through three main stages, namely environmental literacy socialization, program implementation, and plant care assistance. The results of the study indicate that the program is effective in improving students' environmental literacy, evidenced by the average student environmental literacy score of 62.8 before the program (pretest) with a sufficient category and 82.4 after the program (posttest) with a good category, which indicates an increase of 19.6 or 31.2% in environmental literacy scores. This program also contributes to positive changes in student behavior, such as increased awareness of environmental cleanliness and initiatives in caring for plants. Through these activities, students not only gain knowledge about the environment but also real-life experience in maintaining and caring for plants, connecting theory with practice. This program has succeeded in increasing student awareness and involvement in environmental issues, and can be used as a model for similar programs in other schools.

This study aims to analyze in-depth the interest of the Setro Village community in choosing PNM Mekaar Syariah financing as a source of capital for micro and small enterprises (MSMEs). This phenomenon is interesting because it shows how economic, social, and spiritual factors contribute to the financial decisions of rural communities. This research was conducted using a qualitative exploratory approach, using case studies and in-depth interviews with key informants. To explain the relationship between attitudes, subjective norms, and behavioral control on community intentions to choose Islamic financing, data analysis was conducted thematically. The Theory of Planned Behavior (TPB) framework was also used. The study shows that positive community perceptions of ease of access, economic benefits, and compliance with Islamic principles are the main factors shaping community interest in PNM Mekaar Syariah. Fast, easy, and uncomplicated financing increases consumer trust and enhances perceived behavioral control. Conversely, increased business capital and family income enhance the sustainability of micro-enterprises. The institution is given moral legitimacy and spiritual conviction by the implemented Islamic values, such as the yield system and the freedom from usury. Community participation decisions are also driven by social factors such as group support, advice from group leaders, and the quality of service provided by field officers. Theoretically, this study adds local religiosity and socio-cultural aspects to enrich the application of the Theory of Planned Behavior in the context of rural Islamic finance.

Polusi udara partikulat halus (PM2,5) merupakan ancaman serius bagi kesehatan masyarakat di Kabupaten Brebes, Jawa Tengah. Faktor penyumbang utamanya adalah emisi kendaraan di jalur Pantura, aktivitas industri perikanan, serta konsentrasi tinggi selama musim kemarau (Juni–November). Tidak adanya model peramalan sub-jam yang akurat menghambat pengembangan sistem peringatan dini yang efektif. Penelitian ini mengembangkan dan mengevaluasi model deep learning berbasis Transformer untuk memprediksi konsentrasi PM2,5 dengan resolusi waktu 15 menit. Data yang digunakan berasal dari NASA GEOS-CF (band PM25_RH35_GCC) yang diakses melalui Google Earth Engine menggunakan API Python. Dataset mencakup periode 1 Januari hingga 22 November 2025, menghasilkan 7.813 observasi per jam, yang kemudian diinterpolasi linear menjadi 31.249 titik data dengan resolusi 15 menit. Arsitektur Transformer terdiri dari 3 lapis enkoder, 4 kepala perhatian multi-head, dimensi embedding 128, dimensi feed-forward 256, panjang sekuen 60 timestep, dan augmentasi fitur menggunakan rerata bergulir (*rolling mean*, jendela = 3) dan beda pertama (*first difference*). Pelatihan dilakukan dengan TensorFlow-Keras, pengoptimal Adam, penjadwal peluruhan kosinus (*cosine decay scheduler*), dan fungsi kerugian Huber. Pembagian data dilakukan secara kronologis: 70% pelatihan, 30% validasi. Evaluasi pada set uji independen (16 Agustus–21 November 2025, 9.357 observasi atau 97 hari 11 jam 15 menit) menghasilkan MAE 0,7691 µg/m³, RMSE 1,2052 µg/m³, R² 0,9945, dan *Explained Variance Score* 0,9948. Model ini mampu menggambarkan variasi diurnal dan anomali musiman secara akurat, jauh melampaui model LSTM dan GTWR konvensional. Penelitian ini memberikan kontribusi signifikan di bidang Teknologi Informasi melalui kerangka kerja pengolahan *big data* satelit untuk aplikasi lingkungan.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

This study examines how company size, asset growth, tangibility, leverage, and total asset turnover affect profitability in consumer manufacturing companies listed on the Indonesia Stock Exchange from 2019 to 2023, using secondary data collected via purposive sampling. The independent variables in this study include the natural logarithm of total assets, asset growth (this year’s total assets relative to the previous year), and tangibility (the fixed asset ratio to total assets). Leverage uses the debt-to-asset ratio, and total asset turnover uses the total asset turnover ratio, while the dependent variable of profitability uses return on assets. Of the 108 companies in the population, 19 that met the research sample criteria were selected, yielding 95 observations. Data analysis was conducted using multiple linear regression, accompanied by classical assumption tests and hypothesis testing through F-tests and t-tests. The findings of this study reveal that asset growth has a significant positive effect on profitability, while leverage shows a significant negative effect. However, firm size, tangibility, and total asset turnover do not exhibit significant relationships with profitability. This study contributes both theoretically and practically to understanding the internal determinants of financial performance in the consumer sector and serves as a reference for management.

This study aims to analyze the effect of profitability, leverage, and liquidity on firm value in food and beverage sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. Profitability is measured by Return On Assets (ROA), leverage by Debt to Equity Ratio (DER), and liquidity by Current Ratio (CR), while firm value is proxied by Price to Book Value (PBV). The study employs a quantitative approach using multiple linear regression analysis. The sample consists of 25 companies selected through purposive sampling, with a total of 125 secondary data observations obtained from annual financial statements. The results indicate that, partially, profitability, financial risk, and liquidity have a positive and significant effect on firm value. Simultaneously, the three independent variables also significantly affect firm value, with an adjusted R² of 43.4%, meaning that 56.6% of the variation in firm value is explained by other factors outside the model. These findings support agency theory and signaling theory, which suggest that strong financial performance, optimal debt management, and adequate liquidity provide positive signals to investors, thereby enhancing firm value.

In the investment world, stock returns are the leading indicator of a company’s performance and the basis for investor decision-making in the capital market. Fluctuations in stock returns reflect market expectations of the company’s prospects. The retail sector in Indonesia is facing significant pressure from post-pandemic shifts in consumer behavior and increased competition. This study aims to analyze the effect of financial distress, company size, liquidity, operating cash flow, and accounting profit on stock returns in retail sub-sector companies listed on the Indonesia Stock Exchange (IDX) during the period 2021 to 2023. This type of research is causally associated with a quantitative approach. The data used is secondary, in the form of financial statements from retail companies. The sampling technique used was purposive, yielding a total of 39 data points from 13 retail companies. Data testing was carried out using SPSS version 24. The results showed that partially, the variables of financial distress, company size, liquidity, and accounting profit had no significant effect on stock returns. Meanwhile, operating cash flow positively impacts stock returns. These findings indicate that fundamental indicators are not always the main determinants of stock returns. Therefore, investors are advised also to consider external factors such as market sentiment, macroeconomic conditions, and government policies that may have a greater influence on stock performance in the capital market.

This study aims to evaluate the effects of profitability, leverage, liquidity, and cash-flow shocks on the financial distress of companies in the hotel, restaurant, and tourism subsector listed on the Indonesia Stock Exchange during the period 2021 to 2024. The research approach employed is quantitative, using logistic regression analysis. The data analyzed are secondary data obtained from the annual financial statements of the respective companies. The results of the study indicate that, simultaneously, the four independent variables significantly influence financial distress. However, based on partial testing, each variable, namely Return on Assets (ROA), Debt to Equity Ratio (DER), Current Ratio (CR), and cash flow shock, does not show a significant relationship with financial distress. These findings imply that the risk of financial distress in this industry cannot be explained solely through a single financial indicator; instead, a more holistic approach is required. This study provides essential contributions to both management and investors in assessing companies' financial condition and formulating appropriate strategic decisions.