[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125350-en":3,"doc-seo-125350-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125350,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",6,"Technology","Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm","Corrosion is an electrochemical process that degrades metallic materials and creates major risks for industries, including equipment failure, operational shutdowns, and environmental and safety impacts. Conventional corrosion analysis relies on manual data collection with electrode sensors and laboratory procedures, restricting automation, mobility, and predictive performance. A Corrosion Analysis Tool was built using a Pencil Graphite Electrode Sensor with machine learning regression for automated, integrity-focused prediction. Cloud infrastructure and a mobile application enable remote, real-time analysis, and evaluation against laboratory experiments confirms high accuracy and reduced human error.","Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm  \nTang Weng Kitt1, Siti Hazyanti Mohd Hashim 1*, Mohd Azam Osman1, Mohd Hazwan Hussin2  \n1 School of Computer Sciences,  \nUniversiti Sains Malaysia, Pulau Pinang, 11800, MALAYSIA  \n2 School of Chemical Sciences,  \nUniversiti Sains Malaysia, Pulau Pinang, 11800, MALAYSIA  \n*Corresponding Author: [sitihazyanti@usm.my](sitihazyanti@usm.my)  \nDOI: [https://doi.org/10.30880/ijie.2025.17.02.007](https://doi.org/10.30880/ijie.2025.17.02.007)  \nArticle Info  \nReceived: 10 November 2024  \nAccepted: 24 June 2025  \nAvailable online: 18 July 2025  \nKeywords  \nCorrosion, machine learning, corrosion analysis, pencil graphite electrode, corrosion analysis tool  \nAbstract  \nCorrosion is an electrochemical reaction that leads to the deterioration of metallic materials, posing significant challenges across various industries. Traditional corrosion analysis methods require manual data collection using electrode sensors and laboratory-based analysis, limiting automation, mobility, and predictive capabilities. To address these issues, a Corrosion Analysis Tool was developed using a Pencil Graphite Electrode Sensor in combination with machine learning algorithms. The tool integrates regression analysis to enhance data integrity, automate predictions, and minimize human errors. Cloud computing is employed to replace traditional physical servers, facilitating remote access and real-time analysis. A mobile application is also developed to provide users with a convenient and efficient corrosion analysis platform. The system was evaluated by comparing its corrosion rate analysis results with traditional laboratory experiments conducted by chemical science students. Results demonstrated high accuracy, with minimal deviations between the corrosion rate values obtained from the Corrosion Analysis Tool and manually computed rates. The differences observed were 0.236 × 10⁻⁸ for a 7-day immersion, 0.049 × 10⁻⁸ for a 14-day immersion, 0.071 × 10⁻⁸ for a 21-day immersion, and 0.014 × 10⁻⁸ for a 28-day immersion, confirming the system's reliability. The precision test further verified that the tool effectively reduces human errors and enhances data integrity. Furthermore, the tool streamlines project management by centralizing data storage and organization, preventing data redundancy and loss. In conclusion, the Corrosion Analysis Tool successfully automates corrosion analysis, improves mobility, and enhances data-driven decision-making for researchers. The system meets all user requirements, offering a robust solution to traditional corrosion analysis challenges. Its predictive capabilities, powered by machine learning, provide valuable insights for future corrosion prevention strategies. By incorporating cloud-based storage and mobile accessibility, the tool modernizes corrosion analysis and contributes to advancements in materials science and engineering.  \n1. Introduction  \nIn the contemporary business landscape, thriving organizations cannot afford to overlook significant instances of corrosion failure, particularly those that result in human harm, loss of life, unplanned cessation of operations, and environmental pollution. Corrosion may result in breakdowns in plant architecture and machinery, which often incur significant expenses for repairs, losses in terms of product contamination or loss, environmental harm, and even risks to human safety [1] . Corrosion is a process of the deterioration of metals [2] . When a metal undergoes a chemical reaction with the atmosphere or environment around it, corrosion happens [3]. The effect of corrosion may bring harmful potential to the environment. Because of this, buildings [4] and bridges may collapse [5], [6], pipelines may be damaged [7-9] and chemical plants may leak [10] . There are several methods to measure the corrosion rate of a material. One of the most conventional techniques was using electrochemical-assisted","cbCaibF5Rfx3Pvkh","https://ap.wps.com/l/cbCaibF5Rfx3Pvkh","pdf",786925,1,13,"English","en",105,"# Introduction\n## Corrosion impacts and risks\n## Measuring corrosion rate with electrochemical methods\n## Pencil graphite electrodes for electrochemical sensing","[{\"question\":\"Why are traditional corrosion analysis methods limited?\",\"answer\":\"They require manual data collection with electrode sensors and laboratory-based processing, which limits automation, mobility, and predictive capabilities.\"},{\"question\":\"How does the proposed tool improve corrosion analysis accuracy and reliability?\",\"answer\":\"It uses a Pencil Graphite Electrode Sensor combined with machine learning regression to automate predictions and improve data integrity, reducing human error.\"},{\"question\":\"How was the tool validated in the study?\",\"answer\":\"Corrosion rate results from the tool were compared with traditional laboratory experiments performed by chemical science students, showing minimal deviations across multiple immersion durations.\"}]","Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm | 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are traditional corrosion analysis methods limited?","Question",{"text":75,"@type":76},"They require manual data collection with electrode sensors and laboratory-based processing, which limits automation, mobility, and predictive capabilities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed tool improve corrosion analysis accuracy and reliability?",{"text":80,"@type":76},"It uses a Pencil Graphite Electrode Sensor combined with machine learning regression to automate predictions and improve data integrity, reducing human error.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the tool validated in the study?",{"text":84,"@type":76},"Corrosion rate results from the tool were compared with traditional laboratory experiments performed by chemical science students, showing minimal deviations across multiple immersion 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