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All Journal Indonesian Journal of Community Engagement Journal Innovation in Information and Computer Technology
Indah Rahma Sari
Institut Teknologi dan Bisnis Bina Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Lecturer  Mentoring  on  SINTA,  Garuda,  and  Google  Scholar  at STIKES Abdurrahman and ITB Bina Sriwijaya Palembang Muhammad Ridho Ardiansyah; Mahmud; Indah Rahma Sari; Hendriansyah; Martini; Nabila Kintan Oktadinna
Indonesian Journal of Community Engagement Vol. 3 No. 1 (2026): (January) Indonesian Journal of Community Engagement
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/ijce.v3i1.101

Abstract

Mentoring activities in managing SINTA, Garuda, and Google Scholar accounts represent a strategic effort to improve the quality of lecturer publication governance and the performance of higher education institutions. This study aims to describe the process and outcomes of mentoring activities related to the management of SINTA, Garuda, and Google Scholar accounts for lecturers at STIKES Abdurahman Palembang and Institut Bina Sriwijaya Palembang. The study employed a qualitative approach with a descriptive design. Data were collected through observation, documentation, and field notes during the implementation of the mentoring activities. The mentoring was conducted through material presentations, hands-on practice, and individual assistance, focusing on profile updates, publication synchronization, metadata improvement, and the creation of global researcher identifiers such as ORCID and Web of Science. The results indicate a significant improvement in institutional publication performance, particularly at Institut Bina Sriwijaya Palembang, as evidenced by an increase in the number of verified authors, SINTA Overall Score, SINTA 3-Year Score, and SINTA productivity. Meanwhile, at STIKES Abdurahman Palembang, the mentoring activities contributed to improving the accuracy and validity of lecturer publication data through cross-platform account synchronization. Overall, these mentoring activities proved effective in enhancing the quality of lecturer publication management, increasing participant engagement, and supporting sustainable improvements in academic performance and institutional visibility.
Implementation of Canva Training to Improve Digital Design Skills of Students and Teachers at SMA Nusa Bangsa Palembang Muh Ridho Ardiansyah; Amelia Anggraini; Indah Rahma Sari; Andri Saputra; Mahmud
Indonesian Journal of Community Engagement Vol. 3 No. 2 (2026): (May) Indonesian Journal of Community Engagement
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/ijce.v3i2.135

Abstract

The development of digital technology requires students and teachers to have digital design skills that can support learning activities, school publication, and visual communication. Canva is one of the design platforms that is easy for beginners to use because it provides templates, visual elements, text editing features, color arrangement tools, and design export options. This activity aimed to improve the digital design skills of students and teachers at SMA Nusa Bangsa Palembang through practice-based Canva training. The method used a descriptive community service approach consisting of preparation, Canva introduction, poster design demonstration, participant practice, mentoring, poster output review, and reflection. The training was conducted at SMA Nusa Bangsa Palembang and involved students and teachers as participants. The training materials focused on creating school activity posters using Canva, including template selection, text editing, adding design elements, arranging color composition, and downloading the final design. The results showed that participants were able to follow the training process and produce a school activity poster containing important information such as the activity title, schedule, requirements, contact information, and supporting visual elements. This activity provided practical experience for participants in creating school publication media that were neater, more attractive, and more communicative. Therefore, Canva training can be considered a simple and applicable strategy to improve students’ and teachers’ digital design skills in the school environment.
Hybrid Collaborative Filtering Model for Personalized Digital Content Recommendation Systems Bella Paramitha; Indah Rahma Sari
Journal Innovation in Information and Computer Technology Vol. 2 No. 1 (2025): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i1.117

Abstract

The increasing reliance on digital network infrastructures in higher education institutions has significantly increased the exposure of campus networks to various cyber threats. Cyberattacks such as distributed denial-of-service (DDoS), malware infections, and unauthorized access attempts can disrupt academic services and compromise sensitive institutional data. Therefore, developing an effective intrusion detection mechanism is essential to enhance cybersecurity within campus network environments. This study proposes a machine learning framework for cyberattack detection in campus networks by analyzing network traffic patterns using supervised machine learning algorithms. The proposed framework consists of several stages including dataset acquisition, data preprocessing, feature selection, model training, and performance evaluation. Experiments were conducted using the CICIDS2017 dataset, which contains both benign network traffic and multiple types of cyberattack scenarios. Three machine learning models, namely Random Forest, Support Vector Machine (SVM), and XGBoost, were implemented and compared in order to evaluate their effectiveness in detecting malicious network activities. The experimental results indicate that the XGBoost model achieved the highest performance, with an accuracy of 95.3%, outperforming the other evaluated models. These findings demonstrate that machine learning techniques can effectively identify abnormal network traffic patterns and improve cyberattack detection capabilities. The proposed framework provides a promising approach for strengthening cybersecurity monitoring and enhancing the resilience of campus network infrastructures against evolving cyber threats.
Machine Learning Model for Malware Attack Prediction in Computer Network Systems Muhammad Ammar; Indah Rahma Sari
Journal Innovation in Information and Computer Technology Vol. 2 No. 2 (2025): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i2.124

Abstract

The rapid development of information technology has significantly increased the complexity of computer network infrastructures. Along with these developments, cyber threats such as malware attacks have also increased in frequency and sophistication. Malware attacks can cause serious damage to computer systems, including data breaches, service disruption, and financial losses. Therefore, early detection and prediction mechanisms are crucial to enhance network security systems. Machine Learning has emerged as an effective approach for detecting and predicting cyber threats by analyzing large-scale network traffic data and identifying abnormal patterns. This study aims to develop a machine learning-based model for predicting malware attacks in computer network systems. Several machine learning algorithms such as Random Forest, Support Vector Machine, and Decision Tree are evaluated to determine the most effective model for malware prediction. The proposed model analyzes network traffic features and classifies them into normal or malicious behavior using supervised learning techniques. The experimental results demonstrate that machine learning models can significantly improve the accuracy of malware attack prediction and provide an efficient mechanism for proactive network security defense. This research contributes to the development of intelligent cybersecurity systems capable of detecting and predicting malware threats in modern computer networks.
Co-Authors Amelia Anggraini Andri Saputra Bella Paramitha Hendriansyah Mahmud Martini Muh Ridho Ardiansyah Muhammad Ammar Muhammad Ridho Ardiansyah Nabila Kintan Oktadinna