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Penerapan Sistem Informasi Pembelajaran Online (Studi Kasus: Di Sma Negeri 2 Susua) Tafonao, Lianus; Utari, Cut Try; Simanjuntak, Taufik Ismail
Fakultas Teknik dan Ilmu Komputer Vol 4 No 1 (2025): JURNAL PERSEGI BULAT
Publisher : FAKULTAS SAINS DAN TEKNOLOGI - UTND

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36490/jurnalpersegibulat.v4i1.2106

Abstract

Penerapan Sistem Informasi Pembelajaran Online menjadi suatu kebutuhan mendesak dalam era digitalisasi pendidikan saat ini. Studi ini mengambil fokus pada implementasi sistem informasi pembelajaran online di SMA Negeri 2 Susua sebagai studi kasus. Penelitian ini bertujuan untuk mengevaluasi efektivitas dan dampak dari penerapan sistem ini terhadap proses pembelajaran dan pencapaian siswa. Metode penelitian yang digunakan melibatkan survei, wawancara, dananalisis data untuk mendapatkan pemahaman mendalam tentang bagaimana sistem informasi pembelajaran online diimplementasikan dan diterima oleh para stakeholders di SMA Negeri 2 Susua. Temuan penelitian ini diharapkan dapat memberikan wawasan tentang kelebihan dan kendala penerapan sistem informasi pembelajaran online dalam konteks pendidikan menengah. Hasil penelitian menunjukkan bahwa penerapan Sistem Informasi Pembelajaran Online di SMA Negeri 2 Susua memberikan kontribusi positif terhadap efisiensi dan efektivitas proses pembelajaran. ini diharapkan dapat memberikan panduan bagi institusi pendidikan lain yang berencana atau sedang mengimplementasikan sistem serupa.
Analisis Komparasi Algoritma ARIMA dan LSTM pada Prediksi Harga Cabai Merah Keriting Harian Utari, Cut Try; Sembiring, M Thariq Arya Putra; Siregar, M Habibi Rizq Zhafar
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8784

Abstract

Curly red chili is a strategic national commodity characterized by extreme price fluctuations, which significantly impact regional inflation and farmer welfare. Although conventional statistical methods are frequently used for forecasting, these approaches have inherent limitations in capturing non-linear volatility and dynamic price patterns. This research aims to address this gap by comprehensively comparing the performance of the AutoRegressive Integrated Moving Average (ARIMA) statistical model and the Long Short-Term Memory (LSTM) Deep Learning model. This study utilizes a univariate prediction approach based on daily historical price data from January 2024 to October 2025. The dataset is partitioned into 80% for training and 20% for testing purposes. Model performance is rigorously evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (). The experimental results demonstrate that the LSTM model significantly outperforms ARIMA in tracking daily price trends. LSTM achieved an average MAPE of 13.76% (classified as "Good") with an value of 0.92, whereas the ARIMA model recorded a significantly higher MAPE of 41.21% and a negative value. This study concludes that Deep Learning-based algorithms are superior and more effective in handling food commodity price volatility compared to classical linear statistical methods.