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All Journal Journal of Information Systems Engineering and Business Intelligence Communications in Science and Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Syukron Abu Ishaq Alfarozi
Departmen Of Electrical And Information Engineering, Faculty Of Engineering, Universitas Gadjah Mada, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran Sri Suning Kusumawardani; Syukron Abu Ishaq Alfarozi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1134.372 KB) | DOI: 10.22146/jnteti.v9i4.779

Abstract

An online learning system is a very crucial thing nowadays to prevent the spread of COVID-19 virus. However, this system is very difficult to maintain student motivation and engagement because there is no direct interaction between teacher and student. This study reviewed the use of student log data for the needs of learning analytics to predict student performance or drop-out trends from a course by looking at the student interaction log data with the system and student demographic data using open data, namely the Open University Learning Analytics Dataset (OULAD). From reviews of several research articles that refer to these data, we can see: 1) the common problems, i.e., prediction of drop-out student, prediction of student performance and engagement; 2) the features used during modeling, i.e., demographics and interactions, either summarized daily or weekly with various feature representations; 3) learning analysis methods that use machine learning algorithm, i.e., Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). This paper also discusses the risk mitigation process of students, planning and designing data systems that support learning analytics, and problems that are often encountered during the modeling process.
Model Berbasis CNN untuk Estimasi dan Autentikasi Copy Detection Pattern Syukron Abu Ishaq Alfarozi; Azkario Rizky Pratama
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.6205

Abstract

Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy.
A Lightweight Landmark-Based Model for Bahasa Lip-Reading Using Attention-BiLSTM Ferzha Putra Utama; Risanuri Hidayat; Syukron Abu Ishaq Alfarozi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.269-282

Abstract

Background: Most lip-reading studies primarily utilize image-based representations of lip movements, offering extensive visual data while imposing significant computational burdens. Lip landmark-based representations are still not well understood, even though they could be a better way to describe lip dynamics in a smaller and more efficient way. This limitation is even more apparent in Bahasa lip-reading research, where there are few studies and computationally efficient solutions remain essential. Objective: This study examines the shortcomings of image-based lip-reading methods by leveraging lip landmarks as a concise and computationally efficient input representation. The proposed method is tested on the IndoLR open dataset, which contains video data of lip-reading in Bahasa. Methods: In this study, video sequences were transformed into coordinate-based landmark data to minimize computational demands while preserving critical information regarding lip dynamics. An attention-based BiLSTM model was trained to group10 word classes and 4 phrase classes using this dataset. Results: The model achieved accuracies of 93.03% for word classification and 95.18% for phrase classification. The approach also maintained high efficiency, with average inference times of 0.000530 and 0.011366 s per sample and computational costs of only 0.01 and 0.14 GFLOPs, respectively. Conclusion: These results show how well lip landmarks can be combined with a lightweight deep learning model with very few resources. This study makes a significant contribution to research on lip-reading in Bahasa and lays the groundwork for future studies that will use larger and more diverse datasets.   Keywords: Attention, BiLSTM, Bahasa, Landmark, Lip-Reading
A High-Capacity Reversible Watermarking Technique Using Bit-Level Expansion and Pixel Shifting Aulia Arham; Syukron Abu Ishaq Alfarozi; Hanung Adi Nugroho
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1856

Abstract

This paper proposes a high-capacity reversible watermarking method using adaptive bit-level expansion and pixel-class-guided shifting. Pixels are classified into expandable (P0) and non-expandable (P1) according to their 2-bit LSB patterns, and a lightweight reversible transformation converts P1 into P0 with minimal distortion. A shifting map enables exact recovery and avoids overflow/underflow. Secret data are embedded through a 2-bit LSB expansion rule that ensures full reversibility. Experiments on common and medical images demonstrate a consistent embedding capacity of 1.0 bpp, achieving PSNR values above 46 dB and SSIM above 0.97. In addition, the scheme exhibits low computational overhead (<0.7s per image, >380 kbps) while preserving the original histogram distribution. These results demonstrate that the proposed scheme provides an effective balance between embedding capacity, visual quality, and computational efficiency for secure medical imaging and authenticated reversible data embedding.
Co-Authors Aulia Arham Ferzha Putra Utama Hanung Adi Nugroho Pratama, Azkario Rizky Risanuri Hidayat Sri Suning Kusumawardani