Tomatoes need a proper watering system in order to grow and provide optimal yields. The factors that must be considered in watering the tomatoes are soil moisture and air temperature. The soil moisture needed for planting tomatoes is between 60% to 80% with a temperature rate between 24 to 28 degrees Celsius. We propose to implement an IoT based agricultural technology innovation to address the problem of precise watering system based on soil moisture and air temperature rate, which can be controlled remotely via internet connection. This system was designed and assembled using ESP8266 with soil moisture sensor and DHT11. This system was also programmed to be controlled using the Telegram Messenger application. The data read by the sensor could be seen through the Telegram Bot and do watering the plants automatically or manually. Based on several experiments conducted in this study, the system could do watering as well as maintain and control soil moisture and air temperature properly. By using this system, it will be easier for farmers to control and maintain tomato plants from anywhere and anytime through the Telegram Messenger.
The Coronavirus Disease 2019 (COVID-19) has roamed for almost two years now. Every country has applied its strategies in facing and handling this pandemic, including Indonesia. One strategy applied by the Indonesian government in handling this crisis is the enforcement of restrictions on community activities (PPKM) policy. This policy has been acknowledged by many countries' leaders as an effective strategy in handling the COVID-19 pandemic without giving too much burden to the economic sector. However, despite the pros, there are also cons of the policy in society. Therefore, we are interested in conducting a sentiment analysis for the PPKM policy based on Twitter tweets data. We found that most of the tweets were dominated by the neutral sentiment (58.07%), followed by the positive sentiment (27.12%), and lastly by the negative sentiment (14.81%). Furthermore, we also try to build a deep learning model based on long short-term memory (LSTM) networks for the classification task of the collected tweets. We found the proposed deep learning model could reach 92.59% accuracy on the test set, which is pretty high for this sentiment analysis classification task. The built model then was deployed as a simple web-based application that can be accessed freely in the Heroku platform.
Single Nucleotide Polymorphism (SNP) is a type of molecular marker which constitutes the phenotypic variations between individuals in certain species. In recent years, the advantages of SNP were widely considered in many fields, for instance in designing precision medicine in humans and assembling superior cultivars in plant breeding. The main challenge in SNP discovery is imbalanced data distribution between classes, where the number of true SNPs in question is much fewer than false SNPs. While the study in observing the benefit of feature selection in classification problem was widely reported, the use of this technique in solving imbalanced class problem still become interesting topic for research. In this study, we selected the features that most contribute in identifying SNP using Feature Assessment by Sliding Thresholds (FAST) method. FAST evaluates the contribution of each feature in identifying SNPs based on the Area under ROC Curve (AUC) value. SNP identification using 4 best features resulted in improved classifier performance in terms of G-Means compared to using 24 features. In addition, using feature selection techniques can reduce computational time and save resource needed.
We investigated the value of thermal conductivity of particle board using a tool designed with the principle of converting electrical energy into heat energy by varying temperatures. This study aims to determine the value of thermal conductivity of oil certain particle board and to what extent it can function as a thermal insulation material and to determine the performance of the thermal conductivity test equipment. The size of this tool is 10 mm of thickness, 65 mm of width, 140 mm of length and 40 mm of height, which is equipped with the function of measuring the conductivity value of the material. The average error value of the temperature sensor compared to the standard thermocouple is 0.8%. By entering the value of the area and thickness of the sample, the average conductivity value of the composite material is 0,028 W/m°C. The low material conductivity value will be used as a building construction material. The data show that the sensitivity, accuracy, and precision of this tool are very good for testing the conductivity of a material.
Single Nucleotide Polymorphism (SNP) is a form of Deoxyribonucleic Acid (DNA) variation that can be used in predicting phenotypes. Data quality control is a crucial stage in the process of detecting phenotypes using SNP data. In this study, we built a web-based application to carry out the SNP data quality control function. Raw SNP data in string type are filtered by calculating the missing rate, minor allele frequency, and Hardy-Weinberg Equilibrium values. The result is SNP data that has been filtered in numeric form, namely the value 1 represents dominant homozygous, 2 represents heterozygous and 3 represents homozygous recessive. SNP encoding in numerical form aims to make SNP data can be processed into machine learning for the further phenotype prediction step.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.