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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.

The aim of this research is to classify Indonesia's trade balance data using the SVM (Support Vector Machine) method with two features, namely Gross Regional Domestic Product (X1) and Wholesale Price Index (X2). Classification is carried out by comparing two types of kernels, namely polynomial kernels and RBF (Radial Basis Function) kernels. Equality Hyperplaneobtained from the polynomial kernel is: . The Hyperplane equation obtained from the RBF kernel is:  Experimental results show that classification with polynomial kernels provides better performance than RBF kernels. This can be seen in the evaluation results which show that the Polynomial kernel has an average model goodness of 75.93% and for the RBF kernel the average model goodness is 74.07%. Leave One Out cross validation (LOOCV) simulation for training data obtained an average accuracy of 76.67% for the polynomial kernel and 66.67% for the RBF kernel. This shows that in this classification context, kernel polynomials are more effective in separating data classes.

The Family Hope Program (PKH) is a program that provides attention to the community, especially the health category, education category and social welfare category for poor families. The Family Hope Program (PKH) aims to reduce poverty and improve the welfare of the Indonesian population. Due to the large number of residents who want to register themselves as PKH recipients, there are residents who manipulate data or claim to be poor people in order to get PKH. If this continues to happen, and there is no preventive action, it is not impossible that many residents are not right in receiving PKH provided by the Government. One of the efforts that can be made is to test the classification of prospective PKH recipients in Bah Sorma Village. This study aims to classify prospective recipients of the Family Hope Program in Bah Sorma Village. The dataset used is data on prospective PKH recipients in Bah Sorma Village, Pematang Siantar City. This research is a comparative study of previous research using the Naïve Bayes method. The method used in this research is Data Mining with the C4.5 method which is used to see the accuracy of the best method than previous research. The accuracy result obtained by this research is 98.18%. Based on the results obtained, research with the case of classification of prospective PKH recipients in Bah Sorma Village using the C4.5 Algorithm gets better accuracy than previous research using Naïve Bayes obtaining an accuracy of 80%.

Alzheimer's and Parkinson's diseases are neurodegenerative conditions that affect the brain, with Alzheimer's causing cognitive and behavioral decline, while Parkinson's leads to motor and non-motor impairments. Both diseases have significant impacts on the health and quality of life of patients, with prevalence increasing in recent years. Although the exact causes of these diseases are still unknown, MRI (Magnetic Resonance Imaging) is widely used to detect brain activity and serves as one of the diagnostic methods. With technological advancements, intelligent systems in image processing for image classification have been extensively used and have become a popular field due to their ability to replicate human visual capabilities. Image classification is performed using various supervised learning machine learning algorithms based on the shape, texture, and color of the images. This study employs two Convolutional Neural Network (CNN) architectures, ResNet50 and GoogLeNet, to compare the performance of these models in classifying MRI scans of patients with Alzheimer's and Parkinson's diseases. The results show that the ResNet50 model outperforms the GoogLeNet model, with parameters set to 100 epochs, a batch size of 128, a learning rate of 0.0001, and the Adam optimizer, achieving an accuracy rate of 90%.

To effectively comprehend human actions, we have developed a Deep Neural Network (DNN) that utilizes inner spatiotemporal non-locality to capture meaningful semantic context for efficient action identification. This work introduces the Top-Heavy CapsNet as a novel approach for video analysis, incorporating a 3D Convolutional Neural Network (3DCNN) to apply the thematic actions of local classifiers for effective classification based on motion from the spatiotemporal context in videos. This DNN comprises multiple layers, including 3D Convolutional Neural Network (3DCNN), Spatial Depth-Based Non-Local (SBN) layer, and Deep Capsule (DCapsNet). Firstly, the 3DCNN extracts structured and semantic information from RGB and optical flow streams. Secondly, the SBN layer processes feature blocks with spatial depth to emphasize visually advantageous cues, potentially aiding in action differentiation. Finally, DCapsNet is more effective in exploiting vectorized prominent features to represent objects and various action features for the ultimate label determination. Experimental results demonstrate that the proposed DNN achieves an average accuracy of 97.6%, surpassing conventional DNNs on the traffic police dataset. Furthermore, the proposed DNN attains average accuracies of 98.3% and 80.7% on the UCF101 and HMDB51 datasets, respectively. This underscores the applicability of the proposed DNN for effectively recognizing diverse actions performed by subjects in videos.

Cyberbullying is an action related to the use of digital technology to intentionally hurt, humiliate or bully other people online. This research focuses on the classification of cyberbullying comments on social media, especially Instagram comments, where many parties who then become a group of people who don't like something will come together to provide negative opinions and comments, which can cause lowered self-confidence and other bad impacts for other users and account owner. Therefore, a classification of Instagram comments regarding cyberbullying was carried out as an effort to prevent this action. The data used in this research is 2000 data, where this data will go through various processes so that it can be executed. In this research, the Naïve Bayes method was used by dividing two classes, namely Bully and Not Bully. Based on the results of the tests that have been carried out, the results obtained are an accuracy value of 84%, a precision value of 84%, a recall of 84%, and an f1-score of 84%.

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.

The remaining food waste in Indonesia reaches around 46.35 million tons, with economic losses reaching 23 million to 48 million tons per year. This condition has led to various campaigns to reduce food waste from people concerned about the problem of food waste. However, the increase in food waste campaigns has yet to be accompanied by a decrease in the volume of food waste in Indonesia. This research aims to determine public sentiment toward food waste campaigns on Instagram social media and determine the accuracy of the methods used in data classification. The method used is the Naïve Bayes Classifier method. The results obtained were from a total of 118 data regarding the food waste campaign; 79% data showed that the public had a positive sentiment, and 21% other data had a negative sentiment. The accuracy results of using sentiment analysis were 78.94%; this shows that the performance of the Naïve Bayes method in classifying data is quite good.

This research aims to discuss the influence of the Think Pair Share (TPS) learning model based on differentiation learning on students' high-level thinking abilities in the material of classification of living creatures at SMP N 4 Dumoga. This research design uses an experiment with a Posttest-Only Control Group Design research design. The research was carried out in class VII with a total of 50 students divided into two groups, namely VII A as the experimental class and class VIIB as the control class. Test high-level thinking skills in the form of analysis, evaluation, and creation questions. The results of hypothesis testing using the t-test obtained tcount = 4.306 > ttable = 2.010 at α = 0.05, which means H0 is rejected and H1 is accepted. The conclusion is that there is a significant influence of the Think Pair and Share (TPS) learning model based on Differentiated Learning on Higher Level Thinking Abilities. Students think at a higher level at SMP N 4 Dumoga.

In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

The number of incidents occurring on the island of Sumatra, especially West Sumatera and Jambi provinces that affect the vibration of the earthquake in Rengat City on a large scale can lead to the movement of land such as landslides and stagnant ground water sources can lead to liquefaction. The purpose of this research is to know how the arrangement of soil type at research location Rengat City and to know the location reviewed in Rengat City, and its surrounding experience potential liquefaction based on some existing penetration test data. This research was conducted to predict liquefaction potential that occurred in Rengat City of Indragiri Hulu Regency with secondary data of penetration test of konus with Seed and Idriss method (1971). The calculation of potential liquefaction prediction is done by determining the number of soil layers, estimating the weight of the soil volume, determining the soil overburden, determining the effective soil stress, determining the corrected confront resistance, calculating Cyclic Strees Ratio (CSR), calculating Cyclic Resistance Ratio (CRR). After calculating the value of Magnitude Scalling Factor (MSF) and calculating the value of safe factor by comparing the value of Cyclic Strees Ratio (CSR) with Cyclic Resistance Ratio (CRR) if the calculation <1 factor is safe then potentially liquefaction occurs. Based on the results of the analysis of the 7 locations with a total of 2 cone penetration test conus analyzed in this study in Rengat City, obtained the type of soil structure is not uniform and based on the results of analysis, from the 7 research sites with tottal 21 point penetration test konus reviewed at magnitude 5 to 10 in Rengat City Indragiri Hulu Regency. In the type of sand soil in the dominant study area can not be achieved liquefaction, this is reinforced from the calculation (SF)> 1 safe factor (SF: 1.0).

Sabu woven fabric is one of the cultural heritages of Sabu Island. In addition to being a cultural heritage, Sabu woven fabric is one of the handicrafts that still exist today which is preserved by Sabu women. Based on its manufacture, the classification process of Sabu woven fabric is based on color or motif identification. However, the classification process is not an easy process, because the classification process requires time and experts in the field of Sabu woven fabric. In addition to the classification process, the wider community also does not get much information about Sabu woven fabric clearly, because it is necessary to introduce the type of Sabu woven fabric, so that people can know or recognize the type of Sabu ikat woven fabric based on its type. Digital image processing techniques are utilized to build a system that can overcome the problems faced. Furthermore, image feature extraction will be carried out using gray level co-occurrence matrix (GLCM) with 4 features namely contrast, correlation, energy, and homogeneity with angles of 0°, 45°, 90°, and 135°. Each GLCM feature shows the same value even though the original image is rotated. After image feature extraction, the extracted data will be classified using the TensorFlow library. From these results it can be concluded that the program succeeded in selecting the type of Sabu ikat woven fabric class.

Indonesia's democracy has been at the center of intense debate in recent years, with many parties criticizing its increasingly fragile and threatened state. This article aims to analyze why Indonesian democracy is considered destroyed by some parties. First, the analysis begins by examining the decline in the quality of democracy which is reflected in the classification of Indonesian democracy in the implementation of its government. Data showing a decline in civil liberties, suppression of freedom of expression, and abuse of power by those in power that threaten democratic principles. Internal and external factors that contributed to the collapse of democracy in Indonesia. Among the internal factors are abuse of power, corruption, and the inability of institutions to provide adequate protection for citizens' rights. Meanwhile, external factors include geopolitical pressures and the influence of globalization on domestic politics. Such as political polarization that triggers conflict, lack of active participation from civil society, and lack of consolidation of democratic institutions.

Developing accounting theory is very important in making financial reports. Accounting theory is an arrangement of concepts, definitions and propositions that presents a systematic picture of accounting phenomena, as well as explaining the relationships between variables in the accounting structure. This research uses qualitative research methods. The data collection technique uses secondary data which is carried out by literature review and literature study. The results of this study state that the benefits of public sector accounting are indeed very important in preparing financial reports with standard account classifications, recording and journaling procedures that are in accordance with the business circulation of public sector organizations including interpretation, treasury and accounting reports. The standards belonging to public sector financial accounting have been created and developed accordingly according to internationally accepted standards to achieve stable and comparable accounting functionality for all countries or jurisdictions.    

Shipbuilding industrial companies still produce a number of organic waste in the form of twigs, grass and leaves. In line with developments in the current era, organic waste that accumulates will become an issue for the environment if it is not managed properly. This activity was carried out to overcome the problem of organic waste in the shipbuilding industry, by utilizing organic waste for composting. Composting is a simple anaerobic method that can be easily carried out in the shipbuilding industry. This method turns organic waste into compost by carrying out a fermentation process. Organic waste in the form of leaves that are turned into compost can increase the nutrient content in the soil. The composting process is carried out using two different activator materials, namely using Effective Microorganisms 4 (EM 4) and Promoting Microbes (PROMI). The results of the compost research carried out have met the quality standards according to SNI 19-7030-2004. The final results of these two treatments are not too different, the pH resulting from the EM4 activator treatment is 6.9 and the pH from the PROMI activator treatment is 7. The temperature value for both does not exceed the ground water temperature, namely the final temperature of the EM4 activator treatment is 29.9oC and from PROMI activator treatment of 30oC. The observation results show that of the two activators EM4 and PROMI, the one that is more effectively used for composting is the EM4 activator because the microorganism content in EM4 has more classifications compared to the PROMI activator.    

The policy of shutting down TikTok Shop has sparked both pros and cons. On one side, it eliminates jobs for content creators whose income relies on TikTok Shop, while on the other side, it saves UMKM  from predatory pricing wars that harm them. Utilizing the Naive Bayes algorithm, a classification method capable of predicting the likelihood of a class and making decisions based on learning data, the Emotion Recognition research on YouTube comments related to the closure of TikTok Shop is conducted. Data will be classified into five classes: happy, angry, sad, afraid, and surprised. The objective of this research is to find the best emotional model using the Naive Bayes method. The results of user testing with Naive Bayes and Tf-Idf show that the precision values for sad, happy, afraid, and surprised emotions are high, while for anger, the percentage is 59%. The percentages for afraid, happy, sad, and surprised emotions are 91%, 87%, 84%, and 79%, respectively. The overall accuracy is 82%.

. This study aims to describe the process of implementing Talempong Pacik Music learning in grades VIII-1 at SMP Negeri 43 Padang. This type of research is qualitative research with descriptive methods. This research instrument is the researcher himself and is assisted by supporting instruments in the form of stationery and cameras. This research data uses primary data and secondary data. Data are collected through literature study, observation, interviews, documentation. The steps of analyzing data are data classification, analyzing data, and inferring data. The results showed that the implementation of learning Traditional Music Culture Arts (Talempong Pacik) in Grades VIII-1 SMP Negeri 43 Padang consisted of 4 meetings, namely initial activities, core activities and closing activities. The scores of students after the implementation of Traditional Music Culture (Talempong Pacik) learning activities are still many who have not reached KKM, there are 15 students who have not reached KKM and 8 students can only achieve predetermined KKM standard scores and there are 9 students who get scores that exceed KKM. The learning process has not fully obtained satisfactory learning results. Teacher limitations in using teaching aids and limited time in practical activities, teachers continue to try to motivate students, so that students continue to be enthusiastic in the process of learning activities, students still look rigid in carrying out learning activities including in the process of traditional music learner practice activities, so that there are still many student scores below KKM, and the rest only reach the KKM limit. So it is known that students have not received maximum grade results.      

Coughing up blood or hemoptysis is expectoration of blood due to bleeding in the airway below the larynx or bleeding that comes out through the airway below the larynx. The incidence of hemoptysis in lung clinics ranges from 10 to 15 percent and for countries with a high incidence of tuberculosis, it is the cause of massive hemoptysis of 20 percent. Meanwhile, 45 percent are caused by bronchiectasis and 10 percent by tumors. The patient, a 64 year old man, came with complaints of coughing up phlegm mixed with odorless blood after the patient cleaned his house. Complaints are accompanied by stabbing pain in the left chest, fluctuating fever, weakness and difficulty sleeping. Eight years ago, the patient took OAT medication for 6 months and was declared cured. On auscultation, crackles were found in the left hemithorax. On chest x-ray, there was an impression of inactive tuberculosis with visible calcification and fibrosis in the right and left lungs. Pulmonary TB is the most common etiology in each classification of hemoptysis. Recurrent hemoptysis occurs more frequently in patients with bronchiectasis, former pulmonary TB and aspergilloma. From the results of the chest x-ray, the impression of inactive TB was also obtained

This research aims to describe and determine the influence of the Hypnoteaching Method on the Social Sciences Learning Outcomes of Class IV Students at UPT SD Negeri 181 Inpres Pattopakang, Takalar Regency. This research uses a Quantitative Experimental research method. The population in this study were all UPT students at SD Negeri 181 Inpres Pattopakang, Takalar Regency. The sampling technique used in this research is the Purposive Sampling technique, the sample in this research is all 34 class IV students. The instruments used in this research were observation of learning outcomes tests and documentation. The data analysis technique used was descriptive statistics and inferential statistics. The results of the research were based on descriptive analysis using SPSS version 26 regarding fourth grade students at UPT SD Negeri 181 Inpres Pattopakang, Takalar Regency. The average value of learning outcomes before applying the hypnoteaching method was 46.32 and after applying it. increased to 83.68. Based on inferential analysis with the help of SPSS version 26 in the normality test using the Kolmogrov-Smirnov test and it can be said that the data meets the assumption of normality because the significance value is >0.05 and in the independent sample t-test hypothesis test the value obtained sig (2-tailed) < 0.05 in the sense that H0 is rejected and H1 is accepted. From the results of the Normalized Gain Score (N-Gain) test, a value of 0.69 was obtained in the medium category and for the N-Gain percent, a value of 69.60 was obtained with a fairly effective classification. So it can be said that there is an influence of the hypnoteaching method on the social studies learning outcomes of class IV UPT SD students. Negeri 181 Pattopakang Presidential Instruction, Takalar Regency.