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All Journal Julia Jurnal
Muhammad Akbar Mustofa
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Computer Science & IT

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COMPUTER NETWORK ANALYSIS USING NETWORK  MANAGEMENT SYSTEM AT AN NUUR UNIVERSITY Achmad Rizki Ramadhani; Muhammad Akbar Mustofa; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.10

Abstract

During the pandemic, teaching and learning activities have changed. Which originally used the offline format to go online and its combinations. Internet bandwidth usage plays an important role in the success of the teaching and learning process on campus, including at An Nuur University. By using Cacti Network Management System it can be used as a monitoring system to monitor the movement of internet bandwidth whether it meets the needs of the online learning process or not. Internet bandwidth usage is influenced by several factors such as logical topology, physical topology and configuration in computer networks.
PREDIKSI LUAS PANEN DI KECAMATAN PURWOADADI MENGGUNAKAN ALGORITMA REGRESI LINEAR BERGANDA Muhammad Akbar Mustofa; Andri Triyono; Agus Susilo Nugroho
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.23

Abstract

Agriculture, particularly rice cultivation, is highly vulnerable to climate change because it depends on water cycles and weather conditions to maintain productivity. Climate change affects crop growth, development, and yields, as agricultural activities are heavily dependent on weather and climate. This study utilizes data mining to introduce a new breakthrough in addressing rice farming issues in Grobogan Regency, Purwodadi District. The method used is multiple linear regression, with the dependent variable being harvested area and the independent variables including plxanted area and rainfall. The objective of this research is to test and develop data mining methods to predict yield levels, thereby assisting local governments in decision-making during crop failures, based on agricultural data from 20192023. The research process involves data collection, preprocessing, algorithm implementation, and result evaluation. The analysis shows that the multiple linear regression model provides reasonably accurate predictions, with a Root Mean Square Error (RMSE) value of 209.042 and a Relative Root Squared Error (RRSE) of 0.111. Furthermore, the analysis reveals that planted area significantly influence the harvested area. These findings offer insights for local governments as policymakers in providing aid during crop failures. 
Co-Authors Achmad Rizki Ramadhani Agus Susilo Nugroho Andri Triyono Rahmawan Bagus Trianto SN, agus T, Andri