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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi SPIRIT JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) Teknika DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database The Indonesian Journal of Computer Science Multidisciplinary Indonesian Center Journal
Slamet Kacung, Slamet
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Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Sentiment Analysis of Brand Ambassador Influence on Product Buyer Interest Using KNN and SVM Putri, Natasya Kurnia; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Yasin, Verdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29469

Abstract

In the dynamic marketing, companies usually use strategies involving celebrities or influencers to promote their products or brands. The currently popular strategy is using Korean boy bands as brand ambassadors. This collaboration certainly gets a lot of opinion responses through tweets on X app social media. This research aims to analyze sentiment to determine how the product buyer's interest responds to brand suitability, brand image management, and the influence of issues that arise in this collaboration. The research stages consist of data collection, pre-processing, labeling, weighting, and classification with K-Nearest Neighbor and Support Vector Machine and performance evaluation using a confusion matrix. The dataset used was 696 tweets taken using web scrapping techniques. This research uses the Lexicon-based method to divide the dataset into positive, negative, and neutral classes. The SVM method shows superior test results by achieving an accuracy rate of 83.34% compared to the KNN method, which produces an accuracy value of 71.2% in its calculations
Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices Pangestu, Resza Adistya; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Noertjahyana, Agustinus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28594

Abstract

Stock price prediction is a complex and important challenge for stock market participants. The difficulty of predicting stock prices is a major problem that requires an approach method in obtaining stock price predictions. This research proposes using machine learning with the Support Vector Regression (SVR) model and linear regression for stock price prediction—the dataset used in the daily Apple Inc historical data from 2018 to 2023. The hyperparameter tuning technique uses the Grid Search method with a value of k = 5, which will be tested on the SVR and Linear Regression methods to get the best prediction model based on the number of cost, epsilon, kernel, and intercept fit parameters. The test results show that the linear regression model with all hyperparameters k = 5 with the average taken performs best with a True intercept fit value. The resulting model can get an excellent error value, namely the RMSE value of 0.931231 and MSE of 0.879372. This finding confirms that the linear regression model in this configuration is a good choice for predicting stock prices.
Co-Authors Achmad Muzakki Agustinus Noertjahyana Anastasia Lidya Maukar Anik Vega Vitianingsih Damayanti, Erika Dwi Budi Santoso DWI CAHYONO Fadilla, Muhammad Myrza Fardhan Maulana, Abelardi Fauzi, Ariq Ammar Fauzi, Chamdan Fitri Ana Wati, Seftin Hariyanti, Yenny Hengki Suhartoyo, Hengki Hermansyah, David Kesuma Wardanie, Debleng Puja Khoirul Umam Lambang Probo Sumirat, Lambang Probo Larisa, Damasya Ine Lidya Maukar, Anastasia Maulidiana, Putri Dwi Rahayu Muhammad Nasrullah Nugraha, Azariz Ananta Leo Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Poetra, Akbar Rizki Prasetyo, Prasetyo Tri Utomo Pratama, Ferdian Rizki Pujiono, Halim Putri, Natasya Kurnia Sutejo, Davip Warsito Sujatmiko, Achmad Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wijiono, Aditya Kusuma Yasin, Verdi Yudha Adhi Jaya, Villa Indra