PERANCANGAN MODEL HYBRID EXPLAINABLE ARTIFICIAL INTELLIGENCE BERBASIS IOT DAN MACHINE LEARNING UNTUK OTOMATISASI SISTEM INVENTORY DAN DETEKSI RISIKO KECURANGAN PADA PERUSAHAAN

Sumaryanto Sumaryanto (Universitas Sains dan Teknologi Komputer)
Purwati Purwati (Universitas Sains dan Teknologi Komputer)
Setiyo Prihatmoko (Universitas Sains dan Teknologi Komputer)

INFORMATIKA
Vol. 6 No. 2 (2026): Oktober : JURNAL TEKNIK INFORMATIKA DAN MULTIMEDIA

Inventory management in companies faces several challenges, including inaccurate stock records, delayed information, overstocking or stock shortages, and the risk of fraud. This study aims to design a Hybrid Explainable Artificial Intelligence model based on the Internet of Things (IoT) and Machine Learning to automate inventory management systems and detect fraud risks. The proposed model integrates IoT sensor data, inventory transactions, supplier data, and incoming and outgoing goods data. Machine Learning is used to predict stock requirements and detect abnormal transaction patterns, while Explainable Artificial Intelligence (XAI) is employed to provide explanations of the prediction results and risk levels generated by the model. The proposed model is expected to improve inventory monitoring accuracy, detect potential fraud, and support faster, more transparent, and data-driven decision-making.