scholarly journals Prediction System for the Spread of Corona Virus in Central Java with K-Nearest Neighbor (KNN) Method

2021 ◽  
Vol 2 (3) ◽  
pp. 80-85
Author(s):  
Farid Fitriyadi ◽  
Muqorobin Muqorobin
Keyword(s):  
Nearest Neighbor ◽  
Current Data ◽  
Problem Analysis ◽  
Prediction System ◽  
K Nearest Neighbor ◽  
Religious Activities ◽  
Literature Study ◽  
Central Java ◽  
Corona Virus ◽  
The Government

Abstract—Corona Virus is currently spreading very rapidly in many parts of Indonesia, including Central Java Province. According to the current data of corona database in Central Java, today on 17th of August 2021, the number of confirmed cases is; Confirmed in Treatment (Active Cases): 16.344, Confirmed Recovered: 408.697, and Confirmed Dead: 29.148. Therefore, the total number of cases is 454.189, obtained from the sum of the number of being treated, recovered, and dead. Corona Virus is a collection of viruses that can infect the respiratory system, generally mild, such as common cold, although, some forms of diseases like; SARS, MERS, and COVID-19 are more deadly. In anticipating this case, the government has created some policies which include; limiting activities outside the house, having school activities done from home, working from home, and even having religious activities done from home too. The purpose of this study was to predict the possible rate of new cases in one of Central Java areas with confirmed cases of corona virus. Thus, it can be used as information material for the public to anticipate early. The research method applied in this research is problem analysis and literature study, data collection and implementation. The application of the K-Nearest Neighbor (KNN) method is expected to be able to predict the level of spread of COVID-19 in Central Java. The results of the research on testing the prediction system for the new cases level were tested in the Sragen area. Testing is carried out by taking samples for new cases, namely Kudu Regency/City, Confirmed: 17,599, Treated: 89, Recovered: 18,303, Died: 1,721, Suspected: 87 and Discarded Suspected: 1,711. After doing the prediction with K-NN algorithm, it showed the Condition: High.

scholarly journals KLASIFIKASI REGRESI LOGISTIK MULTINOMIAL DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) DALAM PEMILIHAN METODE KONTRASEPSI DI KECAMATAN BULAKAMBA, KABUPATEN BREBES, JAWA TENGAH

2022 ◽  
Vol 10 (4) ◽  
pp. 476-487
Author(s):  
Erysta Risky Rismia ◽  
Tatik Widiharih ◽  
Rukun Santoso
Keyword(s):  
Logistic Regression ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Multinomial Logistic Regression ◽  
K Nearest Neighbor ◽  
Contraceptive Methods ◽  
Family Planning Services ◽  
Central Java ◽  
The Family ◽  
The Government

The characteristics of society in choosing contraceptive methods are also the crucial factors for the government to prepare the family planning services needed at Bulakamba District, Brebes Regency, Central Java. In this case, a classification process needs to be done to assist the process of classifying the characteristics of society in the selection of contraceptive methods. Multinomial Logistic Regression classification is good in exploring data information  meanwhile Fuzzy K Nearest Neighbor (FK-NN) classification is good for handling big data and noise. These two methods used in this study because they are relevant to the data applied and will be compared their classification accuracy through APER and Press's Q calculations.The classification accuracy results obtained based on the APER calculation for Multinomial Logistic Regression is 88,25% and Fuzzy K Nearest Neighbor (FK-NN) is 88,92%.  Meanwhile, the Press's Q value of both methods are 9600,945 and 9518,014 greater than χ 2𝛼,1 which is 3,841, so that it is statistically accurate. Based on the results obtained, it can be concluded that Multinomial Logistic Regression classification method has a better classification accuracy than Fuzzy K Nearest Neighbor (FK-NN) method. 

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 Politik Kebangsaan dalam Membendung Gerakan Radikalisme oleh Nahdlatul Ulama Kabupaten Pati (National Politics in Repressing the Radicalism Movement by Nahdlatul Ulama of Pati Regency)

2021 ◽  
Vol 25 (1) ◽  
pp. 60
Author(s):  
Nevy Rusmarina Dewi ◽  
Wahyu Khoiruzzaman ◽  
Muhammad Fatwa Fauzian ◽  
Abdul Ghofur
Keyword(s):  
Leadership Training ◽  
Research Method ◽  
National Politics ◽  
Literature Study ◽  
Study Approach ◽  
Central Java ◽  
Nahdlatul Ulama ◽  
The Government ◽  
Religious Understanding

ABSTRACTThe radicalism movement is currently one of the centres of attention of the Indonesian government because several incidents have repeatedly occurred in Indonesia. The radicalism movement is a concept that wish changes in society using a narrow religious understanding base which usually leads to bomb terror acts. The Central Java region is one area that is often used as the basis of radicalism movements spreading across various regions. The government cooperates with several Islamic organizations, one of which is (Nahdlatul Ulama), to take part in preventing radicalism that can threaten the integrity of the nation and state. This article aims to reveal the role of Nahdlatul Ulama's national politics in repressing the radicalism movement in Indonesia, especially in the Pati Regency area. The research method applied is qualitative with a literature study approach and through interviews. The NU Branch Leader (PCNU) made several programs to repress radicalism in the Pati Regency area, among others, by solidifying students in the Nahdlatul Ulama Student Association (IPNU) by holding Basic Leadership Training (LDK) in collaboration with the National Military Forces (TNI) and academics. In addition, Ansor and Banser of Pati Regency were active in conducting discussions with the theme of counteracting radicalism. These activities by Nahdlatul Ulama are effective in repressing radicalism in the Pati Branch area.  Keywords: national politics; radicalism; PCNU Pati Kabupaten.ABSTRAKGerakan radikalisme pada saat ini menjadi salah satu pusat perhatian pemerintah Indonesia karena berulang kali aksi ini terjadi di Indonesia. Gerakan radikalisme merupakan paham yang menginginkan perubahan dalam masyarakat yang seringnya menggunakan dasar pemahaman agama yang sempit yang biasanya berujung pada aksi teror bom. Wilayah Jawa Tengah merupakan salah satu wilayah yang sering menjadi basis gerakan radikalisme yang tersebar di berbagai daerah. Pemerintah menggandeng beberapa ormas Islam salah satunya adalah Nahdlatul Ulama yang ikut andil dalam rangka pencegahan radikalisme yang dapat mengancam keutuhan bangsa dan negara.  Artikel ini bertujuan mengungkap peran politik kebangsaan Nahdlatul Ulama dalam membendung gerakan radikalisme di Indonesia khususnya di wilayah Kabupaten Pati. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan studi literatur serta melalui wawancara. PCNU membuat beberapa program yang dilaksanakan dalam rangka membendung radikalisme di wilayah Kabupaten Pati antara lain dengan mensolidkan para pelajar dalam Ikatan Pelajar Nahdlatul Ulama (IPNU) dengan mengadakan Latihan Dasar Kepemimpinan (LDK) yang bekerjasama dengan TNI maupun akademisi.  Selain itu Ansor dan Banser Kabupaten Pati aktif untuk melakukan diskusi dengan tema menangkal radikalisme. Kegiatan-kegiatan tersebut efektif dalam rangka membendung radikalisme oleh Nahdlatul Ulama di wilayah Cabang Pati.Kata kunci: politik kebangsaan; radikalisme; PCNU Kabupaten Pati.

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 Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method

2021 ◽  
Vol 5 (1) ◽  
pp. 60-70
Author(s):  
Diana Rahmawati ◽  
Mutiara Puspa Putri I ◽  
Miftachul Ulum ◽  
Koko Joni
Keyword(s):  
Pathogenic Bacteria ◽  
Nearest Neighbor ◽  
Ocular Lens ◽  
K Nearest Neighbor ◽  
Image Capture ◽  
Chebyshev Method ◽  
Living Things ◽  
Auto Focus ◽  
The Government

Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attached to a webcam camera above the ocular lens as a tool to see bacterial objects and assist in bacterial capture. Rough player rotates automatically (auto-focus) in image capture. In the process of classification and identifying bacteria, the K-Nearest Neighbor method is used, which is a method with the calculation of the nearest neighbor or calculation based on the level of similarity to the dataset. In this study, the bacteria vibrio chlorae, staphylococcus aereus, and streptococcus m. with the highest accuracy is the K = 9 value of 97.77% using the Chebyshev method.

scholarly journals Expert system for diagnosing diphtheria with k-nearest neighbor method

2018 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Chavid Syukri Fatoni ◽  
Ema Utami ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Infectious Disease ◽  
Acute Kidney Injury ◽  
Expert System ◽  
Nearest Neighbor ◽  
Kidney Injury ◽  
Initial Diagnosis ◽  
K Nearest Neighbor ◽  
The Common ◽  
Special Concern ◽  
The Government

The Diphtheria cases have special concern by the Indonesian government and are recorded as an extraordinary case (KLB) in 2017. Diphtheria is an infectious disease and cause complications of dangerous and deadly diseases if have not any treated immediately. Along this time, the communities often underestimate the common symptoms of diseases, such as throat pain, flu, and fever. The similarity of Diphtheria symptoms with common diseases and complications such as myocarditis, obstruction on breath, Acute Kidney Injury (AKI), making Diphtheria are rather difficult to treat due to the infections spread quickly. Some complications of diphtheria can cause a death if have not treated immediately and there must be any identification early for diphtheria. Then, an expert system is needed to help the community and the government in diagnosing the diphtheria. An expert system is an information system containing knowledge from experts in order provide information to be used for consultation. The knowledge from experts in this particular system is used as a basis by the Expert System to answer the questions (consultation). The study used the K-Nearest Neighbor (KNN) method, which the method calculates the similarity value of Diphtheria disease symptom. As the result, it can provide an initial diagnosis for Diphtheria before complications occur. The output of this study is the diagnosis of diphtheria based on the symptoms with the accuracy results of 93.056%, as well as providing an initial diagnosis in order to have immediately treating the diphtheria. 

scholarly journals The Impact of Covid-19 on the Development of Various Business Sectors

2021 ◽  
Vol 2 (4) ◽  
pp. 319-326
Author(s):  
Sunarmin Sunarmin ◽  
Ahmad Junaidi ◽  
Endah Fantini
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Small And Medium Enterprises ◽  
Virus Disease ◽  
Religious Activities ◽  
Business World ◽  
Corona Virus ◽  
The Government ◽  
Business Entities ◽  
The Impact

Outbreaks of the new corona virus or Corona Virus Disease 2019 (Covid-19) are increasingly felt in the domestic economy, especially in terms of consumption, corporate, financial sector, and Micro, Small and Medium Enterprises (MSMEs). The existence of Large-Scale Social Restrictions (PSBB) that has been declared by the government most of the activities involving the public are restricted, such as offices or offices that are closed, restrictions on religious activities and restrictions on public transportation. The business world has not been separated from the shadows of the Corona Covid-19 pandemic. Instead of diminishing, some countries have confirmed that they will experience a second wave of pandemic that began in Wuhan, China. The purpose of this study is to find out whether the impact of Covid-19 will have a widespread effect on business entities and the sustainability of business entities. This research is a descriptive study, using the literature method where research is conducted by studying and collecting data from libraries related to tools, both in the form of journals and scientific studies of research that has been published in public journals. The results of the study concluded that the impact on Covid-19 had a significant influence on the development of the business world. The biggest influences occur in the mall/supermarket, hospitality, manufacturing and various businesses related to the use of mass labor. In this study, not many scientific writers have focused on examining the impact of this 19 on the business world in general. Although the fact in the field of influence of Covid-19 is clearly seen the fact in the life of the wider community due to many businesses limit / lay off and even deduct workers' income from the value of income received.

scholarly journals FACTORS AFFECTING BASIC STUDENTS LEARNING MOTIVATION IN THE COVID-19 PANDEMIC PERIOD

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Silvia Handayani ◽  
Marlina Marlina ◽  
Desyandri Desyandri
Keyword(s):  
Elementary School ◽  
Distance Learning ◽  
Elementary School Students ◽  
Virus Disease ◽  
Learning Motivation ◽  
Literature Study ◽  
School Students ◽  
Factors Affecting ◽  
Corona Virus ◽  
The Government

This study aims to determine the factors that affect the learning motivation of elementary school students during the Covid-19 pandemic (corona virus disease). This study was conducted using the literature study method. The literature studied can be in the form of articles, reports, magazines, news, etc. which can be used as a basis for studying a topic. The results of the study conducted show that there are several factors that affect the learning motivation of elementary school students during the pandemic such as parenting factors, presentation of distance learning carried out by teachers, and support from the government in supporting and assisting in implementing distance learning.

scholarly journals Novel Mathematical Model of Breast Cancer Diagnostics Using an Associative Pattern Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 136 ◽  
Author(s):  
Raúl Santiago-Montero ◽  
Humberto Sossa ◽  
David A. Gutiérrez-Hernández ◽  
Víctor Zamudio ◽  
Ignacio Hernández-Bautista ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Cancer Diagnostics ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Breast Cancer Death ◽  
Positron Emission ◽  
The Government

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

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