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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Fernando Candra Yulianto
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Peramalan Penjualan Laptop Menggunakan Metode Long Short Term Memory (LSTM) Fernando Candra Yulianto; Noor Latifah
JURNAL FASILKOM Vol. 14 No. 2 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i2.7337

Abstract

Industri penjualan laptop tidak hanya dipengaruhi oleh kemajuan teknologi, tetapi juga dipengaruhi oleh perubahan cepat dalam prefernsi konsumen dan perubahan kondisi ekonomi seseorang. Permintaan laptop dari konsumen bervariasi secara signifikan dari waktu ke waktu, yang dipengaruhi oleh beberapa faktor seperti kualitas produk, harga produk, citra merek, dan kecepatan peluncuran model baru. Ketidakstabilan faktor tersebut membuat penjual laptop mengalami kesulitan dalam melakukan restock barang di tokonya, dari beberapa kasus yang ada penjual laptop sering mengalami overstock ataupun stockout. Salah satu metode yang dapat digunakan untuk memprediksi penjualan barang adalah metode Long Short Term Memory (LSTM). Dimana dalam pembuatan model tersebut dibagi menjadi 3 category yaitu low-end category, mid-end category dan high-end category. Untuk low-end category memiliki kriteria harga <= Rp. 8.000.000 sedangkan mid-end category memiliki kriteria harga > Rp. 8.000.000 dan <= Rp. 16.000.000 dan untuk high-end category memiliki kriteria harga > Rp. 16.000.000. Untuk pengoptimalan hasil prediksi digunakanlah metode optimasi Adaptive Moment Gradient (ADAM). Berdasarkan uji coba dengan menggunakan metode tersebut didapatkan hasil berupa RMSE (low-end category): 30.4401 dan R2 Score (low-end category): 0.9804, RMSE (mid-end category): 14.1007 dan R2 Score (mid-end category): 0.9956, dan RMSE (high-end category): 14.5063 dan R2 Score (high-end category): 0.9964.
Pengembangan Sistem Deteksi Kantuk Menggunakan YOLOv9 untuk Keselamatan dalam Berkendara Fernando Candra Yulianto; Wiwit Agus Triyanto; Syafiul Muzid
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i2.18701

Abstract

Drowsiness detection systems play a crucial role in safe driving, considering the high rate of traffic accidents caused mainly by drowsiness. Several drowsiness detection systems built using the eye aspect ratio (EAR), percentage of eyelid closure (PERCLOS), and convolutional neural network (CNN) methods still have limitations in terms of accuracy and response time. This study aimed to overcome these problems by applying the You Only Look Once version 9 (YOLOv9). This method has advantages in terms of speed and accuracy because it can detect objects in real-time in one processing stage. The dataset was collected independently from several sources in a real environment inside the vehicle with various lighting and viewing angles; then, labeling, preprocessing, and modeling processes were conducted. The model performance was evaluated based on precision, recall, F1 score, and mean average precision (mAP) metrics. The best model was optimized using several optimization techniques to determine the most optimal results. The results indicate that the YOLOv9 model trained using Nesterov-accelerated adaptive moment estimation (Nadam) optimization has a better image processing speed than other models. This model yielded a precision, recall, F1 score, mAP@50, mAP@50, mAP@50-95, and processing speed of 99.4%, 99.6%, 99.5%, 99.5%, 85.5%, and 52.08 FPS, respectively. The developed model can detect drivers’ drowsiness signs, such as closed eyes, yawning, abnormal head positions, and unnatural hand movements, in real time. However, this model still has limitations in detecting drivers wearing sunglasses, so further development is needed to improve its performance in these conditions.
Computer Vision-Based Information System for Early Detection of Elderly Patient Falls using YOLOv12 Triyanto, Wiwit Agus; Fernando Candra Yulianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6858

Abstract

Falls in elderly patients are a significant public health problem due to their high frequency and potential to cause serious injury or even death. Traditional fall detection systems often rely on wearable sensors, which can be intrusive and uncomfortable for long-term monitoring. This study proposes a non-intrusive computer vision-based information system for early fall detection using the YOLOv12 (You Only Look Once version 12) object detection model. The system integrates real-time video processing with a lightweight convolutional neural network architecture to detect falls in indoor care settings. A dataset of 10,793 annotated images, including simulated fall scenarios and daily activities, was used to train and validate the proposed model. The proposed system achieved a Mean Average Precision (mAP) of 90.60%, demonstrating robust performance under various lighting conditions and camera angles when compared with the YOLOv8, YOLOv11, and YOLO-NAS models. This study contributes to the development of intelligent healthcare systems that improve the safety and quality of life of elderly patients through proactive monitoring and rapid response capabilities.
Co-Authors Noor Latifah Syafiul Muzid Wiwit Agus Triyanto