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All Journal Jurnal Teknologi dan Manajemen Informatika International Journal of New Media Technology
Putranto, Nicholaus Verdhy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi Putranto, Nicholaus Verdhy; Akrom, Muhamad; Trinapradika, Gustina Alfa
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.11192

Abstract

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.
Development and Implementation of a Corrosion Inhibitor Chatbot Using Bidirectional Long Short-Term Memory Ardyansyah, Nibras Bahy; Putra, Dzaki Asari Surya; Putranto, Nicholaus Verdhy; Trisnapradika, Gustina Alfa; Akrom, Muhamad
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i1.3752

Abstract

This research delves into the intricate phenomenon of corrosion, a process entailing material degradation through chemical reactions with the environment, causing consequential losses across diverse sectors. In response, corrosion inhibitors are a proactive measure to counteract this deleterious impact. Despite their paramount significance, public awareness regarding corrosion and inhibitors remains limited, necessitating intensified educational efforts. The primary focus of this study is developing a Chatbot system designed to disseminate information on corrosion, inhibitors, and related topics. Employing the Machine Learning Life Cycle model, a deep learning approach, specifically the Bidirectional Long Short-Term Memory (BLSTM) architecture, is utilized to construct an optimized Chatbot model. Post-training evaluation of the BLSTM model reveals noteworthy performance metrics, including a remarkable 100% accuracy rate and a substantial 92% validation accuracy over 100 epochs. Training and validation losses are reported as 0.2292 and 0.9342, respectively. In conclusion, the BLSTM algorithm is an effective tool for training and enhancing Chatbot models, ensuring commendable corrosion awareness and inhibition performance.
Co-Authors Akrom, Muhamad Ardyansyah, Nibras Bahy Putra, Dzaki Asari Surya Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa