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All Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal Innovations Computer Science
Alwanto, Hilmi
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Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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Pengenalan Bahasa Isyarat Bahasa Indonesia Real-time Menggunakan Metode SP-Tree Sofyan, Arief; Alwanto, Hilmi; Arif, Sulthan Cendikia
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1371

Abstract

Real-time Indonesian sign language recognition faces several challenges, the most prominent of which is the diversity of hand gestures and different expressions. Often, automated systems face difficulties in interpreting these highly variable gestures, which in turn results in decreased recognition accuracy and efficiency. To improve the performance of sign language recognition in this study, the SP-Tree method is proposed, which utilizes a spatial tree structure to group hand gesture data based on spatial and temporal features. This allows for a faster and more accurate sign language recognition process. It is expected that this technique can accelerate sign language recognition with a high level of accuracy and real-time response, which is very important for everyday applications. We used a public dataset covering various hand gestures in Indonesian sign language to test this technique. The results showed that the SP-Tree method had an accuracy of 92 percent, an F1 score of 0.90, and a feature loss of 0.08. Compared with existing conventional sign language recognition techniques, these figures show significant improvements. The results indicate that the SP-Tree method is an effective way to identify Indonesian signs in real-time. This method has the advantage of being able to interpret and group hand gestures more precisely and efficiently, improving the interaction between the user and the system. We hope that this research will help develop assistive technology for people with hearing disabilities. In the future, it will also provide opportunities to use this technique in other sign languages.
Analysis of Enterprise Network Performance Using the SNMP (Simple Network Management Protocol) Method Alwanto, Hilmi; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.346

Abstract

This study examines the implementation of the Simple Network Management Protocol (SNMP) integrated with the Cacti monitoring platform to evaluate enterprise network performance within a simulated environment using PNETLab. A quantitative approach was applied through continuous data collection and measurement of key performance indicators such as throughput, packet loss, delay, and availability. The experiment utilized virtual Mikrotik routers connected to an Ubuntu-based Cacti server configured for SNMP polling and RRDTool data storage. Real-time visualization enabled efficient tracking of network behavior and early detection of anomalies. The results showed that under normal conditions, the network achieved stable performance with throughput between 70–90% of link capacity, zero packet loss, latency below 150 milliseconds, and availability above 99%, meeting ITU-T/TIPHON Quality of Service (QoS) standards. When faults were simulated, the system accurately detected and displayed traffic interruptions, allowing rapid identification and resolution of network issues. Compared with other monitoring tools such as Zabbix and Nagios, the SNMP–Cacti integration proved simpler to configure while maintaining analytical precision and reliability. These findings confirm that Cacti, supported by SNMP, provides an efficient, scalable, and low-overhead solution for enterprise network monitoring. Future development may incorporate SNMPv3 for enhanced security and automated alert systems or predictive analytics to improve responsiveness and proactive maintenance in larger infrastructures.
Co-Authors Arif, Sulthan Cendikia Betty Yel, Mesra Sofyan Arief