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All Journal Journal of Innovative and Creativity Jurnal Pustaka Cendekia Hukum dan Ilmu Sosial
Neni Nur Laili Ersela Zain
Department of Mathematics, Binus University, Indonesia
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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A Review of Trends and Developments in Document Image Compression Methods from 2016 to 2025 Neni Nur Laili Ersela Zain
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.4217

Abstract

This study aimed to analyze and classify research developments in document image compression from 2016 to 2025, identifying prevailing methods, technical characteristics, and chronological trends. A total of 56 journal articles were retrieved from the ScienceDirect database using the keywords “document image compression,” “text image compression,” and related terms, limited to English language publications in Computer Science, Signal Processing, and Image Processing. The selection followed a three stage process identification, relevance screening, and methodological classification resulting in six papers directly focused on document image compression. The results revealed three main phases of development: classical transform methods (2016-2018) such as JPEG2000, SPIHT, Chain Code, JBIG, JBIG2, and JPEG-LS; hybrid enhancement compression approaches (2017-2019) including DjVu and PDE collaborative models; and learning frameworks (2019-2025) featuring Autoencoder, VAE, GAN, Transformer, and JPEG AI. Overall, the field has evolved from traditional mathematical transforms to adaptive, data models. While research specifically targeting document images remains limited, emerging neural and hybrid methods highlight growing attention to readability preservation, OCR compatibility, and efficient digital archiving.
Autoencoder Based Anomaly Detection of Indonesia's Leading Banks: Penelitian Neni Nur Laili Ersela Zain
Jurnal Pustaka Cendekia Hukum dan Ilmu Sosial Vol. 4 No. 2 (2026): Jurnal Pustaka Cendekia Hukum dan Ilmu Sosial Volume 4 Nomor 2 June - September
Publisher : PT PUSTAKA CENDEKIA GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70292/pchukumsosial.v4i2.441

Abstract

This study This study builds and tests an autoencoder model that hunts for multivariate anomalies inside the financial data of Indonesia's banking sector. Three issuers anchor the analysis, PT Bank Mandiri (Persero) Tbk (BMRI), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) and PT Bank Central Asia Tbk (BBCA). The goal is not fraud detection in the narrow sense but something broader, isolating financial behavior that breaks the pattern investors expect and that might signal deeper trouble. The method layers three techniques. Correlation analysis maps how variables move together. Z Score standardization exposes univariate outliers. An autoencoder trained on an 80 to 20 split between training and test data hunts for anomalies that only reveal themselves across several variables at once. On the test set, BMRI and BCA produced zero anomalies and earned a low-risk label. BBRI stood apart. It logged two anomalies out of thirteen test observations, a rate of 15.38 percent that places it in the moderate risk bracket. Both anomalous quarters, the fourth quarter of 2008 and the first quarter of 2011, trace back to sharp swings in the Z Scores for Revenue, Net Income, Operating Cash Flow, Investing Cash Flow and Financing Cash Flow. The results argue for the autoencoder as a genuinely capable tool for multivariate anomaly detection in financial data. They offer real insight into how issuer risk profiles differ. Still, the test set is small. That limits how far these conclusions should be generalized.
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