The implementation of eco-friendly technology has been become an interesting field for sustainability. No exception with the implementation of wireless technology that used for developing networks infrastructure, it is necessary for saving the usage of energy. As a data forwarding protocol in a computer network, commonly there are two protocols that used for, which are routing and bridging protocol. Technically routing protocol has been confirmed that it is more effective and efficient than bridging protocol. However bridging protocol still becomes the popular protocol for data forwarding because it is easy to use. This research tried to test the energy consumption of the wireless network device that implementing between routing or bridging protocol. The wireless network device that used for this research was MikroTik router RB 433Ah. The data forwarding protocol that was tested consists of bridging, static routing, and RIP routing. Data traffic scenario that used for this research consisted of two scenarios which were HTML data access with packet size 256B and video streaming data access with packet size 1518B. Measuring the energy consumption referred to three parameters which were power consumption, CPU usage, and processor temperature. The result showed that for HTML data access scenario, the RIP routing protocol become the lowest energy consumption with power consumption reached 7.460 W, CPU usage 4.6 %, and processor temperature 38.133^C. While for video streaming scenario, generally the RIP routing protocol still become the lowest energy consumption with power consumption reached 7.567 W, CPU usage 7.33 %, and processor temperature 36.727^C. IntroductionWireless networks are the most considered choice today to build infrastructure computer networks, especially for wide coverage areas. This is because wireless networks allow users to connect the networks flexibly without being limited by hardware like the limitation of conventional wired computer networks. Finally, wireless networks infrastructure is be able to cover almost every places even remote places. There has been increased the use of mobile devices, hotspot areas, and wireless IoT globally [1]. Even wireless infrastructure like telecom tower (BTS), has grown significantly. In 2018, there are 118 thousand towers that cover mostly 95% area of Indonesia for serving 157 million customers [2]. Actually, mobile device that implements wireless technology infrastructure gives energy efficiency, but nowadays the increasing of data usage makes energy saving in vain [3]. It is no doubt that the increase of internet conection and mobile phones impacts the electricity consumption especially in developing country [4].In other hand, the development of wireless networks as information and communication technology (ICT) certainly raises an effect [5], [6]. Theoretically, information and communication technology has a positive and significant relation with energy consumption [7], no exception with wireless technology. Wireless technology is one of the developed techn...
Abstrack -PT. Tunas Ridean Tbk is one of the companies engaged in car service services. In carrying out its services, PT Tunas Ridean Tbk often gets complaints from customers who queue for service registration and wait too long for the car repair process. The aim of building The vehicle booking application is for customers can book vehicle services through interactive messages. The application will determine the appropriate type of service using text mining. Historical service data can be processed into useful information to predict vehicle service duration. Classification of service duration divided into time intervals of less than 1 hour, 1 -2 hours, 2 -3 hours, 3 -4 hours, 4 -5 hours, 5 -6 hours, and more than 7 hours. This historical data processing uses the Naive Bayes method with variable types of service, year of production, and type of vehicle. The feature of the application is receiving interactive messages, processing message data, and processing data history which produces the type of service desired by the customer. Furthermore, the system can predict the duration of service based on jobs at the workshop, and then sends an answer in the form of a short message to the customer the estimated time of arrival of the customer to the workshop.
Dealer kendaraan perlu menjaga hubungan baik dengan pelanggan sehingga inti bisnis dealer dapat berlanjut dan berkembang. Salah satu strategi yang digunakan adalah memprediksi kapan pelanggan akan berkunjung lagi untuk servis kendaraan (layanan perawatan atau perbaikan kendaraan) berdasarkan analisis data riwayat kunjungan pelanggan. Dengan hasil prediksi berupa hari kedatangan pelanggan dimasa depan maka dealer kendaraan dapat mengingatkan pelanggan tentang kapan waktunya servis kendaraan. Support vector machine (SVM) adalah sebuah model pembelajaran mesin (machine learning) yang menggunakan hyperplane dan support-vector untuk memisahkan kelas dalam suatu ruang dimensi secara optimal sehingga sesuai untuk digunakan dalam pemecahan masalah prediksi waktu kedatangan pelanggan. SVM diimplementasikan untuk memprediksi kapan pelanggan akan datang lagi dimasa depan untuk perbaikan atau perawatan kendaraan. Hasil menunjukkan bahwa, dengan pemilihan metode yang tepat, SVM dapat memprediksi waktu kedatangan pelanggan dengan tingkat akurasi mencapai 92.5% berdasarkan validasi K-Fold cross-validation pada data latih dan mencapai rata-rata 97.33% untuk pengukuran nilai presisi, akurasi dan recall pada data uji
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