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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 Journal of Indonesian Tourism, Hospitality and Recreation Jurnal Ilmiah Teknik Industri Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Teknologi dan Manajemen Jurnal Ilmiah Dinamika Rekayasa
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 Health Professions 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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
Customer Satisfaction Analysis of Gofood In Makassar: An Integrated Approach Using CSI, IPA, and PGCV Methods Muthia Raihana Saleha; Armin Darmawan; Agung Sutawinata
Jurnal Ilmiah Dinamika Rekayasa Vol. 22 No. 1 (2026): Jurnal Ilmiah Dinamika Rekayasa - Januari
Publisher : Engineering Faculty, UNSOED

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jidr.2026.22.1.46

Abstract

GoFood is one of the key features in the Gojek application, focusing on online food ordering and delivery, and has become a top choice among Indonesians. Alongside technological developments and rising customer expectations, measuring satisfaction and identifying service improvement priorities have become essential. This study aims to assess customer satisfaction with GoFood services in Makassar using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA), as well as to determine the order of service improvement priorities using the Potential Gain in Customer Value (PGCV) method. Data were collected from 214 GoFood users in Makassar through a questionnaire. The results show a CSI score of 75.975%, which falls into the “satisfied” category. However, the IPA and PGCV analyses indicate that several attributes still require improvement, such as accountability for damaged packaging (X17), product conformity (X9), application issues (X16), and system smoothness (X4). Although customers are generally satisfied, the findings suggest there is still potential to enhance service quality to reach the “very satisfied” level. These results are expected to serve as input for GoFood and its merchants to improve service quality continuously.
Investigating the influence of attractiveness and value for money on revisit intention for Toraja-Londa’s cultural sustainability Darmawan, Armin; Hellavani, Hellavani; Setiawan, Irwan; Handayani, Dwi; Gunawan, Imam
Journal of Indonesian Tourism, Hospitality and Recreation Vol 9, No 1 (2026): April
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jithor.v9i1.90224

Abstract

AbstractThis study examines the factors influencing revisit intention to cultural heritage sites, specifically focusing on the roles of perceived attractiveness and perceived value for money, with customer satisfaction serving as a mediating factor. The proposed conceptual model is empirically tested using data collected from 130 tourists who visited Toraja-Londa, a prominent cultural heritage destination. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis reveals a strong correlation between the antecedents of customer behavioral practices and revisit intention. The findings indicate that both perceived attractiveness and perceived value for money significantly enhance customer satisfaction, which in turn plays a crucial moderating role in shaping revisit intention. This research provides valuable insights for both theoretical and managerial perspectives, highlighting the importance of fostering a positive relationship between perceived attractiveness, value for money, satisfaction, and revisit intention. The study's findings offer practical implications for policymakers and stakeholders, enabling them to make informed decisions that enhance the sustainability and appeal of cultural heritage tourism destinations. By understanding the drivers of revisit intention, destination managers can develop targeted strategies to improve visitor experiences and encourage repeat visits. AbstrakStudi ini mengkaji faktor-faktor yang memengaruhi niat kunjungan ulang ke situs warisan budaya, khususnya berfokus pada peran persepsi daya tarik dan persepsi nilai uang, dengan kepuasan pelanggan sebagai faktor mediasi. Model konseptual yang diusulkan diuji secara empiris menggunakan data yang dikumpulkan dari 130 wisatawan yang mengunjungi Toraja-Londa, sebuah destinasi warisan budaya terkemuka. Dengan menggunakan Pemodelan Persamaan Struktural Kuadrat Terkecil Parsial (PLS-SEM), analisis ini mengungkapkan korelasi yang kuat antara anteseden praktik perilaku pelanggan dan niat kunjungan ulang. Temuan menunjukkan bahwa persepsi daya tarik dan persepsi nilai uang secara signifikan meningkatkan kepuasan pelanggan, yang pada gilirannya memainkan peran moderasi krusial dalam membentuk niat kunjungan ulang. Penelitian ini memberikan wawasan berharga bagi perspektif teoretis dan manajerial, menyoroti pentingnya membina hubungan positif antara persepsi daya tarik, nilai uang, kepuasan, dan niat kunjungan ulang. Temuan studi ini menawarkan implikasi praktis bagi para pembuat kebijakan dan pemangku kepentingan, memungkinkan mereka untuk membuat keputusan yang tepat yang meningkatkan keberlanjutan dan daya tarik destinasi wisata warisan budaya. Dengan memahami faktor pendorong minat berkunjung kembali, pengelola destinasi dapat mengembangkan strategi yang tepat guna meningkatkan pengalaman pengunjung dan mendorong kunjungan berulang. 
Co-Authors -, Nilda A Alief Rachmat Agung Sutawinata 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 Hellavani, Hellavani Imam Gunawan, Imam 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 Muthia Raihana Saleha 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