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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Data Science: Journal of Computing and Applied Informatics Jambura Journal of Mathematics Building of Informatics, Technology and Science Jurnal Mantik INFOKUM Journal La Multiapp Engineering Science Letter Journal of Novel Engineering Science and Technology
Ronsen Purba
Magister Komputer, STMIK Mikroskil
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Consumer Opinion Extraction Using Text Mining for Product Recommendations On E-Commerce Erlina Halim; Ronsen Purba; Andri Andri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10834

Abstract

This study aims to evaluate consumer opinions in text form on e-commerce to determine the accuracy of ratings given by consumers with opinions using text mining with the lexicon approach. The research data was obtained online using a crawling technique using the API provided by Shopee. The conditions of diverse opinions and use of non-standard words are challenges in processing opinions. Opinion must be processed normalization and repairs using dictionary of words before going to extract using lexicon approach. Dictionary of words contain opinions with weights that are worth 1 to 5 for positive opinions and are worth -1 to -5 for negative opinions. For each opinion will be classified using the maximum ratio of the weight of positive opinion compared to the weight of negative opinion. The classification of opinion produced is positive, negative or neutral. Opinion classification is then compared with the rating classification to work out the extent of accuracy. The comparison produces an accuracy of 80.34% by completing an opinion dictionary.
Stock Price Prediction Using XCEEMDAN-Bidirectional LSTM -Spline Kelvin Chen; Ronsen Purba; Arwin Halim
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.14424

Abstract

Bidirectional Long Short Term Memory (Bidirectional LSTM) is a machine learning technique with the ability to capture data context by traversing backward data to forward data and vice versa. However, the characteristics of stock data with large fluctuations, high dimensions and non-linearity become a challenge in obtaining high stock price prediction accuracy values. The purpose of this study is to provide a solution to the problem of stock data characteristics with large fluctuations, high dimensions and non-linearity by combining the Complete Ensemble Empirical Mode Decomposition With Adaptive Noise method for exogenous features (XCEEMDAN), Bidirectional Long Short Term Memory (LSTM), and Splines. The predicted data will go through normalization and preprocessing using XCEEMDAN then the XCEEMDAN decomposition results are divided into high and low frequency signals. The bidirectional LSTM handles high frequency signals and the Spline model handles low frequency signals. The test is carried out by comparing the proposed XCEEMDAN-Bidirectional LSTM-Spline model with the XCEEMDAN-LSTM-Spline model using the same parameters and changing the noise seed randomly 50 times. The test results show that the proposed model has the smallest RMSE average value of0.787213833 while model which is compared only has the smallest RMSE average value of 0.807393567.
Co-Authors Adek Efnaldi Rambe Aldo Alex Sitorus Ali Akbar Lubis Andri Andri, Andri Anisah - Anna Agustiana Anwar Utama Apin Ridwan Aritonang, Mendarissan Arwin Arwin Arwin Halim Arwin Halim Arwin Halim Asif Rana Br Sibarani, Sabrina Adela Erlina Halim Erwin Setiawan Panjaitan Fandi Presly Simamora Feredy Lestari Pandia Frans Agus Purba Harry Darmawan Hendra - Indra Syahputra Irpan Pardosi Jimmy Nganta Ginting Jimmy Peranginangin Kardika Sidabariba Kelvin Chen Lestari, Wulan Sri Manurung, Juliana Damayanti Mark Dhruba Sikder Maya Silvi Lidya Megawaty Simamora Muhammad Fermi Pasha Munthe, Andreas Nadya Sikana Pasha, Muhammad Fermi Pipin, Sio Jurnalis Poerianto Poerianto Ricky Paian Limbong Rivaldi Lubis Rudy Rudy Sari Fatmawati Sastra Wandi Nduru Sibarani, Sabrina Adela Br Sinaga, Frans Mikael Situmorang, Gilbert Fernando Sunario Megawan Vicky, Vicky Winardi, Sunaryo Wita Clarisa Ginting Yudi Pratama Hasibuan Yundari, Yundari