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Regional economic development requires a comprehensive understanding of the structure, potential, and dynamics of economic sectors so that formulated policies can be targeted and sustainable. Bojonegoro Regency as one of the regions in East Java Province has unique economic characteristics with the dominance of certain sectors, so it is necessary to conduct an in-depth analysis of the economic sectors that play a role in driving regional growth. This study aims to identify basic and non-basic sectors, analyze the dynamics of changes in economic sectors, and assess the sectoral competitiveness of Bojonegoro Regency compared to East Java Province. This study uses a quantitative approach with secondary data in the form of Gross Regional Domestic Product at constant prices by business field obtained from the Central Statistics Agency. The analytical methods used include Location Quotient, Dynamic Location Quotient, and Shift Share. The results show that the mining and quarrying sector remains the sector with the most dominant relative advantage in the economic structure of Bojonegoro Regency. However, the analysis of dynamics and competitiveness indicates that several non-extractive sectors are starting to show faster development and growth potential. This finding suggests an opportunity for transformation of the regional economic structure towards a more diverse pattern. The implications of this research emphasize the importance of regional economic development strategies that do not only rely on traditional leading sectors, but also encourage the development of more sustainable potential sectors.

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

Used cooking oil is a household waste that is often disposed of carelessly, potentially polluting the environment and endangering health. In Battu Winagun Village, the availability of used cooking oil from household activities and the abundant potential of lemongrass plants have not been optimally utilized. This community service activity aims to increase the community's knowledge and skills in processing used cooking oil and lemongrass plants into environmentally friendly products with economic value, namely natural aromatherapy candles. The implementation method includes socialization of the negative impacts of used cooking oil on the environment, training in the process of filtering and processing used cooking oil, extracting lemongrass aroma, and assistance in making and packaging aromatherapy candles. The results of the activity showed that the Battu Winagun Village community was able to understand the importance of managing used cooking oil waste and utilizing lemongrass plants as a natural additive. This activity has an impact on reducing environmental pollution and opening up creative business opportunities based on the village's local potential. Thus, the use of used cooking oil and lemongrass plants can be a sustainable solution in waste management while improving the economy of the Battu Winagun Village community.

Tafsir tarbawi is an approach to interpreting the Qur’an that emphasizes the extraction of educational values to shape holistic human character. This study aims to analyze the implementation of tafsir tarbawi in the process of Islamic education and examine its relevance in shaping students’ character in the modern era. The research employs a qualitative method with a library research approach, where data are collected through the analysis of various academic sources such as books, scholarly journal articles, and relevant previous studies. The findings indicate that tafsir tarbawi can be effectively implemented through the integration of Qur’anic values into educational curricula, the application of value-based learning methods, and the reinforcement of teachers’ exemplary behavior as role models. Furthermore, this approach encourages the internalization of spiritual, moral, and social values within students. Therefore, tafsir tarbawi plays a strategic role in developing learners who are not only intellectually competent but also possess strong character, noble morals, and the ability to face life’s challenges wisely.

The Soxhlet extraction method uses repeated heating and solvent circulation to separate substances from mixtures, producing more extract faster than maceration with less solvent. However, this method requires pure solvents and is not suitable for thermolabile compounds as they can be degraded by heat. Soxhlet extraction is more effective for limited quantities of dry and fine herbal materials. This method is widely used to extract phytochemical compounds such as flavonoids, tannins, and curcumin, and has potential in cosmetic raw materials, herbal medicines, and antioxidant products. Although it uses more energy, this technique is efficient and continuous. Modern innovations such as combining it with Ultrasonic Assisted Extraction (UAE) or environmentally friendly microextraction are expected to increase extraction efficiency while reducing the use of organic solvents. Modifications to Soxhlet, including automation and assistive technologies such as high pressure, ultrasound, and microwaves, open up opportunities for commercialization and further research with more optimal results and more practical operations. The modified Soxhlet is considered a “panacea” in extraction due to the significant performance improvements achieved.

Soursop leaves (Annona muricata L.) are known to contain secondary metabolites such as alkaloids, flavonoids, tannins, saponins, and polyphenols, which have antibacterial potential. This study aimed to formulate soursop leaf extract into a gel dosage form and to evaluate its antibacterial activity against Pseudomonas aeruginosa. This research was conducted as an experimental laboratory study. Soursop leaf extract was obtained using the maceration method with 96% ethanol as the solvent and then formulated into gel preparations with extract concentrations of 5%, 10%, and 15%. Physical evaluation of the gel preparations included organoleptic test, homogeneity, pH, spreadability, and viscosity. Antibacterial activity was evaluated using the well diffusion method on Nutrient Agar medium. The results showed that all gel formulations met the physical requirements for topical preparations. The antibacterial activity test demonstrated that the soursop leaf extract gel inhibited the growth of Pseudomonas aeruginosa, with the 15% concentration producing the largest inhibition zone of 10 mm compared to other concentrations. In conclusion, soursop leaf extract gel has potential to be developed as a topical antibacterial agent against Pseudomonas aeruginosa.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Decoction is a traditional extraction method rooted in ethnobotany; however, meeting quality standards in modern pharmaceutical research remains a major challenge. This study aims to map global research trends regarding phenolic and flavonoid compounds in decoctions over the 2015–2025 period through bibliometric analysis. Data were retrieved from the Scopus database and analyzed using VOSviewer 1.6.20 software, employing the fractional counting method to ensure a more proportional weighting of keyword relationships. The results indicate a fluctuating trend that significantly increased toward the end of the period, peaking at 78 documents in 2025, with India and China emerging as the primary contributors. Network visualization and research density analysis reveal that the global research focus remains centered on antioxidant capacity (DPPH, TPC, and TFC), while decoction itself occupies a supporting position within the research map. This study concludes that decoction has not yet become a central focus in modern pharmaceutical research but serves primarily as a vehicle for presenting active compounds. There remains a significant gap between traditional decoction use and the application of advanced analytical technologies such as HPLC and antibacterial testing, representing a substantial opportunity for future research to validate the safety and efficacy of decoctions more scientifically and through standardized approaches.

Health issues related to free radicals remain a serious concern in Indonesia as they can trigger oxidative stress and degenerative diseases. Red ginger (Alpinia purpurata) is a rhizome plant with potential as a source of natural antioxidants due to its secondary metabolite content; however, its effectiveness is highly influenced by extraction techniques. Although numerous experimental studies have been conducted, a systematic research mapping on this topic is still lacking. This study aims to perform a bibliometric analysis of scientific publications regarding the antioxidant potential of red ginger, focusing on extraction techniques and free radical scavenging activity. The research method employs a quantitative analysis using data sourced from the Scopus database for the 2015–2025 period. Through specific inclusion and exclusion criteria, 38 relevant articles were obtained and analyzed using VOSviewer 1.6.20 software. The results indicate that publication trends have fluctuated, reaching a peak in 2024. Research distribution is dominated by Asian countries, with India, Thailand, and Indonesia as the primary contributors. Network visualization reveals three main clusters focusing on bioactivity, phytochemistry, chemical analysis, antimicrobial activity, and extraction techniques. A research gap was identified for the Alpinia purpurata species compared to Alpinia galanga, as well as opportunities for developing advanced instruments such as LC-MS and other complex analytical techniques. The implications of this study highlight the need for further exploration into 'nanoemulsion' and 'green extraction' to enhance the bioavailability of red ginger's antioxidant compounds in the development of future innovative pharmaceutical products

Curriculum and education is an inseparable relationship because the curriculum is very important in education, if there is no curriculum then education will not be realized because the curriculum is a guideline for the maintenance of education in addition to that the curriculum is always adapted to the existing situation and circumstances. One of the educational institutions that are active in applying K-13 is MA Putri 1 Al-Amien Prenduan, where this institution is under the umbrella of Al-Amien Prenduan Pesantren Pondok. Pondok Pesantren Al-Amien Prenduan is composed of 4 parent institutions, namely Tarbiyatul Banaat Diniyah Al-Amien (TIBDA), Madrasah Tsanawiyah Al-Amien (MTsA) accredited status in 2005, Madrasah Aliyah Al-Amien (MAA) accredited status in 2004, Madrasah Aliyah Skills (MAK), and the Middle School of Information Technology (SMK IT) established in 2008. This study uses a qualitative-descriptive approach. As for the data collection technique in this research, it is using methods of interview, observation and documentation. The interview method used is a structured interview to extract detailed data from the source. From the findings in the field obtained based on the results of interviews, documentation, and observations can be concluded into several points: 1) the K13 learning plan in improving student performance performed by the teacher is by first mapping the KD by establishing the theme that is in the teacher's book. 2) K13 learning process in improving student learning performance more emphasizes cognitive aspects with emotional, and psychomotor support. 3) Authentically evaluate in K13 learning to improve this student’s learning performance using various techniques and starting instruments

The decline in seed quality due to storage beyond the shelf life is a major problem in rice cultivation. One effort that can be made to improve seed viability is through invigoration techniques using natural ingredients, such as bean sprout extract (Phaseolus radiatus), which contains growth hormones. This study aims to analyze the effect of bean sprout extract on the viability of Ciherang rice seeds that have exceeded their shelf life and to determine the best treatment. The study was conducted from August to November 2024 at the Biology Laboratory of the UPTD Seed Center, Supervision and Certification of Agricultural Seeds of Gorontalo Province. The study used a two-factor Randomized Block Design (RBD) with bean sprout age (3, 5, and 7 days after sowing) and bean sprout extract concentration (20 g/L, 40 g/L, and 60 g/L) as factors, with four replications. The parameters observed included germination rate, maximum growth potential, and sprout length. The data were analyzed using ANOVA and DMRT post-hoc test at a 5% level. The results showed that bean sprout extract had a significant effect on all observed parameters. The treatment of 5-day-old bean sprouts with a concentration of 20 g/L gave the best results with a germination rate of 95.5%, maximum growth potential of 98.5%, and the highest sprout length. Sprout extract has the potential to be used as a natural alternative to improve the quality of rice seeds that have passed their storage period.

Permanent tooth loss is a common oral health problem experienced by patients. This condition can be caused by pathological conditions such as tooth decay, leading to tooth extraction. Rehabilitation treatment for partial posterior tooth loss aims to restore masticatory function, maintain occlusal relationships and periodontal health, and improve aesthetics. A 34-year-old man came with a complaint that his lower right posterior tooth had been extracted and wanted a permanent dentures to replace his missing tooth. The intraoral and periapical radiograph examination revealed that 46 was missing, and 47 had enamel caries. The diagnosis for 46 was determined to be partial edentulous. The treatment plan was a PFM fixed-fixed bridge with a sanitary pontic for 46, a rigid connector, and a full crown retainer for 45 and 47. In this case, the choice of PFM bridge is based on several considerations, i.e., high chewing loads, relatively low cost, and high long-term success. The PFM bridge has been the primary choice for posterior tooth rehabilitation, due to its durability and cost-effectiveness.

The increasing need for insulation in electric power systems encourages the discovery of high-performance and sustainable dielectric materials. This study presents a Literature Review of biomass-based composite insulator research from 2018-2025 to synthesize the effect of filler type and treatment on the electrical, thermal, and mechanical properties of polymer composites. Literature was analyzed from reputable databases with inclusion criteria, and thematic analysis data extraction. Processing methods generally include washing, acid/alkali treatment, calcination, and advanced production techniques such as sol-gel and ultrasonication, integration of biomass fillers especially at low fractions (3-7%). The results show 1) the dominance of the use of rice husk as a source of biosilica for the main matrix filler of the insulator. 2) the performance of biomass composite insulators is highly dependent on the quality of purification, particle size, and surface modification of the filler. 3) there is a significant increase in the insulator's breakdown strength, resistivity, and thermal stability with the addition of biomass fillers to the main matrix. 4) the long-term stability of biomass materials against humidity and thermal aging has not been evaluated in depth.  

Inflammation is a biological response that occurs as a defense mechanism of the body against tissue damage, infection, or exposure to harmful stimuli. Excessive or prolonged inflammation can lead to various chronic diseases and negatively affect overall health. Natural compounds derived from medicinal plants have gained attention as alternative anti-inflammatory agents due to their relatively lower side effects compared to synthetic drugs. Jatropha curcas is a plant traditionally used in herbal medicine and is known to contain various bioactive compounds. This study aims to examine the effect of Jatropha curcas leaf extract on inflammatory responses. The research method used was an experimental laboratory approach with extract preparation through maceration techniques. The anti-inflammatory effect was evaluated based on changes in inflammatory indicators observed during the treatment process. The results showed that Jatropha curcas leaf extract demonstrated potential anti-inflammatory activity, which was indicated by a reduction in inflammatory signs. The presence of secondary metabolites such as flavonoids, tannins, and saponins is suspected to contribute to this effect. These findings suggest that Jatropha curcas leaf extract has promising potential as a natural anti-inflammatory agent. Further research is recommended to explore dosage optimization and toxicity levels for safe therapeutic use.  

This study aims to examine the activity of ginger (Zingiber officinale) on metabolism and digestion through a laboratory experimental approach. Metabolism and the digestive system play a crucial role in maintaining physiological balance, and disturbances in these systems can affect nutrient utilization and overall health. Ginger has long been used as a traditional herbal remedy and is known to contain bioactive compounds such as gingerol, shogaol, and zingerone, which are believed to support digestive and metabolic functions. The research was conducted using ginger extract prepared through an extraction process under controlled laboratory conditions. Observations focused on changes in metabolic responses and digestive activity following the administration of ginger extract. The study employed a descriptive experimental design to provide an objective overview of ginger’s biological activity. The results indicated a gradual improvement in digestive activity and metabolic responses after treatment with ginger extract. These changes suggest that ginger extract has the potential to enhance digestive efficiency and support metabolic processes. The findings provide scientific support for the traditional use of ginger as a natural ingredient in maintaining metabolic and digestive health and may serve as a preliminary reference for further research using quantitative and clinical approaches.

The increasing consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.