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Dalam meningkatkan monitoring performa harian mesin eCRM (Cash Recycling Machine), diperlukan suatu aplikasi yang dapat memantau mesin secara mendetail. CRM merupakan alat yang berfungsi untuk menyalurkan, menyimpan dan mengatur data-data kegiatan engineer terhadap mesin dari engineer kepada supervisor dan manager sehigga dapat dilakukannya evaluasi kegiatan pada lapangan, banyak digunakan untuk memenuhi kebutuhan konsumen dalam bidang perbankan khususnya Automated Teller Machine (ATM). Pengembangan fitur downtime reporting diperlukan untuk menyimpan data jumlah downtime seluruh mesin di Indonesia, sehingga memudahkan manager dan supervisor dalam memanage mesin dan engineer berdasarkan data peforma mesin di lapangan. Sistem dibangun menggunakan php framework CodeIgniter.dengan prinsip kerja Model-View-Controller sehingga dapat digunakan untuk mengembangkan aplikasi secara efisien dan dinamis serta lebih memudahkan dalam melakukan pemeliharaan aplikasi.  Kata Kunci: eCRM, downtime reporting, CodeIgniter

No abstract

The usage of wireless system and dry electrode on electroencephalography (EEG) device becomes widely demanding, particularly in commercial purposes. While the wireless system is needed for lesser cable interference and practical function for mobility, the dry electrode is very important for signal consistency in longer period of brainwave acquisition. Previously, a wireless EEG device was developed in our laboratory; however, the evaluation of the acquired brainwave is needed for further usage and development. This research aimed to compare the signal acquired by the developed EEG device using Emotiv Insight device as a benchmark, which is already an established wireless and dry electrode-based EEG on the market. The brainwave acquisitions were conducted on the subject while resting with eyes closed. AF3 and AF4 of frontal lobe channels were used as the electrode placements. The results were then characterized using frequency band analysis, SNR comparison, and general signal inspection. The result showed that the signal patterns on both devices were visually similar. A minor difference on the amplitude scale can be adjusted by normalization method. The result of alpha band calculation, which is normally detected in resting activity, found similar on both devices. Furthermore, the SNR result from developed device was considered fairly close to the benchmarking device. This study showed that developed EEG device was considered comparable to Emotiv Insight in detecting alpha band extracted from resting frontal lobe, as well as in the brainwave filtering process and accuracy.

Brainwave is widely used as an indicator of brain activity and can be detected by electroencephalography (EEG). The development of EEG device has become more advanced along with the invention of low-cost tiny electronic modules and wireless technology. This research aimed to develop a low-cost wireless modular device for brainwave acquisition based on Arduino microcontroller. The system was designed into sensor block for brainwave receiver and conditioning, and mainboard block for data processing. Dry-active electrode was developed as the sensor, followed by preamplifier module which was also installed at the sensor block. Active filter and DRL circuits were developed on the mainboard part. Arduino UNO was used as the main processor of the device. The developed modules were then evaluated using signal generator to examine the module characteristics and consistency. As the result, the preamplifier module was detected to reach 40.34 dB on gain ability. The cutoff frequency on the active filter module was calculated on 31 Hz. Furthermore, Arduino UNO was identified to have a consistency on input and output voltage.