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All Journal JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Infotekmesin Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Information Technology Education Journal Journal of Vocational, Informatics and Computer Education
Muhammad Nur Faiz, Muhammad Nur
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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Konsep Pendidikan Akhlak Perspektif Hadratus-Syaikh KH. Muhammad Hasyim Asy’ari Mukhlishah, Ulfatul; Faiz, Muhammad Nur; Jumari, Jumari
JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Vol 22 No 2 (2023): September 2023
Publisher : LP2M STAI Miftahul 'Ula (STAIM) Nganjuk

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/lentera.v22i2.1254

Abstract

The goal of moral education in Islam is to develop positive traits in humans so that their morality will develop, so that they are always open to doing good and able to distance themselves from all forms of evil. Thus, a person can become a human being who has good morals and is responsible. Meanwhile, according to the concept of Hadratus-Shaykh KH. Hasyim Asy'ari in the book Adabul Alim Wal Muta'alim has provided an educational treatise that talks a lot about the moral education of teachers and students so that in the book we can understand and know in depth about the content of moral education properly. And he also colored education in Indonesia and among Islamic boarding schools. This research is a library research (Library Research) which is included in qualitative research. Data was collected through primary and secondary data sources after which the data was analyzed using a descriptive approach. This study states that in the book Adabul Alim wal Muta'alim, moral education is thoroughly integrated into the learning process. KH. Hasyim Asy'ari emphasized the importance of being calm and gentle for a teacher in interacting with his students. Likewise, students are expected to have morals that sincerely respect their teachers. In the context of learning, morality for students means always prioritizing learning by starting from the most important things to the depth of the discussion. The moral relationship between teacher and student actually reflects mutual respect and trust, which is built on awareness and respect between the two during the learning process. Ethics in interacting between teachers and students is considered as a real form of obedience to Allah and His Messenger. All of this shows that the harmonious relationship between the two parties cannot be separated from the spiritual values that are upheld in Islamic teachings
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Sari, Laura; Faiz, Muhammad Nur; Muhammad, Arif Wirawan
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Classification of DDoS Attacks based on Network Traffic Patterns Using the k-Nearest Neighbor (k-NN) Algorithm Faiz, Muhammad Nur; Maharrani, Ratih Hafsarah; Sari, Laura; Muhammad, Arif Wirawan; Supriyono, Abdul Rohman
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1834

Abstract

Many server attacks disrupt industrial or business operations. Attacks that flood bandwidth with simultaneous requests can overwhelm a system, leading to significant downtime and financial losses. Additionally, breaches that compromise sensitive data can damage a company's reputation and erode customer trust. DDoS attacks, or Distributed Denial of Service attacks, are among the most common types of server attacks. DDoS has been proven to cause server downtime, and one effective way to mitigate this attack is to detect and classify it using a machine learning approach. The K-Nearest Neighbor (KNN) algorithm, a simple yet effective classification method based on similarity measures, is known for its high accuracy. The current research builds upon two stages: the feature extraction stage and the classification stage, with the ultimate goal of improving the accuracy of DDoS identification using the CICDDoS2019 dataset. Based on this premise, the detection accuracy can be improved by enhancing these two stages. At a value of k equal to 3, this study produces an accuracy of 99.73%.
Comparison Analysis of Cloning-Hashing Applications for Digital Evidence Security Faiz, Muhammad Nur
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1844

Abstract

The development of the Internet has resulted in an increasing variety of cyber crimes. Cybercrime is closely related to digital evidence, so cybercriminals tend to delete, hide, and format all collected data to eliminate traces of digital evidence. This digital evidence is very vital in proving at trial, so it is necessary to develop applications to secure digital evidence. This study aims to compare the results of cloning and hashing in securing digital evidence and evaluate the performance of a digital forensic application developed under the name Clon-Hash Application v1 compared to applications commonly used by investigators including Autopsy, FTK Imager, md5.exe in terms of its function, the result, CPU usage. The results of the research conducted show that the cloning process is perfectly successful, as evidenced by the hash value results which are the same as paid applications and there are even several other applications that have not been able to display the hash value. Hash values in the Clon-Hash v1 application also vary from MD5, SHA1, and SHA256 which do not exist in other applications. Applications developed are better in terms of function, results, and CPU usage.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Faiz, Muhammad Nur; Muhammad, Arif Wirawan; Sari, Laura
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Explaining Continuance Intention in Artificial Intelligence-Supported Learning through Artificial Intelligence Literacy and Technology Acceptance Model Faiz, Muhammad Nur; Yahya, Muhammad; Abdul Muis Mappalotteng; Sanatang; Fathahillah; Fitria Arifyanti; Selen Subasi
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.372

Abstract

Purpose – Despite the growing use of artificial intelligence in higher education, prior studies have focused more on initial adoption than on continuance intention, while the role of AI literacy in sustaining AI use remains underexplored. This study examines an integrative model linking AI literacy, perceived usefulness, attitude toward using AI, behavioral intention, and continuance intention in AI-supported learning.Methods – A quantitative cross-sectional survey was conducted with 324 students from the Educational Informatics and Computer Engineering and Computer Engineering programs at Universitas Negeri Makassar. Data were collected from January to February 2025 using a 25-item Likert-scale questionnaire and analyzed with PLS-SEM in SmartPLS 4 using bootstrapping with 10,000 subsamples.Findings – All hypothesized relationships were significant: AI literacy → perceived usefulness (β = 0.449, p < 0.001), AI literacy → attitude toward using AI (β = 0.177, p < 0.001), perceived usefulness → attitude toward using AI (β = 0.686, p < 0.001), attitude toward using AI → behavioral intention (β = 0.757, p < 0.001), and behavioral intention → continuance intention (β = 0.800, p < 0.001). Research implications – The cross-sectional design, self-reported data, purposive sampling, and single-institution context limit causal inference and broader generalizability.Originality – This study extends TAM by positioning AI literacy as a competence-based antecedent within a post-adoption sequence that explains continuance intention through perceived usefulness, attitude, and behavioral intention. Practically, the findings suggest that sustainable AI integration in higher education requires not only access to AI tools but also structured support for students’ AI literacy.
Explaining Students’ Continuance Intention to Use Artificial Intelligence in Higher Education: A Post-Adoption TAM Model Faiz, Muhammad Nur; Muhammad Yahya; Rosidah; Sanatang
Information Technology Education Journal Vol. 5, No. 1, February (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i1.267

Abstract

Purpose – This study examines continuance intention to use artificial intelligence in higher education by integrating system quality, self efficacy in artificial intelligence usage, and artificial intelligence literacy into a post adoption technology acceptance framework. The study clarifies whether sustained use is driven more by technical and usability related evaluations than by literacy alone. Design/methods/approach – A quantitative cross sectional survey was conducted with 324 undergraduate students from the Informatics and Computer Engineering Education and Computer Engineering study programs at Universitas Negeri Makassar. Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling with SmartPLS 4. Findings – Self-efficacy in artificial intelligence usage significantly affected artificial intelligence literacy (β = 0.447, p < 0.001), system quality significantly affected perceived ease of use (β = 0.733, p < 0.001) and perceived usefulness (β = 0.266, p < 0.001), perceived ease of use significantly affected perceived usefulness (β = 0.502, p < 0.001), and perceived usefulness significantly affected continuance intention (β = 0.637, p < 0.001). Artificial intelligence literacy did not significantly affect perceived usefulness (β = 0.072, p = 0.093). Research implications/limitations – The findings are limited by the cross-sectional design, self-reported data, and the focus on two technology-oriented study programs in one university. Originality/value – This study contributes a focused post-adoption explanation of sustained artificial intelligence use by showing that continuance is shaped more strongly by system performance and perceived academic value than by artificial intelligence literacy alone.
Co-Authors Abdul Muis Mappalotteng Arif Wirawan Muhammad Fathahillah Fitria Arifyanti Jumari Jumari, Jumari Maharrani, Ratih Hafsarah Muhammad Yahya Muhammad Yahya Mukhlishah, Ulfatul Rosidah Sanatang Sari, Laura Selen Subasi Supriyono, Abdul Rohman