scholarly journals Support Vector Machine (SVM) For Toddler’s Nutritional Classification in Palu City

INSIST ◽  
2016 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Andi Hendra ◽  
Gazali Gazali
Keyword(s):  
Support Vector Machine ◽  
Nutritional Status ◽  
Support Vector ◽  
Software Support ◽  
Software Application ◽  
The People ◽  
Slow Condition ◽  
Computer Based ◽  
The Government ◽  
Nutritional Status Of Children

Abstract—Toddlers are groups who are vulnerable about the health nutrition problems. Nutritional status of children is one of the indicators that can describes the level of social welfare in the city. Nutritionists are the people that can determined the nutritional status. The problem that arises is the limited number of the nutrition experts in each area, this problem causes the children’s malnutrition in the Palu city is detected in very slow condition. The aims of this study is to help the health professionals in the health centers or the hospitals to determine the children’s nutritional status computerized, so the malnutrition problem in the Palu city can be detected earlier. Besides that, to help the government in policy making about nutrition of the toddlers in Palu city. This study uses a Support Vector Machine (SVM) which implemented in computer-based software application to analyze nutrition of the toddlers.Keywords—Nutrition, Software, Support Vector Machine (SVM), Toddlers, Palu city.

scholarly journals Analisis Pengaruh Faktor Kemiskinan Terhadap Tingkat Kesehatan Dan Gaya Hidup Masyarakat Desa Suralaga, Lombok Timur, Menggunakan Algoritma Support Vector Machine (SVM)

2021 ◽  
Vol 4 (1) ◽  
pp. 11-19
Author(s):  
Muhammad Wasil ◽  
◽  
Mahpuz Mahpuz ◽  
Keyword(s):  
Support Vector Machine ◽  
Rural Areas ◽  
Lower Class ◽  
Support Vector ◽  
Poverty Level ◽  
Village Community ◽  
Health Level ◽  
The People ◽  
Svm Algorithm ◽  
The Government

The level of poverty in Indonesia is currently an important task for the government, both in cities and in villages. As time goes by and the nation's economy is arguably unstable, the poverty level of the community cannot be controlled properly. Especially for rural areas or remote and remote villages, such as in the province of NTB, East Lombok Regency, especially in Suralaga Village. The people in Suralaga Village, who generally only have income from farming and raising livestock, are unable to meet their daily needs, which are increasingly soaring. Not only to meet economic needs, they may find it difficult to fulfill their educational needs. The results of farming or raising them, which has a grace period from planting to harvest, require a lot of money. Therefore, the income they have is not stable. This instability causes the economy of the community in the village to be classified as middle to lower class. To find out the extent of the influence of the poverty factor on the level of health, an analysis of the influence of the poverty factor on the health level and lifestyle of the people of Suralaga Village, East Lombok was carried out using the Support Vector Machine (SVM) algorithm. The experimental results shown, have concluded that data processing to determine the purpose of this study, using the Support Vector Machine algorithm, poverty to the health level of the Suralaga Village community is very large and provides an illustration that the average Suralaga Village community is included in the category of people who do not pay attention to the elements. health with an accuracy rate of 73.77%, when viewed based on the distribution of data used.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals Twitter sentiment analysis of the relocation of Indonesia's capital city

2020 ◽  
Vol 9 (4) ◽  
pp. 1620-1630
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Point Of View ◽  
Capital City ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Algorithm ◽  
The Government ◽  
The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media. 

Learning Management System Under Digital India Program

2018 ◽  
pp. 205-225
Author(s):  
Shilohu Rao N. J. P. ◽  
Shveta Sahal
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Knowledge Exchange ◽  
Government Officials ◽  
Software Application ◽  
Computer Based Learning ◽  
Computer Based ◽  
Competency Based ◽  
Learning Management ◽  
The Government

To foster continuous learning in governance, it is imperative to use technology in such a way that learning and knowledge exchange becomes a normal engagement without external interventions (http://digitalindia.gov.in/newsletter/2016_july/index.php). Web- or computer-based learning is easy, anytime and anywhere. It has in fact become a well-established, diversely applicable practice through a software application, known as learning management system (LMS). The LMS designed for e-governance under Digital India initiative is unique and one of a kind; it takes forward the vision of competency-based learning and is a tool to deliver right knowledge and skills to right personnel. LMS deployed by National e-Governance Division serves as a tool for learning and training the government officials and other stakeholders involved in planning, developing, implementing, monitoring, and sustaining governance in Government of India. This chapter broadly discusses the significant facets of the LMS like its prominent features and framework, key benefits, services rendered, and the outcomes and impact as a consequence of its structured implementation.

scholarly journals Nutritional adequacy of menus offered to children of 2 to 5 years in registered child care facilities in Inanda

2015 ◽  
Author(s):  
◽  
Phindile Favourite Nzama
Keyword(s):  
Child Care ◽  
Nutritional Status ◽  
Food Storage ◽  
Food Handlers ◽  
Nutritional Adequacy ◽  
Severe Stunting ◽  
Care Facilities ◽  
The Government ◽  
Nutritional Status Of Children ◽  
Child Care Facilities

Introduction: According to the American Dietetic Association, Child care facilities (CCFs) play an essential role in the nutritional status of children as children typically spend 4-8 hours a day at a facility. As a result, the meals should provide at least 50 – 60% of daily nutritional requirements. Worldwide CCF feeding has been found to be nutritionally inadequate as energy and most micronutrient requirements are not met by the meals provided, due to the lack of nutrition knowledge of the caregivers. Studies have shown that with appropriate training there has been improvement in nutritional standards. Aim: The aim of this study was to analyse the nutritional adequacy of menus offered; and to determine the nutritional status of children aged two to five years old in registered child care facilities in the Inanda area. Methodology: CCFs (n=10) in the Inanda area were randomly selected from multiple options to participate in the study. This study was conducted on children (boys (n= 91) and girls (n=109)) of ages two to five years old. Trained fieldworkers and teachers assisted in interviewing parents to complete the socio-demographic questionnaire. The researcher gathered menus and recipes for analysis, using Foodfinder Version 3 Software. The researcher also conducted plate-waste studies to determine consumption patterns during CCF meal times. Anthropometric measurements for weight and height were collected. In order to establish BMI-for-age and height-for-age, the WHO Anthro Software and WHO AnthroPlus Software were used. Ten food handlers (FHs) were interviewed by the researcher on food preparation and serving. Results: Most children (79.40%) originate from extended families that are female-headed. The highest form of education attained by most caregivers in the sample is standard 10 (47.74%) and 45.73% are unemployed. Of the 54.27% employed, 64.71% are informally employed. Most respondents (72.87%) are living on a total household income of less than R2500. The anthropometric results of the children show very low prevalence of severe stunting (1.74%) and stunting (5.42%). Less than halve (34.48%) of the children were at a possible risk of being overweight, 13.79% were overweight and 2.46% obese. The top 20 foods served in CCFs in Inanda were cereal-based staples of rice and maize meal more frequently than meat, dairy products and fruit and vegetables – all served far less frequently. All the CCFs did not meet the 60% of daily requirements for energy, fibre, calcium and vitamin C in foods served. The CCFs have well-equipped, designated kitchens for food storage, preparation, serving and good hygiene practices. Conclusion: Meals served to two to five year olds in registered CCFs in the Inanda area are nutritionally inadequate as most facilities do not contain 60% of the daily nutrient requirements from both daily meals served. Recommendations: CCF owners and Food handlers should receive proper training and retraining on food safety and hygiene and menu planning. The government should increase the subsidy to CCFs in order to meet the nutritional needs of children in order to aid in the alleviation of under-nutrition.

scholarly journals Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

2019 ◽  
Vol 13 (1) ◽  
pp. 43
Author(s):  
Junanda Patihullah ◽  
Edi Winarko
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Hate Speech ◽  
Support Vector ◽  
Speech Detection ◽  
The People ◽  
Gated Recurrent Unit ◽  
Rule Out

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

People Counting Based on the Analysis of the Mass

2014 ◽  
Vol 945-949 ◽  
pp. 1875-1879
Author(s):  
Tao Li ◽  
Dong Mei Li ◽  
Ren Jie Huang ◽  
Xue Zhu Zhao
Keyword(s):  
Support Vector Machine ◽  
Video Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
The Novel ◽  
People Counting ◽  
The People ◽  
Objects Tracking ◽  
Counting Model ◽  
Novel Algorithm

In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the analysis of the mass is proposed. The novel algorithm of objects tracking is designed to aim at people counting, and the people counting model is obtained by training a support vector machine (SVM) classifier with the input of the feature of mass. The experimental results show that the accuracy of counting is over 93%.

People Counting Based on the Moving Feature of the Mass

2014 ◽  
Vol 989-994 ◽  
pp. 2540-2542
Author(s):  
Peng Zhe Qiao ◽  
Tao Li ◽  
Tao Xiang ◽  
Xi Zhi Zhang
Keyword(s):  
Support Vector Machine ◽  
Energy Density ◽  
Optical Flow ◽  
Video Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
People Counting ◽  
The People ◽  
Flow Algorithm ◽  
Counting Model

In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the moving feature of the mass is proposed. We obtain the orientation and energy density of mass through the optical flow algorithm, and get the information about the size of mass to design the feature of mass. The people counting model is obtained by training a support vector machine (SVM) classifier with the moving feature and shape feature of mass. The experimental results confirm that our approach improves the accuracy of people counting.

An Evaluation of the National Food and Nutrition Policy of Bangladesh

2003 ◽  
Vol 24 (2) ◽  
pp. 183-192 ◽  
Author(s):  
M. A. Mannan
Keyword(s):  
Nutritional Status ◽  
Monitoring And Evaluation ◽  
Nutrition Policy ◽  
Lessons Learned ◽  
Political Commitment ◽  
Consensus Document ◽  
The People ◽  
Food And Nutrition ◽  
The Government ◽  
Observational Techniques

Food and nutrition policy activities directed toward improvement of the nutritional status of the people of Bangladesh began in the 1980s. The government formulated a national food and nutrition policy and approved it in 1997. Qualitative methods, including observational techniques, in-depth interviews of the key informants, and focus group discussions, were used to collect information on the strengths, weaknesses, opportunities, and threats (SWOT) of the policy. The information obtained has been transcribed and analyzed using this model. The strengths of the policy are that it is a consensus document that emphasizes human rights, was formulated by a multisectoral approach, complements other government policies, and has broad goals and wide-ranging objectives. The weaknesses include lack of implementation, monitoring, and evaluation guidelines; lack of strong government commitment; inadequate support of policy makers; perhaps an excessively ambitious target; and ignorance of past lessons learned. The opportunities include the scope of social mobilization, the wide scope of the policy, suggested programs and measures to improve nutritional status, a congenial policy environment, and the ability to modify the scope of the policy as needed. The threats to the policy are lack of knowledge of the policy, lack of resources to implement the policy, tension between technical people and bureaucrats, vested business interests, and, possibly, discontinuity of political commitment. The key to reducing the weaknesses of the food and nutrition policy of Bangladesh and minimizing the threats to it is for the stakeholders in the policy to coordinate efforts to use the strengths and opportunities of the policy to effectively implement it.

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