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All Journal The Asian Journal of Technology Management (AJTM) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Kairupan, Indah Yessi
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Computer Science & IT Decision Sciences, Operations Research & Management

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An Extreme Gradient Boosting Approach for Classification and Sentiment Analysis Kairupan, Indah Yessi; Angdresey, Apriandy; Arif, Hamdani; Emor, Kenshin Geraldy
The Asian Journal of Technology Management (AJTM) Vol. 16 No. 3 (2023)
Publisher : Unit Research and Knowledge, School of Business and Management, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12695/ajtm.2023.16.3.5

Abstract

Since 2020, when the coronavirus epidemic was at its peak, the Indonesian Ministry of Health's social media accounts have been constantly followed by a big number of individuals. The Indonesian Ministry of Health account is a fantastic resource for social media users, particularly Twitter users. The Republic of Indonesia's Ministry of Health's Twitter account publishes a wide range of content at random. As a result, it is usually difficult for Twitter users to determine the type of information provided by the Ministry of Health of the Republic of Indonesia.  The positive and negative responses of Twitter users to material released by the Indonesian Ministry of Health's Twitter account are frequently noted.  The decision tree algorithm is tree-based, similar to the extreme gradient boosting method (XGBoost). The extreme gradient boosting approach has been successfully implemented with high performance in the classification process. This classification is separated into two primary categories: general and essential information categorization and sentiment analysis, which is classified into three classes: positive, neutral, and negative. Both the classification work and the sentiment analysis produced outstanding accuracy levels. Based on 2243 tweets, an accuracy rate of 89.35% has been achieved for classification, supported by a precision of 88.76% and a recall value of 88.58% when using 80 data training and 20 data testing.  Similarly, the maximum accuracy in sentiment analysis was achieved utilizing the same 80-20 data partitioning, with a 91.22% accuracy rate. Using 304 comments data, accuracy was calculated to be 89.17% and recall was calculated to be 89.06%.  It's worth noting that an 80-20 split for training and testing consistently produced the best results for both the sentiment analysis and classification tasks.
Leveraging Convolutional Neural Networks for Multiclass Waste Classification Angdresey, Apriandy; Kairupan, Indah Yessi; Mongkareng, Andre Gabriel
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9373

Abstract

The impact of population growth on waste production in Indonesia emphasizes the urgent need for effective waste management to mitigate environmental and health risks. Segregating waste into organic and inorganic categories is essential for sustainable management, enabling processes like composting and recycling. Employing convolutional neural networks (CNN) through machine learning presents a promising solution for waste classification. This study utilizes a CNN algorithm to achieve significant accuracy and precision in multi-class waste classification, with particular attention to areas for improvement, such as cardboard classification. Based on the MobileNetV2 architecture and Adam optimizer, the model demonstrates high accuracy and precision, with training and validation accuracy of 95.28% and 89.48%, respectively. High precision and recall values confirm its accurate waste classification. The evaluation of unseen data maintains an accuracy of 86.36%, indicating its generalization ability. However, variations in accuracy among waste classes suggest opportunities for refinement, particularly in cardboard classification.
Co-Authors Arif, Hamdani Emor, Kenshin Geraldy Mongkareng, Andre Gabriel