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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT
Heikhmakhtiar, Aulia Khamas
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Computer Science & IT Library & Information Science Mathematics

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Development of a laravel-based web information system for network device maintenance management using the rapid application development method Bimorogo, Sembada Denrineksa; Lediwara, Nadiza; Heikhmakhtiar, Aulia Khamas; Aulia, Regifia Ningrum Nur; Sunami, Yoga; Priyani, Kadek Jana; Azahra, Manda Fatimah
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.456

Abstract

Information and Communication Technology (ICT) infrastructure is essential for government operations, particularly in managing network devices. Within the ICT Hardware Infrastructure Subdivision (Subbidang Harinfra TIK) of the Data and Information Center at the Indonesian Ministry of Defense, documentation of maintenance activities remains fragmented, making monitoring, analysis, and historical data storage less effective. This study developed a web-based information system using the Laravel framework and the Rapid Application Development (RAD) approach to address these issues. The system automates documentation, monitoring, and reporting, ensuring more structured, transparent, and efficient processes. Black Box testing confirmed reliable functionality, data validation, and improved efficiency in maintenance activities. Unlike previous studies that focused on general asset or helpdesk systems, this research emphasizes ICT infrastructure maintenance in a defense environment, highlighting security and adaptability for sensitive data. The implementation enhances systematic documentation and operational transparency, with future improvements directed toward intelligent notifications and platform integration in line with Industry 4.0 trends.
Comparison of Naïve Bayes Classifier and Support Vector Machine for sentiment analysis on civil military relations conflict among Rohingya refugees as recommendation for defense policy making Putri, Nanda Selviana; Saragih, Hondor; Heikhmakhtiar, Aulia Khamas
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 3 (2024): Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i3.255

Abstract

This research focuses on the evaluating the performance of various sentiment analysis techniques using the Naive Bayes Classifier and Support Vector Machine in identifying civil-military conflicts among Rohingya refugees. The goal is to assist leaders in formulating defense policies. This research uses text data from news sources on Twitter, with a total of 5018 data that have been processed to become clean data, then divided into 1004 test data and 4018 training data to be classified using the Support Vector Machine and Naive Bayes methods. This research analyzes the sentiment and polarity of public opinion related to the issues that occur in this situation. The results of the sentiment analysis from the two methods are then classified using the Support Vector Machine and Naive Bayes methods, and then compared to determine which method is more effective in capturing the complex dynamics of sentiment. The findings of this research indicate that the Support Vector Machine method has a higher accuracy in identifying sentiments related to the civil-military conflict among Rohingya refugees, with an accuracy of 87.95%, compared to the Naive Bayes Classifier with an accuracy of 85.16%. The analysis results in the form of frequently occurring words in the true positive word cloud, namely apology, human, angry, and solidarity, are handed over to experts to be formulated into recommendation sentences and can be used to assist in the formulation of policies for defense decision-makers in more effectively addressing the Rohingya refugee issue.
Co-Authors Aulia, Regifia Ningrum Nur Azahra, Manda Fatimah Bimorogo, Sembada Denrineksa Hondor Saragih Lediwara, Nadiza Priyani, Kadek Jana Putri, Nanda Selviana Sunami, Yoga