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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Scientific Journal of Informatics CSRID
Qusyairi, Mohammad Mirza
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Malware Detection pada Static Analysis Windows Portable Executable (PE) Menggunakan Support Vector Machine dan Decision Tree Qusyairi, Mohammad Mirza; Rio Guntur Utomo; Rahmat Yasirandi
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.2.2023.93-102

Abstract

Malware has become a major issue for computer system security today. Due to its ability to spread rapidly and negatively impact system performance, malware detection becomes crucial. One of the methods for malware detection is performing classification using Machine Learning, which learns the variable values of an application without executing it. In this study, the author evaluates the method of malware detection in the static analysis of Windows Portable Executable (PE) using Support Vector Machine (SVM) and Decision Tree. The author uses a dataset of PE files related to malware and safe applications from malware Using SVM and Decision Tree algorithms to classify the PE files as malware or not, determining the best machine learning algorithm for malware detection in PE files.
Implementasi dan Analisis Teknologi Digital Menu Boards pada Agropedia Space Menggunakan Metode System Usability Scale Arrauf, Moh Fawwaz; Yasirandi, Rahmat; Qusyairi, Mohammad Mirza; Anom, Rahmat Indira Pratama; Ramadhan, Arga
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6514

Abstract

Dalam industri makanan dan minuman, menu berperan sebagai alat komunikasi yang mencerminkan layanan, kualitas, dan harga. Dalam era transformasi digital, kafe seperti Agropedia Space mengadopsi teknologi dengan menggunakan menu digital berbasis website. Meskipun Agropedia Space telah memadukan inovasi hidroponik dalam pengelolaan menu, pemilihan produk masih menggunakan format tradisional menu kertas. Penelitian ini bertujuan mengembangkan digital interaktif menu yang terkoneksi dengan tablet, memungkinkan konsumen menjelajahi dan memesan produk melalui layar sentuh. Visual dan deskripsi menyeluruh diharapkan mengurangi ketidakpastian konsumen dan meningkatkan pengalaman pengguna. Evaluasi usability menggunakan System Usability Scale (SUS) diharapkan memberikan pemahaman mendalam tentang sejauh mana menu interaktif memenuhi harapan pengguna dalam pengoperasian, kepuasan dan kemudahan untuk memilih menu yang disediakan. Diperoleh hasil skor System Usability Scale sebesar 64,07 berada pada Acceptable Level “OK” dan Marginally Level di level Marginally High. Sehingga dapat disimpulkan bahwa menu digital ini dapat digunakan untuk menggantikan menu tradisional, namun harus dievaluasi lebih lanjut untuk mengidentifikasi masalah spesifik, dengan mengumpulkan umpan balik dari pengguna untuk memberikan user experience yang lebih baik.
Automated Essay-Answer Grading using Transformer and GenAI-Based Error Analysis on Answer Sentence Qusyairi, Mohammad Mirza; Yusep Rosmansyah
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.39859

Abstract

Purpose: This study aims to address the limitations of manual essay assessment, which is time-consuming and prone to subjectivity, by developing an automated essay grading system that is faster, more efficient, and more objective. In addition to producing scores, the system is intended to support learning by providing meaningful feedback on students’ writing errors. Methods: The research applies a transformer-based approach to automated essay scoring in order to capture deeper semantic relationships between student answers and reference answers. Unlike conventional word embedding methods, transformers are used to model contextual meaning at the word, phrase, and sentence levels. The system is further enhanced with an AI-based error analysis module that identifies categories of student errors, including content omissions, conceptual misunderstandings, and structural inaccuracies, enabling the generation of corrective feedback alongside numerical scores. Result: The experimental results show that the proposed model achieves strong agreement with human scoring, indicated by a Pearson correlation coefficient (r) of 0.8432 and a Mean Absolute Error (MAE) of 0.6013. These results demonstrate an improvement in prediction accuracy compared to word embedding-based approaches. Furthermore, the system successfully generates relevant and actionable error feedback that supports students in understanding their mistakes and improving their essay quality. Novelty: The novelty of this research lies in the integration of transformer-based automated essay scoring with an AI-driven error analysis module. Rather than focusing solely on score prediction accuracy, the proposed approach combines quantitative scoring with qualitative error feedback, enabling the system to function not only as an assessment tool but also as a learning support mechanism that helps students understand and improve their writing.
Co-Authors Anom, Rahmat Indira Pratama Arrauf, Moh Fawwaz Rahmat Yasirandi Ramadhan, Arga Rio Guntur Utomo Rosmansyah, Yusep