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INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Teknokrat Indonesia
ISSN : 27973492     EISSN : 27972011     DOI : https://doi.org/10.33365/jatika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 31 Documents
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Comparison of Modern NLP with Classical Machine Learning Algorithms in Evaluating Food Security Programs Sinaga, Anita Sindar; Sijabat, Dameria Esterlina; Saputri, Bella; Aulia, Nadia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 4 (2025): Volume 6 Number 4 Desember 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i4.1395

Abstract

The success of food security programs faces various challenges. Most of the available data is in the form of unstructured text reports, news, and policy documents. The BERT (Bidirectional Encoder Representations from Transformers) model allows the system to read reports and news by considering the relationship between words in sentences. Compared to Support Vector Machines (SVMs) that rely on numerical data. The dataset is expanded to improve the generalization of the IndoBERT Classifier. There are 6 commodity data and 3 labels used in IndoBERT Modeling, represented by a 768-dimensional feature vector resulting in Accuracy 0.8333 (83.33%) indicating 5 correct predictions, with one misclassification. Tuned Min-Max on Support Vector Machines (SVM) is used in each dimension to find the optimal hyperplane contributing. The feature matrix x with size (39,10) and the target variable y with size (39) show Accuracy 0.92 (92.0%) that the data division process maintains the class proportion consistently. SVM performed better than IndoBERT. Classification evaluation of the models showed IndoBERT with Accuracy 83% and SVM Sccuracy 87%.

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