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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Tsabit
Darjat Saripurna
Universitas Islam Sumatera Utara
Computer Science & IT

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Perancangan Sistem Keamanan Aplikasi E-Voting Untuk Pemilihan Ketua Badan Eksekutif Mahasiswa Fakultas Teknik UISU Dengan Menggunakan Algoritma MD5 Muhadi M.Ilyas Gultom; Darjat Saripurna
ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Vol 3, No 2 (2019): November 2019
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.553 KB) | DOI: 10.30829/algoritma.v3i2.6438

Abstract

E-voting Is the use of hardware and software to establish an electronic system that is useful in the electoral process by making electronic votes that replace ballot papers. E-Voting was introduced by several e-Government especially in Europe in a request to serve the voting provisions by providing a control system, so that voters can vote whenever and wherever. To avoid all forms of fraud and damage to data which is certainly very important for the voting process, it is necessary to secure data by applying cryptography to the data. Currently there are many methods or algorithms that can be used to protect data from various types of attacks, but of course each algorithm has advantages and disadvantages of each. MD5 is a concise message algorithm developed by Ron rivest. MD5 has actually been rooted in a series of concise message algorithms, which are the predecessors of md5, all developed by Rivest. The original digest message is called md.
Implementation of Seasonal ARIMA (SARIMA) for Sales Prediction of Pet Food Products: A Case Study of Abadi Petshop Fikri Haikal Harahap; Antoni; Darjat Saripurna
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit104

Abstract

The rapid growth of pet ownership in Indonesia has driven a steady increase in demand for pet food products, which typically follows a recurring seasonal pattern across the year. Abadi Petshop, a local pet supply store, has so far relied on manual recording and the owner's intuition to estimate future sales, an approach that does not systematically account for historical sales patterns and frequently leads to inaccurate stock planning, resulting in either overstock or stockouts. This study aims to design and implement a web-based sales prediction system for pet food products using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, applied to monthly sales data of a Whiskas cat food product collected from January 2023 to December 2024 (24 observations). Non-seasonal differencing (d = 1) and seasonal differencing (D = 1, s = 12) were applied to achieve stationarity, and the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots were used to identify the model order, resulting in a SARIMA(1,1,1)(1,1,0)12 model with parameters φ = 0.6, Φ = 0.3, and θ = 0.4. The system was developed using the Laravel framework with a MySQL database, following the Unified Software Development Process (USDP), and functional black-box testing confirmed that all core features — including login, sales data input, prediction processing, forecast visualization, and result export — operated successfully. The resulting forecast for the twelve months following the training period projected a gradual upward trend, starting at 140.20 kg in the first month and reaching 175.50 kg by the twelfth month, consistent with the increasing trend and annual seasonal pattern observed in the historical data. These findings indicate that the SARIMA method is capable of automatically modeling trend and seasonal components in pet food sales data and can support Abadi Petshop in making more accurate, data-driven decisions regarding inventory planning and sales strategy.
Co-Authors Antoni Fikri Haikal Harahap Muhadi M.Ilyas Gultom