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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Desain dan Analisis Teknologi Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Maslan, Andi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Development of Conventional Lathe Machine Manual User by Using Augmented Reality Frameworks Hamid, Abdul; Puan, Loretta Anak; Tamin, Norfauzi; Maslan, Andi; A.S, Darmawan
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2712

Abstract

Machining is one of the familiar subjects in the field of Technical and Vocational Education and Training (TVET) and has been offered at several Vocational Colleges and Institutes of Higher Education (IPT) throughout Malaysia. However, the level of dominance is limited to a handful of students in understanding the learning content and achieving learning outcomes at the end of the course's teaching and learning process. Therefore, this research intends to design and develop a machine manual using an interactive multimedia concept characterized by Augmented Reality (AR). The method of creating forms and developing interactive multimedia routinely uses the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model as a reference model and guideline for implementing learning. The research instruments used were product development expert review forms and student investigation questionnaires. The research respondents consisted of 80 TVET students from Universiti Tun Hussein Onn Malaysia (UTHM) and Tanjung Piai Vocational School. The data obtained is collected and analyzed periodically using statistical-based software. An evaluation is conducted on the product's design, form, content, and functionality. The results of the analysis on the use of interactive multimedia concepts indicate that the average minimum standard for all variables exceeds 3.25, which is interpreted as Highly Acceptable for the Use of Multimedia-Based Learning. Three experts in the field of multimedia and engineering agree that the product developed has a shape that matches the design and can function effectively. Overall, the research found that the design form, content, and functionality of conventional interactive machines can enhance students' visualization abilities in the teaching and learning process, as well as improve their skills when practicing with the devices.
Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques Maslan, Andi; Mohamad, Kamaruddin Malik Bin; Hamid, Abdul; Pangaribuan, Hotma; Sitohang, Sunarsan
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1533

Abstract

Various forms of distributed denial of service (DDoS) assault systems and servers, including traffic overload, request overload, and website breakdowns. Heuristic-based DDoS attack detection is a combination of anomaly-based and pattern-based methods, and it is one of three DDoS attack detection techniques available. The pattern-based method compares a sequence of data packets sent across a computer network using a set of criteria. However, it cannot identify modern assault types, and anomaly-based methods take advantage of the habits that occur in a system. However, this method is difficult to apply because the accuracy is still low, and the false positives are relatively high. Therefore, this study proposes feature selection based on Hybrid N-Gram Heuristic Techniques. The research starts with the conversion process, package extract, and hex payload analysis, focusing on the HTTP protocol. The results show the Hybrid N-Gram Heuristic-based feature selection for the CIC-2017 dataset with the SVM algorithm on the CSDPayload+N-Gram feature with a 4-Gram accuracy rate of 99.86%, MIB- Dataset 2016 with the 2016 algorithm. SVM and CSPayload feature +N-Gram with 100% accuracy for 4-Gram, H2N-Payload Dataset with SVM Algorithm, and CSDPayload+N-Gram feature with 100% accuracy for 4-Gram. As a comparison, the KNN algorithm for 4-Gram has an accuracy rate of 99.44%, and the Neural Network Algorithm has an accuracy rate of 100% for 4-Gram. Thus, the best algorithm for DDoS detection is SVM with Hybrid N-Gram (4-Gram).
Co-Authors A.S, Darmawan Abdul Hamid Bin Mohamad, Kamaruddin Malik Cik Feresa Mohd Foozy, Cik Feresa Dedy Fitriawan Elisa, Erlin Fadzri, Alief Achmad fajrin, alfannisa annurrallah Fajrin, Alfannisa Annurullah Gary, Gary Hasugian, Joni Heri Gunawan ihsana, angga novenzo Ihwan Ghazali Imam Budi Utomo, Imam Budi Kremer, Hendri Mariyanto Mariyanto Mohamad, Kamaruddin Malik Bin Nasaruddin, Andira Nofedrinata, Windri Nopri Hidayat Pangaribuan, Hotma Puan, Loretta Anak Putri, M.Kom, Anggia Dasa Rafsyanzani s, Firman Firmansyah Rahman, Azan Ramot Argenta Pasaribu, Fabian Simanjuntak, Pastima Simbolon, Hery Sanjaya Sitohang, Sunarsan Tamin, Norfauzi Tampubolon, Piala Wiyono, Pieter Yonanta, Rhesty Dian Ziliwu, Krisma Budi