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Rosita
Universitas Negeri Surabaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : journal of applied informatics research

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Student Clustering Based on Subject Grades: A K-Means Approach to Clustering Study Groups Rosita; Fitria
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44597

Abstract

Pengelompokan siswa berdasarkan nilai akademik merupakan pendekatan strategi untuk meningkatkan efektivitas pembelajaran dalam pendidikan vokasi. Penelitian ini mengimplementasikan algoritma K-Means untuk mengelompokkan siswa Sekolah Menengah Kejuruan (SMK) ke dalam tiga klaster utama: Kelompok Bimbingan (nilai rendah), Kelompok Potensial (nilai sedang), dan Kelompok Unggulan (nilai tinggi). Data yang digunakan mencakup nilai dari 12 mata kuliah dalam program keahlian Teknik Komputer Jaringan dan Telekomunikasi. Hasil analisis menunjukkan bahwa pengelompokan mampu mengidentifikasi pola belajar siswa dan memberikan rekomendasi strategi pembelajaran yang tepat untuk setiap klaster. Klaster 1 memerlukan bimbingan intensif, Klaster 2 dapat diarahkan pada tantangan akademik, dan Klaster 3 memerlukan program pengayaan untuk mengoptimalkan potensinya. Penelitian ini menunjukkan bahwa pendekatan berbasis data, seperti pengelompokan, memberikan manfaat yang signifikan dalam mendukung pembelajaran yang dipersonalisasi dan mempersiapkan siswa untuk kebutuhan dunia kerja.
Campus Network Design and Construction Using Cisco Packet Tracer Simulation Fitria; M Adamu Islam Mashuri; Rosita
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44676

Abstract

The development of information technology encourages educational institutions to have a reliable and structured computer network infrastructure. This study aims to design a campus computer network using Cisco Packet Tracer simulation with a VLAN segmentation approach and star topology. The methods used include identifying network requirements, designing topology, allocating IP subnets using subnetting techniques, and configuring devices such as routers, switches, and access points. The RIPv2 protocol is used to support communication between VLANs, and DHCP is implemented to facilitate automatic IP address assignment. The simulation results show that all devices in the network can be connected to each other well, as evidenced by connectivity tests using the ping and tracert commands. All tests produced positive responses without any packet loss or failed communication routes. This study proves that Cisco Packet Tracer is an effective tool in designing and testing networks before physical implementation.
Decision Support System For Seed Aid Recipients Using The Combined Compromise Solution Method (Case Study: Among Tani Farmer Group) Anis Putri Purwanti; Andi Iwan Nurhidayat; Dodik Arwin Dermawan; Rosita
Journal of Applied Informatics Research Vol. 2 No. 1 (2026): July
Publisher : Universitas Negeri Surabaya

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

Abstract

The distribution of seed assistance in the Among Tani Farmer Group, Magetan, has traditionally relied on manual assessment and subjective judgment, increasing the risk of inconsistent recipient selection. This study develops a web-based Decision Support System (DSS) that applies the Combined Compromise Solution (CoCoSo) method to prioritize 148 farmer candidates based on five evaluation criteria: economic condition, cultivated land area, land readiness, previous aid history, and land tenure status. Primary data were collected through interviews and field observations, then converted into standardized 1–5 ratings using predefined assessment rules established with the farmer group administrators before being processed through the CoCoSo algorithm. The system was implemented using Laravel 12 and MySQL to provide transparent and traceable ranking results. The proposed system generated a complete ranking of all candidates, supporting the prioritization of 100 farmers according to the available assistance quota, while the remaining 48 farmers were categorized as requiring further consideration. Computational verification against an independent Microsoft Excel implementation produced identical Ki values for all 148 alternatives, confirming the correctness of the CoCoSo implementation. Functional testing across 72 black-box test scenarios indicated that all system features operated as intended. In addition, a comparative analysis with the Simple Additive Weighting (SAW) method revealed notable differences in ranking outcomes, particularly among highly ranked alternatives, demonstrating the distinct aggregation characteristics of the CoCoSo method.
Co-Authors Andi Iwan Nurhidayat Anis Putri Purwanti Dodik Arwin Dermawan Fitria Fitria M Adamu Islam Mashuri