Nama Dosen:
Dr. Eng. Adi Wibowo, S.Si., M.Kom.
Program Studi:
Informatika (Fakultas Sains dan Matematika, Universitas Diponegoro)
Kepakaran Dosen:
Berdasarkan profil pada scholar.undip.ac.id, Dr. Eng. Adi Wibowo memiliki kepakaran di bidang kecerdasan buatan (Artificial Intelligence), machine learning, data mining, serta sistem cerdas berbasis data. Fokus penelitiannya mencakup pengembangan model deep learning, reinforcement learning, dan analisis data kompleks untuk berbagai domain.
Bidang riset yang tampak dari publikasinya antara lain:
- Deep learning dan computer vision, seperti segmentasi citra medis dan deteksi sinyal gempa untuk sistem peringatan dini
- Time series dan forecasting, termasuk prediksi lalu lintas dan data ekonomi menggunakan model LSTM dan fuzzy time series
- Internet of Things (IoT) dan blockchain, khususnya untuk monitoring dan deteksi anomali (misalnya konsumsi listrik)
- Smart system dan e-government, seperti penerapan AI dan big data untuk pengambilan keputusan pemerintah serta evaluasi smart village
- Reinforcement learning dan optimasi, termasuk aplikasi pada sistem transportasi dan trading
Secara aplikatif, keahliannya digunakan dalam pengembangan sistem cerdas untuk transportasi pintar, deteksi bencana, kesehatan berbasis citra medis, serta pengambilan keputusan berbasis data skala besar.
Publikasi:
- Ratio intervals with weighted fuzzy time series forecasting
https://doi.org/10.1063/5.0326678 - Integrated Evaluation Model for Smart Village Effectiveness: A Mixed-Methods and Thematic Analysis Approach
https://doi.org/10.1109/ACCESS.2026.3665754 - Integration of Big Data, Dynamic Systems, and Artificial Intelligence in Local Government Decision Making: A Systematic Literature Review
https://doi.org/10.1109/IC3INA68387.2025.11325648 - A Novel SW-KMA-Bi-LSTM Approach for Improving Traffic Flow Prediction
https://doi.org/10.22266/ijies2025.0831.07 - Deep learning for real-time P-wave detection: A case study in Indonesia’s earthquake early warning system
- Optimization of Computational Resources for Real-Time Product Quality Assessment Using Deep Learning and Multiple High Frame Rate Camera Sensors
- Lightweight encoder-decoder model for automatic skin lesion segmentation
- Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation
- Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia
- Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology