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 Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain

2020 ◽  
Vol 1 (1) ◽  
pp. 19-26
Author(s):  
Rakhmi Khalida ◽  
Siti Setiawati
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Traffic Violations ◽  
The Government ◽  
Bayes Algorithm ◽  
User Friendly

Abstract   The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impression. Twitter social media is one of the objects to express opinions because it is user friendly, updated topics, and openly accesses tweets. Opinions on Twitter are collected, then the preprocessing stage is performed, then the selection of information gain features helps reduce noise caused by irrelevant labels, the next step is the classification of sentiments with the Naïve Bayes algorithm and finally polarity sentiments. This research resulted in an accuracy of 41.82%, a precision of 50.51% and a recall of 45.45%.   Keywords: Sentiment analysis, E-ticketing, Information Gain, Naive Bayes   Abstrak   Pemerintah Indonesia melakukan langkah perubahan untuk memperbaiki sistem pelayanan publik dalam pelanggaran berlalu-lintas yaitu dengan menerapkan sistem e-Tilang. Sistem ini menjadi solusi mendisiplinkan para pengendara kendaraan bermotor dari banyaknya melakukan pelanggaran berlalu-lintas. Keberadaan e-Tilang juga menjadi solusi mencegah kenakalan penegak hukum dari pungutan liar, istilah damai ditempat, hingga akuntabilitas uang denda. Dalam penelitian ini melakukan analisis sentimen tentang sistem e-Tilang atau opinion mining untuk mengelompokan ragam komentar masyarakat yang memberikan kesan positif, negatif atau netral. Media sosial Twitter menjadi salah satu objek untuk menyampaikan opini karena user friendly, topik ter-update, dan terbuka mengakses tweet. Opini pada twitter dikumpulkan, lalu dilakukan tahapan preprocessing, selanjutnya dengan seleksi fitur information gain membantu mengurangi noise yang disebabkan oleh label-label yang tidak relevan, tahap selanjutnya adalah klasifikasi sentimen dengan algoritma Naïve Bayes dan terakhir sentimen polarity. Penelitian ini menghasilkan accuracy 41,82%, presisi 50,51% dan recall 45,45%.   Kata kunci: Analisis sentimen, E-Tilang, Information Gain, Naive Bayes

scholarly journals PERANCANGAN SISTEM INFORMASI PELAYANAN PUBLIK PADA RUKUN WARGA 05 CENGKARENG TIMUR DENGAN PENERAPAN METODE NAÏVE BAYES

Petir ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Meliana Meliana ◽  
Riri Fajriah
Keyword(s):  
Public Service ◽  
Public Services ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Birth Certificates ◽  
Administrative Services ◽  
The Public ◽  
Work Program ◽  
Service Information ◽  
The Government

Public service is a service provided by the government as the state administrator of the community to meet the needs of the community itself and has a purpose to improve the welfare of the community. The forms of service that are in the neighborhood of Citizenship 05 include basic administrative services,for example:services for making Family Cards, Birth Certificates, Death Letters, KTPs, Not Available Certificate (SKTM) and many others. The problem faced by the pillars of 05 is the administration and bureaucracy that have not been computerized, causing public services to be long. In addition, there are still many public service irregularities in the Rukun Warga 05, especially in the deviation from giving an insufficient certificate. Previous research on poor SKTM recipients in Jambi City was one of the bases of this research. The design of the public service information system in the RW 05 area was designed using several modules, namely the citizen reporting module, RW work program evaluation module, RT work program distribution module, citizen administration module, evaluation of SKTM giving, socialization module and citizen information. The implementation of this public service uses a prototype method and uses PIECES analysis with the application of naive bayes for selection of inadequate certificates so that public services and the provision of SKTM can be done effectively, efficiently and on target.

scholarly journals SMS CLASSIFICATION: CONJOINT ANALYSIS OF MULTINOMIAL NAIVE BAYES APPLICATION

2021 ◽  
Vol 9 (08) ◽  
pp. 1165-1173
Author(s):  
Vedad Burgic ◽  
◽  
Dino Keco ◽  
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Text Processing ◽  
Training Model ◽  
Naïve Bayes ◽  
Processing Technologies ◽  
Collection Data ◽  
Bayes Algorithm ◽  
And Training

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.

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%

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

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

2021 ◽  
Author(s):  
Sulthan Rafif ◽  
Pramana Yoga Saputra ◽  
Moch Zawaruddin Abdullah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Research Fields ◽  
Naive Bayes Method

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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