scholarly journals Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor

2021 ◽  
Vol 6 (1) ◽  
pp. 96
Author(s):  
Ikhsan Romli ◽  
Shanti Prameswari R ◽  
Antika Zahrotul Kamalia
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Manhattan Distance ◽  
K Nearest Neighbor ◽  
Distance Calculation ◽  
K Nearest Neighbor Algorithm ◽  
Similarity Distance

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point.

scholarly journals Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Danny Sebastian
Keyword(s):  
Nearest Neighbor ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Product Data ◽  
K Nearest Neighbor Algorithm ◽  
Similarity Distance ◽  
Brand Product

E-marketplace has gained popularity with the Indonesian society resulting in the increment of products offered. Consequently, customers require more effort to search for products. In this study, we classified products from several e-marketplaces. The classification was carried out using TF-IDF method for the weighting, cosine similarity to calculate product similarity distance, and k-nearest neighbor algorithm. Based on the first testing result using 150 product data, the k-nearest neighbor method with k=5 successfully classified 146 data with 4 data classified into the wrong class. This k=5 value gives the best result for this case, with an accuracy of 97.33%. The second testing result using 150 mixed brand product data, the k-nearest neighbor method successfully classified 145 data with 5 data classified into the wrong class. The accuracy of the second testing is 96.67%.

scholarly journals PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR

2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Akhmad Deviyanto ◽  
Muhammad Didik Rohmad Wahyudi
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Term Weighting ◽  
K Nearest Neighbor Algorithm ◽  
Cosine Similarity Measure ◽  
Analysis System ◽  
Test Result ◽  
Python Package

AbstractThis research is made to implement the KNN (K-Nearest Neighbor) algorithm for sentiment analysis Twitter about Jakarta Governor Election 2017. The object is 2000 data tweets in Indonesia collected from Twitter during Januari 2017 using Python package called Twitterscraper. The methode used in sentiment analysis system is KNN with TF-IDF term weighting and Cosine similarity measure. As the test result, the highest accuracy is 67,2% when k=5, the highest precision is 56,94% with k=5, and the highest recall 78,24% with k=15.Keywords : K – Nearest Neighbor, Twitterscraper, TF-IDF, Cosine Similarity Penelitian ini dibuat untuk mengimplementasikan algoritma KNN (K - Nearest Neighbor) dalam analisis sentimen pengguna Twitter tentang topik Pilkada DKI 2017. Data tweet yang digunakan adalah sebanyak 2000 data tweet berbahasa Indonesia yang dikumpulkan selama bulan Januari 2017 menggunakan package Python bernama Twitterscraper. Menggunakan algoritma KNN dengan pembobotan kata TF-IDF dan fungsi Cosine Similarity, akan dilakukan pengklasifikasian nilai sentimen ke dalam dua kelas : positif dan negatif. Dari hasil pengujian diketahui bahwa nilai akurasi terbesar adalah 67,2% ketika k=5, presisi tertinggi 56,94% ketika k=5, dan recall 78,24% dengan k=15.Kata Kunci : K – Nearest Neighbor, Twitterscraper, TF-IDF, Cosine Similarity

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

scholarly journals ASPECT BASED SENTIMENT ANALYSIS DATA KUESIONER DI RUMAH SAKIT MUHAMMADIYAH LAMONGAN MENGGUNAKAN ALGORITMA K-NN.

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 506
Author(s):  
Mustain Mustain Mustain
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
Analysis Data ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Space Model

Kesulitan untuk mengorganisir data kuesioner yang bersifat konvensional melatarbelakangi penelitian ini. Oleh karena itu dibuat sistem yang memudahkan pengelompokan data kuesioner secara otomatis yang lengkap dengan sentimen yang terkandung didalamnya. Dataset yang digunakan dalam penelitian ini adalah data kuesioner rumah sakit Muhammadiyah lamongan. Penelitian ini hanya menangani kuesioner yang berbentuk teks. Data dengan fisik kertas direkap kemudian diinput ke database lengkap dengan kategori unit kerja dan sentiment. Selanjutnya dataset tersebut di dilakukan pre-prosesing yang meliputi penanganan negasi case folding, tokenizing, filtering dan stemming. Sebagai data uji komentar dari kuesioner akan dilakukan pre-prosesing selanjutnya dihitung tingkat kemiripan document dengan menggunakan metode K- Nearest Neighbor dan Vector Space Model. Jumlah data yang ditangani mempengaruhi performa system terutama dari akurasi dan kecepatan pada saat proses klasifikasi. Hasil dari sistem yang dibuat berupa ranking dokumen yang paling mirip dengan dataset berdasarkan urutan nilai cosine similarity. Ujicoba klasifikasi berdasarkan kelas kategori menghasilkan nilai akurasi 91 %. Ujicoba berdasarkan Kelas Sentimen sebesar 94 %.dari kombinasi keduanya system berhasil mendapat akurasi sebesar 86 %

scholarly journals An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7269
Author(s):  
Ling Ruan ◽  
Ling Zhang ◽  
Tong Zhou ◽  
Yi Long
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Local Clustering ◽  
Positioning Method ◽  
K Nearest Neighbor Algorithm ◽  
Space Range ◽  
Fingerprint Data

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

scholarly journals Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Forests ◽  
2014 ◽  
Vol 5 (7) ◽  
pp. 1635-1652 ◽  
Author(s):  
Leonhard Suchenwirth ◽  
Wolfgang Stümer ◽  
Tobias Schmidt ◽  
Michael Förster ◽  
Birgit Kleinschmit
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Carbon Stocks ◽  
Riparian Forests ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Self Organizing Maps ◽  
K Nearest Neighbor Algorithm ◽  
Self Organizing

N-Gram and K-Nearest Neighbor Algorithm for Sentiment Analysis on Capital Relocation

2021 ◽  
Author(s):  
Muhammad Ilham Ramadhon ◽  
Arini Arini ◽  
Fitri Mintarsih ◽  
Iik Muhamad Malik Matin
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
N Gram

Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

scholarly journals Recent trends in big data using hadoop

2019 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Chetna Kaushal ◽  
Deepika Koundal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Nearest Neighbor ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Clustering Techniques ◽  
Recent Trends ◽  
K Nearest Neighbor Algorithm

<span>Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. </span>

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