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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.

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%.

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).

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.

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%.

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.

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.

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.

This tofu factory industry has existed since 2000 and in general, the people who set up the tofu factory were originally a meatball seller who had lived in Batu Merah village for a long time. From the beggining, the tofu factory industry was established until now, the industry is still operating and has never done production cost classification, break even point calculations, profit planning calculations, and margin of safety precentage ratios. The purpose of this study was to determine the break-even point that must be achieved in the tofu factory business of Mr. Haji Rahim in Batu Merah Village, Sirimahu District, Ambon City. The results showed that the total cost of production in March 2024 was Rp. 33,400,000 consisting of a total fixed cost of Rp. 27,700,000 and a total variable cost of Rp. 5,700,000. By showing that the total sales of tofu during March 2024 were Rp. 39,200,000. The number of tofu sold during March was 1,120 units with a unit price of  Rp. 35,000 per unit.

Indonesia is committed to achieving zero hunger as one of the goals of fulfilling the Sustainable Development Goals (SDGs) where this commitment focuses on addressing the problem of food availability but also ensuring that every individual has access to sufficient, nutritious, and safe food throughout the year for everyone. However, reviewing the current conditions in Indonesia, there is still an imbalance in food availability that will cause food vulnerability. Therefore, a prediction of food vulnerability in the future is needed where discriminant analysis is one of the appropriate statistical methods to analyze qualitative dependent and quantitative independent variables. This study uses secondary data from the official website of the food agency and the central statistics agency. The results of the study show that the characteristics of the data have small variations, asymmetric distribution, and there are outliers in several categories. The assumptions of multivariate normality, the suitability of the dependent variables, and the identity of the variance-covariance matrix have been met. Through discriminant analysis, the variables of the percentage of poverty and the percentage of households with access to clean drinking water are proven to significantly affect the IKP category. The discriminant model produces one significant function that is able to group the IKP category with a model accuracy rate of 86.8% and a classification accuracy of 64.7%.

This research aims to analyze students' errors in solving quadratic equation questions based on the Kastolan classification with test instrument questions adapted to the cognitive level of Bloom's Taxonomy. The subjects of this research were six students of class IX MTsS Simpang Tanjung Nan IV. The type of research used is descriptive qualitative. Instruments include questions, interview guidelines and documentation. Based on this research, conceptual errors were found with a percentage of 58.3%, procedural errors with a percentage of 33%, and technical errors with a percentage of 25%. The dominant errors found are conceptual errors, the teacher's role is needed in preventing and overcoming these errors.

Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiments. The purpose of sentiment analysis is given by internet users on social media to provide a personal assessment or opinion. Paga Lewu Shop that often gets user sentiment through social media is Paga Lewu Shop. The existence of consumer opinion sentiments about Paga Lewu Shop can be analyzed and utilized to obtain useful information for other customers and the Paga Lewu Shop. By using the Text Mining technique classification method, a sentiment will be known as positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about the Paga Lewu Shop. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the conversion of emotion icons, the accuracy value can be increased to 98%.

The high level of internet access among children aged 5 years and over in Indonesia with a percentage of 12.43%, the 3rd highest in the classification of internet use based on age after those aged 25 years and over in first place and those aged 19-24 years in second place. This research aims to determine the relationship between the duration of device use and cognitive development in 6 year old children at SDN Sukaraya 04 in 2024. This research design uses a cross-section technique with the entire population of 82 6 year old students at SDN Sukaraya 04 and their parents. the. The sampling technique was a total sampling of 82 students. Data were collected using primary data by distributing questionnaires with data analysis using univariate analysis and bivariate analysis using the chi square test. Based on the research results, it shows that there is a relationship between the duration of device use and the cognitive development of 6 year old children at SDN Sukaraya 04 in 2024 with a statistical p value of 0.002 (a<0.05) and the OR result was 6.026. Based on the research results, 21 respondents (47.7%) who used devices in the risk category experienced poor cognitive development. It is hoped that this research can be a guide for parents to pay more attention to the duration of device use for children aged 6 years so as not to interfere with cognitive development.

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.

The reliability of sterilization equipment, such as autoclaves, is essential to ensure patient safety, infection control, and operational continuity in healthcare facilities. Damage or malfunction of autoclaves may disrupt sterilization processes and pose significant risks to medical services. This study aims to develop an expert system for autoclave damage detection using the fuzzy logic method to support faster and more accurate diagnostic decision-making. The proposed system applies fuzzy inference to evaluate the level of damage based on input symptoms provided by users. By handling uncertainty and varying symptom intensities, the fuzzy logic approach enables proportional assessment rather than rigid rule-based classification. The system was designed through knowledge acquisition from technical experts and implemented using fuzzy membership functions and inference rules to determine damage severity levels. Experimental testing was conducted to evaluate system performance and diagnostic accuracy. The results indicate that the expert system successfully generated diagnosis outputs for all tested scenarios, achieving functional diagnostic accuracy within the defined test cases. The system was also able to calculate a quantified damage severity value of 11.6235981% based on the given symptoms, demonstrating its capability to assess damage levels numerically and objectively. Furthermore, the developed system significantly reduces the time required for damage detection compared to manual diagnostic procedures. Automating the evaluation process, it assists electromedical technicians in identifying faults more efficiently and taking preventive or corrective actions promptly. Overall, the implementation of a fuzzy logic-based expert system provides an effective, accurate, and practical solution for improving autoclave maintenance management and supporting healthcare service reliability.

The student study period is one of the important aspects to measure the quality of a higher education institution. The length of the student study period can be assumed to come from internal factors and external factors, so it is necessary to conduct research that aims to identify and model the factors that affect the student study period. The method used in this research is logistic regression and the data used is primary data obtained from distributing questionnaires. The target of this research is aimed at alumni students from the Mathematics Study Programme of Udayana University from 2011 to 2019. In this study, the best model produced has a classification accuracy of 98.17% and the independent variables that have a significant effect on the study period are gender, tuition fees and interest in majors.

Human resources are a company's most dominant asset, because they can play an important role in the company's business development. An outsourcing company is a legal entity and is obliged to comply with business licenses issued by the Central Government. The eligibility process for new employees is supported based on the level of ability and competency determined by the company. The existence of difficulties for companies in determining the eligibility of new employees which makes the reason for the ineffectiveness of the processes carried out by the company at this time, is used as a goal for the authors for the purposes of a study. By using the classification method in a data mining with the C4.5 algorithm (Decision Tree) and a RapidMiner application as a tool in the analysis process carried out to find a factor supporting the process of a new employee eligibility. With the data of 960 applicants used as a sample, this data was taken from 2021-2022. From the data divided into several attributes used, the highest Gain value obtained from these attributes through the results of Test 2 of 0.417152421 which will be used as the root in the process of determining employee eligibility and has the highest accuracy value of 98.44%.