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All Journal Jurnal Teknik Informatika (JUTIF) Review: Journal of Multidisciplinary in Social Sciences
Shofro, Puteri Awaliatush
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Increasing The Payload of Reversible Data Hiding Scheme Using Modified Pixel Value Ordering (PVO) Shofro, Puteri Awaliatush; Barmawi, Ari Moesriami
Review: Journal of Multidisciplinary in Social Sciences Vol. 1 No. 05 (2024): April 2024
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v1i05.326

Abstract

Reversible Data Hiding (RDH) is a kind of data hiding technique that allows the embedded data can be retrieved as needed and can restore the stego image exactly as the original image after the extraction of embedded data. Recently, RDH has been applied for medical record management, where any distortion of digital images is not allowed. Many new RDH techniques have been developed such as Pixel Value Ordering (PVO). This method is very popular because it has the Management of Medical Records: Facts and Figures for Surgeons advantage of reducing the number of pixels shifted, such that it can improve the stego image quality. However, the existing method has a low embedding capacity (31%). To overcome this problem, this paper proposed a modified Pixel Value Ordering where the difference value is expanded after sorting the pixel values resulting from the embedding process based on different values of 0 and 1. In this case, the difference values of -2, 0, 1, and 3 are used to embed the data, instead of only using the difference values 0 and 1. The proposed method also uses the frequency of bit 0 and 1 in the message to determine the pixels mapping that can be used to embed the message. The experiments results show that the proposed method achieved an average embedding capacity (37%) and obtained a higher PSNR value of 61 dB.
A Web-Based Expert System Using Forward Chaining for Identifying Engine Power Loss Problems in the BMW 3 Series E36 Fawzi, Muhammad Ihsan; Permadi, Ipung; Chasanah, Nur; Ramadhany, Emha Diambang; Amirulbahar, Azis; Shofro, Puteri Awaliatush
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.6307

Abstract

The BMW 3 Series with production code E36, built between 1991 and 1998, remains widely owned in Indonesia, yet its age of more than two decades makes power loss a frequent complaint. Although the E36 engine is partially computerized, owners living far from an authorized or specialist workshop equipped with a diagnostic scanner cannot easily determine the cause of the power loss they experience. This study builds a web-based expert system, “Si Pak-E”, that identifies power loss problems on the BMW 3 Series E36 engine and recommends solutions. Knowledge was acquired through structured interviews with a BMW specialist mechanic and represented as a knowledge base of 22 problems, 33 symptoms, 22 solutions, and 22 production rules derived from a 33 × 22 decision table. Forward chaining was selected as the inference engine because diagnosis proceeds from observed symptoms toward a conclusion, while the waterfall model guided development. The system was implemented in PHP, MySQL, and Bootstrap with three user roles. Evaluation combined functional black-box testing with knowledge-base verification against the expert. Black-box testing executed 95 scenarios across 36 test cases and three roles, and all 95 (100%) produced the expected output. Rule-coverage verification traced all 22 production rules as consultation cases, and the system returned the problem and solution expected by the expert in 22 of 22 cases (100% agreement). The findings show that forward chaining is effective for symptom-driven automotive fault identification and that the system is a practical, transparent, and accessible diagnostic aid for E36 owners. Keywords: BMW E36, expert system, fault diagnosis, forward chaining, power loss, web-based.
Co-Authors Amirulbahar, Azis Ari Moesriami Barmawi Fawzi, Muhammad Ihsan Ipung Permadi Nur Chasanah ramadhany, emha diambang