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The Analyzing Online Learning Satisfaction and The Use of LLS pietra dorand
Journal of Informatics and Communication Technology (JICT) Vol 2 No 2 (2020)
Publisher : PPM Institut Teknologi Telkom Telkom Jakarta

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

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.

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

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

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

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.

A Literature Review for Understanding the Development of Smart Parking Systems Cahyadi, Nanang; Dorand, Pietra; Rozi, Nurwan Reza Fachrur; Haq, Laksamana Aidzul; Maulana, Refsi Indra
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

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 Cahyadi, Nanang; Nurgaida Yutia, Syifa; Dorand, Pietra
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

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.

Enhancement of images compression using channel attention and post-filtering based on deep autoencoder Wirabudi, Andri Agustav; Fachrurrozi, Nurwan Reza; Dorand, Pietra; Royhan, Muhamad
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1499

Image compression is a crucial research topic in today's information age, especially to meet the demand for balanced data compression efficiency with the quality of the resulting image reconstruction. Common methods used for image compression nowadays are based on autoencoders with deep learning foundations. However, these methods have limitations as they only consider residual values in processed images to achieve existing compression efficiency with less satisfying reconstruction results. To address this issue, we introduce the Attention Block mechanism to improve coding efficiency even further. Additionally, we introduce post-filtering methods to enhance the final reconstruction results of images. Experimental results using two datasets, CLIC for training and KODAK for testing, demonstrate that this method outperforms several previous research methods. With an efficiency coding improvement of -28.16%, an average PSNR improvement of 34%, and an MS-SSIM improvement of 8%, the model in this study significantly enhances the rate-distortion (RD) performance compared to previous approaches.

Komunikasi Cahaya Tampak untuk Model Sistem Pintu Otomatis Berbasis Internet of Things NATALI, YUS; R. F, NURWAN; NURHAYATI, ADE; RIZKY, M.; A, M. NABIL; M, MOSES; ROIHAN, M.; S, ALVA NURVINA; DORAND, PIETRA; SUYATNO, SUYATNO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 4: Published October 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i4.938

ABSTRAKKomunikasi cahaya tampak (Visible Light Communication) merupakan solusi untuk komunikasi berkecepatan tinggi pada sistem berbasis Internet of Things. Model pintu otomatis berbasis IoT menggunakan komunikasi cahaya tampak berhasil dibuat untuk keamanan rumah. Komunikasi tersebut dengan panjang gelombang 650 nm berwarna merah diujicobakan untuk jarak 20 cm. Penerima fotodioda mengaktifkan motor servo untuk membuka pintu dengan maksimal sudut rotasi 120 derajat. Ada enam macam kondisi pintu terbuka yang ditampilkan di LCD dan dikirimkan melalui internet ke website. Secara keseluruhan sistem berjalan dengan baik. Komunikasi cahaya tampak juga diujicobakan sebagai sinyal pembawa dengan mendeteksi tegangan yang dikirimkan oleh laser di fotodioda. Berdasarkan ujicoba didapatkan data yang dikirimkan dapat diterima dengan baik, walaupun perubahan tegangan turun sampai dengan 1.5% di fotodioda.Kata kunci: komunikasi cahaya tampak (Visible Light Communication), Internet of Things, pintu otomatis, keamanan, sinyal pembawa ABSTRACTVisible Light Communication (VLC) is a solution for high-speed communication that can be utilized for Internet of Things (IoT) systems. An automatic door model based on IoT using VLC has been successfully assembled for user security at home. This communication, with a wavelength of 650 nm in red light, was tested for 20 cm. The photodiode receiver activates the servo motor to open the door with a maximum rotation angle of 120 degrees. The open door process consists of 6 different conditions displayed on the LCD and transmitted via the internet to the website. In a comprehensive evaluation, the system operates optimally. In addition, VLC was also tested as a carrier signal to examine the voltage sent by the laser. Based on the experiment, the data sent can still be received well, even though there is a voltage change up to 1.5% at the photodiode receiver.Keywords: Visible Light Communications (VLC), Internet of Things, automatic door, security, carrier signal

Smart Home Security System Using Object Recognition with the EfficientDet Algorithm: A Real-Time Approach Suyatno, Suyatno; Natali, Yus; Reza Fachrurrozi, Nurwan; Roihan, Muhamad; Dorand, Pietra; Ghani, Naufal
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.400

The EfficientDet method, which is implemented on the Raspberry Pi for real-time detection in resource-constrained contexts, is the basis for the smart home security system presented in this study. The system integrates CCTV cameras, motion sensors, and detectors to identify and classify objects, sending notifications via WhatsApp via the Twilio API. The EfficientDet-D0 model achieves an accuracy of 94.8%, an average processing time of 45 ms, and a memory usage of about 850 MB. When compared to moving individuals or non-human things, testing shows that stationary human items have a higher detection accuracy. Notifications are transmitted roughly every three seconds, with an average latency of 1.4 to 1.8 seconds. The suggested method provides object recognition, real-time monitoring, and configuration flexibility in contrast to traditional IoT-based systems. These results highlight the potential of EfficientDet as a reliable and adaptable solution for home security. Future improvements include improving accuracy in a variety of environmental conditions and implementing adaptive learning.

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