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Building fires can significantly degrade the strength and integrity of steel structures, so post-incident evaluation is crucial to ensure building safety and feasibility. This study aims to evaluate the condition of the steel tower structure after the fire through a visual inspection method. A total of 35 structural elements were examined, including columns, beams, and bracing, to identify damage caused by heat exposure. The inspection results showed that 6 elements (17%) were in the category of Acceptable, 8 elements (23%) Needs Attention, 5 elements (14%) Not Acceptable, and 1 element (3%) Not Applicable because they had been removed. Steel columns generally remain upright without deformation, but suffer damage to the protective layer (coating). In contrast, most blocks lose their protective layers, are directly exposed to fire, show early signs of corrosion, and some suffer severe damage such as flange tears and cuts. These findings confirm the importance of systematic documentation and classification of element conditions as the basis for technical decision-making for structural improvement. Visual inspection proved effective as an initial step in the evaluation process, providing a relevant initial picture of the extent of damage and the need for intervention. This study recommends follow-up in the form of advanced structural analysis and material testing to ensure the feasibility of reusing the affected steel elements.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Activities that have tasks and functions according to their provisions require fixed assets, which are an important component in supporting their operations. Indonesia uses Government Accounting Standard Statement (PSAP) Number 07 on fixed asset accounting as the asset treatment system. Every government agency that reports on its accounting treatment, including recognition, valuation, presentation, and disclosure, must use this standard statement. In addition, as a task support unit for the Tarakan City Tourism Office, the Tourism Office also provides financial reports. The purpose of this study is to determine whether this office has presented financial statements in accordance with PSAP No. 07. This research uses a qualitative descriptive approach with data collection through interviews, observation, and documentation. The results of this study indicate that the asset components of PSAP No. 07, namely asset classification, recognition, measurement, valuation, expenditure, depreciation, termination and disposal, and disclosure have met the principles of PSAP No. 07 on fixed asset accounting.

This study aims to utilize QGIS as a spatial analysis tool to map the distribution of divorces based on economic factors and disputes in Surakarta City during the 2020–2023 period. The data used includes spatial data in the form of Surakarta City's administrative map in shapefile format and non-spatial data comprising the number of divorces obtained from BPS Surakarta. Non-spatial data were integrated into spatial data using the "join attribute" feature in QGIS. The analysis process was conducted using classification methods to identify areas with the highest divorce density. The findings reveal that divorces due to economic factors are concentrated in low-income areas, such as Banjarsari and Jebres, while divorces caused by disputes exhibit a more evenly distributed pattern. The thematic maps were then exported into GeoJSON format for implementation on an interactive website accessible to the public and policymakers. This study contributes to the utilization of GIS technology in supporting data-driven decision-making.

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

Background:   The fovea ethmoidalis and the lateral lamella of the cribriform plate of the ethmoid bone are the parts of the skull base that are most vulnerable to iatrogenic problems during functional endoscopic sinus surgery. The vertical height of the cribriform plate's lateral lamella, which is divided into three groups based on Keros types. According to the Keros, The likelihood of iatrogenic injury and issues increases with the cribriform plate's lateral lamella height.   Aim of the study:   The aim of this study is to assess the discrepancies in the ethmoid roof elevation (the depth of the olfactory fossa) amongst the adult Kirkuk population using multi-detector computed tomography.   Patients and methods: 160 persons who were referred for a CT scan to evaluate their paranasal sinuses participated in the study. Participants in this study were not allowed to have any pathological abnormalities affecting the ethmoid roof. According to the Keros classification, which was split into three groups (Keros I from 1 to 3 mm, and from 4 to 7 mm considered Keros II, while Keros III should be from 8 mm and more), the vertical height of the lateral lamella of the cribriform plate was measured using the coronal portion of a CT image. Results: The patients' average age was 35.25 ±14.16 years. The range of the left lateral lamella height is 2.1 to 10.0 mm, and the range of the right lateral lamella height is 2.0 to 9.9 mm. Of these, 43.75% had Keros type I, 55% had Keros type II, and 1.25% had Keros type III.   Conclusion: Keros type II was present in the majority (about 55%) of the adult population in that was studied while Keros type I was (about 43.75%). However, just (about 1.25%) of the adults population in the sample had Keros type III.

This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

Song or Music Copyright with the provisions stipulated in Law Number 42 of 1999 concerning Fiduciary Guarantees, explains that "Song copyrights can be used as Fidusa Guarantees, however in practice, banks refuse to process loans originating from songs and music because so far there are no songs and music that are guaranteed to get a loan, as stipulated in Law Number 10 of 1998 concerning Banking. The purpose of this study is to (1) find out how the classification of song copyrights that can be registered as objects of fidusa guarantees, and (2) find out the model of legal arrangements for song copyrights that are registered as fidusa guarantee objects carried out by banks. The research used to examine this issue is of a normative juridical type and uses statutory and comparative approaches. Indonesian copyright law forms the basis of the legal information used in this research. Some of the data used in this study were collected from various journal papers on song copyright as well as findings from studies on copyright laws. The research findings show that (1) the Copyright Law does not sufficiently explain or describe the process for registering a song copyright as a fidusa guarantee and (2) the Bank's legal policy regarding song copyrights as a fidusa guarantee is based on Article 16 paragraph (3) of the Copyright Law.

The news about the proposal of the government of the Republic of Indonesia regarding the postponement of the 2024 elections is certainly an interesting discussion. In this research, sentiment analysis will be carried out on the issue of postponing the election. In this study, a dataset obtained using the crawling technique was obtained in the amount of 1280 tweet data about the postponement of the 2024 election. Data labeling in this study uses lexicon-based techniques with Indonesian dictionaries. By applying this technique, the details of the data in the positive class are 67.7%, namely 157 opinion data, and 32.3% negative, namely 75 opinion data. The sentiment classification system's training and test data yield a 9:1 ratio when the Naïve Bayes Classifier method is applied, and word weighting using TF-IDF yields an accuracy value of 91.67%, precision of 90.91%, recall of 100%, and f1-score of 95.24%.

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.

This research explores the implementation of computer vision technology in AI-based e-commerce platforms to enhance product identification and improve user experience. The study specifically examines the use of deep learning algorithms, particularly Convolutional Neural Networks (CNN), to automate product recognition and classification. The results indicate that AI-driven image search features significantly increase the speed and accuracy of product search, leading to greater customer engagement. However, challenges such as the need for high-quality datasets, varying image quality, and high initial investment costs were identified as barriers to effective implementation. The findings suggest that overcoming these obstacles can lead to improved operational efficiency and customer satisfaction. The success of AI in e-commerce depends on robust infrastructure, data quality, and skilled workforce training.

This study looks at feminism in Melly Goeslow's song Bunda. This study aims to shed light on how popular music, in particular, shapes societal perceptions of women's roles. The research type is descriptive qualitative, and the methodology is qualitative. An analysis of feminism in Melly Goeslow's song Bunda serves as the study's data source. Melly Goeslow's song Bunda serves as the study's data source. Techniques for reading and taking notes were used in the data collection process. Finding significant patterns, comprehending phenomena, organizing, isolating, and arranging data, and drawing conclusions were all part of the data analysis process. According to the study's findings, there are four verses in Melly Goeslow's song Bunda that are analyzed for feminism. Marxist feminism, radical feminism, liberal feminism, and socialist feminism are the classifications for one verse, one verse, and one verse, respectively.

Foreign language learning anxiety is a prevalent emotional phenomenon among English major students that significantly impacts their learning effectiveness and motivation. This study comprehensively examines the definition, classification, causes, and complex effects of foreign language learning anxiety from a theoretical perspective. Through analyzing the interplay of personal factors (such as self-esteem, learning styles, learning motivation) and environmental factors (such as teaching methods, classroom atmosphere, test pressure, cultural differences), it reveals the multidimensional nature of foreign language learning anxiety. The research indicates that this anxiety not only reduces learners' motivation and efficiency but also interferes with their cognitive processes, affects the development of speaking and listening skills, and even negatively impacts their physical and mental health. To address this issue, based on the Affective Filter Hypothesis, Humanistic Psychology, Constructivist Learning Theory, and Social Interaction Theory, this study proposes educational intervention strategies, including optimizing curriculum design, improving teaching evaluation systems, establishing psychological counseling mechanisms, and strengthening the emotional support function of teachers. These strategies aim to create a positive, interactive learning environment to help students overcome anxiety and enhance learning outcomes.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

This study examines the virtues of knowledge and the classification of education based on the perspectives of two prominent Islamic scholars, Al-Ghazali and Ibn Qayyim, grounded in the framework of the Qur'an and Hadith. Islam assigns a distinguished position to knowledge, as reflected in Qur'anic verses such as Al-Mujadilah 11, which promises elevated ranks for those who believe and possess knowledge, and the supplication for increased knowledge in Thaha 114. In Hadith, knowledge is likened to beneficial milk that must be shared with others. Al-Ghazali emphasizes the importance of knowledge as a path to happiness in both this world and the hereafter, while Ibn Qayyim highlights the role of knowledge in drawing closer to Allah and shaping a complete human character. This research employs a literature review method to explore the concepts of the virtues of knowledge and education in Islam through the perspectives of Al-Ghazali and Ibn Qayyim. The findings reveal that both scholars present complementary views on the value of knowledge as an act of worship that brings one closer to Allah SWT, while categorizing knowledge based on its benefits for individuals and society. This study underscores that seeking knowledge is an obligation for every Muslim and a path to paradise, as affirmed in the Qur'an and Hadith. These findings provide valuable contributions to the development of Islamic education, particularly in instilling the virtues of knowledge in modern educational curricula.

In the process of learning activities, teachers should be required to be able to choose the right learning media in delivering learning materials to students, so that students more easily understand and recall the subject matter presented by the teacher, and create an attraction for students to be more active in participating in the learning process. teach. Therefore, I am interested in researching the Effect of Video Learning Media on Increasing Student Learning Motivation in Civics Subjects at SMP IT Nur Al Rahman Cimahi. The problems that will be discussed in this research are; 1) how to use video learning media in Civics subjects; 2) how is the motivation of students in PPKn subjects; 3) how much influence the use of video media in increasing students' motivation in learning Civics. The purpose of the study was to determine the effect of video learning media on students' learning motivation. The research method used in this study is a quantitative method. The sampling technique is by random sampling. The results of the study are 1) The value for the video learning media variable (X) is 49.02. The classification is said that the video learning media variable (X) belongs to the "very good" category, students participate in Civics learning using video learning media effectively. 2) The value for the learning motivation variable (Y) is 47.54. The classification can be said that the learning motivation variable (Y) belongs to the "Very Good" category, indicating that students' learning motivation has increased. 3) The results of the F test can be taken to determine the value of fcount 74,917 > ftable 4.00 then H0 is rejected and H1 is accepted which means there is an overall effect between the independent variable (video learning media) on the dependent variable (Learning Motivation)4) The coefficient of determination is 55.5% and the remaining 44.4% is determined by other variables outside of this research.

Burns are a type of trauma caused by various external factors such as heat, electrical current, chemicals, or lightning, which can damage the skin, mucosa, and deeper tissues. Extensive burns can affect the metabolism and overall function of the body. Burns are classified based on their severity: first-degree burns (affecting only the epidermis with symptoms of erythema and pain), superficial second-degree burns (extending into the epidermis and part of the dermis, accompanied by blisters and severe pain), deep second-degree burns (involving the entire dermis), and third-degree burns (involving the epidermis, dermis, and subcutaneous tissue, with damage to blood vessels that reduces blood flow to the affected area). Burn management aims to prevent infection and allow epithelial cells to proliferate and close the wound. Additionally, burns can lead to serious complications such as hypovolemic shock, pneumonia, urinary tract infections, cellulitis, and skin contractures. Therefore, prompt and proper initial treatment is crucial to prevent further complications. With a better understanding of burns, it is hoped that the quality of patient care can be improved, accelerating the healing process and reducing the risk of serious complications.

UMKM is an economic platform for the community which is a basic component in the development of economic pillars starting from the lowest level, small and medium enterprises (UKM/UMKM) are types of companies in Indonesia which are owned by individuals or business entities in accordance with the criteria set by Law No. 20 of 2008. UMKM is able to defined as business run by individuals, households, or small-sized business entities. The classification of UMKM is based on limits on annual revenue turnover, total assets, and number of employees. The development and quality improvement needs to get serious support for its further growth, this is not only in terms of capital and ease of doing business, but also needs to be done in terms of management up to the ability of the perpetrators, one of that the UMKM perpetrators must learn about management, especially in financial management, starting with simple things such as preparing a cash book. Most UMKM are run by individuals who do not have special skills. UMKM run as they are and do not have a good financial planning system and financial management system, this is one of the weaknesses of UMKM, so they cannot make plans for future business development. The cash book preparation training aims to increase the financial management capacity of Micro, Small and Medium Enterprises (UMKM). The training method is designed to provide a basic understanding of accounting principles, financial statement preparation techniques, and practical applications that are relevant to the needs of UMKM. This training program is expected to improve the quality and financial performance of UMKM, so that they can be more competitive and sustainable in the market.

The growth of e-commerce in Indonesia has led to the emergence of various online shopping platforms, with Shopee being one of the most popular in Semarang City. User reviews on the Shopee application serve as a valuable data source for analyzing customer satisfaction levels; however, the large volume of data requires a systematic and accurate analytical approach. This study aims to analyze user review sentiments of the Shopee application using three machine learning algorithms: Random Forest, Naïve Bayes, and Support Vector Machine (SVM), as well as comparing the accuracy of these three algorithms. This research utilized 1000 reviews collected through web scraping from the Play Store, which were categorized into three classifications: positive, neutral, and negative sentiments. The analysis process encompassed pre-processing stages, feature extraction using TF-IDF, and classification using Random Forest, Naïve Bayes, and Support Vector Machine algorithms. The results demonstrated that the Random Forest algorithm achieved the highest accuracy at 96.19%, followed by Support Vector Machine with 95.71% accuracy, and Naïve Bayes with 84.76% accuracy. This research highlights the effectiveness of Random Forest and SVM in classifying user review sentiments towards the Shopee application.

Excessive sugar consumption has become a serious public health problem. Increasing patterns of food and drink consumption in line with changes in modern lifestyles have contributed to an increase in the prevalence of non-communicable diseases such as obesity, type 2 diabetes and cardiovascular disorders. This study analyzes and analyzes the use of Artificial Intelligence (AI), especially Deep Learning techniques and Neural Network algorithms, in the classification of sugar content in sweetened drinks. The Systematic Literature Review (SLR) method was used to filter relevant studies published between 2020-2024. The study results show that AI is able to provide more efficient and accurate solutions than manual methods. However, although the literature results show great potential, the application of AI in sugar content classification still requires further empirical research. This study emphasizes the importance of developing AI models tailored to the characteristics of sweetened drinks to support consumer decision making regarding healthier drink choices.