Digital Imaging Light Energy Saving Lamp Based On A Single Board Computer

Pencitraan Digital Nyala Lampu Hemat Energi Berbasis Single Board Computer

  • Hadid Tunas Bangsawan Kementerian Perindustrian
  • Lukman Hanafi Kementerian Perindustrian RI
  • Deny Suryana Kementerian Perindustrian RI
Keywords: computer vision (CV), lamp imaging, single board computer


Computer Vision (CV) is an interdisciplinary scientific field that discusses how computers can gain a high-level understanding of digital images or video. A system has been created that is capable of detecting a compact fluoresence lamp (CFL) light. However, in previous research there is no justification that the lamp is only a part that can glow on the lamp alone and has not been done in multi-lamp testing. This study aims to compare the lamp segmentation when it goes OFF and ON so that it could be justified the accuracy of this system and does multi-lamp testing. The method used is an experiment with collecting data by direct observation of the results of the system made. The system consists of a single board computer and a common webcam. The result is the difference between the lamp segmentation when it goes OFF and ON is small with the appropriate threshold setting. So that lamp light imaging had been made could function with good reability.



Download data is not yet available.


Di Mauro, S., Musumeci, S., & Raciti, A., 2017. Analysis of electrical and photometric quantities of CFL and LED bulb lamps. In 2017 IEEE Industry Applications Society Annual Meeting (pp. 1-8). IEEE.

Prayoga, H. A. (2014). Intensitas pencahayaan dan kelainan refraksi mata terhadap kelelahan mata. KEMAS: Jurnal Kesehatan Masyarakat, 9(2), 131-136.

Klette, R., 2014. Concise Computer Vision. London: Springer.

Szeliski, R., 2011. Computer Vision: Algorithms and Applications. London: Springer.

Ortmeyer, C., 2014. Then and now: a brief history of single board computers. Electron. Des. Uncovered, 6, 1-11.

Bangsawan H.T, Hanafi L, 2019. Desain Alat Monitoring Umur Lampu Berbasis Computer Vision. Seminar Nasional Teknik Elektro dan Informatika (SNTEI 2019), Makassar, 19 September 2019.

Wei, C. C., Song, Y. C., Chang, C. C., & Lin, C. B., 2016. Design of a solar tracking system using the brightest region in the sky image sensor. Sensors, 16(12), 1995.

Palaloi, S., 2015. Pengujian dan Analisis Umur Pakai Lampu Light Emitting Diode (LED) Swabalast Untuk Pencahayaan Umum. Jurnal Energi dan Lingkungan (Enerlink), 11(1).

Wijaya, A. A., & Prayudi, Y., 2010. Implementasi Visi Komputer Dan Segmentasi Citra Untuk Klasifikasi Bobot Telur Ayam Ras. Seminar Nasional Aplikasi Teknologi Informasi (SNATI). Juni 2010.

Lee, C. D., Huang, H. C., & Yeh, H. Y., 2013. The development of sun-tracking system using image processing. Sensors, 13(5), 5448-5459.

Carballo, J. A., Bonilla, J., Berenguel, M., Fernández-Reche, J., & García, G., 2019. New approach for solar tracking systems based on computer vision, low cost hardware and deep learning. Renewable energy, 133, 1158-1166.

Bangsawan, H. T., Mardiyanto, R., & Sardjono, T. A., 2015. Six key points lip's feature extraction using adaptive threshold segmentation. In 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA). Surabaya, Mei 2015, IEEE.

Song, B., Yang, X., Pei, Y., Zhao, M., Zhao, C., & Xiao, G., 2012. Electronic ballast for fluorescent lamp based on class Φ 2 inverter with parallel resonant tank. In 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC) (pp. 2179-2183). IEEE

How to Cite
Hadid Tunas Bangsawan, Lukman Hanafi, & Deny Suryana. (2020). Digital Imaging Light Energy Saving Lamp Based On A Single Board Computer. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 751 - 756.
Artikel Rekayasa Sistem Informasi