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The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Non-communicable diseases (NCDs) are one of the biggest health challenges globally. Based on data from the World Health Organization or WHO, data shows that of the 56 million deaths that occurred in the world in 2021, there were 38 million or almost three quarters. Based on data from the Semarang City Health Service, the gender of those receiving health services for Hypertension Sufferers in 2022 shows that the female gender is 161,877 (56%) which is greater than the male gender which is 129,033 (44%), and in the working area of ​​the Gunungpati Community Health Center it is in the fifth highest position. Hypertension sufferers. Nutritional status is also influenced by a good diet and is also influenced by age, especially in women of childbearing age less than 40 years because they have a greater potential for experiencing hormonal imbalances in the body. The respondents in this research were WUS in the Sadeng Gunung Pati sub-district, Semarang city, totaling 101 respondents. The aim of this study was to determine the relationship between nutritional status and the incidence of hypertension in suburban age women (WUS) in the Sadeng Gunung Pati sub-district, Semarang City. This research method uses a cross sectional approach which was carried out in the Sadeng Gunung Pati sub-district. Data collection was carried out from March to May 2024. The results showed that the correlation between nutritional status and the incidence of hypertension in women of childbearing age (WUS) in Sadeng subdistrict was carried out using the Chi Square test, obtained with a P value = 0.0001, OR = 5.15 , CI = (2.16 – 12.26), where the majority of respondents with hypertension were 57.4% (aged 15-49 years) with a BMI classification in the thin category of 18.5. The conclusion of this study is that there is a relationship between nutritional status and the incidence of women of suburban age (WUS) in the Gunung Pati sub-district, Semarang city.

Every parent has an obligation to educate their children to become pious and pious children based on the Koran and Sunnah. Parents are also obliged to provide support to each child, to gain knowledge for the sake of happiness in this world and the hereafter. The aim of this research is to describe the responsibilities of parents in early childhood education according to Al-Maraghi's interpretation of Surah An-Nisa verse 9 and Surah At Tahrim verse 6. Apart from that, it is also to reveal the concept of the objectives of Islamic education regarding the educational role of parents in educating their children according to Al-Quran and Sunnah. The method used in this research is a qualitative method with a library research approach, data was obtained through basic information from the tafsir al-Maragh which contains the thoughts of Imam al-Maragh. Data analysis was carried out through the following stages: data processing (unification), data classification, data interpretation. The results of this research show; Firstly, Al-Qur'an Surah Al-Nisa verse 9 contains a warning to every parent that they will be worried and afraid if in the future they have to leave their child in a weak and helpless state. Second, parents are responsible for educating young children. The areas of education that are the responsibility of parents are physical and spiritual education, religious education, and character education.    

Twitter is one of the media used by the Indonesian people to express their opinions regarding the 2024 Presidential Election. However, there is no scientific calculation that can determine the tone of public opinion regarding the 2024 presidential election. In this study, sentiment analysis was carried out on the tweets of the Indonesian people related to the 2024 Presidential Election (Pilpres 2024). The purpose of this study is to find out the opinions of Indonesian Twitter users regarding the 2024 Presidential Election using Natural Language Processing (NLP) Technology and Long Short Term Memory (LSTM) algorithms. NLP techniques are used to understand natural language and extract meaning from tweet copy, and LSTM is used to analyze the accuracy and accuracy of classification. The data used in this study was 1,004 tweets with the topic "Presidential Election", this data researchers obtained through the process of crawling using the tweet harvest library. In this study, 53.2% had positive emotions, 3.5% had neutral emotions, and 43.3% had negative emotions. 78% accuracy, 67% precision, and 67% recall.

GoPay as one of the digital payment applications in Indonesia faces challenges in understanding user perceptions in the midst of fierce competition. This study aims to develop a user review sentiment analysis model by comparing two approaches to text representation, namely TF-IDF and BERT, as well as two machine learning algorithms, namely Random Forest and Logistic Regression. Review data is obtained from the Google Play Store and processed through pre-processing, feature extraction, and sentiment modeling. The results showed that the combination of BERT + Logistic Regression provided the best performance with an F1 Score of 0.86, showing the superiority of BERT in understanding the semantic context compared to TF-IDF. An important feature analysis identifies financial-related words such as "duitnyaapakah" and "kompensasi" as key issues. This research makes a practical contribution by helping app developers improve the user experience through prioritizing relevant features and solutions to key problems complained of.

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.

Problem-based learning is an educational approach that centers around the exploration of real-world problems, with an emphasis on finding genuine solutions through realistic investigation. The objective of this study is to ascertain the outcomes of students' learning achievement through the implementation of a problem-based learning approach, specifically focusing on the topic of animal classification. This study employs the methodology of classroom action research. The research was conducted at SMP Negeri 8 Kei Besar. The participants of the study consisted of 33 pupils in the seventh grade. The research findings indicated that the rate of classical learning achievement in the first cycle was 27%, with a mean score of 62%, while in the second cycle it increased to 81.9%, with an average score of 76%. The findings of this study demonstrate that the utilization of a problem-based learning approach in teaching animal classification can enhance students' academic achievements.    

Archives is a record of activities or a source of information in various forms created by institutions, organizations, or individuals in the context of their activities. Archives can take the form of letters, documents, deeds, charters, books, and others, which can serve as valid evidence for actions and decisions. With the advancement of technology, archives can also take the form of audio, video, and digital content. This research was conducted at one of the government institutions, namely the BMKG office in Palembang, SMB II Meteorology Station. The archiving procedure applied here is still done manually or semi-manually. The purpose of this research is to design an archive classification system at the BMKG office in Palembang, SMB II Meteorology Station, to assist in the development of the existing archiving system in the institution. The result of the research is an interface design that illustrates how the archive information system can be implemented in the institution. In this information system design, the author provides an overview and a simple display of the archive classification system, which is expected to help the institution manage its company archives

This research was conducted with the aim of determining the influence of PMIK (Medical Recorder and Health Information) professional competence on the performance of medical record officers at the Dr. Air Force Hospital. Esnawan Antariksa. The research method used is quantitative with inferential analysis. The data collection techniques used were observation, interviews and questionnaires. The sampling technique used a total saturated sampling technique of 14 people. Obtained from 7 PMIK competency standard indicators (Noble Professionalism, Ethics and Legal, Introspection and Personal Development, Effective Communication, Health Data and Information Management, Clinical classification skills, Disease coding and other Health Problems as well as Clinical Procedures, Health Statistics Applications, Basic Epidemiology, and Biomedicine, Management of Medical Records Services and Health Information, there are still 2 indicators in the percentage that are not good, and in the 5 performance indicators of medical record officers (Quality of Work, Quantity of Work, Supervision, Attendance, Conservation) there are still 2 indicators in the percentage not good. It was found that 3 officers had a D-III RMIK education and 11 officers still had a high school education. Based on the results of the T test, it was found that the Sig. value was 275. It could be concluded that there was no influence of PMIK competency on the performance of medical records officers.    

The Koran has explained how important education is which is very influential in the lives of shaping the character of students to become Muslims with noble morals, especially in the field of developing religious education which is used as a basis for learning both in terms of formal, informal and non-formal institutions which have a very important role in it. The aim of this research is to reveal the important position of students in the view of the Qur'an. The method used in this research is a literature study research method, the data source is analyzed interactively using a literature review based on the latest references. The results of the research in this discussion show that based on the presentation of the verses of the Koran, forms of classification regarding students have been explained and of course they have given meaning to the most important subjects and objects in education, namely students, where they are always prioritized to gain an understanding of learning and formation. character, this cannot be separated from the role of an educator who always directs students to become human beings who are not only intellectual in general knowledge but also intelligent in developing their morals.  

The Koran has explained how important education is which is very influential in the lives of shaping the character of students to become Muslims with noble morals, especially in the field of developing religious education which is used as a basis for learning both in terms of formal, informal and non-formal institutions which have a very important role in it. The aim of this research is to develop and foster an academic culture in the school environment. Where schools can improve learning and education effectively and sustainably. The method used in this research uses a qualitative approach, with a research design (research library) sourced from books, the latest journals and other sources of information as well as those that support the writing of this article, for example trusted national newspapers. The results of the research in this discussion show that based on the explanation of the Al-Quran verses that have been explained in Q.S At-Taubah 9/122 and Q.S An-Nisa 4/170 regarding forms of classification regarding students and of course they have given meaning to the subjects and objects that The most important thing in education is students, where they are always prioritized to gain an understanding of learning and character formation, this cannot be separated from the role of an educator who always directs students to become human beings who are not only intellectual in general knowledge but also intelligent in developing their morals.

The focus of this research is on how transgender communication strategies relate to social stereotypes in the city of Bukittinggi. In life, a person is never separated from the name of communication, this is because communication is an inherent activity in humans who are classified as social beings, humans cannot live without communication anda communicationt itselt is an important element that forms and anables society. In principle, society cannot be separated from social problems because society has developed and changed from time to time, stereotypes are labels given by society to certain groups. This research is motivated by transgender communication strategies in which they always get social stereotypes but they can still be persistent even carrying out their activities, how to conduct interviews and observations on informants related to this phenomenon. This research resulted in an explanation of the social stereptypes of people in the city of Bukittinggi. The first social stereotypes here is  divided into two, namely the classification of condition in which this the  classification of this condition  is someone who has personality traits and irability,  namely the behavioral traits that are inherent in a person. Two communication strategies, this is where a transgender will carry out a communication strategies when he gets a stereotype so that he can survive and move on with his life.

It has become an axiom that parents are responsible for their children's education. Parents are the first teachers for children. It is appropriate that since waiting for the birth of a child, even before marriage, prospective parents have planned how to raise and educate their children. The aim of this research is to describe the responsibilities of parents in raising children at an early age based on al-Maragh's interpretation of the Al-Qur'an verse 9 of Surah An-Nisa. This research uses a qualitative research method with a library approach, the main source of information is obtained from reading materials from books, magazines, articles and interpretations of the al-Maragh hadith which contain the thoughts of Imam al-Maragh. Data analysis was carried out through the following stages: data processing (unification), data classification, data interpretation. This research produced several results. First, the Al-Qur'an, Surah Al-Nisa verse 9, contains a warning to every parent that they will be worried and afraid if in the future they have to leave their child in a weak and helpless state. Second, parents are responsible for educating young children. The areas of education that are the responsibility of parents are physical and spiritual education, religious education, and character education.

Disease control of chili leaf citra plants is an important aspect in modern agriculture to increase crop yields and reduce losses due to pest attacks on chili leaf citra plants. In this research, identification of chili leaf diseases uses Gray Level Co-Occurrence to obtain image features, and the Support Vector Machine (SVM) method is used to classify the feature extraction results according to leaf disease categories in the test image. Based on the disease class using the test image. .As a classification tool for identifying plant pests in images of chili leaves, the dataset used in this research consists of images of leaves that represent normal conditions and conditions attacked by pests. The pest identification process consists of several stages, including image pre-processing, feature extraction, as well as training and testing. SVM model.

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Web Usage Mining (WUM) is the use of data mining methods to extract knowledge from web usage data. One function of WUM is to support Business Intelligence (BI) purpose in which one of the important information needed is the classification of web users that can be used for acquisition, penetration, and user retention activity. There are two main problems encountered in conducting the classification of web users. The first is the determination of antecedent attributes as a term of classification rules, which is a major problem in data mining classification function in general. The second problem is the preprocessing activity which involves preparing the supporting data for the web users’ classification need which is the most difficult stage in WUM. For the web user classification method, we propose a classification method based on ant colony optimization method (ACO) as a distributed intelligent system using heuristic function which is in line with the problem areas. We proposed a heuristic functions for web user classification based on web usage data that uses entropy of antecedent candidate, information gain from attribute of total number of web user access  and average of access duration of web user.  For preprocessing purpose, a method of data preparation that can support the needs of web users’ classification is proposed. The data used consists of web access log data, web user profile data and web transaction data. The preprocessing activity consists of parsing, data cleansing, and extraction of the web user sessions using heuristic method concerning web page access timeout and differences in web browser agent. Testing is done by comparing the performance of the proposed algorithm with Ant-Miner algorithm, cAnt-Miner algorithm, and the Continuous Ant-Miner algorithm. The results of testing of four web data shows that the performance of the proposed algorithm is better in terms of accuracy of rules and simplification of rules.

This research is motivated by the use of social media Twitter which attracts its users with informative and popular content. Twitter has succeeded in reaching users from various groups, from officials to the general public. The features in it allow for unlimited communication. This research focuses on analyzing the principles of jokes in tweets from the Twitter account @kaesangp. The principle of joking is a method that is intended to offend feelings by being friendly or in the form of a basis for making conclusions that are true and false; an intentional violation of the maxims of politeness; and disclosure of taboo things in a speech. In accordance with the focus of the research objectives, pragmatic theory is used and is related to the principle of jokes. This research use desciptive qualitative approach. The listening method and note-taking technique were used as data collection methods. As the end of the research objective, results were obtained in the form of utterances in tweets from the Twitter account @kaesangp which contained utterances with the principle of joking. The research results were obtained from the classification and decomposition based on the type of joking speech, including satire, banter and jokes. The benefit of this research is to find out the principles of jokes contained in the tweets of related Twitter accounts.

Computer vision technology is used to improve work safety in the construction industry. The key in this project is the utilization of the YOLO method on the Roboflow platform. In addition to the Convolutional Neural Networks (CNN) algorithm, YOLO efficiently divides the image into a grid and classifies the objects in the grid by bounding box and confidence score. With the integration of YOLO, this project can achieve accurate and fast PPE detection. This project uses the YOLO method to detect head and body parts from input images. The detected body parts are then cropped and processed using the CNN method for classification. This project will also implement computer vision algorithms, including Deep Learning methods that currently have the most significant results in image recognition is CNN method, to automatically detect and monitor the use of PPE. This model achieves mAP 64.1%, Precision 73.2%, and Recall 60.2%. The Streamlit framework was used for deployment, creating a web application for PPE compliance tracking. This project, ''Health and Safety PPE Compliance Tracking'', aims to improve work safety in the construction industry. This project uses Computer Vision technology to detect, monitor, and ensure worker compliance with the use of appropriate PPE. The suggestion is to conduct further trials using other datasets in the form of photos or videos that can be done in real-time by ensuring that the colors of hats and vests do not vary too much to detect the conformity of labeling with PPE use.