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Antibacterial activity of active fraction of sweet corn hair extract (zea mays f saccharata kornicke & werner) against methicillin resistant staphylococcus aureus (MRSA) growth
No full textMethicillin-resistant Staphylococcus aureus (MRSA) is a Staphylococcus aureus bacterium that is resistant to methicillin-type antibiotics. This study aims to determine the antibacterial activity of the active fraction of sweet corn hair extract on the growth of Methicillin-Resistant Staphylococcus aureus (MRSA) bacteria. Extraction was carried out by the remuneration method in 70% ethanol. In fractionation, liquid vacuum column chromatography with n-hexane, ethyl acetate, and methanol solvents was used. The fraction with the largest clear zone of methanol (100%) is with a clear zone diameter of 0.752 cm. Testing the antibacterial activity of ethanol extract at concentrations of 10%, 15%, and 20% obtained by clear zone with an average of 1,013; 1,073; 1,159 cm and in the active fraction with an average of 0,703; 0.903; 1,004 cm. Ciprofloxacin 0.005% was used as a positive control and DMSO as a negative control. The results showed that ethanol extract and an active fraction of sweet corn hair (Zea mays f saccharata Kornicke & Werner) had antibacterial activity against Methicillin-Resistant Staphylococcus aureus (MRSA). One-way ANAVA test results showed that there were significant differences in providing antibacterial activity (p> 0.05) between ethanol extract and active fractions at concentrations of 10%, 15%, and 20%. Flavonoid compounds, alkaloids, saponins, tannins, and triterpenoids in the active fraction of corn hair have antibacterial activity against MRSA bacteria
Phytochemical contents and diuretic activity of ethanolic extract of the red leaf lettuce (Lactuca Sativa l.)
No full textThis study was conducted to evaluated of phytochemical content and diuretic activity of the red leaf lettuce. Red leaf lettuce were extracted by cold maceration method using 70% ethanol for the 3 days and remaceration for 1 days. The phytocemical content in ethanolic extract were evaluated by qualitatif method. A total of 25 male rats were divided into 5 groups with CMC Na, a standard drug (furosemide 10 mg/kg), and three different doses (200, 300, and 400 mg/kg) of ethanol extract. Parameters used to determine diuresis activity include first urine latency, urine pH and cumulative urine volume. The ethanolic extract induced diuresis in a dose dependent manner as compared to the negative control. Extracts at doses of 200, 300, and 400 mg/kg produced significant diuresis effects (p<0.05) compared to negative controls with values of diuretic action 1.35; 1.43; and 1.53, respectively. In addition, there was a slightly change in the pH of urine samples of the extract-treated group compared with the negative control. Phytochemicals analysis revealed the presence of alkaloids, flavonoids, phenolics, and tannins
Flying Trap (Fly-T): An Automatic Termite Trapping Based on IoT and Hybrid Energy System using NodeMCU
Get PDFThis paper proposes an automated productive caste termite trap device based on a hybrid energy system and the Internet of Things called Flying Trap (Fly-T). This tool is equipped with ultraviolet light with a frequency of 365 nm which is used to attract termites to enter and trap into Fly-T storage tank until they die. Dead termites will be detected by an ultrasonic sensor with a certain limit value then the relay cuts off electric current so that the light turns off and the tank door automatically opens to expel dead termites. The automatic control system on Fly-T is built using the NodeMCU ESP32 microcontroller to optimize the performance of sensors, relays, servo, and wifi connections in recording data to an IoT-based cloud database. The Fly-T is also controlled by command via a Telegram Bot equipped with solar panels and a windmill turbine generator. The results show that Fly-T can run automatically, easily, and save time efficiently, and is environmentally friendly
The Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm
No full textIn this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model
Game design documents for mobile elementary school mathematic educative games
No full textMobile games or games in this era are very much in demand by young people and small children as a medium of entertainment. Even the elderly are still often encountered playing this mobile game. This has prompted many game developer programmers to want to make mobile-based games. This aims to add insight, especially at the age of children, so that they are more enthusiastic about learning through this educational game. The academic side of this game comes from simple and fun math puzzles. From within this game, players can enjoy games that have 2D animations and are based on Android, as well as enriching children's knowledge and learning basic mathematical calculations by answering using games or this educational game. The assessment of this educational game is assessed when based on the number of correct answers. The method used in this research is in the form of collecting information and data, which includes recording and studying the literature and will conduct searches using the internet, as well as data sources relating to the problems in this research game Development. Lifecycle (GDLC) is used as a system development method. GDLC is a guide or guidelines that can regulate the rules in making this educational game. The results of this research will be the realization of mobile or android-based games with construct 2 for elementary school children from grades 3, 4, and 5. This android-based educational game is expected to provide experience to children in the world of learning and can increase elementary school children's interest in arithmetic, especially in counting
Content-based filtering using cosine similarity algorithm for alternative selection on training programs
Get PDFThe large selection of training programs provided by the Ministry of Manpower of the Republic of Indonesia makes it difficult for prospective trainees to choose a training program that suits their interests and needs. The purpose of this research is to support the selection process so that an appropriate method is needed to recommend the selection of training programs that match the interests and needs of users. One of the selection methods that can be used is the Content-Based Filtering method with similarity measurement using Cosine Similarity. The content-based filtering method is a content-based filtering method, which recommends training programs based on the suitability between the description of the training program and the interests of prospective trainees using the cosine similarity distance measurement. The test results using the Content-Based Filtering method were able to achieve an average precision value of 88%, indicating the ability of the system to provide training program recommendations that are very relevant and in accordance with the interests and needs of the trainees
Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16
Get PDFCrown density and foliage transparency are important parameters for tree crown conditions. Previously, observers carried out crown density and foliage transparency assessments manually, which could be a less efficient process.This research aims to use the VGG-16 deep learning architecture to classify the density and transparency of broadleaves tree crowns. In this study, broadleaves tree crown datasets were collected for four types of broadleaves tree: cacao (theobroma cacao), durian (durio zibethinus), rubber (havea brasiliensis), candlenut (aleurites moluccana); then the data is labeled based on the crown density and foliage transparency scale card. The research applies resize and augmentation preprocessing. The model training process uses a scenario of 80% train data, 10% test data, and 10% validation data. After training using the VGG-16 model, the test results showed impressive accuracy, with the highest accuracy reaching 98.40% for candlenut trees, rubber (96.00%), cacao (92.00%), and durian (86.60%). This research shows quite good results in classifying the scale of crown density and foliage transparency with four types of broadleaves tree (cacao, durian, rubber and candlenut) using VGG-16
Hoax classification in indonesian language with bidirectional temporal convolutional network architecture
Get PDFThe increasingly massive rate of information dissemination in cyberspace has had several negative impacts, one of which is the increased vulnerability to the spread of hoaxes. Hoax has seven classifications. Classification problems such as hoax classification can be automated using the application of the Deep Learning model. Bidirectional Temporal Convolutional Network (Bi-TCN) is a type of Deep Learning architectural model that is very suitable for text classification cases. Because the architecture uses dilation factors in its feature extraction so it can generate exceptionally large receptive fields and is supported by Bidirectional aggregation to ensure that the model can learn long-term dependencies without storing duplicate context information. The purpose of this study is to evaluate the performance of Bi-TCN architecture combined with pre-trained FastText embedding model for hoax classification in Indonesian and implement the resulting model on website. Based on the research that has been done, the model with Bi-TCN architecture has satisfactory performance with an accuracy score of 92.98% and a loss value that can be reduced to 0.191. Out of a total of 13,673 data tested with this model, only 414 data or in other words around 3% of the total data were incorrect predictions
Antibacterial activity test of sweet arum mango peel extract gel preparation (Mangifera indica L.) against growth methicillin resistant staphylococcus aureus
No full textInappropriate use of antibiotics can make bacteria’s reistance that cause skin diseases, the example is acne. Acne tretment usually uses synthethic drugs that causes side effects, so the alternative are used by plants that come from nature. On of the plats that has the potential as an antibacterial is the arum manis mango (Mangifera indica L.) because contains compounds that can inhibit the growth of Mathicillin Resistant Staphylococcus aureus. The research of this study was to determine the antibacterial activity of extract gel’s arum manis mango peel to c. Extract of arum manis mango peel was obtained remaserated using 70% ethanol solvent. The extract obtined were phytochemicel screening and affirmation tests using the TLC (Thin Layer Chromatography) method to ensure the compounds contained. Arum manis mango peel is preparation of gel with a concentration of 5%, 10%, 15%, 20%, 25%. The results showed that extract of arum manis mango peel preparation gel with concentrations of 5%, 10%, 15%, 20%, 25%. The results showed that extract of arum manis peel preparation gel with concentration 5%, 10%, 15%, 20%, 25% showed significant difference in potential of antibacterial
Analysis of technology foresight for metaverse in tourism sector by integrating quantitative approaches
Get PDFThe name "metaverse" is a combination of the words "meta" and "universe." The metaverse refers to both present and future digital platforms that are interconnected and focuses on virtual and augmented reality. The purpose of this research to identify the drivers of the future of metaverse in tourism and study the future trend of metaverse in tourism. The target respondents are select and cover mainly by developers and organizational users of metaverse in tourism. In the conduct of this research, both quantitative and qualitative research methods have been taken, and both methods will be apply in the process of the data analysis and data interpretation. In this research, the STEEPV analysis is apply. The STEEPV technique will be utilized in order to determine or identify all the drivers of metaverse in tourism. Data from the questionnaire are analyze using "Social Science Statistics Package" (SPSS). It shows the result of drivers of metaverse in tourism portable devices in the second phase of the research with impact-uncertainty analysis. The top two drivers are “government policy in digitizing the nation” and “technology reliability”. Four different scenarios have been formed based on the top two drivers chosen from the impact-uncertainty analysis. These four alternate scenarios reflect the four potential outcomes between 2022 and 2032. Hence, this research can help future researchers and developers increase their awareness of adopting the metaverse in tourism sector in future. A further explanation of the findings has been given in the discussion