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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Teknologi Informasi dan Komunikasi Jurnal Publikasi Ilmu Manajemen Journal of Technology Informatics and Engineering
Joseph Teguh Santoso
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Journal of Technology Informatics and Engineering

 1 
CREDENTIAL ANALYSIS FOR SECURITY CONFIGURATION ON CUSTOM ANDROID ROM Joseph Teguh Santoso; Fujiama Diapoldo Silalahi; Laksamana Rajendra Haidar
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.v1i3.149

Abstract

Android is an operating system with open source and consists of several layers, with the different layers its duties and responsibilities. Various parties in the customization chain such as device vendors such as Samsung, Xiaomi, Oppo, Huawei, and others, operators such as Telkomsel, Smartfren, XL, etc., and hardware manufacturers can customize one or more layers to adapt devices for different purposes, such as supporting specific hardware and providing different interfaces and services. The purpose of this study was to investigate systematically for any inconsistencies that arose as a result of the processes involved in this study and to assess their various security implications. This research runs DroidDiff to perform a substantial-balance diverse investigation on images collected by the analytical methodology. DroidDiff found a lot of differences when it comes to the selected features. The method used in this study is the method of five differential analysis algorithms. As a result, by comparing the security configurations of similar figures, important security changes that could be accidentally introduced during customization can be found. The results show that DroidDiff can be used by vendors to check the configuration of various security features in a given image. DroidDiff will extract those features from the image, and compare them to other image configuration sets, then DroidDiff will flag the inconsistent ones for further investigation by vendors who have the source code and tools to check their effect. For future work, improvements to DroidDiff to more accurately detect risky inconsistencies are highly recommended. Improving DroidDiff will help reduce the number of false positives and determine risky configurations more accurately.
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 Budi Raharjo Danang Danang Fujiama Diapoldo Fujiama Diapoldo Silalahi Khoirur Rozikin Laksamana Rajendra Haidar Siti Kholifah Sulartopo Sulartopo Unang Achlison