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Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

Tooth extraction is a procedure to remove a tooth from its alveolar bone socket. The causes for tooth extraction include caries, periodontitis, fractures, impacted teeth, the need for orthodontic treatment, and persistent primary teeth. Post-extraction bleeding is the most common complication that occurs. Hemostasis is a mechanism to stop bleeding from blood vessels to prevent excessive blood loss when an injury occurs, ensuring that blood continues to flow smoothly. In stopping bleeding, there are three processes involved: vasoconstriction (the narrowing of blood vessels), platelet activity, and the activity of blood clotting factors. Bleeding time is the time interval from when blood exits the blood vessel until the bleeding stops. The normal range for bleeding time is 1 to 3 minutes. Balakacida leaves contain active compounds including alkaloids, tannins, flavonoids, saponins, and phenolics. To determine the effect of Balakacida leaf extract (Chromolaena odorata) as a hemostatic agent following tooth extraction in Wistar rats (Rattus norvegicus). This study uses an experimental method with a Post-Test Only Control Group Design. The samples used in this research are male Wistar rats (Rattus norvegicus), aged 2–3 months, weighing between 200–250 grams. The research data were processed and analyzed using the One-Way ANOVA test. The results showed that treatments at concentrations of 10%, 20%, and 30% were able to significantly accelerate bleeding time compared to the control group. The administration of Balakacida leaf extract is effective as a hemostatic agent following tooth extraction in Wistar rats.  

The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.

This study investigates the effect of adding onion peel extract as a corrosion inhibitor on the corrosion rate and hardness of radiator pipes. The research employed an experimental method with inhibitor concentrations of 0 ppm, 100 ppm, 200 ppm, and 300 ppm. Corrosion rate testing was conducted using electrochemical methods, while hardness was measured using the Vickers method. The findings reveal that the addition of onion peel extract at a concentration of 300 ppm significantly reduced the corrosion rate to 0.081 mmpy, achieving an inhibition efficiency of 56.45%. Furthermore, the same concentration enhanced the surface hardness of radiator pipes to 255.403 Kgf/mm². These results demonstrate that onion peel extract has strong potential as an eco-friendly organic corrosion inhibitor. Its dual function in reducing corrosion and improving mechanical properties highlights its applicability in radiator pipe protection and sustainable engineering practices. The study contributes to the development of natural inhibitors as alternatives to synthetic chemicals, aligning with environmental preservation efforts and advancing green technology in material protection.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

Brotowali stem (Tinospora crispa L.) is a plant that grows abundantly in Central Kalimantan and has been empirically used for generations as an antidiabetic remedy by the Dayak Ngaju community. Brotowali stem contains secondary metabolite compounds, including alkaloids, which possess various pharmacological activities, one of which is antidiabetic activity. This study aimed to determine the alkaloid content of Tinospora crispa stem extract in aquadest, ethyl acetate, and n-hexane fractions. The research employed an observational descriptive method by analyzing qualitative data through color reaction tests and quantitative data using UV-Vis spectrophotometry to determine alkaloid levels. The qualitative analysis results showed positive color reactions indicating the presence of alkaloid compounds. Quantitative analysis using UV-Vis spectrophotometry revealed that the total alkaloid content in the aquadest fraction was 20.19 mg or 20.19%, in the n-hexane fraction was 20.54 mg or 20.54%, and in the ethyl acetate fraction was 31.07 mg or 31.07%. The highest total alkaloid content was found in the ethyl acetate fraction. In conclusion, the extract of Tinospora crispa stem positively contains alkaloids, with the highest alkaloid content obtained in the ethyl acetate fraction at 31.07%.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Gaya hidup remaja masa kini sangat erat kaitannya dengan penggunaan smartphone, di mana hampir seluruh remaja telah memiliki smartphone pribadi. Penggunaan smartphone yang tidak terkontrol berisiko menimbulkan kecanduan yang dapat berdampak negatif terhadap kualitas hidup remaja. Kondisi ini berdampak pada status gizi yang tidak normal pada remaja, diantaranya kekurangan berat badan (underweight) maupun kelebihan berat badan (overweight). Tujuan penelitian ini adalah mengetahui hubungan kecanduan smartphone terhadap Indeks Massa Tubuh. Desain penelitian ini cross sectional dengan sampel 242 remaja usia 15-18 tahun dan menggunaan teknik purposive sampling. Instrumen penelitian yang digunakan adalah Smartphone Addiction Scale-Short Version yang sudah diuji validitas dan reliabilitasnya dengan nilai cut off point kuesioner untuk laki-laki >31 dinyatakan kecanduan smartphone sedangkan untuk perempuan >33 dinyatakan kecanduan smartphone, selain itu dilakukan pengukuran IMT berdasarkan berat badan dan tinggi badan. Analisa data yang dilakukan menggunakan uji statistik Chi-Square dengan uji alternatif Fisher Extract melalui SPSS versi 25. Hasil yang diperoleh yaitu p-value (0,014 < 0,05) yang menunjukan terdapat hubungan antara kecanduan smartphone terhadap Indeks Massa Tubuh (IMT).

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.

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.