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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Fajar, Fauzan Ihza
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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N-SOFT SETS ASSOCIATION RULE AND ITS APPLICATION FOR PROMOTION STRATEGY IN DISTANCE EDUCATION Fatimah, Fatia; Kharis, Selly Anastassia Amellia; Fajar, Fauzan Ihza
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1865-1878

Abstract

In everyday life, we always encounter obstacles in seeing the interrelationships between several events to make the right decisions. Universitas Terbuka is a pioneer in distance education that implements digital transformation for new student registration, student services, and alums. The obstacle faced is determining a suitable promotion strategy for new students. As a result, a representative model is needed to handle such cases. As an extension of soft sets, N-soft sets can handle decision-making for binary and non-binary assessments. However, research has yet to be related to N-soft sets decision-making in data mining, especially association rule classification. This article proposes a new combination of N-soft sets with Association Rule (NSSAR). This article also introduces and applies the decision-making procedure using NSSAR to real. The population is new students of Universitas Terbuka Jakarta in the 2023/2024 odd semester. Samples were taken randomly using a questionnaire—primary data obtained by 201 new students. The following results are obtained based on the processed sample data using the NSSAR algorithm: 1) new students from Universitas Terbuka Jakarta are predominantly from Vocational High Schools domiciled in Bekasi, majoring in Bachelor of Management from the Faculty of Economics and Business; 2) The most favorite media information used by new UT Jakarta students is Instagram. Based on the results, the NSSAR algorithm gave relationship patterns between the number of new students based on region, study program, diploma of origin, and information media. Therefore, policymakers should consider the right promotional strategy to increase the number of students.
Analisis Kinerja Model CNN-LSTM Berbasis Optical Character Recognition untuk Ekstraksi Informasi e-KTP Berdasarkan Kategori Teks Fajar, Fauzan Ihza; Kharis, Selly Anastassia Amellia; Tarigan, Asmara Iriani
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37593

Abstract

Optical Character Recognition (OCR) technology plays an important role in automating information extraction from identity documents such as the Electronic Identity Card (e-KTP). However, recognizing long text sequences and handling complex character variations remain significant challenges. These issues can lead to high error rates. This study aims to address these limitations by exploring a deep learning–based OCR model that integrates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Connectionist Temporal Classification (CTC) in an end-to-end framework without explicit character segmentation. CNN is employed to extract visual features, LSTM captures sequential dependencies, and CTC enables flexible alignment between input images and output text. The main contribution of this study lies in analysing the performance of a CNN-LSTM model with CTC in extracting e-KTP information across text categories with different complexity levels, namely Date and Place of Birth (TTL), name, and national identification number (NIK). Performance is evaluated using the Character Error Rate (CER). The results show that the model achieves the best performance on TTL with a CER of 0.84%, followed by NIK at 1.29%, and Name at 4.33% indicating higher difficulty in recognizing more complex text patterns. These findings demonstrate that model performance is influenced by text characteristics, particularly variability and sequence length. Overall, the proposed approach is effective for end-to-end e-KTP information extraction and provides insights for developing more adaptive OCR models.
Co-Authors Fatia Fatimah Selly Anastassia Amellia Kharis Tarigan, Asmara Iriani