scholarly journals Penerapan Algoritma Naive Bayes pada Penentuan Kelayakan Calon Tenaga Kerja Indonesia

2019 ◽  
Vol 1 ◽  
pp. 771
Author(s):  
Winda Hana Purba ◽  
Poningsih Poningsih ◽  
Dedi Suhendro ◽  
Irfan Sudahri Damanik ◽  
Ilham Syahputra Saragih
Keyword(s):  
Unemployment Rate ◽  
Migrant Workers ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Level Of Education ◽  
High Unemployment ◽  
Low Level ◽  
High Unemployment Rate ◽  
The Government

Indonesian Manpower is a potential that is a huge potential for the progress of the country. However, the difficulty of employment and the high unemployment rate in Indonesia requires that some people seek perfect employment abroad, in order to improve economic levels. The lack of selection resulted in many problems in the workforce, the low level of education of prospective migrant workers resulted in them having an easy risk on other party tricks, non-violence, unpaid salaries and so on. In accordance with what has been surveyed, it turns out that the sending of these workers is actually not feasible, given the level of education, skills and abilities that are lacking for employment abroad. This study aims to facilitate the government or companies engaged in the field to channel selected workers using the Naive Bayes Method.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

scholarly journals Legal Protection Model for Indonesian Migrant Workers

2018 ◽  
Vol 3 (02) ◽  
pp. 291-304
Author(s):  
Anis Widyawati
Keyword(s):  
Saudi Arabia ◽  
Migrant Workers ◽  
Middle Eastern ◽  
Legal Protection ◽  
Legal Assistance ◽  
Legal Systems ◽  
Level Of Education ◽  
Low Level ◽  
The Republic ◽  
The Government

The emergence of several large cases of migrant workers in Malaysia and Singapore as well as in several Middle Eastern countries, especially Saudi Arabia, made all the nation's components flinch. Many people argue that the problem occurs because of the low level of education of migrant workers. There are also those who say that this problem occurs because employers of Indonesian labor services companies (Pengerah Jasa Penyalur Tenaga Kerja Indonesia, PJTKI, now called Perusahaan Penyalur Tenaga Kerja Indonesia Swasta, PPTKIS) are not nationally minded and only pursue profit (profit-oriented). There were also those who argued that the cases of migrant workers occurred due to the inactivity of regulative and punitive functions of the Government of the Republic of Indonesia. Based on the background above, the problem can be formulated is how the urgency of legal protection for Indonesian migrant workers abroad and how the legal protection model for Indonesian migrant workers abroad. Research carried out at BP3TKI and the Semarang Manpower and Transmigration Office underlined that legal protection for Indonesian migrant workers abroad is very important. The urgency in legal protection due to fulfillment of the rights of victims who work legally abroad but also cannot be fully implemented properly, due to differences in legal systems with migrant workers recipient countries that do not necessarily want to protect the rights of migrant workers who experience treatment not please from their own citizens. The migrant workers who work illegally the government has not been able to fully protect the rights of victims who have experienced criminal acts. The legal protection model for migrant workers currently emphasizes the fulfillment of victims’ rights who work legally abroad, such as obtaining legal assistance from a local lawyer appointed by the ambassador of the Republic of Indonesia in the country receiving the migrant workers, mentoring by psychologists and clergy, bringing the families of victims, compensation, and insurance claims. And at the same time, for migrant workers who work illegally the government has not been able to fully protect the rights of the victims.

scholarly journals PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal)

2021 ◽  
Vol 10 (1) ◽  
pp. 11-20
Author(s):  
Reza Dwi Fitriani ◽  
Hasbi Yasin ◽  
Tarno Tarno
Keyword(s):  
Family Planning ◽  
Naive Bayes ◽  
Population Control ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The Family ◽  
The Government ◽  
Efficient Learning ◽  
Naive Bayes Method

The Family Planning Program (KB) launched by the Government of Indonesia to address the problem of population control does not always produce the desired program results. In 2017, there were 7 users of the IUD contraceptive type of contraceptive who failed from 1,102 new IUD users in Kendal Regency so that the ratio of success and failure to the IUD KB program when compared to users of the new IUD KB is 0.64%: 99.36% . The ratio of success and failure of family planning programs which tend to be unbalanced makes it difficult to predict. One of the handling imbalanced data is oversampling, for example using Random Oversampling (ROS). Naive Bayes is used for classification because it’s easy and efficient learning model. The data in this study used 14 independent variables and 1 dependent variable. The results of this study indicate that the G-mean of Naive Bayes is less than 60%. The G-mean of ROS-Naive Bayes is 96.6%. It can be concluded that in this research, the ROS-Naive Bayes method is better than the Naive Bayes method for detecting the success status of IUD family planning in Kendal Regency. Keywords: Naive Bayes, Random Oversampling, G-mean

scholarly journals IDENTIFIKASI JENIS OBAT BERDASARKAN GAMBAR LOGO PADA KEMASAN MENGGUNAKAN METODE NAIVE BAYES

2016 ◽  
Vol 3 (2) ◽  
pp. 125
Author(s):  
Surya Rahayuda
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Naive Bayes ◽  
Modern Medicine ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Different Types ◽  
The Government ◽  
Numeric Data

<p><em>There many types of drugs have been approved by the government and circulating in the community, but many people don’t know. In this study, I want to create an application that can identify the type of drug based on the logo on the packaging. I’m using 4 different types of modern medicine and 3 types of herbal medicine, total there will be as many as 7 different logo that will be used. Pictures will be entered into the application, then detected the edges of the image using the Edge Detection, to get the shape of the logo image, after it is extracted using methods GLCM, extraction will produce output in the form of numbers, the numeric data is then classified using Naïve Bayes classification and will get the results in the form of the type of drug. From the experiments it was found that the resulting level of accuracy is quite high, there are 3 categories of types of drugs that have a high accuracy on Obat Bebas, Obat Bebas Terbatas and Obat Keras. From the results of these trials concluded that the Naïve Bayes method can be used to mengkalsifikasi types of drugs is based on the logo on the packaging of drugs</em>.</p><p><strong><em>Keywords: </em></strong><em>logo, drug, image processing, edge detection, GLCM, naïve bayes</em></p><p><em>Terdapat banyak jenis obat telah disetujui oleh pemerintah dan beredar di masyarakat, namun banyak masyarakat tidak mengetahuinya. Pada penelitian ini saya ingin membuat suatu aplikasi yang dapat mengindentifikasi jenis obat berdasarkan logo pada kemasan. Saya menggunakan 4 jenis obat moderen dan 3 jenis obat herbal, total akan terdapat sebanyak 7 macam logo yang akan digunakan. Gambar akan diinputkan ke dalam aplikasi, kemudian dideteksi tepian gambarnya menggunakan metode Edge Detection, untuk mendapatkan bentuk dari gambar logo, setelah itu diekstraksi menggunakan metode GLCM, hasil ekstraksi akan menghasilkan output berupa angka, data angka ini kemudian diklasifikasikan menggunakan metode Naïve Bayes dan akan mendapatkan hasil klasifikasi berupa jenis obat. Dari percobaan yang dilakukan didapatkan bahwa tingkat akurasi yang dihasilkan cukup tinggi, terdapat 3 buah kategori jenis obat yang memiliki akurasi yang tinggi yaitu pada jenis Obat Bebas, Obat Bebas Terbatas dan Obat Keras. Dari hasil percobaan tersebut disimpulkan bahwa metode Naïve Bayes dapat digunakan untuk mengkalsifikasi jenis obat berdasarkan logo pada kemasan obat.</em> <em></em></p><p><strong><em>Kata kunci: </em></strong><em>logo, obat, image processing, edge detection, GLCM, naïve bayes</em></p>

scholarly journals PENERAPAN ALGORITMA NAIVE BAYES DALAM MENENTUKAN KONSENTRASI SISWA TERHADAP PROSES BELAJAR MENGAJAR DI SMP TAMAN ASUHAN

2021 ◽  
Vol 4 (2) ◽  
pp. 142-155
Author(s):  
Farhannah Silmi Az Zahra Farhannah ◽  
Solikhun Solikhun
Keyword(s):  
Learning Process ◽  
Teaching And Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Research Data ◽  
Bayes Method ◽  
A Value ◽  
The Government

The purpose of this study is to analyze whether students concentrate or not on the teaching and learning process at Pematangsiantar Park in SMP. To determine the concentration of students in the teaching and learning process, the Naive Bayes classification of data mining methods is used. Sources of research data were obtained using a questionnaire distributed to Pematangsiantar Park Middle School. So hopefully this research can help the government and the school in monitoring the concentration of students so that it can help in improving the quality and quality of schools. Based on that research that has been done,the writer uses the Naïve Bayes Method to predict student concentration resulting in a value of 95.31%, while the predicition of lack of concentration results in a value of 100.00%

scholarly journals Unemployment as one of the leading problems of depopulation in border municipalities of Banat

2010 ◽  
pp. 279-292
Author(s):  
Milana Pasic ◽  
Andjelija Ivkov-Dzigurski ◽  
Aleksandra Dragin
Keyword(s):  
Unemployment Rate ◽  
Research Study ◽  
Unemployed People ◽  
Demographic Situation ◽  
Level Of Education ◽  
High Unemployment ◽  
High Unemployment Rate ◽  
The Unemployed ◽  
Different Levels

Bordering area of the Banat region consists of nine municipalities. These municipalities border with Romania and have the furthest, peripheral eastern position in Vojvodina. Demographic situation of this area is generally not favourable and one of the main problems is high unemployment rate. In order to improve the current demographic situation, it is necessary to offer certain proposals for revitalisation. The unemployment rate could be changed with a long-term plan and its realisation conducted in several stages. What needs to be taken into account is proper planning of all activities at different levels. A research study conducted in the period from January 2007 to October 2008 shows that all bordering municipalities of Banat decreased the number of unemployed people in relation to the beginning of the year 2007. Municipality Bela Crkva has the highest percent of the unemployed, whereas Coka and Novi Knezevac have the lowest percentage. The unemployed people are mainly older categories with a lower level of education. .

scholarly journals Classification of Community Complaints Against Public Services on Twitter

2020 ◽  
Vol 1 (1) ◽  
pp. 7-10
Author(s):  
Muqorobin Muqorobin ◽  
Siti Rokhmah ◽  
Isnawati Muslihah ◽  
Nendy Akbar Rozaq Rais
Keyword(s):  
Public Service ◽  
Public Services ◽  
Naive Bayes ◽  
Text Processing ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The Government ◽  
Bayes Algorithm

Abstract— Information on public services is an important part of increasing community satisfaction with government policies. Complaints and Complaints of the community become mediators to improve public services according to community needs.Twitter is one of the most widely used social media in the community to post activities, experiences, and complaints about public services through the internet easily and realtime.The amount of information on Twitter is mixed between satisfaction and extensibility of public services, making it difficult for the government to make decisions in public policy. The role of Big Data can be a solution to classifying data to predict satisfaction or extensibility of public services with parameters: markets, transportation and hospitals.Data sources taken from Twitter are 700 data texts. The twitter classification of public service complaints is built using the Naïve Bayes Algorithm Method, because the algorithm can classify based on probability values. Text processing is done by filtering text and selecting text to be ordered.The results of this study indicate that the Naïve Bayes Method is able to properly classify public service complaints based on 3 parameters, transportation, markets and hospitals. System testing using 700 data obtained the best results accuracy value: 86%, and precision: 72%, recall 81% and f-measure: 83%.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

2020 ◽  
Vol 3 (1) ◽  
pp. 22-34
Author(s):  
Komang Aditya Pratama ◽  
Gede Aditra Pradnyana ◽  
I Ketut Resika Arthana
Keyword(s):  
Naive Bayes ◽  
Intelligent System ◽  
Naïve Bayes ◽  
The State ◽  
State University ◽  
Bayes Method ◽  
New Students ◽  
Computer Based ◽  
Entrance Test ◽  
Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

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