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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Artificial Intelligence (ITALIC)
Salahuddin Alam
Universitas Hasanuddin
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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Kapabilitas Analitik Digital dan Transformasi Model Bisnis dalam Meningkatkan Ketangkasan Organisasi: Kemampuan Analitik Digital, Transformasi Model Bisnis, dan Kelincahan Organisasi dalam Bisnis Digital Ryan Saputra Alam; Salahuddin Alam
ADI Bisnis Digital Interdisiplin Jurnal Vol 7 No 2 (2026): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v7i2.1688

Abstract

Latar Belakang: Perkembangan teknologi digital mendorong organisasi untuk meningkatkan kemampuan dalam mengolah data dan menyesuaikan model bisnis secara cepat terhadap perubahan lingkungan. Objek: Penelitian ini menganalisis hubungan antara kapabilitas analitik digital, transformasi model bisnis, dan ketangkasan organisasi dalam menghadapi dinamika pasar dan perubahan teknologi. Metode: Penelitian menggunakan pendekatan kuantitatif dengan survei terhadap pelaku organisasi yang telah memanfaatkan teknologi digital dalam aktivitas bisnisnya. Data dianalisis menggunakan Structural Equation Modeling berbasis Partial Least Squares untuk menguji pengaruh kapabilitas analitik digital terhadap transformasi model bisnis serta kontribusinya terhadap ketangkasan organisasi. Hasil: Penelitian menunjukkan bahwa kapabilitas analitik digital berperan dalam memperkuat kemampuan organisasi mengidentifikasi perubahan, menginterpretasikan informasi bisnis, dan mendukung pengambilan keputusan berbasis data. Transformasi model bisnis juga berperan sebagai mekanisme strategis yang menghubungkan pemanfaatan kemampuan analitik dengan peningkatan ketangkasan organisasi, terutama melalui penyesuaian proses bisnis, proposisi nilai, dan konfigurasi sumber daya. Kesimpulan: Temuan penelitian menegaskan bahwa pengembangan kapabilitas analitik digital tidak hanya berfungsi sebagai instrumen pendukung keputusan, tetapi juga menjadi fondasi strategis bagi transformasi model bisnis dan peningkatan ketangkasan organisasi. Penelitian ini memberikan implikasi bagi organisasi untuk mengintegrasikan analitik digital dengan inovasi model bisnis agar mampu merespons perubahan pasar secara lebih cepat, fleksibel, dan berkelanjutan.
A Hybrid Fuzzy Neural Network Model for Intelligent Traffic Congestion Prediction in Smart Cities Ryan Saputra Alam; Salahuddin Alam; Maulana Abbas; Asher Nuche
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1097

Abstract

Rapid urbanization and the increasing number of vehicles in metropolitan areas have intensified traffic congestion problems, creating significant challenges for transportation efficiency, environmental sustainability, and urban mobility management in smart cities. This study aims to develop a hybrid fuzzy neural network model capable of improving traffic congestion prediction accuracy by effectively handling the uncertainty and nonlinear relationships inherent in urban traffic systems. The proposed model integrates fuzzy logic with neural network learning mechanisms to capture ambiguous traffic patterns and adaptively learn complex interactions among traffic-related variables, including traffic volume, vehicle speed, traffic density, weather conditions, time intervals, and road occupancy rates. The experimental evaluation was conducted using historical traffic datasets collected from smart city transportation environments, while model performance was assessed using Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, accuracy, and the coefficient of determination. The results demonstrate that the proposed hybrid fuzzy neural network model achieved superior predictive performance and greater robustness compared with conventional machine learning approaches, particularly in scenarios characterized by dynamic traffic fluctuations and uncertain environmental conditions. Furthermore, the integration of fuzzy reasoning significantly improved the model's capability to represent gradual transitions between congestion levels and reduce sensitivity to noisy traffic observations. The study concludes that hybrid fuzzy neural network approaches provide an effective and scalable solution for intelligent traffic congestion prediction and can support data-driven traffic management strategies in future smart city ecosystems. 
Co-Authors Asher Nuche Maulana Abbas Ryan Saputra Alam