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All Journal MEDIA KOMUNIKASI TEKNIK SIPIL CIVED (Journal of Civil Engineering and Vocational Education) JURNAL KARYA TEKNIK SIPIL IPTEK Journal of Proceedings Series Jurnal Transportasi Jurnal Konstruksia Jurnal Optimasi Sistem Industri Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Journal of Advanced Civil and Environmental Engineering Jurnal Vokasi Indonesia Journal of Information Systems and Informatics Jurnal Pasopati : Pengabdian Masyarakat dan Inovasi Pengembangan Teknologi
Mochamad Agung Wibowo
Departemen Teknik Sipil, Fakultas Teknik, Universitas Diponegoro
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Transportation

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Journal : Journal of Information Systems and Informatics

 1 
Multimodal Implicit Sentiment Analysis for Tourism Development: A Systematic Literature Review Yoannes Romando Sipayung; Mochamad Agung Wibowo; Ridwan Sanjaya
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1436

Abstract

This study aims to examine the application of multimodal approaches in implicit sentiment detection within the tourism sector to support data-driven digital development strategies. This review identifies prevailing trends, methodologies, datasets, and scientific novelties in multimodal sentiment analysis capable of capturing hidden emotions, such as sarcasm and ambiguity, in tourist reviews. Using a systematic literature review approach, ten core studies published between 2020 and 2025 were analyzed to identify prevailing research trends, dominant methodological frameworks, commonly used datasets, and emerging scientific contributions. Results demonstrate that multimodal deep learning models—particularly those employing attention-based fusion and contrastive learning—consistently outperform unimodal approaches in recognizing nuanced tourist emotions that are not explicitly stated in text. Despite these advances, the review reveals a significant gap in tourism-specific and Indonesian-context studies, as well as an overreliance on general-purpose social media datasets. This review provides a conceptual and methodological foundation for implementing multimodal implicit sentiment analysis in tourism decision-making systems, enabling destination managers and policymakers to develop early warning mechanisms for tourist dissatisfaction, enhance destination quality assessment, and support more targeted and sustainable tourism development strategies.
A Hybrid Ensemble Stacking Framework Integrating Long Short-Term Memory and Random Forest for Bitcoin Price Forecasting Akhlis Munazilin; Mochamad Agung Wibowo; Rizky Parlika
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1537

Abstract

Bitcoin is a non-linear and non-stationary digital asset that has become a highly volatile asset challenging the usual prediction models. In this paper, the authors present a problem-specific Hybrid Ensemble Stacking approach, the proposed approach, which combines the benefits of Long Short-Term Memory (LSTM) in terms of capturing long-term temporal variations with the power of Random Forest (RF) to process complex technical characteristics. The model follows a two-tier structure with a split ratio of 90:10 using BTC/USD historical data of Yahoo Finance and Binance (20102025) to combine the predictions of base learners with the use of a Linear Regression meta-learner. Findings show that pure LSTM has a low RMSE and MAE, but the Hybrid model has the best Mean Absolute Percentage Error (MAPE) of 3.54%. This means that the stacking mechanism will provide a more balanced error percentage, that is, it will enhance stability in forecasting at the phases of price discovery. It is novel in the sense that it uses macro-technical indicators to stabilize predictions in the face of market anomalies as a stacking scheme. These results have real-life implications on developers of financial systems in creating consistent crypto-asset risk management instruments.
Real-Time Explainable Concept Drift Detection for Eco-Driving in Mining Trucks using KSWIN and Event-Triggered SHAP Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1551

Abstract

Fuel consumption represents a significant operational cost in mining, where real-time eco-driving optimization is hindered by dynamic and non-stationary operating conditions. Variations in operator behavior and environmental factors often induce concept drift, which diminishes the reliability of static machine learning models and constrains the effectiveness of conventional drift detection methods. This study proposes a distribution-aware, event-triggered Explainable Artificial Intelligence (XAI) framework for detecting and diagnosing fuel consumption anomalies in streaming telematics data. A Hoeffding Tree Regressor was evaluated using a prequential scheme on 1,927,867 real-world observations, achieving a Mean Absolute Error (MAE) of 19.43 under non-stationary conditions. Concept drift was monitored using the Kolmogorov–Smirnov Windowing (KSWIN) algorithm, which detected 1,874 drift events. Upon detection, an event-triggered SHAP module identified contributing factors, indicating that behavioral features such as engine speed and accelerator position were dominant contributors in early drift events. The primary contribution of this study is the integration of distribution-based drift detection with event-triggered explainability within a unified streaming framework, facilitating both anomaly detection and interpretable root-cause analysis.
Co-Authors Ahmad Hilmi Saifunuha Akhlis Munazilin Andini Nugrahardani Arif Haidar Nur Shidqi Arif Hidayat Bagus Hario Setiadji Broto Sunaryo Dedi Novrizal Eko Marsudi Utomo Ferry Hermawan Ferry Hermawan Frida Kistiani Frida Kristiani Geofanny Dominica Sihaloho Hari Budieny Ifan Azwar Nasution Iqbal Setyo Jatmiko Jati Utomo Dwi Hatmoko Kusnawi Moammar Zachari Moh Nur Sholeh Naniek Utami Handayani Rani Pranita Rezza Mahendra Putra Ridwan Sanjaya Riqi Radian Khasani Rizki Fatmawati Rizky Parlika Satria Sentik Herman Merukh Sriyana Sriyana Syafrudin Syafrudin Undayani Cita Sari Yoannes Romando Sipayung Yoga A. Harsoyo