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Ditemukan: 2 dokumenANALISIS PARAMETER PEMBORAN SECARA REAL-TIME UNTUK DETEKSI DINI ANOMALI PEMBORAN DAN SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN PENDEKATAN MACHINE LEARNING TANPA SUPERVISI DENGAN INTERPRETASI BERBASIS ATURAN
This study aims to develop a real-time drilling anomaly detection framework by combining unsupervised machine learning methods with a rule-based approach. The primary objectives are to determine the most effective and efficient machine learning model for real-time anomaly detection, develop feature engineering as model inputs, identify early signs of drilling anomalies, and validate detection results by comparing them against historical data and field reports. The case study was conducted on three wells in South Sumatra, Indonesia (Well A, Well B, and Well C) using historical drilling parameter data simulated as if it were streaming in real-time. Well A was focused on kick detection validation, Well B on lost circulation, and Well C on stuck pipe, while bit balling cases were validated using the Drilling Anomaly Index (DAI) and lithology due to the absence of specific incident records. Data pre-processing included separating data by drilling section and handling missing values, while model preparation involved tuning the Isolation Forest algorithm hyperparameters and determining the severity threshold to balance sensitivity and noise tolerance. The results indicate that the developed framework can detect anomalies earlier than conventional observations recorded in the Daily Drilling Report (DDR) or Final Well Report (FWR) and can identify early signs such as kick potential, lost circulation potential, differential sticking, and mechanical sticking. Validation using the DAI demonstrated consistent detection of abnormal drilling behaviors across the wells. Integration with a rule-based system also enhanced decision support capabilities by providing recommended actions before anomalies escalate. This study emphasizes the importance of high-quality real-time drilling parameter data for prediction accuracy and operational safety. The developed framework provides a robust platform for proactive drilling anomaly management, with potential for further development in non-rotary operations and expanded rulebased determination. Overall, the combination of feature engineering, unsupervised learning, and a rulebased system proved effective in improving safety, efficiency, and decision-making in drilling operations.
PERKEMBANGAN AI DALAM INDUSTRI KEUANGAN DAN PERBANKAN : ANALISIS EFEK BAGI NASABAH DAN PENINGKATAN LAYANAN PERBANKAN
Kecerdasan Buatan (AI) merupakan serangkaian teknologi yang mencakup visi komputer, pemrosesan bahasa alami, dan pembelajaran mesin, yang bekerja sama untuk menganalisis data, meningkatkan pengambilan keputusan, dan mengotomatisasi proses. Di dalam sektor perbankan, kecerdasan buatan biasanya digunakan untuk meningkatkan layanan pelanggan, memperkuat keamanan, dan menawarkan solusi keuangan yang dipersonalisasi. Penelitian ini dibutan dengan bertujuan untuk menguji sejauh mana pelanggan merasa nyaman menggunakan layanan berbasis kecerdasan buatan. Metode penelitian yang digunakan melibatkan statistik deskriptif dan analisis regresi dua sederhana untuk menganalisis data yang dikumpulkan melalui kuesioner terstruktur. Tujuan utama dari penelitian ini adalah untuk memahami pandangan nasabah mengenai integrasi AI dalam layanan perbankan dan untuk meningkatkan layanan perbankan berbasis AI di masa depan berdasarkan pendekatan nasabah tersebut. Hasilnya menunjukkan bahwa meskipun pelanggan pada umumnya menghargai efisiensi dan personalisasi yang diberikan oleh AI, kekhawatiran mengenai keamanan data masih tetap ada. Studi ini menekankan pentingnya bagi bank untuk mengatasi masalah keamanan data sekaligus meningkatkan alat AI agar dapat lebih memenuhi kebutuhan nasabah dan meningkatkan kepuasan. Pembahasan dan keterbatasan penelitian disajikan lebih lanjut dalam penelitian ini.