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All Journal SPEKTRUM INDUSTRI Journal of Indonesian Tourism and Development Studies Journal of Engineering and Management in Industrial System Jurnal Optimasi Sistem Industri Journal Industrial Servicess Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Teknik Industri Industria: Jurnal Teknologi dan Manajemen Agroindustri Journal of Research and Technology Indonesian Journal of Artificial Intelligence and Data Mining JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Ilmiah Teknik Industri Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Teknologi dan Manajemen
Armin Darmawan
Department Of Industrial Engineering, Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analysis of Spotify User Sentiment to Improve Customer Satisfaction Using Opinion Mining and Latent Dirichlet Allocation Based on E-Satisfaction Dimensions Mutawakkil Samjas; Armin Darmawan
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study aims to enhance Spotify customer satisfaction by analyzing user reviews on the Google Play Store using sentiment analysis techniques and identifying relevant topics related to customer satisfaction based on the dimensions of electronic satisfaction. The methods used in this analysis are Support Vector Machine (SVM), Naïve Bayes (NB), and Latent Dirichlet Allocation (LDA). The results show that SVM is the most effective technique for text classification, with accuracies of 87%, 87%, 81%, and 84%, respectively, along with precision, recall, and F1-score of 0.93, 0.93, and 0.84. LDA was utilized to extract various topics within the e-satisfaction dimensions, with serviceability emerging as the top priority for improvement. Identified topics include connectivity and accessibility, performance and user experience, premium services, app quality, content and playlists, app features, and sound/music quality. These findings suggest that improvements in server infrastructure, the implementation of AI-driven chat support, enhanced ad management, and improved song lyrics databases could substantially enhance Spotify's customer satisfaction.
Comparative Deep Learning Analysis: Unveiling the Power of LSTM, BiLSTM, GRU, and BiGRU for Agricultural Stock Price Forecasting on the Indonesian Stock Exchange Muhammad Fadhlurrahman; Armin Darmawan
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.73-70

Abstract

This study aims to analyze the performance of deep learning algorithms in predicting agricultural sector stock prices on the Indonesia Stock Exchange (IDX) by comparing four models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). Daily historical data of six agricultural sector stock issuers (AALI, BISI, DSNG, LSIP, SIMP, SSMS) for the period 2017–2025 was used as the dataset. The research methods included data pre-processing (normalization, 80:20 training-test data split), model training with optimal hyperparameters (unit=512, dropout rate = 0.3, epoch = 50–150, learning rate = 0.0001), and evaluation using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), R² Score , and computation time metrics. The results show that BiGRU is the most accurate model with the lowest RMSE (7.43–17.20) and the highest R² (0.99 on BISI and SSMS), thanks to the Bidirectional architecture that processes bidirectional data to capture complex temporal patterns. However, GRU is more efficient with a training time of 40–43 seconds, suitable for real-time applications . LSTM and BiLSTM have lower accuracy, especially on volatile stocks such as DSNG (RMSE LSTM = 130.51). This study provides practical recommendations: BiGRU for long-term investment strategies that prioritize accuracy, while GRU for quick decisions based on efficiency. Theoretical implications strengthen the effectiveness of the Bidirectional architecture in financial time series analysis
Co-Authors -, Nilda A Alief Rachmat Alamsyah Alamsyah Ali, Syafrillah Amrin Rapi Amrin Rapi Amrin Rapi Amrin Rapi Andry Yusdi Bahri, Syamsul Bahri, Syamsul Bohari, Abdul Rahman Dwi Handayani Fansuri, Ilham Fauziah Fauziah Irfan Rahman IRWAN SETIAWAN Irwan Setiawan Muthalib Kifayah Amar Maula Sidi Muhammad Muhammad Fadhlurrahman MULYADI HAMBALI, MULYADI Mulyadi Mulyadi Mulyadi Mulyadi Mulyadi Mulyadi Muslimin, Rahmat Hidayat Mutawakkil Samjas Nur Khaerani Busri Nur Khaerani Busri Parenreng, Syarifuddin Mabe Pujikharisma, Andi Rahmadani, Fachriah Nur Rahman, Irfan Rahmat Hidayat Muslimin Saiful - Salam, Awal Rakhmat Sapta Asmal Syafrillah Ali Syamsul Bahri Syamsul Bahri Syamsul Bahri Syamsul Bahri Syamsul, Diniary Ikasari Tahir, Nurfaidah