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All Journal Jurnal Lahan Suboptimal Jurnal Akademika Biologi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer Jurnal Rekayasa Sipil dan Desain Jurnal Akta PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) Jurnal Teknologi Informasi dan Komunikasi Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Journal of Engineering, Electrical and Informatics (JEEI) Formosa Journal of Social Sciences (FJSS) Jurnal Publikasi Ilmu Komputer dan Multimedia Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Journal of Technology Informatics and Engineering JGuruku: Jurnal Penelitian Guru International Journal of Graphic Design
Budi Raharjo
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Journal : Jurnal Teknologi Informasi dan Komunikasi

 1 
5G NETWORK TRAFFIC FORECASTING USING MACHINE LEARNING Budi Raharjo; Mars Caroline Wibowo
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 13 No 2 (2022): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v13i2.850

Abstract

The idea of network chunks being described as virtual subsets of the physical resources of 5G infrastructure is used in standards for 5G communications. The efficiency of ML predictors for traffic prediction in 5G networks has been established in recent research so that it becomes to assess the capability demands of each network slice and to see how it progresses as a large number of network slices are deployed over a 5G network over time to be very important. The main objective of this research is to establish the model that has the potential to help network management and resource allocation in 5G networks with machine learning performance analysis in predicting network traffic on high-dimensional spatial-temporal cellular data, in addition to investigating the effectiveness of various neural network models in traffic prediction from univariate and multivariate perspectives. The research method used is a quantitative research method using correlation analysis, statistical analysis, and distribution analysis on the temporal and spatiotemporal frameworks developed to predict traffic from a univariate and multivariate perspective. To predict 24-hour mobile traffic requires combining spatial and temporal dependencies. The univariate analysis will be carried out by applying a temporal framework that includes FCSN, 1DCNN, SSLSTM and ARLSTM to capture temporal dependencies. The results of various experiments in this study show that the proposed spatiotemporal model outperforms the temporal model and other techniques in the mobile traffic forecasting literature including internet, SMS, and calls.
PERFORMANCE EVALUATION OF PENETRATION TESTING TOOLS IN DIVERSE COMPUTER SYSTEM SECURITY SCENARIOS Joseph Teguh Santoso; Budi Raharjo
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 13 No 2 (2022): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v13i2.851

Abstract

This study aims to scrutinize various tools and techniques employed in vulnerability assessment, to furnish a comprehensive guide regarding the efficacy of computer system penetration testing tools, and to offer a post-exploitation analysis approach to aid security professionals in selecting security tools. The increasing interconnectivity and complexity of computer systems in this ever-evolving digital age have led to the growing sophistication of cyber threats such as hacking, malware, and data theft. To counter these threats, penetration testing has become the primary method for securing computer systems. However, in diverse environments, efficient and adaptive penetration testing tools are needed. The selection of the right tools, with a focus on their efficiency in detecting vulnerabilities and providing mitigation solutions, is a paramount and highly crucial consideration. Additionally, post-exploitation analysis to develop more effective protection strategies after a successful attack is also becoming increasingly important. This research contributes to the fields of Communication Networks and System Security, offering insights into the challenges of selecting the right tools for penetration testers and underscoring the importance of vulnerability assessment in securing computer systems. The research approach employed comprises static analysis and manual analysis, encompassing techniques such as fingerprinting, vulnerability scanning, fuzzing, Nmap scanning, and the utilization of a database search tool called search-sploit. The results of this study indicate that the tools and techniques employed in this research can assist in identifying and mitigating vulnerabilities in computer systems. However, due to certain limitations, the research findings may not apply to diverse scenarios.
5G NETWORK TRAFFIC FORECASTING USING MACHINE LEARNING Budi Raharjo; Mars Caroline Wibowo
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 13 No. 2 (2022): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v13i2.850

Abstract

The idea of network chunks being described as virtual subsets of the physical resources of 5G infrastructure is used in standards for 5G communications. The efficiency of ML predictors for traffic prediction in 5G networks has been established in recent research so that it becomes to assess the capability demands of each network slice and to see how it progresses as a large number of network slices are deployed over a 5G network over time to be very important. The main objective of this research is to establish the model that has the potential to help network management and resource allocation in 5G networks with machine learning performance analysis in predicting network traffic on high-dimensional spatial-temporal cellular data, in addition to investigating the effectiveness of various neural network models in traffic prediction from univariate and multivariate perspectives. The research method used is a quantitative research method using correlation analysis, statistical analysis, and distribution analysis on the temporal and spatiotemporal frameworks developed to predict traffic from a univariate and multivariate perspective. To predict 24-hour mobile traffic requires combining spatial and temporal dependencies. The univariate analysis will be carried out by applying a temporal framework that includes FCSN, 1DCNN, SSLSTM and ARLSTM to capture temporal dependencies. The results of various experiments in this study show that the proposed spatiotemporal model outperforms the temporal model and other techniques in the mobile traffic forecasting literature including internet, SMS, and calls.
PERFORMANCE EVALUATION OF PENETRATION TESTING TOOLS IN DIVERSE COMPUTER SYSTEM SECURITY SCENARIOS Joseph Teguh Santoso; Budi Raharjo
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 13 No. 2 (2022): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v13i2.851

Abstract

This study aims to scrutinize various tools and techniques employed in vulnerability assessment, to furnish a comprehensive guide regarding the efficacy of computer system penetration testing tools, and to offer a post-exploitation analysis approach to aid security professionals in selecting security tools. The increasing interconnectivity and complexity of computer systems in this ever-evolving digital age have led to the growing sophistication of cyber threats such as hacking, malware, and data theft. To counter these threats, penetration testing has become the primary method for securing computer systems. However, in diverse environments, efficient and adaptive penetration testing tools are needed. The selection of the right tools, with a focus on their efficiency in detecting vulnerabilities and providing mitigation solutions, is a paramount and highly crucial consideration. Additionally, post-exploitation analysis to develop more effective protection strategies after a successful attack is also becoming increasingly important. This research contributes to the fields of Communication Networks and System Security, offering insights into the challenges of selecting the right tools for penetration testers and underscoring the importance of vulnerability assessment in securing computer systems. The research approach employed comprises static analysis and manual analysis, encompassing techniques such as fingerprinting, vulnerability scanning, fuzzing, Nmap scanning, and the utilization of a database search tool called search-sploit. The results of this study indicate that the tools and techniques employed in this research can assist in identifying and mitigating vulnerabilities in computer systems. However, due to certain limitations, the research findings may not apply to diverse scenarios.
Co-Authors Agung Suprihadi Aminuyati Anak Agung Istri Sri Wiadnyani Anindita Ayu Pratiwi Annisaa Widyasari Anto Budiharjo Antonius Sarjiya Devi Ayu Ningsih Editha Praditya Duarte Endang Kusdiyantini Hadi Ali Irda Yunianto Iskandar Sudirman Joseph Teguh Santoso Khoirul Huda Kristiani Dwiatmi Mamik Setyowati MG Isworo Rukmi Moeslich Hasanmihardja Nur Cahyanti Nur Sabrina Hasyyati Prawatya Cahyani Prayogo, Fitra Adi Priyo Pratomo PRIYO WAHYUDI Reza Auliarahman Bhaktinagara Riris Nurmila D. Rutty Wulandari Sarjito, Aris Sekar Ayu Yogyaswari Setiawan Wicaksono Sih Parmiyatmi Sofyan Fauzi Larosa Sri Endah Wahyuningsih Sri Handoko Sumaryanto Sumaryanto w wijanarka Wahyu Utaminingrum Wibowo, Mars Caroline Yanter Hutapea Yuli Fitriyanto