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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi dan Manajemen Informatika Prosiding SNATIKA Vol 01 (2011) Record and Library Journal Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Research and Technology Indonesian Journal of Artificial Intelligence and Data Mining JKTP: Jurnal Kajian Teknologi Pendidikan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) SPIRIT Building of Informatics, Technology and Science Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) SKANIKA: Sistem Komputer dan Teknik Informatika Teknika KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Data Science, Engineering, and Analytics (IJDASEA) Decode: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Informatics, Electrical and Electronics Engineering Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Anik Vega Vitianingsih
UNITOMO, Surabaya
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
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 Deep Learning Methods for Predicting the Value of the Standard & Poor's Global Supply Chain Intelligence (S&P GSCI) Nickel Stock Index Rahmansyah, Ragada; Vitianingsih, Anik Vega; Hamidan, Rusdi; Lidya Maukar, Anastasia; Budi Suprio, Yoyon Arie
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36129

Abstract

The development of information technology has opened up new opportunities in stock market forecasting, especially in nickel commodities, which are increasingly strategic in the global energy transition. This study uses a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a Gated Recurrent Unit (GRU) to forecast the movement of the S&P GSCI Nickel stock index value. Yahoo Finance time series data for the years 2018–2024 are used in the dataset. The study's findings are used to evaluate each model's capacity to forecast changes in nickel stock prices. The RNN model is used in this study because it can work with sequential information, while LSTM works with three memory gates (input, forget, output), and GRU works with 2 gates, namely update and reset. Mean Absolute Percentage Error (MAPE) presents the results of open and closed variable forecasting errors with the lowest average for the RNN model of 2.08%, the LSTM model of 2.505%, and the GRU model of 1.505%. This study is expected to contribute to investor decision-making and the identification of the most accurate forecasting model for the nickel stock index
Sentiment Analysis of BCA Mobile App Reviews Using K-Nearest Neighbour and Support Vector Machine Algorithm Zandroto, Yosefin Yuniati; Vitianingsih, Anik Vega; Maukar, Anastasia Lidya; Hikmawati, Nina Kurnia; Hamidan, Rusdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37773

Abstract

The rapid evolution of digital technology has significantly transformed the financial services landscape, especially in the realm of mobile banking. BCA Mobile stands among the most popular apps for digital banking in Indonesia. Despite its widespread adoption, user reviews reflect diverse viewpoints and sentiments about the app. The objective of this research is to examine the user sentiments regarding the BCA Mobile app, based on reviews sourced from the Google Play Store and App Store. Two classification models, namely Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), are used in the analysis process. The collected review data undergoes several pre-processing stages and is labeled automatically using a Lexicon-Based technique. For feature weighting, the TF-IDF (Term Frequency-Inverse Document Frequency) approach is used.. Sentiment classification is then carried out using both K-NN and SVM, with performance evaluated through a matrix of confusion based on measurements like F1-score, recall, accuracy, and precision.  The findings show that the SVM algorithm outperforms K-NN in terms of performance, with an accuracy of 94%, while K-NN achieves an accuracy of 82%. This study offers valuable insights for BCA management in understanding user sentiment and enhancing service quality through the application of artificial intelligence
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 Abdul Rezha Efrat Najaf Achmad Choiron Ade Susianti, Febrina Adharani, Salza Kartika Agustinus Noertjahyana Ahmad, Sharifah Sakinah Syed Al-Karaki, Jamal N. Ana Wati, Seftin Fitri Anastasia Lidya Maukar ANGGI FIRMANSYAH Arumsari, Andini Dwi Arya Darmansyah, Mochammad Dzikri Ayomi, Jose Mario Aziiza, Arizia Aulia Azzahra, Morra Fatya Gisna Nourielda Badrussalam, Nanda Budi Suprio, Yoyon Arie Cahyono, Cahyono Kaelan Damayanti, Erika DWI CAHYONO Dwi Indrawan, Dwi Dwi Prasetyo, Septian Efendi, Kacung Fardhan Maulana, Abelardi Fauzan, Rizky Fauzi, Ariq Ammar Fawaidul Badri Febrian Rusdi, Jack Firmansyah, Deden Fitri Ana Wati, Seftin Fitri, Anindo Saka Ghibran Jhi S, Moch Hamidan, Rusdi Hengki Suhartoyo, Hengki Hermansyah, David Hikmawati, Nina Kurnia Jazaudhi’fi, Ahmad Kacung Hariyono Khusnaini, Geovandi Gamma Krismantoro, Putu Gede Ari KRISTIAWAN KRISTIAWAN Li, Shuai Lidya Maukar, Anastasia Ma'rifani Fitri Arisa Maukar, Anastasia L Maukar, Anastasya Lidya Maulidiana, Putri Dwi Rahayu Miftakhul Wijayanti Akhmad, Miftakhul Wijayanti Minggow, Lingua Franca Septha Mudinillah, Adam Mustafa, Zulfikar Amirul Muzaki, Mochammad Rizki Nabil, Muh Niken Titi Pratitis Oktafamero, Yomara Omar, Marwan Ongko, Bagus Kustiono Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Pradana, Dwifa Yuda Pramisela, Intan Yosa Pramudita, Atanasia Pramudita, Krisna Eka Pujiono, Halim Puspitarini, Erri Wahyu Putra Selian, Rasyid Ihsan Putri, Jessica Ananda Putri, Natasya Kurnia Rahmansyah, Ragada Ramadhan, Prayudi Wahyu Ramadhani, Illham Ratna Nur Tiara Shanty, Ratna Nur Tiara Rijal, Khaidar Ahsanur Riza , M. Syaiful Rizal, Moch Arif Samsul Rusdi Hamidan Rusdi, Jack Febrian Salmanarrizqie, Ageng Salsabilah, Azka Sari, Dita Prawita Seftin Fitri Ana Wati Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Slamet Winardi Sufianto, Dani Suyanto Suyanto Suyanto Tiara Shanty, Ratna Nur Titus Kristanto Tjatursari Widiartin Tri Adhi Wijaya, Tri Adhi Umam, Azizul Voni Anggraeni Suwito Putri Warsito Sujatmiko, Achmad Wati , Seftin Fitri Ana Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wijiono, Aditya Kusuma Wikaningrum, Anggit Wikanningrum , Anggit Yasin, Verdi Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Yuliani, SY. Yunior, Kevin Heryadi Zandroto, Yosefin Yuniati Zangana, Hewa Majeed