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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Data Analytics, Information, and Computer Science (JDAICS)
Sanjaya, Rizki Dwi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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ANALISIS PARAMETER QOS (QUALITY OF SERVICE) PADA JARINGAN INTERNET DI PERPUSTAKAAN UNIVERSITAS ESA UNGGUL TANGERANG Sanjaya, Rizki Dwi; Syamdova, Dian; Farhan, Muhammad; Laksana, Ryan Putra
Journal of Data Analytics, Information, and Computer Science Vol. 2 No. 1 (2025): Januari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jdaics.v2i1.1819

Abstract

Penelitian ini menganalisis parameter pada jaringan internet di Perpustakaan Universitas Esa Unggul Tangerang untuk mengevaluasi kinerja jaringan dan kualitas layanan. Pengukuran yang dianalisis meliputi seperti throughput, packet loss, delay, dan jitter. Hasil penelitian nilai throughput sebesar 682 Kbps dikategorikan buruk, mengindikasikan rendahnya kecepatan transfer data. Sebaliknya, parameter lain seperti packet loss 0% (sangat bagus), delay 10,847 ms (sangat bagus), dan jitter 10,975 ms (bagus) menunjukkan kualitas jaringan yang lebih baik dan memadai. Studi ini menyimpulkan bahwa meskipun stabilitas jaringan memadai untuk mendukung aplikasi real-time, rendahnya throughput menjadi kendala utama. Oleh karena itu, disarankan untuk meningkatkan kapasitas bandwidth dan mengoptimalkan infrastruktur jaringan guna memenuhi kebutuhan pengguna yang terus berkembang.
ANALISIS PREDIKSI PROFIT PADA TOKO ESKA DENGAN METODE REGRESI LINEAR Sanjaya, Rizki Dwi
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.12563

Abstract

Eska Store, as a grocery store business, faces challenges in predicting profits accurately for more efficient business planning. This study aims to develop a profit prediction model using a simple linear regression method, which analyzes the relationship between the number of orders and Eska store revenue for three years, namely 2022 to 2024. Based on the results of the analysis, it was found that linear regression is effective in modeling the relationship between the independent variable (number of orders) and the dependent variable (revenue), with an R² value reaching 0.88 to 0.95. From the prediction results, this model shows that each additional order can increase revenue by around IDR 54,482.87 in 2022, IDR 25,163.94 in 2023, and IDR 47,782.64 in 2024. In addition, the results of the model evaluation show that although there are some errors in the prediction, overall this model can provide fairly accurate results for projecting revenue based on the number of orders. This study suggests that Eska Store uses this model periodically to support better decision making in terms of marketing strategy and production planning. It is expected that by using this linear regression-based prediction model, Eska Store can improve operational efficiency and profitability sustainably. Keywords: Number of Orders, Prediction Model, Profit Prediction, Revenue, Simple Linear Regression
Co-Authors Laksana, Ryan Putra Muhammad Farhan Syamdova, Dian