Komparasi Algoritma Klasifikasi Pada Analisis Review Hotel

Utami, Lila Dini

doi:10.33480/pilar.v14i2.1023

Cited by 7 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, the evaluation was carried out by calculating the accuracy. To get the value of the accuracy results, this study uses a confusion matrix as an evaluation test [15].…”

Section: Discussionmentioning

confidence: 99%

The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method

Taufiqurrahman,

Siswa

2023

J.Sci.Eng

View full text Add to dashboard Cite

Muhammadiyah University of East Kalimantan in carrying out its operational activities relies on funds from students, one of which is tuition fees (SPP). This creates problems if students are late in making tuition payments, because tuition payments can improve the quality of education and facilitie. Therefore, the purpose of this study is to determine indicators, then implement the logistic regression algorithm by dividing the data into 70:30 and evaluating the performance results of the algorithm using the confusion matrix. The data obtained are sourced from the Academic Administration Section and the Financial Administration Section as many as 12,408 data with several attributes such as the faculty, study program, class, gender, father's income, mother's income, father's education, mother's education and label (late or not late). From the results of the data test that has been carried out, it gets the results of an accuracy of 55.89%.

show abstract

“…In this study, the evaluation was carried out by calculating the accuracy. To get the value of the accuracy results, this study uses a confusion matrix as an evaluation test [15].…”

Section: Discussionmentioning

confidence: 99%

The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method

Taufiqurrahman,

Siswa

2023

J.Sci.Eng

View full text Add to dashboard Cite

show abstract

“…Term Frequency-Inverse Document Frequency (TF-IDF) merupakan teknik statistik numerik yang dapat menentukan bobot untuk setiap term atau kata dalam setiap dokumen [9]. Penelitian kedua [10] melakukan analisis review hotel dengan membandingkan metode klasifikasi Naïve Bayes, Support Vector Machine, dan KNN (K-Nearest Neighbor). Didapat dari hasil pengujian bahwa metode KNN (K-Nearest Neighbor) menghasilkan nilai akurasi yang paling tinggi dibandingkan dengan Naïve Bayes dan Support Vector Machine.…”

Section: Pendahuluanunclassified

Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN

2022

View full text Add to dashboard Cite

As the times move forward, technologies are following to develop with the times, especially internet technology. Nowadays, people can find out information about shopping center via the Internet. One of the best-known shopping centers in Bandung is Paris van Java resort lifestyle place. The person's consideration of going to the shopping center is based on the location and opinions of the person who once visited that shop. With a huge amount of opinion and an underlying amount of information that is of great value causes the information to become dangerous if the person misunderstood it. With all these problems, sentiment analysis one of the solution that can prevent the problems of these misunderstanding, in which sentiment analysis works to analyze each text to determine the level of positive or negative sentiment values. In this study, the sentiment analysis dataset goes first to the preprocessing stage, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature then classified using the K-Nearest Neighbor method, The K-Nearest Neighbor method was chosen because this method is one method that can classify text and data, besides K-Nearest neighbor method has a good classification accuracy where the classification process is easy and quite simple in its implementation. With a system that built using TF-IDF Unigram and Euclidean Distance, the best accuracy value is 88.29%.

show abstract

“…Lila Dini Utami, Hilda Rachmi, dan Dini Nurlaela melakukan Komparasi Algoritma Klasifikasi Pada Analisis Review Hotel dengan membandingkan Naïve Bayes, Support Vector Machine dan K-Nearest Neighbor. Hasil pengujian didapatkan bahwa algoritma K-Nearest Neighbor menghasilkan nilai akurasi yang paling tinggi dibandingkan dengan Naïve Bayes dan Support Vector Machine [11].…”

Section: Penelitian Sebelumnya Penelitian Dilakukan Oleh Abdul Maunclassified

K-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia

Pajri

Umaidah²,

Padilah³

2020

JuTISI

View full text Add to dashboard Cite

Tokopedia is a popular marketplace used by e-commerce in Indonesia. Customers’ perception of Twitter towards Tokopedia can be used as an important source of information and can be processed into useful insights. Sentiment analysis is a solution that can be used to process the customers’ perception using K-Nearest Neighbor based on Particle Swarm Optimization. The purpose of this study is to classify customers’ perception based on positive, neutral, and negative classes. The test is carried out with four different scenarios and k values which are evaluated using a confusion matrix. Evaluation results showed the distribution of the dataset is 90:10 and the value of k = 1 is the best evaluation result, which is 88.11%. The feature selection was used for results by using Particle Swarm Optimization. The Particle Swarm Optimization used 20 iterations and 10 particles. It produced 97.9% the best evaluation accuracy, 96.17% precision, 96.62% recall, and 96.39% f-measure.

show abstract

Komparasi Algoritma Klasifikasi Pada Analisis Review Hotel

Cited by 7 publications

References 2 publications

The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method

The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method

Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN

K-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia

Contact Info

Product

Resources

About