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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.

Along with the advancement of technology, online gambling games that were initially only considered entertainment, turned into a source of income or known as online gambling. On this basis, the government issued Law Number 11 of 2008 concerning Information and Electronic Transactions to regulate the management of information and electronic transactions in Indonesia. Since its enactment in 2008, Law No. 11 of 2008 has not undergone major changes to keep pace with the rapid advancement of information technology, especially related to online gambling. This research is a normative judicial research supported by a legislative approach, a sociolegal approach and a conceptual approach using primary legal materials, secondary legal materials and tertiary legal materials. This study uses data collection techniques in the form of observation and literature studies. The processing technique of legal materials is carried out in several stages, namely inventory, classification, systematization and verification, after which it will be analyzed qualitatively judicially according to the information obtained from various legal materials. The results of this study show that (1) Regulation of online gambling in Indonesia is regulated in 9 laws and regulations, namely in laws and regulations (2) Law enforcement against online gambling in Indonesia has not been running effectively, seeing that the data on online gambling cases from year to year online gambling cases are increasing. (3) Legal protection for people involved in online gambling, having legal protection rights guaranteed by Law Number 8 of 1981 concerning the Criminal Procedure Law (KUHAP).

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 background of this research is students’ mistakes in writing review texts based on linguistic level studies. This research analyzes the fields of phonology, morphology, semantics and syntax in the review text of the film entitled Alive. This research aims to obtain results of language analysis errors in grade 8 students at Shuffah Al-Jamaah Tasikmalaya Middle School. The research method uses analytical descriptive research. The sample selection method in this study used a simple random sampling method. The data collection method used is (1) accumulating the results of students’ review texts at school as a data source, (2) reading carefully all data sources, (3) marking and assigning classification codes to the data, (4) classifying data based on the form of error. Language use, (5) presenting and describing data based on forms of language use errors. The instrument used is data analysis. Based on the results of data analysis, the most dominant errors that emerged were errors in the field of phonology, namely the omission of phonemes. Apart from that, it is also often found in the field of morphology regarding reduplication, namely the use of the word repeat with a square sign. The deletion of phonemes and incorrect reduplication of writing occurs, which is done by students to shorten the writing of a word so that the writing process can be completed more quickly without paying attention to good and correct linguistic rules. Students need to concentrate and be more careful in the process of writing activities. Apart from that, teachers also need to provide direction in the form of knowledge and insight regarding the basics of good and correct linguistics to minimize the occurrence of language mistakes and mistakes.

The background of this research is students’ mistakes in writing review texts based on linguistic level studies. This research analyzes the fields of phonology, morphology, semantics and syntax in the review text of the film entitled Alive. This research aims to obtain results of language analysis errors in grade 8 students at Shuffah Al-Jamaah Tasikmalaya Middle School. The research method uses analytical descriptive research. The sample selection method in this study used a simple random sampling method. The data collection method used is (1) accumulating the results of students’ review texts at school as a data source, (2) reading carefully all data sources, (3) marking and assigning classification codes to the data, (4) classifying data based on the form of error. Language use, (5) presenting and describing data based on forms of language use errors. The instrument used is data analysis. Based on the results of data analysis, the most dominant errors that emerged were errors in the field of phonology, namely the omission of phonemes. Apart from that, it is also often found in the field of morphology regarding reduplication, namely the use of the word repeat with a square sign. The deletion of phonemes and incorrect reduplication of writing occurs, which is done by students to shorten the writing of a word so that the writing process can be completed more quickly without paying attention to good and correct linguistic rules. Students need to concentrate and be more careful in the process of writing activities. Apart from that, teachers also need to provide direction in the form of knowledge and insight regarding the basics of good and correct linguistics to minimize the occurrence of language mistakes and mistakes.

Javanese literature in the form of songs is currently favored by people in Indonesia. One of these songs is Lestari. Lestari contains the theme of love and admiration for a woman. The theme is presented with lyrics that contain language styles. The formulation of this research problem is how the use of language styles in the lyrics of the song 'Lestari'. This study aims to analyze the use of language style in the lyrics of the song 'Lestari'. The theory used is language style by Gorys Keraf. The methods used are data collection, data analysis and classification of language styles, and presentation. The data source comes from the Youtube channel Khatulistiwa Record which is transcribed independently. The results show that the song lyrics contain rhetorical language styles in the form of alliteration, assonance and hyperbole, as well as figurative language styles including metaphor and simile.

This research analyzes a violation of airspace sovereignty by unmanned free balloons for espionage purposes. Each country has full and exclusive sovereignty over its entire territory which includes the surface of the earth and the contents of the earth beneath the surface, including air space. A country that violates sovereignty over airspace without permission entails responsibility for the passing country towards the lower country. The research method used is normative juridical using problem approaches such as the statute approach, conceptual approach, and case approach. The research findings show that unmanned free balloons are the same as the classification of aircraft regulated in Annex 2 of the 1944 Chicago Convention regarding unmanned free balloons which are defined as unpowered, unmanned, and lighter than air aircraft in air flight. The use of unmanned free balloons for espionage purposes in peacetime has no legal regulations, so that they have implications for violations of state sovereignty which give rise to state responsibility. ICAO or International Civil Aviation Organization needs to reconstruct the legal ground towards unmanned free balloons usage outside the function of meteorological purposes in order to avoid the false accused and declare specified sanctions towards the violators.

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 develops a fruit classification system using Convolutional Neural Network (CNN) in the educational application FruityLens, which helps children recognize different types of fruits through image recognition. The application can identify four types of fruits: apple, banana, orange, and watermelon, utilizing an image dataset from open sources. The research methods include dataset collection, image pre-processing, CNN model training, and classification accuracy evaluation. The results indicate that the developed CNN model achieves high accuracy, supporting children's learning about fruits. This implementation is expected to contribute to the advancement of artificial intelligence technology, specifically in the field of fruit object recognition.

This article discusses the importance of syntax in forming sentences, phrases and clauses orally and in writing, but the author examines more about syntax in forming sentences. A sentence is the smallest unit of language that contains a complete message expressed orally or in writing. The method used is a qualitative approach based on literature which aims to examine the importance of syntax in forming sentences orally and in writing. The results of the study are then narrated in descriptive form. It can be concluded that the importance of syntax in forming sentences orally and in writing includes the nature of the sentence, syntactic function in basic sentences, derivative sentences (transformation sentences), classification of sentences based on (clause, predicate, and situational relationships), diathesis, and grammatical.

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.

Dengue fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of the Aedes aegypti mosquito. This disease is a major health problem in many tropical countries, including Indonesia. Identification and classification of DHF patients is very important to prevent further spread and to provide appropriate medical treatment. In this study, the classification of DHF disease is carried out using the K-Means algorithm, which is one of the methods in machine learning used to classify data based on similarity of features. This study aims to apply the K-Means algorithm in classifying DHF cases based on data on symptoms that appear in patients, such as high fever, joint pain, skin rashes, and others. The data used includes patient medical records that record various clinical and demographic parameters. The K-Means algorithm is used to group the data into clusters that describe the severity category or potential risk of dengue disease. The results showed that the K-Means algorithm can be used to cluster DHF patients well, with the division of groups that can describe the severity of the disease. Evaluation was conducted using metrics such as silhouette and cluster validity to assess the effectiveness of the algorithm in performing classification. This model is expected to help medical personnel in decision-making, provide early warning, and improve rapid response to dengue cases.

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.

This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.

This study aims to find out and analyze the effectiveness of the implementation of the duties of the civil police unit in the management of dormitories/lodging in Central Ende District, Ende Regency. This research is an empirical juridical research that uses three approaches, namely the socio-legal approach, the conceptual approach and the doctrinal approach using primary data and secondary data collected using observation, interview and literature study methods. This study uses a method of processing legal materials which is processed in several stages, namely inventory of legal materials, classification of legal materials, systematization of legal materials and verification of legal materials after which it is analyzed in a qualitative descriptive manner. The results of this study show that (1) the effectiveness of the implementation of the duties of the Pamong Praja Police Unit in controlling dormitories/lodging in Central Ende District, Ende Regency can be said to be effective, because the Satpol PP of Ende Regency in collaboration with other agencies carries out their duties and authorities following the existing procedures, even though the implementation is not perfect, (2) Obstacle factors for the implementation of Satpol PP's duties in ordering dormitories/lodgings,  namely responsiveness and confrontation, compliance and understanding, limited resources, difficult cases and infrastructure constraints

This study presents an advanced approach to multi-class skin lesion classification by leveraging an ensemble model comprising the Inception-V3, ResNet-50, and VGG16 architectures. The classification task focuses on categorizing skin lesions into distinct classes, including Melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), using the ISIC dataset, a comprehensive collection of dermoscopic images. In order to properly balance the dataset, the oversampling strategy is utilized, as some lesion types are underrepresented due to inherent imbalances in the dataset. By ensuring that the model is trained on a more representative dataset, this balancing improves the algorithm's capacity to categorize all lesion types properly and impartially. By combining the complementary features of ResNet-50, Inception-V3, and VGG16, the ensemble technique improves the overall classification performance. ResNet-50 is chosen for its deep feature extraction capabilities, which help capture fine details in lesion patterns. Inception-V3 is selected for its multi-scale processing, allowing it to effectively analyze lesions at varying resolutions and sizes. VGG16 is included due to its simple yet highly effective architecture for image classification tasks. The ensemble model with data augmentation significantly outperforms individual models in skin lesion classification for both the original and balanced ISIC datasets regarding accuracy, precision, recall, and F1-score. This method offers a robust solution for skin lesion classification, contributing to more accurate and reliable diagnostic tools in dermatology.

Conventional methods in Mine Countermeasures operations are becoming increasingly ineffective in today’s world, especially due to the increasingly complex challenges in mine detection, classification, and neutralization. The use of Unmanned Underwater Vehicles (UUVs) has been identified as a potential solution to address these issues. This study aims to evaluate the potential use of UUVs in improving the effectiveness and safety of TPR. Through data collection and comparative analysis, the results of the study indicate that UUVs have significant potential in reducing the risk to human personnel, improving the accuracy of mine detection, and increasing overall operational efficiency. UUVs can operate in hazardous environments without putting human personnel at direct risk, and the advanced technology applied to UUVs allows for higher precision mine detection and classification compared to conventional methods. Practical implications of these findings include increased efficacy and safety in TPR, making UUVs an effective tool in improving the effectiveness and safety of naval mine countermeasures. Thus, this study contributes to the use of UUVs as an effective tool in improving the effectiveness and safety of mine countermeasures.