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 Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 43-51
Author(s):  
Hendrik Setiawan ◽  
Ema Utami ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Support Vector Machine ◽  
World Health ◽  
Support Vector ◽  
Online Lectures ◽  
Svm Algorithm ◽  
The World ◽  
The Government ◽  
Bayes Algorithm ◽  
Health Organization ◽  
Performance Results

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm  

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.

Paradigma Pembangunan Desa dalam Pengelolaan Keuangan Desa Berdasarkan Undang-Undang Nomor 6 Tahun 2014 Tentang Desa

2020 ◽  
Vol 3 (2) ◽  
pp. 117-132
Author(s):  
Betha Rahmasari
Keyword(s):  
Regional Development ◽  
Financial Management ◽  
Sustainable Use ◽  
Economic Potential ◽  
Village Community ◽  
Development Organizations ◽  
The People ◽  
The Government ◽  
The Right ◽  
The Village

This article aims to find out the developmentidea or paradigm through village financial management based on Law Number 6 of 2014 concerning Villages. In this study, the researcher used a normative research methodby examining the village regulations in depth. Primary legal materials are authoritatuve legal materials in the form of laws and regulations. Village dependence is the most obvious violence against village income or financial sources. Various financial assistance from the government has made the village dependent on financial sources from the government. The use of regional development funds is intended to support activities in the management of Regional Development organizations. Therefore, development funds should be managed properly and smoothly, as well as can be used effectively to increase the people economy in the regions. This research shows that the law was made to regulate and support the development of local economic potential as well as the sustainable use of natural resources and the environment, and that the village community has the right to obtain information and monitor the planning and implementation of village development.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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 Implementasi Metode SMARTER Untuk Rekomendasi Penerima Bantuan Raskin Masa Covid 19

2021 ◽  
Vol 5 (2) ◽  
pp. 549
Author(s):  
Julfikar Rahmad ◽  
Volvo Sihombing ◽  
Masrizal Masrizal
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Rural Areas ◽  
Lower Class ◽  
Developed Countries ◽  
Decision Support Model ◽  
Head Of Household ◽  
Classic Problem ◽  
The Government ◽  
The Right

The problem of poverty is a classic problem that occurs in every country, both developed countries and developing countries like Indonesia. In every country, there are many programs carried out by the government to overcome the problem of poverty, one of which is the RASKIN program carried out by the Indonesian government. The method used to complete this research is SMARTER (Simple Multi Attribute Rating Technique Exploiting Ranks). During the Covid 19 pandemin, which is currently happening, various kinds of assistance are needed for middle and lower class people in rural areas, thus to distribute assistance, assistance distribution techniques are needed so that it reaches the right people. The SMARTER method was chosen because it is a form of decision support model used in decision making with multi attributes that will be used to solve decision-making problems. The research was conducted in Sei Beluru Village, Meranti District, Asahan Regency. In Sei Beluru Village, several criteria were obtained from direct observation of the field, namely the area of the house floor, the type of floor of the house, the type of house wall, the toilet facilities, the source of drinking water, lighting, materials. fuel used, frequency of eating, ability to buy meat, ability to buy clothes, ability to seek treatment, monthly income, education of the head of household, ownership of assets. Decision support systems using the Smarter method are able to analyze data on people who are entitled to receive Raskin assistance. The results obtained from this study are that from several prospective recipients of Raskin assistance with the specified criteria, it is found that the most prioritized alternative has the highest value of 0.603 using the Smarter method.

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. 

scholarly journals PENGUATAN PERAN APARATUR PEMERINTAHAN DESA DALAM MELAKSANAKAN TUGAS PEMERINTAHAN DESA DI KABUPATEN INDRAMAYU JAWA BARAT

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Rasji Rasji
Keyword(s):  
Central Government ◽  
Government Officials ◽  
Village Community ◽  
The People ◽  
The Republic ◽  
The Government ◽  
Village Government ◽  
The Village ◽  
Technical Guidance

Village government is the lowest level of government in the Government of the Republic of Indonesia. Its existence is very strategic for the implementation of programs of the central government, local government, and the wishes of the village community, so that the village government can help create a balance between the goals desired by the state and those desired by the people, namely the welfare of the people. For this reason, the role of village government officials is important to achieve the success of implementing village government tasks. In fact, there are still many village government officials who have not been able to carry out their duties and authorities properly and correctly. How are efforts to strengthen the role of village government officials so that they are able to carry out their duties and authority properly and correctly? One effort that can be done is to provide technical guidance to village government officials regarding village governance, the duties and authorities of village government officials, as well as the preparation of village regulations. Through this activity, it is hoped that the role of the village government apparatus in carrying out their duties and authorities will be strong, so that their duties and authorities can be carried out properly and correctlyABSTRAK;Pemerintahan desa adalah tingkat pemerintahan terendah di dalam Pemerintahan Negara Republik Indonesia. Keberadaannya sangat strategis bagi penerapan program pemerintah pusat, pemerintah daerah, dan keinginan masyarakat desa, sehingga pemerintah desa dapat membantu terciptanya keseimbangan tujuan yang diinginkan oleh negara dan yang diinginkan oleh rakyat yaitu kesejahteraan rakyat. Untuk itu peran aparatur pemerintahan desa menjadi penting untuk mencapai keberhasilan pelaksanaan tugas pemerintahan desa. Pada kenyataannya masih banyak aparatur pemerintahan desa yang belum dapat melaksanakan tugas dan wewenangnya dengan baik dan benar. Bagaimana upaya menguatkan peran aparatur pemerintahan desa, agar mampu menjalankan tugas dan wewenangnya secara baik dan benar? Salah satu upaya yang dapat dilakukan adalah memberikan bimbingan teknis kepada aparatur pemerintahan desa mengenai pemerintahan desa, tugas dan wewenang aparatur pemerintah desa, maupun penyusunan peraturan desa. Melalui kegiatan ini diharapkan peran aparatur pemerintahan desa dalam melaksanakan tugas dan wewenangnya menjadi kuat, sehingga tugas dan wewenangnya dapat dilaksanakan dengan baik dan benar.

scholarly journals Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Zhichuan Zhu ◽  
Alin Hou ◽  
Qingdong Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pulmonary Nodule ◽  
Multiple Kernel Learning ◽  
Pso Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Inertia Weight ◽  
Fitness Value ◽  
Svm Algorithm ◽  
Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

scholarly journals Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 86 ◽  
Author(s):  
Ping Liu ◽  
Xi Chen
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Support Vector ◽  
Sufficient Information ◽  
Classification Result ◽  
Rotation Forest ◽  
Svm Algorithm ◽  
Multi Temporal ◽  
Occurrence Matrix

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area.

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