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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Sinus Indonesian Journal of Electrical Engineering and Computer Science Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Nusantara Science and Technology Proceedings Teknika Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Info Kripto Jurnal Pelita Pengabdian Jurnal Informatika: Jurnal Pengembangan IT Media of Computer Science
Dimas Febriyan Priambodo
National Cyber And Crypto Polytechnic
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards Ajhari, Abdul Azzam; Priambodo, Dimas Febriyan; Yulianti, Henny
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2017

Abstract

The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.
Collaborative Intrusion Detection System with Snort Machine Learning Plugin Priambodo, Dimas Febriyan; Faizi, Achmad Husein Noor; Rahmawati, Fika Dwi; Sunaringtyas, Septia Ulfa; Sidabutar, Jeckson; Yulita, Tiyas
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2018

Abstract

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.
Enhancing Security in Cross-Border Payments: A Cyber Threat Modeling Approach Amiruddin, Amiruddin; Briliyant, Obrina Candra; Windarta, Susila; Setiadji, Muhammad Yusuf Bambang; Priambodo, Dimas Febriyan
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3205

Abstract

Cross-border payment (CBP) systems are critical to the global economy but are increasingly susceptible to cyber threats due to their complex structures and diverse transaction models. This paper analyzes cyber vulnerabilities across four CBP models: correspondent banking (SWIFT), infrastructure (ApplePay), closed-loop (PayPal), and peer-to-peer (Ripple). It employs the STRIDE methodology and adapts the cyber threat modeling framework proposed by Khalil et al. Key objectives include identifying vulnerabilities, assessing the impact of threats, and proposing mitigation strategies. The corresponding banking model shows the highest threat impact due to extensive transaction elements crossing trust boundaries. In contrast, the closed-loop model demonstrates lower vulnerability because of fewer components outside its trust boundary. Peer-to-peer and infrastructure models present moderate risk levels influenced by blockchain transparency and infrastructure dependencies. Critical threats identified include abuse of authority, malware, and script injection, which can result in significant losses, such as financial theft, service outages, and data breaches. Results indicate that interactions between processes across trust boundaries exacerbate cyber risks. Strategic recommendations include reducing system complexity, reinforcing security protocols at trust boundaries, and integrating advanced threat detection mechanisms. The study highlights these vulnerabilities and risks and underscores the need for robust cybersecurity measures to protect CBP systems. This research contributes to the existing knowledge by providing a detailed threat assessment and practical insights for improving CBP security. Future studies should explore alternative modeling methods, update security contexts to reflect real-world scenarios, and analyze the impact of open banking technologies.
Co-Authors Abdul Abror Achmad, Fahdel Adiati, Nadia Paramita Retno Aditama, Whisnu Yudha Afif, Yusrizal Agus Reza Aristiadi Nurwa Ahmad Ashari Ajhari, Abdul Azzam Akhmad Rizal, Akhmad Amiruddin Amiruddin Amiruddin Amiruddin Amiruddin Annisa Nurul Puteri ARIZAL Arya, Primadona Asep Dadan Rifansyah Awalin, Lilik Jamilatul Azzahra, Arsya Dyani Beatrix, Yehezikha Briliyant, Obrina Candra Dhana Arvina Alwan Diaz Samsun Alif Dozy Arti Insani Fachrurozy, Rizky Fadlilah Izzatus Sabila Faizi, Achmad Husein Noor Farida, Yeni Furqan Zakiyabarsi Ghiffari Adhe Permana Girinoto Girinoto, Girinoto Gusti Agung Ngurah Gde K.T. D Hafidz Faqih Aldi Kusuma Handayani, Annisa Dini Henny Yulianti Hermawan Setiawan I Komang Setia Buana, I Komang Indarjani, Santi Ira Rosianal Hikmah Jayanti Yusmah Sari Jeckson Sidabutar La Ode Ahmad Saktianyah La Ode Hasnuddin S. Sagala Lestari, Andriani Adi Mahar Surya Malacca Muhammad Hasbi Muhammad Hasbi Muhammad Yusuf Bambang Setiadji Muhammad Yusuf Bambang Setiadji Mukhamad Najib Nanang Trianto Nanang Trianto Naufal Hafiz Nirsal Nirsal Noorhasanah Zainuddin Nurwa, Agus Reza Aristiadi Obrina Candra Briliyant Olga Geby Nabila Pandi Vigneshwaran Pandi Vigneshwaran Prasetyo, Arbain Nur Prayoga, Arga Prisma Megantoro Purwoko, Rahmat Rabiah Adawiyah Rahmat Purwoko Rahmat Purwoko Rahmawati, Fika Dwi Rizki Putra Prastio Rizky Fachrurozy Sabela Trisiana Oktavia Saptomo, Wawan Laksito Yuly Siswantyo, Sepha Sri Siswanti Suci Pricilia Lestari Suharsono Bantun Sunaringtyas, Septia Ulfa Syaban, Kharis Syahrul Syahrul Tiyas Yulita Wahyu Riski Aulia Putra Windarta, Susila Yulandi Yusuf Bambang Setiadji