Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM

Efendi, Syahril; Sihombing, Poltak

doi:10.30812/matrik.v21i3.1898

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Studi lain telah membandingkan algoritma K-Nearest Neighbor dan SVM. Hasil algoritma SVM mempunyai akurasi lebih tinggi dibandingkan K-Nearest Neighbor (KNN), [9].…”

Section: Pendahuluanunclassified

Application Penerapan support vector machine pada analisis sentimen kebijakan pembelajaran tatap muka saat pandemi 2021

Dhianur Alam Putra,

Didi Juardi,

Arif Sholehudin

et al. 2023

infotech

View full text Add to dashboard Cite

Dampaknya dikeluarkannya Surat Edaran 4 Mendiknas tahun 2020 tentang kebijakan pendidikan pada masa darurat akibat penyebaran wabah Covid-19 menjadi pembelajaran daring. Sentimen yang disampaikan masyarakat mengenai kebijakan Pembelajaran Tatap Muka terbatas dapat dianalisis dan hasilnya digunakan sebagai feedback dari kebijakan untuk mengetahui efektifitas dari kebijakan tersebut. Tujuannya a). mengklasifikasikan sentimen pengguna sosial media twitter terhadap kebijakan pembelajaran tatap muka menggunakan support vector machine. b). menganalisis hasil performan dari penerapan algortima support vector machine berdasarakan nilai akurasi menggunakan metode confusion matrix. Penelitian mempunyai 4 tahap penelitian yang dilakukan, yaitu tahap pendahuluan, tahap pengumpulan data, tahap pengolahan data, tahap analisis dan pembahasan, dan kesimpulan. Metode Knowledge Discovery in Database terdiri dari, Data Selection, Preprocessing, Transformation, Data Mining, dan Evaluasi. Data yang digunakan bisa berupa satu tweet dari aplikasi Twitter atau sebanyak 200 data, splitting data sebanyak 80% data digunakan untuk pelatihan dan 20% digunakan untuk pengujian sebelum tahap penemuan data. Information Gain menghasilkan 665 fitur yang tidak relevan. Hasil Support Vector Machine dan Information Gain yang digunakan dalam pemrosesan data dan evaluasi kinerja ini sebesar Accuracy sebesar 85,00%, Precision sebesar 40,00%, dan recall sebesar 66,67% untuk kernel linear sedangkan Accuracy sebesar 82,50%, Precision sebesar 0%, dan recall tidak bernilai untuk kernel Radial Basis Function

show abstract

“…Studi lain telah membandingkan algoritma K-Nearest Neighbor dan SVM. Hasil algoritma SVM mempunyai akurasi lebih tinggi dibandingkan K-Nearest Neighbor (KNN), [9].…”

Section: Pendahuluanunclassified

Application Penerapan support vector machine pada analisis sentimen kebijakan pembelajaran tatap muka saat pandemi 2021

Dhianur Alam Putra,

Didi Juardi,

Arif Sholehudin

et al. 2023

infotech

View full text Add to dashboard Cite

show abstract

“…The concept of a Support Vector Machine (SVM) sends high-dimensional data to low-dimensional vectors. The Support Vector Machine (SVM) method classifies the extracted features as an image classification method [16].…”

Section: Introductionmentioning

confidence: 99%

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Wibowo,

Prasetyo,

Sembiring

2023

matrik

View full text Add to dashboard Cite

The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.

show abstract

“…Meanwhile, other studies use the Nave Bayes Classification [7][8][9] and KNN [10,11] methods in building a text classification model. Based on studies from research [12,3,13] that compares the performance of several text classification methods, SVM is reported to have the best model performance.…”

Section: Introductionmentioning

confidence: 99%

Recognize The Polarity of Hotel Reviews using Support Vector Machine

Saraswati,

Diatri Indradewi

2022

matrik

View full text Add to dashboard Cite

A brand is very dependent on consumer perceptions of the product or services. In assessing consumer perceptions of products and services, companies are often faced with data analysis problems. One of the data that is very useful to produce a picture of consumer perceptions of the products and services is review data. So that the company's ability to process review data means that the company has a picture of the strength of the brand it has. Some of the most popular machine learning algorithms for creating text classification models include the naive Bayes family of algorithms, support vector machines (SVM) and deep learning algorithms. In this research, SVM has been proven to be a reliable method in pattern recognition. In particular, this study aims to produce a model that can be used to classify the polarity of hotel reviews automatically. The experimental data comes from review data on hotels in Europe sourced from TripAdvisor with a total of 38000 reviews. We also measure the quality of the classification engine model. The test results of the SVM model built from hotel review data are quite good. The average accuracy of the classification engine is 92.48%. Because the recall and precision values are balanced, the accuracy value is considered sufficient to describe the quality of the classification.

show abstract

Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM

Cited by 3 publications

References 14 publications

Application Penerapan support vector machine pada analisis sentimen kebijakan pembelajaran tatap muka saat pandemi 2021

Application Penerapan support vector machine pada analisis sentimen kebijakan pembelajaran tatap muka saat pandemi 2021

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Recognize The Polarity of Hotel Reviews using Support Vector Machine

Contact Info

Product

Resources

About