Article Per Year (5 Year)
p-Index From 2021 - 2026
2.804
P-Index
This Author published in this journals
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
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences Other

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Journal of Technology Informatics and Engineering

 1 
THREAT ATTRIBUTES HANGING IN THE WILD ANDROID Irda Yunianto; Mars Caroline Wibowo; Budi Raharjo
Journal of Technology Informatics and Engineering Vol 1 No 3 (2022): December: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i3.150

Abstract

Android is a complicated system that applications and component are usable and support for multiple work together, giving rise to highly complex interdependence relationships. Meanwhile, the Android environment is notable for being greatlty disparate and decentralized: different Operation System version is personalized and re-personalized by different parties about fast and used by whoever that can develop an application for that version. Android secure its explanation sources over an app sandbox and permissions model, where each application execution in this part can entrance only suspectible overall assets and another application component (value providers, services, activities, publication receivers) by the appropriate liscense. This study uses Harehunter measurement to automatically detect Hare vulnerabilities in Android system applications. Harehunter and HareGuard performance evaluations were carried out in this study, both of which proved to be highly effective. The approach used here is divergent investigation, by searching all quoted, decompiled script, and obvious data for targeted attribute determination as an initial step, and running an XML parser. The outcome of this research show that the impact of Hares is very significant. The application of HareGuard in this study proved to be effective in detecting all attack applications that were made. Further evaluation of the performance impact on the minimum system host. For future research, to make Harehunter more effective, it is suggested to use a more qualified analyzer. So that this direction can be explored in more depth.
Innovation In Project Management Utilizing Machine Learning Technology Joseph Teguh Santoso; Budi Raharjo
Journal of Technology Informatics and Engineering Vol 2 No 3 (2023): December : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v2i3.163

Abstract

The successful adoption of programmable machines for complex tasks opens up opportunities for productivity and more efficient communication, but also poses serious challenges in IT project management. This study aims to tackle the issue of high project failure rates caused by inadequate planning. It aims to assist project managers in enhancing their project planning by implementing real-time solutions through the utilization of machine learning (ML) algorithms and a user-friendly graphical interface. This research is divided into two key phases. The initial phase involves an examination of existing literature in the field of machine learning to identify relevant concepts applicable to project management. In the subsequent stage, two distinct types of ML algorithms, namely example-based learning and regression modeling, will be integrated into a user-friendly platform. This research develops a system that utilizes machine learning algorithms to assist project managers in real-time or near real-time through a user-friendly graphical interface, with a focus on improving project planning and risk mitigation. This research shows that machine learning algorithms provide positive results in overcoming human factors and preventing risks based on the experience of project managers.
THREAT ATTRIBUTES HANGING IN THE WILD ANDROID Irda Yunianto; Mars Caroline Wibowo; Budi Raharjo
Journal of Technology Informatics and Engineering Vol. 1 No. 3 (2022): December: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i3.150

Abstract

Android is a complicated system that applications and component are usable and support for multiple work together, giving rise to highly complex interdependence relationships. Meanwhile, the Android environment is notable for being greatlty disparate and decentralized: different Operation System version is personalized and re-personalized by different parties about fast and used by whoever that can develop an application for that version. Android secure its explanation sources over an app sandbox and permissions model, where each application execution in this part can entrance only suspectible overall assets and another application component (value providers, services, activities, publication receivers) by the appropriate liscense. This study uses Harehunter measurement to automatically detect Hare vulnerabilities in Android system applications. Harehunter and HareGuard performance evaluations were carried out in this study, both of which proved to be highly effective. The approach used here is divergent investigation, by searching all quoted, decompiled script, and obvious data for targeted attribute determination as an initial step, and running an XML parser. The outcome of this research show that the impact of Hares is very significant. The application of HareGuard in this study proved to be effective in detecting all attack applications that were made. Further evaluation of the performance impact on the minimum system host. For future research, to make Harehunter more effective, it is suggested to use a more qualified analyzer. So that this direction can be explored in more depth.
Innovation In Project Management Utilizing Machine Learning Technology Joseph Teguh Santoso; Budi Raharjo
Journal of Technology Informatics and Engineering Vol. 2 No. 3 (2023): December : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v2i3.163

Abstract

The successful adoption of programmable machines for complex tasks opens up opportunities for productivity and more efficient communication, but also poses serious challenges in IT project management. This study aims to tackle the issue of high project failure rates caused by inadequate planning. It aims to assist project managers in enhancing their project planning by implementing real-time solutions through the utilization of machine learning (ML) algorithms and a user-friendly graphical interface. This research is divided into two key phases. The initial phase involves an examination of existing literature in the field of machine learning to identify relevant concepts applicable to project management. In the subsequent stage, two distinct types of ML algorithms, namely example-based learning and regression modeling, will be integrated into a user-friendly platform. This research develops a system that utilizes machine learning algorithms to assist project managers in real-time or near real-time through a user-friendly graphical interface, with a focus on improving project planning and risk mitigation. This research shows that machine learning algorithms provide positive results in overcoming human factors and preventing risks based on the experience of project managers.
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