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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Imam Fahrur Rozi
Politeknik Negeri Malang, Malang, Indonesia
Computer Science & IT

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Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection Imam Fahrur Rozi; Ahmadi Yuli Ananta; Endah Septa Sintiya; Astrifidha Rahma Amalia; Yuri Ariyanto; Arin Kistia Nugraeni
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4367

Abstract

In the era of advanced education, assessing lecturer performance is crucial to maintaining educational quality. One aspect of this assessment involves evaluating the textbooks authored by lecturers. This study addresses the problem of efficiently detecting International Standard Book Numbers (ISBNs) within these textbooks using optical character recognition (OCR) as a potential solution. The objective is to determine the effectiveness of OCR, specifically the Tesseract platform, in facilitating ISBN detection to support lecturer performance assessments. The research method involves automated data collection and ISBN detection using Tesseract OCR on various sections of textbooks, including covers, tables of contents, and identity pages, across different file formats (JPG and PDF) and orientations. The study evaluates OCR performance concerning image quality, rotation, and file type. Results of this study indicate that Tesseract performs effectively on high-quality, low-noise JPG images, achieving an F1 score of 0.97 for JPG and 0.99 for PDF files. However, its performance decreases with rotated images and certain PDF conditions, highlighting specific limitations of OCR in ISBN detection. These findings suggest that OCR can be a valuable tool in enhancing lecturer performance assessments through efficient ISBN detection in textbooks.
Logistic Regression-Based Classification of Food Security Vulnerability in East Java Districts Ahmadi Yuli Ananta; Rudy Ariyanto; Rakhmat Arianto; Imam Fahrur Rozi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6002

Abstract

The problem addressed in this study is the limited capacity of district-level food-security monitoring in Indonesia to anticipate deterioration in the following year, particularly when prediction relies on short longitudinal histories and must account for repeated observations, class imbalance, temporal change, and regional variation. The method involved constructing 2,056 temporally ordered prediction instances from Food Security Index data covering 514 Indonesian districts and cities during 2019–2024, representing six regional indicators through their current values and annual changes, and evaluating Logistic Regression, Random Forest, and eXtreme Gradient Boosting through district-grouped crossvalidation, alternative imbalance treatments, and an untouched 2023–2024 out-of-time test; temporal ablation, cluster-robust Logistic Regression, SHapley Additive exPlanations, sensitivity analysis, anddirect assessment in East Java were subsequently conducted. The result showed that Logistic Regression achieved the strongest screening-oriented performance, with a recall of 0.690, an F1-score of 0.450, a Receiver Operating Characteristic Area Under the Curve of 0.615, and a Precision–Recall Area Under the Curve of 0.404, while annual-change features improved F1-score and Precision–Recall Area Under the Curve across all three classifiers. However, performance declined in East Java, where two of four deterioration cases were detected, and 22 false-positive warnings were generated. The implication is that parsimonious temporal features provide useful predictive information beyond current regional conditions, although the model is more appropriate for screening and prioritization than for autonomous administrative classification, while operational use requires local calibration, longitudinal data auditing, threshold assessment, and validation across additional provinces and later annual transitions.
Co-Authors Ahmadi Yuli Ananta Arin Kistia Nugraeni Astrifidha Rahma Amalia Endah Septa Sintiya Rakhmat Arianto Rudy Ariyanto Yuri Ariyanto