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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Media Borneo Journal of Information Technology, Software Engineering and Computer Science
Megawaty, Dyah Ayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Building of Informatics, Technology and Science

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Komparasi Performa Klasifikasi Sentimen Masyarakat Terhadap Kurikulum Merdeka di Sekolah Menggunakan SVM dan KNN Apriyani, Risa Fitria; Megawaty, Dyah Ayu
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6877

Abstract

The Independent Curriculum is a strategic education policy that aims to increase learning flexibility and develop student competencies in the 21st century. This research focuses on analyzing public sentiment towards the implementation of the Independent Curriculum using two machine learning algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). One of the main challenges in this study is the imbalance of sentiment data that includes negative, neutral, and positive classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) technique was applied to balance the distribution of data between classes. The results show that the SVM method is superior to KNN with an overall accuracy of 92% and a high F1-score in the majority class (Neutral: 96%), although the performance in the minority class (Negative: 43% and Positive: 40%) still needs improvement. In contrast, the KNN method recorded a lower overall accuracy of 31% but had a more even distribution of errors. After the implementation of SMOTE, there was a significant improvement in both methods, especially in recognizing minority classes. This study concludes that SVM is more effective for sentiment classification tasks on datasets with class imbalances, and recommends further exploration of ensemble methods to improve the quality of prediction and model generalization.
Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tanda Tangan Digital Yadin, Yuli; Megawaty, Dyah Ayu
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6982

Abstract

In the fast-paced digital era, identity security has become crucial, and digital signatures play an important role in verification and authentication. This study focuses on the analysis and comparison of the performance of the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms in digital signature pattern recognition. Both algorithms are widely used in classification tasks, and this study aims to identify which algorithm is most effective in recognizing and classifying digital signatures with the highest accuracy. Digital signature data was collected from various sources, including public datasets and directly collected data. Key features were extracted using the Gray-Level Co-occurrence Matrix (GLCM) method, which is effective in describing the texture and pattern of the signature. These features were used to train the KNN and SVM classification models. The performance of both algorithms was evaluated based on accuracy, precision, and recall metrics. The results showed that KNN with a value of k = 3 achieved an accuracy of 91.42%, while SVM with a linear kernel excelled with an accuracy of 97.06%. In addition, SVM is also more stable in handling complex signatures and has higher precision and recall than KNN, at 97.52% and 97.06%, respectively. On the other hand, KNN is faster in the training process and has a simpler implementation. This study provides valuable insights into the selection of optimal classification algorithms for digital signature recognition applications. The results of this study can be a guide for security and authentication system developers in choosing the most effective method to protect identity and prevent signature forgery.
Co-Authors - Damayanti Aditia Yudhistira Agustina, Intan Ahmad Ari Aldino Akmal Junaidi Andi Nurkholis Apriyani, Risa Fitria Ari Sulistiyawati Chandra, Iryanto Damayanti Damayanti Dedi Darwis Erwanto Favorisen R. Lumbanraja Gibtha Fitri Laxmi Heni Sulistiani Irsan, Aqilla Hattami Kurniawan, M. Fadli Palupiningsih, Pritasari Parjito, Parjito Permata, Permata Riska Aryanti Ryan Randy Suryono Saputra, Williyandi Setiawan, Dandi Setiawansyah Setiawansyah Sitna Hajar Hadad Sumanto, Sumanto Susanto, Gregorius Nugroho Sutyarso, - Ulum, Faruk Wahyuda, Hana Wang, Junhai Waqas Arshad, Muhammad Wardana, Suwarman Adi Widiyanti, Adella Wirahudha, Muhammad Arif Yadin, Yuli Yuri Rahmanto