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All Journal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rozi Kesuma Dinata
Universitas Malikussaleh
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparative Analysis of K-Nearest Neighbor and Support Vector Machine Methods for Assessing Quality Standards of Palm Oil Bunches Siti Hajar; Rozi Kesuma Dinata; Maryana
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Oil palm (Elaeis guineensis Jacq) is a crucial crop in the agricultural sector, particularly in Indonesia, as it produces various economically valuable products. The quality of oil palm fruit bunches (TBS) significantly influences the production process of crude palm oil (CPO), making accurate quality assessments essential for maintaining industry standards. This study aims to compare the effectiveness of two machine learning methods, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM), in determining the acceptable quality of TBS. Using TBS data from the years 2019 to 2023, the research analyzes several variables, including maturity level and yield percentage, to develop a web-based system for classifying TBS. The classification process involves preprocessing the data, applying the algorithms, and evaluating their performance based on key metrics such as accuracy, recall, and precision. The results indicate that the K-NN method outperforms SVM, achieving an accuracy of 100%, a recall of 100%, and a precision of 100%. In contrast, the SVM method demonstrates an accuracy of 91%, a recall of 100%, and a precision of 91%. These findings highlight the effectiveness of K-NN in classifying TBS quality while also demonstrating the reliability of SVM. This research is expected to provide valuable insights and effective solutions for decision-making regarding the acceptance of TBS quality, ultimately benefiting stakeholders in the palm oil industry and serving as a reference for future studies in data mining classification.
The Implementation of Support Vector Machine to Analyze Compliance of Land and Building Taxpayers Nurul Nafisa; Rozi Kesuma Dinata; Rizki Suwanda
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Land and Building Tax (LBT) is an important source of revenue for local governments, supporting development and community welfare. However, low taxpayer compliance rates often pose a challenge in achieving the targets for Local Own-Source Revenue (LOSR). This study aims to develop a data-driven classification system to map areas with varying levels of LBT taxpayer compliance in Lhokseumawe City and to implement the Support Vector Machine (SVM) method to improve the accuracy of predicting taxpayer compliance. The research data was obtained from the Regional Financial Management Agency (RFMA) of Lhokseumawe City, encompassing LBT data from 2021 to 2023, with variables such as principal amount, penalties, total payments, due dates, and payment dates. This classification system divides taxpayers into two categories: Compliant and Non-Compliant. The results of testing the SVM model indicate that Banda Sakti sub-district has a compliance rate of 98%, Muara Satu has a compliance rate of 99%, Muara Dua has a compliance rate of 99%, and Blang Mangat has a compliance rate of 100%. The accuracy metrics from the implementation of the Support Vector Machine method for assessing land and building tax compliance show a Precision of 86%, a Recall of 100%, and an Accuracy of 86%. By applying the SVM method, it is hoped that there will be an increase in efficiency in the tax collection and management processes, thereby optimally increasing Local Own-Source Revenue (LOSR) and supporting better regional development.
Co-Authors Maryana Maryana, Maryana Nurul Nafisa Rizki Suwanda Siti Hajar