scholarly journals Analysis Model for Identifying Negative Posts Based on Social Media

2021 ◽  
Vol 11 (10) ◽  
pp. 104-108
Author(s):  
Ade Febriany ◽  
◽  
Ditdit Nugeraha Utama
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Analysis Model ◽  
Research Hypothesis ◽  
Animal Psychology

Cyberbullying is an act that violates where this crime is committed on social media, e.g. the Twitter application. This action is difficult to detect, thus someone has to report the case before detection. Identification of cyberbullying tweets aims to classify tweets containing the bullying content. Several studies gave output results in the identification of whether the tweet is positive or negative, or bully or not. It can be confusing when analyzing the classification results as it only results in two classes. In this research, by using the conception of text mining Naïve Bayes, the model that can categorize into more detail was developed. It does not only categorize the contents are bullying or not, however it can classify the contents into five detail categories. The classification process done based on the dataset and label where the schema to build dataset was proposed scientifically from this study. The contribution of this research is to offer the algorithm to collect and label the Indonesian language dataset and then classify the types of sarcasm, namely animal, psychology and stupidity, disabled person, attitude, and general bullying. The research hypothesis is that analysis from the classification results can be improved by classifying bully content into the five classes. Dataset was collected by the researcher and labelling was done manually based on study literature. The result proves the model can use to classify cyberbullying content in social media with 99.15% accuracy.

scholarly journals Text Mining Literature Review on Indonesian Social Media

2021 ◽  
Vol 7 (2) ◽  
pp. 226
Author(s):  
Angelina Pramana Thenata
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes

Era sekarang jumlah berita dari berbagai media sosial yang tersebar dalam waktu singkat dan kebutuhan masyarakat untuk mengkonsumsi berita dalam berbagai referensi dapat mempengaruhi kehidupan masyarakat. Hal ini menyebabkan data yang tersebar dapat dikumpulkan dan dimanfaatkan oleh pemerintah, pengusaha, analisis, ataupun peneliti untuk mengidentifikasi tren, mengembangkan bisnis, memprediksi perilaku pelanggan dan lain sebagainya. Pengumpulan data berita dari media sosial tersebut dapat menggunakan text mining yang melibatkan algoritma yakni Naive Bayes, K-NN, dan SVM. Namun, penggunaan algoritma pada studi kasus yang tidak sesuai dapat memberikan hasil yang tidak optimal. Oleh karena itu, penelitian ini akan menganalisis algoritma text mining yang diimplementasikan pada media sosial berbahasa Indonesia dengan memakai metode systematic literature review. Metode ini dimulai dengan melakukan tahap planning yang menetapkan pertanyaan penelitian, kata pencarian, sumber literatur digital, dan standard literatur. Dilanjutkan dengan tahap conducting yang memilih dan mencocokan standard literatur, serta ekstraksi data. Kemudian tahap reporting yang melakukan analisis hasil ekstraksi data sehingga bisa menemumkan informasi dan pengetahuan. Tolak ukur yang menjadi acuan untuk perbandingan yakni pengujian confusion matrix berupa accuracy, precision, dan recall. Adapun hasil dari penelitian ini ditemukan algoritma Naive Bayes memberikan hasil yang stabil tapi kurang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia. Sedangkan algortima K-NN dan SVM ditemukan memberikan hasil yang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia yang dibuktikan dengan accuracy (50%-98.13%), precision (58.22%-98.48%), dan recall (21.05%-98%).  

scholarly journals CLASSIFICATION OF CUSTOMER COMPLAINTS ON INSTAGRAM COMMENTS USING NAÏVE BAYES ALGORITHM WITH N-GRAM FEATURE EXTENSION

2020 ◽  
Vol 17 (2) ◽  
pp. 109-116
Author(s):  
Fachri Amsury ◽  
Nanang Ruhyana ◽  
Irwansyah Saputra ◽  
Daning Nur Sulistyowati
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Electronic Mail ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Customer Complaints ◽  
N Gram ◽  
Bayes Algorithm ◽  
And Performance ◽  
Usual Process

Customer complaints about the company can be used as a form of self-evaluation and performance that has been carried out by the company, based on customer complaints the company can find out the weaknesses that exist in the company and fix them. The forms of submitting customer complaints are very diverse, currently not only by telephone, but customers also submit suggestions or complaints, customers can submit suggestions or complaints via electronic mail or e-mail or forums in cyberspace that are indeed created by product-producing companies to accommodate various complaints, suggestions, and direct criticism from consumers, especially social media that are free to express opinions on the delivery services used. Instagram is a social media that is more inclined towards images and on the other hand, has captions and comments text, a study is needed for the problem of customer complaints from shipping service users on an Instagram account of a delivery service company. Based on this background, a solution is needed in solving problems for text mining classification using Naïve Bayes with SMOTE techniques and N-Gram feature extraction with the usual process for text mining so that it can produce Naïve Bayes and SMOTE accuracy with an accuracy of 88.54%, before implementation. N-Gram and the accuracy rate increased by 1.44% after the N-Gram Term was applied to 89.98% by using a dataset of 776 Instagram comment text records that had to preprocess text.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Analisis Text Mining dari Cuitan Twitter Mengenai Infrastruktur di Indonesia dengan Metode Klasifikasi Naïve Bayes

2019 ◽  
Vol 1 (2) ◽  
pp. 92
Author(s):  
Bimananda W ◽  
Insan Riski ◽  
Karina Dwi ◽  
Rani Nooraeni ◽  
Theresa Siahaan ◽  
...  
Keyword(s):  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals MODEL KLASIFIKASI ABSTRAK SKRIPSI MENGGUNAKAN TEXT MINING UNTUK PENGKATEGORIAN SKRIPSI SESUAI BIDANG KAJIAN

2018 ◽  
Vol 9 (2) ◽  
pp. 1091-1098 ◽  
Author(s):  
Angga Cahyo Pradikdo ◽  
Aidina Ristyawan
Keyword(s):  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes

Dengan melakukan observasi pada Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri, penulis mendapati bahwa dokumen skripsi pada Program Studi tersebut selalu bertambah setiap tahun, sehingga dapat dijadikan referensi pemilihan bidang penelitian yang sesuai untuk Mahasiswa Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri. Selain itu penulis juga pernah melakukan penelitian tentang pemodelan klasifikasi abstrak prosiding yang bisa digunakan untuk penyusunan letak skripsi pada Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri. Dari hasil penelitian tersebut penulis mendapatkan saran tentang data yang digunakan. Saran tersebut berupa penggunaan data penelitian mahasiswa sebelumnya pada Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri, supaya lebih tepat dan sesuai dengan studi kasusnya. Maka dari itu penulis terinspirasi untuk melakukan penelitian dengan menggunakan data penelitian mahasiswa Program Studi Sistem Informasi yang tersimpan di SIMKI (Sistem Informasi Manajemen Karya Ilmiah) Universitas Nusantara PGRI Kediri. Dengan memanfaatkan data penelitian mahasiswa sebelumnya serta metode teknik text mining  diantaranya prepocesing dan trasformation dengan didukung dengan algoritma naive bayes sebagai proses untuk menghitung nilai probabilitas tertinggi sebagai proses klasifikasi yang akan digunakan untuk menguji data tersebut. Dari hasil pengujian 9 siklus menghasilkan pengetahuan bahwa siklus ke 1 merupakan siklus terbaik dengan akurasi 82,76%, yang dapat digunakan sebagai model klasifikasi skripsi pada Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri, untuk dapat membantu memudahkan mahasiswa untuk mencari referensi karena sudah memuat bidang kajian yang sesuai dan Program Studi Informasi mendapatkan model klasifikasi dengan data hasil dari skripsi mahasiswa Program Studi Sistem Informasi Universitas Nusantara PGRI Kediri.

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