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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 aim of this Systematic Literature Review research is to find a way that can help overcome the problems faced and identify different points of view regarding the problem being researched and reveal theories that are relevant to the problems in the research. The research method used is literature study. Electronic databases used by researchers include Google Scholar, Garuda. Next, all articles are extracted and only articles that are relevant and meet the inclusion criteria will be analyzed. The results of the research suggest that the main type of error in mathematical proof is conceptualization error. Students do not understand the meaning of the questions due to a lack of basic understanding and lack of literacy to understand the meaning of the questions given. This must be explored further to strengthen understanding of concepts and mastery of techniques and strategies in mathematical proof.

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.

Profiling of corrupt perpetrators is done by looking for information such as office or profession, educational level, employment history and criminal background. Because increased tax receipts are not proportionate to the extraction of benefits to the public, it creates mistrust and reluctance to meet tax obligations. It endangers national security in many aspects, such as social, cultural, moral, political, and legal. Literature Review is an in-depth study of a particular topic with a qualitative approach. The purpose of this writing is to discuss and analyze further about the profiling of perpetrators and the massive impact caused by the case of Rafael Alun Trisambodo PNS class III in the General Directorate of Tax Kemenkeu who served as Head of the General Division of Kanwil DJP South Jakarta. His motive for corruption and money laundering was to enrich himself, his family, or anyone else by abusing office. The conclusion of this writing is that efforts to combat corruption are still not optimal as many sides tempt wealth and take advantage of opportunities.

Porang flour, extracted from the tuber of the porang plant (Amorphophallus muelleri), is known as a food ingredient rich in fiber and low in calories. This study aims to measure the crude fiber content in porang flour tested at the Food and Agricultural Product Technology Laboratory, Faculty of Agricultural Technology, Universitas Gadjah Mada. Based on the analysis, the average crude fiber content in porang flour was found to be 0.79%, with a standard deviation of 0.02%. However, no crude fiber content was found in the porang flour gel sample. These findings provide important insights into the potential of porang flour as a fiber source in the development of food products. The results also demonstrate product quality consistency, which can serve as a foundation for the development of healthier food industries.

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 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.

. This research investigates the correlation between mastery of idiomatic expressions and reading comprehension among tenth-grade students at one of high schools in Way Pengubuan during the second semester of the 2022/2023 academic year. Mastery of idiomatic expressions, which improves through childhood and adolescence into adulthood, is essential for accurate comprehension. Reading comprehension is the ability to extract specific information from texts. The study involved 138 students, with a sample of 64 selected through cluster random sampling. Data were collected via tests comprising 20 items each for idiomatic expression and reading comprehension, ensuring the validity of the test items. Analysis was conducted using SPSS 25.0. The findings revealed a high correlation between students' idiomatic expression mastery and their reading comprehension, with a correlation coefficient of 0.986, indicating a strong relationship. This suggests that proficiency in understanding idiomatic expressions significantly enhances reading comprehension.

Staphylococcus aureus infection  is one of the causes of increasing number of diseases and deaths. It is estimated that currently around 2-3% of the general population has MRSA in their bodies. People with MRSA on their bodies are estimated to have a 64% higher chance of dying. The purpose of this study was to determine the antibacterial activity of red guava leaves against Staphylocoocus aureus bacteria  and to determine the amount of inhibition of methanol extract from red guava leaves against the activity of Staphylocoocus aureus bacteria. This type of research is experimental research The object of this study is the inhibitory power of Staphylococcus aureus bacteria  in methanol extract of red guava leaves. The sample used in this study was methanol extract of red guava leaves. This research was conducted using maceration extraction method with the solvent used is methanol. The measurement results showed that red guava leaf methanol extract at concentrations of 25%, 50%, 75% and 100% had an inhibitory zone on the antibacterial activity of Staphylococus aureus. The greatest bland result at a concentration of 50% of 0.49 mm. The lowest inhibitory power is at a concentration of 75% of 0.26 mm.      

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.

The use of antibiotics can cause drug resistance if their use is not appropriate, so alternative treatment from plants is needed, one of which is soursop leaves (Annona muricata L.). Chemical substances contained in soursop leaves can prevent development until they can kill bacteria, one of which is flavonoids. The purpose of this study was to determine whether soursop leaf methanol extract (Annona muricata L.) at concentrations of 25%, 50%, 75% can inhibit the growth of Staphylococcus aureus bacteria and can determine at what concentration soursop leaf extract (Annona muricata L.) can effectively inhibit Staphylococcus aureus. The simplisia extract was done by maceration strategy using methanol as a solvent. The antibacterial testing method uses the disc diffusion method and uses clindamycin as a positive control and distilled water as a negative control. The results showed that Annona muricata L. extract can provide antibacterial action at concentration variants of 25%, 50% and 75% which is characterized by the presence of transparent areas with an average diameter of 8.97mm, 11.36 mm and 13.3mm respectively with positive control with a diameter of 23.25 and negative control with a diameter of 0 mm. Soursop leaf methanol extract (Annona muricata L.) is able to suppress the growth of Staphylococcus aureus bacteria with the most effective concentration variant of 75%.    

African leaf plants (Vernonia amygdalina Del.) is one of tradisional medicine and has pharmacological contain secondary metabolite such as Flavonoids, Saponin, Tanin and Alkaloid. This study aimed to determine the antibacterial activity of African leaf extract (Vernonia amygdalina Del.) in handsoap formulation on the growth of Escherichia coli. African left extract was obtained from maceration process with ethanol and phytochrmical screening was carried out.This study uses concentration of 0,5%, 1% and 2%. Antibacterial activity testing using scratch and disc diffusion methods. Tis study also evaluated the pyshical quality of the preparation which included organoleptic observation, homogeneity test, high foam test and antibacterial activity test. Antibacterial activity testing using scratch and disc disfusion methods. African pyctochemical screening was carried out. The result showed that the percentage of African leaf extract yield was 24,958%. The pH test gets a pH of 10-11 and 5-6 cm foam height test. The highest concentration of positive control of preparation containing alcohol (Dettol) with an average number og 4,72 mm ± 1,45 mm. Liquid soap preparation 1% with a diameter of inhibition of 3,93 mm ± 1,97 mm. The result of this study inhibit the growth of Eshcherichia coli bacteria.

This research discusses "Information Search Behavior Using the Electronic Journal Portal "ScienceDirect" in Fulfilling Student Information Needs". The subject matter of this research is the information search behavior of students of the Library Science Study Program at the State Islamic University of North Sumatra. This type of research uses descriptive methods, with a qualitative research approach. This approach aims to identify information search behavior using ScienceDirect Information search behavior using the ScienceDirect electronic journal portal has conducted searches using the stages put forward by Ellis. The results showed that the purpose of information searchers using ScienceDirect was to find references in completing coursework and looking for journals only as reading material. The information sought is articles related to courses majoring in library science and looking for scientific papers on losing weight. In Ellis' theory, there are 8 stages that are passed in the information search process, namely, starting, chaining, browsing, differenting, monitoring, extracting, verifying, and ending. From the analysis, it can be concluded that all informants go through these 8 stages to get the information they need. Some of the differences between using ScienceDirect and using other portals: (a) Availability of articles (sometimes in ScienceDirect not in other portals, and vice versa) (b) Articles obtained are in English, other portals use Indonesian (c) Provides scientific papers from international authorized authors Some obstacles in searching for information using ScienceDirect electronic journals: (a) The articles searched are not very complete (b) Articles in English (c) Network constraints (d) Many paid articles (e) Having to register Many articles are paid (e) Must register for an account (f) Search settings are not easy.

Some food industries still use synthetic dyes. Synthetic dyes, if consumed, can harm your health. Turmeric can be used as a natural food coloring. This natural coloring is applied to sweet jelly. This natural coloring agent must be used in fresh juice. To extend the use time of the natural dyes turmeric-bay leaves, ginger-bay leaves, this research was carried out in a variety of storage conditions without using preservatives. The aim of the research was to determine the results of the content of secondary metabolite compounds in turmeric-laurel extract, curcuma-laurel extract, turmeric extract and curcuma extract, in making solid preparations using preservatives, namely citric acid, maltodextin and sucrose. This research uses experimental methods with Sari method is done on a mixture of turmeric-bay leaves and ginger-bay leaves using a water solvent using the distillation method. Determination of variations in storage conditions was carried out for 7 days and the effect of storage temperature. Color stability and intensity measurements were carried out using visible spectrophotometry and characterization methods. The results of this research were obtained. The absorbance intensity test on turmeric-bay leaves was carried out at a wavelength of 425.14 nm with a result of 0.442 (day 1), ginger-bay leaves had an absorbance of 0.490 ( day 1). In the color stability test, there was a change in color from day 3 at drying cupboard temperature, at refrigerator temperature and at room temperature, the color remained stable.

The trunk of the kepok banana tree contains flavonoids that have activity as a sunscreen that can protect the skin from the adverse effects of UV rays. This study aims to formulate and determine the SPF value of the cream preparation of banana kepok tree tree extract (Musa paradisiaca L) which meets the requirements of good physical quality as a sunscreen. The method used is experimental research consisting of making maceration method extracts with 70% ethanol solvent. The resulting extract is formulated with a cream base formula with extract concentrations of 30% and 40%. The study was analyzed using ANOVA test and SPSS software. The results showed that the formulation of sunscreen cream extract of banana kepok tree trunk extract (Musa paradisiaca L) with concentrations of 30% and 40% showed good physical quality with the effectiveness of SPF at concentrations of 30% and 40% was 3.1 and 4.7 in the moderate protection category without any reference or formula. Based on the results of the article's research, it was concluded that the extract of the banana kepok tree trunk (Musa paradisiaca L) which was formulated as a cream has good physical quality as a sunscreen and based on the ANOVA test, one factor showed a difference in concentration affecting the SPF value obtained.

Arcangelisia flava Merr, which has been known empirically by the Dayak community in Central Kalimantan as natural herbs. This study aimed to determine the antibacterial effect of the Arcangelisia flava Merr extract against Escherichia coli. This study used two concentration of 25% and 50%, Ciprofloxacin as a positive control and water for injection as a negative control. The existence of the effect of the Arcangelisia flava Merr. extract against bacteria Escherichia coli is characterized by the formation of inhibition zone at a concentration of 25% obtained by the mean 2,005 mm, concentration of 50% obtained by the mean 2,435 mm, positive control obtained mean 7,975 mm, while for the negative control has no effect on Escherichia coli. The results showed that the Arcangelisia flava Merr. can significantly inhibit the growth of Escherichia coli bacteria. And there was the tendency of an increase in the concentration of Arcangelisia flava Merr. The higher concentration of Arcangelisia flava Merr. Extract the higher the resulting inhibit zone.

Inflammation is the body's response when it experiences injury, infection, antigen or cell damage, so it is a sign of something that is harmful to the body or disease. Galangal rhizomes (Kaempferia galanga L.) and Srikaya leaves (Annona squamosa L.) are plants that contain flavonoid compounds which have anti-inflammatory potential by inhibiting the enzymes cyclooxygenase and lipooxygenase. The aim of this research was to determine the effectiveness of a combination cream preparation of galangal rhizome extract and srikaya leaf extract as an anti-inflammatory. The various combinations used are a combination of 15 grams of kecur rhizome extract and 10 grams of srikaya leaf extract, 12.5 grams of srikaya rhizome extract and 12.5 grams of srikaya leaf extract, 10 grams of srikaya rhizome extract and 15 grams of srikaya leaf extract. The anti-inflammatory test uses 1% carrageenin induction of 0.1 ml, then the swelling that occurs is measured using a plastinometer. Based on the results obtained, a good anti-inflammatory power value was a combination of 15 grams of kencur rhizome extract with 10 grams of srikaya leaf extract with a value of 21.6216%. The results of the One Way Annova analysis obtained a p-value >0.05, which means there is no significant difference. So it can be concluded that various combinations of galangal rhizome extract and srikaya leaves are effective in mice induced by carrageenin. A post hoc Tuckey test was carried out on the AUC value, obtaining a p-value >0.05, so it can be concluded that there was no significant difference in the positive controls for formulas 1, 2 and 3.

Many things are found in the market with various brands and forms, such as in liquid form, instant powder or tablets. The tendency of people today is to prefer to use products whose packaging and presentation are more practical and fast, because it does not need to require a lot of time in preparing it. One example of a health drink that can be found is ginger extract instant drink, where the product is generally made by taking the juice from the ginger rhizome and then further processing. Most of these products are found in powder form, while some are made in tabled or water form.

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.

Vermicelli is an alternative food to replace rice, a type of noodle that is still in great demand by the public. Vermicelli is a popular food made from rice flour processed through an extrusion process so that it is shaped like a thread. The substitution of potato starch and the addition of celery extract will increase the antioxidant activity content of the vermicelli. The purpose of this research was produce vermicelli which has high antioxidant activity and produce vermicelli with characteristics according to Indonesian National Standard No. 01-2975-2006. Complete Randomized Design factorial with two factors were used in this research. The first factor was the ratio of rice flour and potatoes flour (10:90, 15:85, 20:80). The second factors was the levels of celery extract to water addition of dough (0,78; 1,04; 1,56%). The vermicelli were then tested for moisture content, ash content, crude fiber content and antioxidant activity as well as sensory analysis. The data analyse using Analysis of Variance (ANOVA). The best treatment was on ratio of substitution potatoes flour 20:80(g) with celery extract (0,78%). The fermicelli produced has the following characteristics water 10,65%, ash content 0,52%, fiber content 3,45% and 19,25% antioxidant activity. Femicelli produced in accordance with the quality requirements of SNI No. 01- 2975-2006. The sensory test of fermicelli that having color for dry fermicelli (3,82) color is yellowish dark green, color for wet fermicelli (3.62) is slightly faded green, texture (2,68) is not chewy, after taste (3.36) the taste of celery that lingers after being swallowed and overall preference (3,42).