scholarly journals KLASIFIKASI SENTIMEN SARA, HOAKS DAN RADIKAL PADA POSTINGAN MEDIA SOSIAL MENGGUNAKAN ALGORITMA NAIVE BAYES MULTINOMIAL TEXT

2020 ◽  
Vol 14 (2) ◽  
pp. 68
Author(s):  
Febry Eka Purwiantono ◽  
Addin Aditya
Keyword(s):  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Application Programming ◽  
Programming Interface

Penelitian ini bertujuan untuk menerapkan sebuah algoritma klasifikasi yang dapat menjustifikasi sentimen pada kumpulan cuitan Twitter yang diposting oleh masyarakat Indonesia. Penerapan algoritma ini nantinya akan mengklasifikasikan cuitan mana yang mengandung unsur pelanggaran yang diatur dalam UU-ITE. Dengan adanya penerapan algoritma klasifikasi ini diharapkan dapat membantu pemerintah khususnya Kepolisian Republik Indonesia dan Badan Intelijen Negara dalam merumuskan kebijakan mengenai tindakan pencegahan pelanggaran UU-ITE serta mencegah penyebaran paham radikalisme, informasi palsu dan isu SARA di Negara Indonesia. Teknik pengumpulan data yang dilakukan pada penelitian ini yaitu menggunakan Twitter API (Application Programming Interface). Sedangkan algoritma klasifikasi yang digunakan pada penelitian ini yaitu Naive Bayes Multinomial Text. Algoritma ini dipilih karena mampu mengklasifikasikan dokumen dengan memperhitungkan jumlah kemunculan kata. Dari hasil kompilasi dan data yang diolah, algoritma ini mampu menjustifikasi sentimen secara akurat kurang lebih 99,62%.

scholarly journals Twitter Sentiment Analysis using Na¨ive Bayes Classifier with Mutual Information Feature Selection

2018 ◽  
Vol 2 (2) ◽  
pp. 106-111
Author(s):  
Maria Arista Ulfa ◽  
Budi Irmawati ◽  
Ario Yudo Husodo
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Application Programming ◽  
Programming Interface

Analisis sentimen merupakan suatu teknik idetifikasi terhadap emosi yangdiekspresikan melalui teks. Tujuan analisis sentimen adalah menentukan apakah suatupendapat dalam kalimat atau dokumen termasuk kategori positif ataunegatif. Twitter merupakan salah satu media sosial yang sering digunakan dalammenyampaikan pendapat. Twitter memungkinkan penggunanya (user) untuk menulispendapat mereka mengenai berbagai topik dalam sebuah tweet. Data twitter dalampenelitian ini didownload melalui twitter Application Programming Interface (API).Data twitter tersebut terdiri dari 500 tweet tentang pariwisata Lombok dengan hashtag#lombok dan #woderfullombok. Fitur informasi dari setiap tweet diseleksimenggunakan metode Mutual Information dan dianalisis menggunakan modelklasifikasi Naïve Bayes (Naïve Bayes Classifier). Hasil pengujian klasifikasisentimen twitter pada kategori positif dan negatif menggunakan 10-fold crossvalidation memperoleh akurasi rata-rata sebesar 97,9%.Kata kunci : Analisis Sentimen, Twitter, Naïve Bayes Classifier, Mutual Information

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

scholarly journals Advanced soft robot modeling in ChainQueen

Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik
Keyword(s):  
Application Programming Interface ◽  
Material Point ◽  
Soft Robotics ◽  
Soft Robot ◽  
Inference Control ◽  
Simulation And Optimization ◽  
Efficient Simulation ◽  
Application Programming ◽  
Robot Modeling ◽  
Programming Interface

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

A Layered Mapping of Ada 202X to OpenMP

2021 ◽  
Vol 40 (2) ◽  
pp. 55-58
Author(s):  
S. Tucker Taft
Keyword(s):  
Operating System ◽  
Application Programming Interface ◽  
Light Weight ◽  
Main Interest ◽  
Thread Scheduling ◽  
Environment Variables ◽  
Application Programming ◽  
Parallel Loops ◽  
Programming Interface

The OpenMP specification defines a set of compiler directives, library routines, and environment variables that together represent the OpenMP Application Programming Interface, and is currently defined for C, C++, and Fortran. The forthcoming version of Ada, currently dubbed Ada 202X, includes lightweight parallelism features, in particular parallel blocks and parallel loops. All versions of Ada, since its inception in 1983, have included "tasking," which corresponds to what are traditionally considered "heavyweight" parallelism features, or simply "concurrency" features. Ada "tasks" typically map to what are called "kernel threads," in that the operating system manages them and schedules them. However, one of the goals of lightweight parallelism is to reduce overhead by doing more of the management outside the kernel of the operating system, using a light-weight-thread (LWT) scheduler. The OpenMP library routines support both levels of threading, but for Ada 202X, the main interest is in making use of OpenMP for its lightweight thread scheduling capabilities.

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