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Perancangan Jaringan Virtual Local Area Network Menggunakan Cisco Packet Tracer Pada SMK Islam Assa’adatul Abadiyah Noviriandini, Astrid; Bachtiar, Denny; Indriyani, Luthfi
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 2 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v5i2.389

Abstract

Computer network is a relationship between two or more separate computer systems, through a communication medium to carry out data communication with one another in order to share resources. SMK Islamic Assa’adatul Abadiyah was founded in 1984, which stands on a land area of 1237 m2, which has three majors namely: Office Administration, Accounting, Commerce. However, in 2011 SMK Islamic Assa’adatul Abadiyah added a competency competency department, namely the competency competency in Computer and Network Engineering. Internet network is one of the human needs to date. So many human activities have become easier with the internet network. However, problems arise on a network that is not working properly, making information and communication managers less optimal in the process of sending data and teaching/learning processes in the school lab. The solution provided to deal with problems on a network that is less than optimal is to design a VLAN (Virtual Local Area Network) network and change a HUB component to a Switch as network integration, install premium antivirus on computers, allow users to access the network without any problems and security on computers are safer.
LAN Bandwidth Management Using the Queue Tree Method Safinatunnaza, Salwa; Noviriandini, Astrid; Indriyani, Luthfi; Fauziah, Sifa
Golden Ratio of Data in Summary Vol. 5 No. 1 (2025): November - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i1.887

Abstract

The advancement of technology, particularly in computer networks, has enabled global connectivity through the Internet. Computer networks connecting various devices allow for information sharing and communication. One common issue is slow internet speed due to suboptimal bandwidth utilization. To address this issue, bandwidth management becomes crucial, especially in managing multiple applications at PT. XYZ, bandwidth management is implemented using a Mikrotik router using the Queue Tree method. This method allows for flexible and fair bandwidth allocation, ensuring every device has a stable internet connection. This method helps enhance efficiency and ensures bandwidth allocation is aligned with user needs, resulting in smooth and evenly distributed connectivity across the network.
APPLICATION OF THE BERT MODEL IN MEASURING USER PERCEPTION OF THE MAGIC INVESTMENT APPLICATION ON THE GOOGLE PLAY STORE Tabina Fasya Benedicta; Ade Setiawan; Luthfi Indriyani; Astrid Noviriandini; Sandra Dewi Saraswati
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 10 No. 4 (2025): November
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Investment is one of the most effective ways to achieve long-term financial gains. Nowadays, numerous digital platforms offer investment services, including the Ajaib application. The growing public interest in investing has been driven by influencers and online advertisements, yet it has also led to the rise of fraudulent schemes and fake investment platforms. Therefore, evaluating user satisfaction through sentiment analysis of application reviews becomes essential. This study aims to analyze user sentiments toward the Ajaib investment application based on reviews collected from the Google Play Store. The dataset consists of Indonesian-language reviews from the period 2019–2024, processed using Google Colab and the BERT (Bidirectional Encoder Representations from Transformers) algorithm. The classification results yielded 1,393 reviews, comprising 696 positive and 697 negative sentiments, indicating that negative opinions were slightly more dominant. The model achieved an accuracy of 85%, F1-score of 85%, recall of 85%, and precision of 87%, demonstrating that the BERT algorithm performs effectively in sentiment analysis for investment-related applications.