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All Journal Jurnal Teliska Indonesian Journal of Artificial Intelligence and Data Mining
Abdurrahman Abdurrahman
Politeknik Negeri Sriwijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PENENTUAN KEBUTUHAN LAMPU UNTUK RUANGAN KANTOR Sudirman Yahya; Abdurrahman Abdurrahman
TELISKA Vol. 5 No. 2 (2013): Edisi 14, Volume 5, Nomor 2, Mei 2013
Publisher : Teknik Elektro Polsri

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Abstract

Dalam penentuan lampu di setiap ruangan tidaklah sama karena ukuran panjang,lebar dan tinggi ruangan berbeda serta setiap ruangan yang dibuat di peruntungkanya berbeda pula,sehingga penentuan lampu setiap ruangan dilihat dari fungsi ruangan itu digunakan memerlukan intesitas cahaya tertentu. Intensitas cahaya diruangan yang dipancarkan oleh suatu lampu dapat dipengaruhi oleh repleksi warna langit-langit,repleksi warna dinding,dan repleksi warna lantai. Selain itu juga jenis armature lampu yang digunakan dapat menentukan sistem penerangang yang digunakan seperti penerangan langsung,penerangan tak langsung,penerangan difus dan sebagainya. Untuk mendapatkan intensitas cahaya yang dihasilkan oleh lampu merata dalam ruangan maka diperlukan jarak pemasangan armature lampu sama rata menurut panjang,lebar ruangan,dan jarak antara armature lampu. Dalam perhitungan jumlah lampu ini dapat digunakan beberapa metode diantaranya metode indek ruang yang digunakan dalam penulisan ini.Kata kunci: fungsi ruangan,armature lampu,intensitas cahaya,sistem penerangan,repleksi warna.
Comparative Study: Performance Comparison of You Only Look Once and Convolutional Neural Networks Algorithms in Human Object Detection Dewi Permata Sari; M. Akbar Tri Ramadhani; Abdurrahman Abdurrahman
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.
Co-Authors Dewi Permata Sari M. Akbar Tri Ramadhani Sudirman Yahya