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All Journal IJIIS: International Journal of Informatics and Information Systems JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI
Les Endahti
AMIK-YPAT Purwakarta, Indonesia
Humanities Computer Science & IT Education Mathematics Social Sciences Other

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PERENCANAAN ARSITEKTUR ENTERPRISE SISTEM INFORMASI SEKOLAH MENGGUNAKAN TOGAF ADM Jalaludin Jalaludin; Les Endahti; Denada Fatimah Zahra
PENDIDIKAN SAINS DAN TEKNOLOGI Vol 10 No 3 (2023)
Publisher : STKIP PGRI Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47668/edusaintek.v10i3.883

Abstract

Perancangan arsitektur enterpise menggunakan metodologi Framework The Open Group Architecture Framwork (TOGAF) Administration Development Method (ADM), mempertimbangkan kepentingan organisasi secara keseluruhan dan tahapan-tahapan dari metodologi tersebut diterjemahkan kedalam aktivitas perancangan arsitektur enterprise. Tahapan perancangan arsitektur enterprise sangatlah penting dan akan berlanjut pada tahapan berikutnya yaitu rencana implementasi. Luaran dari tahapan ini akan menghasilkan sebuah arsitektur enterprise yang pada nantinya bisa dijadikan oleh MTs dan MA Al-Irfan Purwakarta untuk mencapai tujuan yang strategis sesuai dengan visi dan misi sekolah. Perancangan arsitektur enterprise sistem informasi akan menghasilkan empat arsitektur domain yang berbasis pada empat pilar yaitu architekture bisnis, data, aplikasi dan teknologi.
Predicting Demand for MSME Products Using Artificial Neural Networks (ANN) Based on Historical Sales Data Les Endahti; Muhammad Shihab Faturahman
International Journal of Informatics and Information Systems Vol. 8 No. 4: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.288

Abstract

Accurate demand forecasting plays a crucial role in supporting inventory and sales strategies, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face resource constraints. This study aims to develop a predictive model using Artificial Neural Networks (ANN) to forecast product demand based on historical sales data. The ANN model is trained and evaluated using a structured experimental approach, adjusting parameters such as the number of hidden layers, learning rate, and epochs to identify the best-performing architecture. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) are used to measure model performance. The results demonstrate that the ANN model is capable of capturing complex nonlinear relationships in multidimensional data and producing accurate demand forecasts. The model particularly performs well in predicting demand trends for products in the Electronics and Household categories. These findings provide valuable insights for MSME stakeholders in optimizing inventory planning and making data-driven business decisions.
Co-Authors Denada Fatimah Zahra Jalaludin Jalaludin Muhammad Shihab Faturahman