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All Journal Jurnal Pendidikan Vokasi Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Knowledge Engineering and Data Science Bulletin of Social Informatics Theory and Application
Hammad, Jehad A.H
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Computer Science & IT Education Engineering Social Sciences

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Blocking pornography sites on the internet private and university access ; Irawan, Ninon Oktaviani; Nurfadila, Piska Dwi; Ristanti, Putri Yuni; Hammad, Jehad A.H
Bulletin of Social Informatics Theory and Application Vol. 3 No. 1 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v3i1.161

Abstract

Promoting Covid-19 vaccination with Instagram Fahmi, Ahmad Maulanal; Azizah, Hanifah Nur; Mahendra , I Putu Arda; Fatkhiatin , Irma; Hammad, Jehad A.H
Bulletin of Social Informatics Theory and Application Vol. 5 No. 1 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v5i1.408

Abstract

The COVID-19 Vaccination Program is essential information during this pandemic. Information about COVID-19 vaccination is disseminated through social media, one of which is Instagram. During the COVID-19 pandemic, the Ministry of Health's Instagram accounts in various countries provided much information about COVID-19, including vaccinations. This research was made to determine the role of Instagram as a media to promote the invitation to vaccinate for COVID-19 by using the calculation of engagement rates in each post category on the Instagram account of the Ministry of Health of Indonesia, Malaysia, the United States, and Australia. The results obtained in this study are that these accounts have implemented the right strategy in COVID-19 vaccination, and several strategic refinements need to be done.
Ransomware detection: patterns, algorithms, and defense strategies Amro, Manar Y; Dwieb, Mohamed; Hammad, Jehad A.H; Wibawa, Aji Prasetya
Bulletin of Social Informatics Theory and Application Vol. 8 No. 1 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i1.689

Abstract

In the contemporary digital landscape, rapid technological advancements present unprecedented challenges for developers in the hardware and software realms. The ubiquitous presence of the Internet, the Internet of Things (IoT), and widespread digital solutions bring numerous benefits and escalating risks. This study investigates the pervasive threat of ransomware attacks, a daily menace that imperils the operational and security dimensions of the digital sphere for enterprises and individuals. The research objective is to identify the most effective algorithm for detecting ransomware viruses, a persistent and evolving threat that significantly challenges institutions, companies, and governmental organizations. The dynamic nature of ransomware necessitates robust detection mechanisms to safeguard sensitive data. To achieve this goal, we conducted a comparative analysis of four prominent algorithms recognized for their efficacy in combating and detecting viruses. Emphasis was placed on the algorithm exhibiting the most promising results. A detailed examination of its impact on existing data involved comprehensive analysis and a comparative assessment against previous studies. Results, derived from extensive studies and experiments on a diverse dataset, illuminate the critical role of ransomware detection algorithms and underscore their effectiveness. The findings contribute valuable insights to the ongoing discourse on cybersecurity strategies, providing a foundation for enhanced ransomware defense measures.
Random Forest Algorithm to Measure the Air Pollution Standard Index Setiawan, Ariyono; Wibowo, Untung Lestari; Mubarok, Ahmad; Larasati, Khoirunnisa; Hammad, Jehad A.H
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p86-100

Abstract

This study uses the Random Forest algorithm to measure and predict the Air Pollution Standard Index (APSI) at Blimbing Banyuwangi Airport. Air pollution data, including concentrations of O3, CO, NO2, SO2, PM2.5, and PM10, were collected from air monitoring stations at the airport from April 15-30, 2024. APSI measurement followed established formulas by relevant authorities. Data analysis utilized statistical approaches and computational algorithms. The findings reveal that air quality at the airport is generally "Moderate," with occasional "Good" days. The Random Forest algorithm effectively predicts APSI based on existing pollution data. These results provide insights for improving air pollution management at the airport and surrounding areas, emphasizing the need for continuous air quality monitoring. Days classified as "Moderate" suggest health risks for sensitive groups, indicating the need for targeted mitigation strategies. Recommendations include increasing green spaces, optimizing flight schedules to reduce peak pollution, and raising public awareness about air quality. The effectiveness of the Random Forest algorithm suggests its potential application in other airports for proactive air quality management. Future research could integrate real-time data and advanced machine learning models for more accurate and timelier APSI predictions.
Virtual Reality-Based Learning Performance in Civil Engineering Education Kuncoro, Tri; Siswahyudi, Dwi; Hammad, Jehad A.H
Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Vol 9, No 3: MARCH 2024
Publisher : Graduate School of Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/jptpp.v9i3.25335

Abstract

This research aims to create virtual reality (VR) based learning media for build-ing construction courses and assess how effective it is in improving students' understanding. VR can create an immersive and interactive simulated environ-ment that allows students to learn complex concepts visually and practically. This is the reason why VR is the chosen option. The development processes used were needs analysis, design, development, testing, and evaluation. The final product is a VR application that allows students to interact with building models in three dimensions, perform construction simulations, and better un-derstand various aspects of construction. The results show that virtual reality significantly positively affects the learning process. Students who used virtual reality showed significant improvements in concept understanding, practical skills, and desire to learn. They also expressed positive feedback on the ease of use of virtual reality and its benefits for learning. Conclusion: This research shows that virtual reality has an enormous capacity to improve the quality of construction education. It is recommended that virtual reality be further incor-porated into the civil engineering education curriculum because providing a more engaging and effective learning experience can overcome some of the barriers in conventional learning, such as time and facility limitations.
An AI-driven framework for learning analytics and operational optimization in technology and vocational education: Bridging industrial engineering and informatics Nurdiyanto, Heri; Hernandes , Leonel; Hammad, Jehad A.H; Kindiasari, Aktansi
Jurnal Pendidikan Vokasi Vol. 15 No. 3 (2025): November
Publisher : ADGVI & Graduate School of Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpv.v15i3.95617

Abstract

This study aims to present an artificial intelligence-based framework that combines learning analytics with operational optimization, which can address the ever-present problems concerning technology and vocational education. In the case of vocational institutions, it has been noticed that while learning environments are increasingly embracing the incorporation of digital technologies, the connection between the use of data for educational outcomes and operational decision-making remains disconnected. In many instances, learning-related data is analyzed separately from production-oriented activities, which include scheduling, resource allocation, and process efficiency, despite the fact that these activities are part of the learning process in the factory and learning environments. This study aims to address the disconnect between the use of learning-related data and production-oriented activities through the incorporation of perspectives from industrial engineering and informatics, which are integrated into a single framework that is oriented towards artificial intelligence. Machine learning is utilized for the representation of learning processes, while optimization techniques are used for decision-making regarding task allocation, scheduling, and resource allocation. Instead of being restricted to a particular application domain, the framework is developed with the idea of adaptability so that it can be used across different contexts of vocational education. An empirical study was conducted within a particular context of a technology-oriented vocational education domain to assess the viability of the proposed framework. It was found that the integration of learning analytics with operational optimization provides a more consistent outcome compared to the individual analysis of these factors. It was also found that the proposed AI-based framework provides a better outcome for the assessment of competency as well as the prediction of performance, which leads to the efficiency of managing a production-oriented learning process. Such findings indicate the ability of the application of AI to support the field of vocational education more comprehensively. This study contributes to the field of research by proposing an interdisciplinary framework that goes beyond the idea of individual technological tools to offer a more comprehensive perspective on the adoption of AI within the context of vocational education.
Co-Authors Aji Prasetya Wibawa Amro, Manar Y Azizah, Hanifah Nur Dwieb, Mohamed Fahmi, Ahmad Maulanal Fatkhiatin , Irma Hernandes , Leonel Irawan, Ninon Oktaviani Kindiasari, Aktansi Larasati, Khoirunnisa Mahendra , I Putu Arda Mubarok, Ahmad Nurdiyanto, Heri Nurfadila, Piska Dwi Ristanti, Putri yuni Setiawan, Ariyono Siswahyudi, Dwi Tri Kuncoro Wibowo, Untung Lestari