Article Per Year (5 Year)
p-Index From 2021 - 2026
1.665
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Charity : Jurnal Pengabdian Masyarakat Journal of Informatics and Communication Technology (JICT) International Journal of Engineering Continuity Jurnal Pengabdian Masyarakat Sultan Indonesia Journal of Informatics and Communication Technology (JICT)
Pietra Dorand
Telkom University
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Informatics and Communication Technology (JICT)

 1 
The Analyzing Online Learning Satisfaction and The Use of LLS: The investigation of students' satisfaction towards the frequent use of LLS pietra dorand
Journal of Informatics and Communication Technology (JICT) Vol. 2 No. 2 (2020)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v2i2.58

Abstract

This mixed-method approach study is conducted to find out the positive relationship between students’ satisfaction and their LLS use in the context of ICT for ELL. Having wed out for the sake of validity and reliability, 46 sets of both SERVQUAL and SILL questionnaires were then administered to 83 students of telecommunication engineering at Akademi Telkom Jakarta. The result of the study was explained in inferential and descriptive as well as qualitative in nature. The statistical result confirmed the positive effect of online learning satisfaction on the use of LLS. The low correlation coefficient is detected between students’ satisfaction and the use of LLS (r =.235, p <.05); however, the regression model Y=74.3+0.20X is then eligible to estimate and generalize. Thus, by paying attention to SERVQUAL dimensions, the students will more explore their LLS use. The findings provide a greater understanding of students’ satisfaction and LLS use. LLS – CALL integration model could be developed as the implications of the research for further study. Keywords: ICT, Language Learning Strategies, Online Learning, Student Satisfaction, SERVQUAL
A Literature Review for Understanding the Development of Smart Parking Systems Nanang Cahyadi; Pietra Dorand; Nurwan Reza Fachrur Rozi; Laksamana Aidzul Haq; Refsi Indra Maulana
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.196

Abstract

Smart parking systems that use AI, data analytics, and IoT are a result of urbanization and rising automobile utilization. These systems are designed to enhance user experience, shorten search times, and make the most of available space. AI analyzes real-time data, proposes open places, and projects demand in the future. Infrastructure costs, stakeholder cooperation, and system compatibility continue to be issues, nevertheless. To maintain user confidence, privacy, and ethical usage in the study of smart parking systems, in-depth literature reviews are essential. The systematic literature review (SLR) method was used to examine AI-based smart parking solutions, such as wireless sensor networks, ultrasonic sensor nodes, reservation-based systems, intelligent parking guidance, IoT-based on-street infraction monitoring, central parking management systems, and energy-efficient automated solutions. Budgetary restrictions, stakeholder participation, interoperability concerns, data privacy, security, and moral ambiguities are all problems. To test scenarios, understand rules, processes, and algorithms in limited contexts, researchers need to develop reliable outdoor sensor and data technologies for outside application.
Enhancing SQL Injection Attack Prevention: A Framework for Detection, Secure Development, and Intelligent Techniques Nanang Cahyadi; Syifa Nurgaida Yutia; Pietra Dorand
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 2 (2023)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i2.233

Abstract

SQL injection attacks (SQLIAs) pose increasing threats as more organizations adopt vulnerable web applications and databases. By manipulating queries, SQLIAs access and destroy confidential data. This paper delivers three contributions around improving SQLIA detection research: first, a literature review assessing current detection/prevention systems to produce an SQL injection detection framework; second, specialized deep learning models optimizing session pattern analysis and feature engineering to enhance performance; third, comparing proposed models against previous defenses to surface promising research directions. Results highlight opportunities like real-time systems generalizing across attack variants through emerging techniques. Additionally, with attack complexity rising, systematized SQLIA investigation is warranted. Despite extensive study, current perspectives lack cohesive guidance informing mitigation strategies. Therefore, a framework is proposed holistically mapping knowledge gaps around contemporary SQLIAs, seminal threats in web applications, and security solutions. Furthermore, a multi-faceted framework examines research trends divided into hardening existing apps, detecting attacks on production systems, and integrating secure development practices. Literature suggests comprehensive resilience requires concurrent strength across these areas. Finally, future work remains in integrated frameworks, deep reinforcement learning adoption, automated AI auditing, and differential privacy to advance real-world SQL injection detection and prevention.
Co-Authors A, M. NABIL Ade Nurhayati, Ade Aisyah Novfitri Angga Setiawan Atilla Pramudya Nugroho Faiq Rizky Firmansyah Haq, Laksamana Aidzul Kamelia Laksamana Aidzul Haq M, MOSES Maulana, Refsi Indra Muhamad Roihan Muhamad Roihan Muhammad Agung Pratama Muhammad Nurhadi Nanang Cahyadi Naufal Ghani Nurwan Reza Fachrurrozi Refsi Indra Maulana RIZKY, M. ROIHAN, M. Royhan, Muhamad S, ALVA NURVINA Sevierda Raniprima Suyatno Suyatno Suyatno Suyatno Syifa Nurgaida Yutia Wirabudi, Andri Agustav Yus Natali