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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Ilmiah Teknologi dan Rekayasa TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) ComTech: Computer, Mathematics and Engineering Applications Jurnal Optimalisasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Ilmu Komputer dan Informatika
Suharjito Suharjito, Suharjito
Bina Nusantara University
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Indonesian online travel agent sentiment analysis using machine learning methods Abimanyu Dharma Poernomo; Suharjito Suharjito
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp113-117

Abstract

Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment.  From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda
Foreign exchange prediction based on indices and commodities price using convolutional neural network Rian Rassetiadi; Suharjito Suharjito
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp494-501

Abstract

The level of accuracy in predicting is the key in conducting forex trading activities in gaining profits. Some predictions are made only by using historical currency data to be predicted, this makes predictions less accurate because they do not consider external influences. This study examines external factors that can influence the results of predictions, by looking for the relationship between the value of indices such as NTFSE and S & P 500 and the value of commodities such as gold and silver to the prediction process of EUR / USD. Prediction carried out using a deep learning algorithm with the Convolutional Neural Network method uses 2 1-dimensional convolution layers with ReL activation. The data used is the value of Open, High, Low and Close prices on forex, indices and commodities which are combined into one with the close forex value target for the next 5 days. Testing of EUR / USD test data only gets MSE results of 0.00081894. While the results of testing of the combined test data between EUR / USD, indices and commodities producing MSE vary between 0.00068717 to 0.0109606 where the best combination is a combination of FTSE 100 and Natural Gas values. So it can be concluded that other factors included in predicting have an influence on the results obtained.
Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa Andika, Rizky; Suharjito, Suharjito
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1662-1671

Abstract

Sentiment analysis was a system for recognizing and extracting opinions in documents. There were two weaknesses in sentiment analysis. The first weakness was preprocessing in sentiment analysis can’t recognize slang words so that important words that should have been recognized became unrecognizable. The Second was the feature extraction process in sentiment analysis only recognized words based on the form of the word but can’t recognize the similar word. In this paper, we proposed spelling checker and wordnet to fix these weaknesses. We also used k-nearest neighbor (KNN), Naïve Bayes, and decision tree as methods for check classify the text. The purpose of this research was first to know the effects of used Wordnet and spelling checkers in sentiment analysis and second was to improve the data processing process in the existing sentiment analysis. The dataset that we used in the research was a list of tweets in Bahasa. The results showed wordnet and spelling checker make a decrease in the valued of false positives, false negatives, and true negatives in the calculation of the confusion matrix. It can increase the accuracy of the K-NN from 43% to 72%, Naïve Bayes from 41% to 71% and decision tree from 47% to 75%.
Co-Authors Abba Suganda Girsang, Abba Suganda Abdul Latief Qohar Abimanyu Dharma Poernomo Andika, Rizky Andriyansyah, Andriyansyah Arta, Mikhael Chandra Asroll, Muhammad Astrid Lestari Tungadi Aulia, Yunita Demi Adidrana Dian Puspita Tedjosurya, Dian Puspita Halim, Marwa Ida Ratna, Ida Imran, Bahtiar Indra Alfredo Ishartati Jimmy Jimmy Josef Bernadi, Josef Leonardus Hardjo, Leonardus Maulivia, Vivia Surya Muhammad Rifqi Nensi, Sri Wahyu Nico Surantha, Nico Nur Anwar, Nur Perdana, Panji Arief Putri, Yulia Aneke Raymond Raymond Rian Rassetiadi Stefani Agusta Tinambunan, Gusmariani Widodo, Bambang Yundari, Yundari Yuwono Abdillah