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Abstrak :
[ABSTRAK
Meningkatnya interaksi manusia dengan komputer, perangkat teknologi dan jaringan, telah membawa pada kebutuhan akan adanya sistem lokalisasi multi divais pada sebuah area tertentu. Akan tetapi, saat ini belum ada sistem yang cukup tangguh, yang mampu melakukan lokalisasi divais dengan akurasi yang baik, dengan toleransi kurang dari 10 cm. Dalam konteks ini, kami meneliti sebuah teknik yang inovatif dalam usaha lokalisasi dalam ruangan yang berbasis komunikasi nirkabel, WiFi. Tantangannya adalah bagaimana cara melakukan lokalisasi divais tanpa melakukan modifikasi pada perangkat divais, baik itu perangkat keras dan lunak, juga pada perangkat jaringannya. Dan dalam rangkan menjawab tantangan itu, kami mengembangkan sistem lokalisasi dalam ruangan ini. Proyek yang saya kerjakan ini khusus melakukan capture MAC address dari setiap divais yang berada pada lingkup area tertentu. Proyek ini menggunakan LabView sebagai bahasa pemrograman, dan NI-USRP dari National Instrument sebagai perangkat kerasnya.

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ABSTRACT
The increase of human interaction to gadgets, computers and networks, has needed an ability to localize multi devices or gadgets in a certain area. But nowadays, no robust technology can estimate a position and localization with sufficient accuracy (<10cm). In this context, we wish to study the technique of indoor localization system based on innovative approach of communication media wireless (WiFi). The challenge is how to define multi devices localization without any modification in hardware, software and wireless device. To answer this challenge, we need to develop a system of internal localization. The potential impact of this solution is significant to the general public, to extent that these networks are very common. And the concern of this project is how to recovery and capture the MAC Address from devices inside the area of WiFi localization, using LabView as the programming language and NI-USRP from National Instrument as the hardware. ;The increase of human interaction to gadgets, computers and networks, has needed an ability to localize multi devices or gadgets in a certain area. But nowadays, no robust technology can estimate a position and localization with sufficient accuracy (<10cm). In this context, we wish to study the technique of indoor localization system based on innovative approach of communication media wireless (WiFi). The challenge is how to define multi devices localization without any modification in hardware, software and wireless device. To answer this challenge, we need to develop a system of internal localization. The potential impact of this solution is significant to the general public, to extent that these networks are very common. And the concern of this project is how to recovery and capture the MAC Address from devices inside the area of WiFi localization, using LabView as the programming language and NI-USRP from National Instrument as the hardware. ;The increase of human interaction to gadgets, computers and networks, has needed an ability to localize multi devices or gadgets in a certain area. But nowadays, no robust technology can estimate a position and localization with sufficient accuracy (<10cm). In this context, we wish to study the technique of indoor localization system based on innovative approach of communication media wireless (WiFi). The challenge is how to define multi devices localization without any modification in hardware, software and wireless device. To answer this challenge, we need to develop a system of internal localization. The potential impact of this solution is significant to the general public, to extent that these networks are very common. And the concern of this project is how to recovery and capture the MAC Address from devices inside the area of WiFi localization, using LabView as the programming language and NI-USRP from National Instrument as the hardware. , The increase of human interaction to gadgets, computers and networks, has needed an ability to localize multi devices or gadgets in a certain area. But nowadays, no robust technology can estimate a position and localization with sufficient accuracy (<10cm). In this context, we wish to study the technique of indoor localization system based on innovative approach of communication media wireless (WiFi). The challenge is how to define multi devices localization without any modification in hardware, software and wireless device. To answer this challenge, we need to develop a system of internal localization. The potential impact of this solution is significant to the general public, to extent that these networks are very common. And the concern of this project is how to recovery and capture the MAC Address from devices inside the area of WiFi localization, using LabView as the programming language and NI-USRP from National Instrument as the hardware. ]

Abstrak :
Masalah pokok penggunaan fingerprinting Receive Signal Strength (RSS) pada indoor localization adalah pengaruh lingkungan terhadap hasil pengukuran RSS, menyikapi variabilitas nilai RSS dan akurasi penentuan posisi. Penelitian ini mengkombinasikan penggunaan keunggulan teknologi LoRa dengan metode deep learning yang menggunakan semua variasi hasil pengukuran nilai RSS di setiap posisi sebagai fitur alami dari kondisi dalam ruangan sebagai fingerprinting untuk melatih model pada deep learning. Teknik ini diberi nama DeepFi-LoRaIn, yang menggambarkan teknik untuk menggunakan data fingerprinting dari RSS perangkat LoRa pada indoor localization menggunakan metode deep learning. Penelitian ini dilakukan tidak hanya sebatas pengujian dan pembuktian metode menggunakan pendekatan testbed dan simulasi, namun berlanjut hingga tahapan implementasi menggunakan RSS fingerprinting dari hasil pengukuran sebenarnya. Skenario pengujian yang digunakan untuk mengevaluasi model adalah skenario tanpa gangguan dan skenario dengan memberikan gangguan. Skenario gangguan dilakukan dengan cara memberikan gangguan pada nilai RSS yang diterima di beberapa anchor node. Pada pengujian menggunakan dataset simulasi diperoleh hasil prediksi posisi dengan nilai akurasi 100% untuk skenario tanpa gangguan. Sedangkan pada skenario dengan gangguan diperoleh hasil akurasi prediksi posisi sebesar 86,66%. Hasil pengujian prediksi posisi menggunakan data pengukuran langsung diperoleh nilai akurasi sebesar 96,22%, untuk skenario tanpa gangguan dan 92,45%. untuk skenario pengujian dengan gangguan. Berdasarkan hasil penelitian menggunakan data simulasi dan data pengukuran sebenarnya pada implementasi, diperoleh kesimpulan bahwa, penggunaan Teknik DeepFi-LoRaIn mampu mengatasi permasalahan pada variabilitas nilai RSS didalam ruangan dan mampu menjaga akurasi prediksi posisi jika terjadi gangguan yang disebabkan oleh perubahan kondisi lingkungan. ......The main problem using fingerprinting Receive Signal Strength (RSS) in indoor localization is the influence of the environment on the results of RSS measurements, addressing the variability of RSS values and positioning accuracy. This study combines the use of the advantages of LoRa technology with a deep learning method that uses all variations of the RSS value measurement results in each position as a natural feature of indoor conditions as fingerprinting to train models in deep learning. This technique is named DeepFi-LoRaIn, which describes a technique for using RSS fingerprinting data from LoRa devices in indoor localization using deep learning methods. This research is not only limited to testing and proving the method using a testbed and simulation approach, but continues to the implementation stage using RSS fingerprinting from the actual measurement results. The test scenarios used to evaluate the model are the without interference scenario and the with interference scenario. The inteference scenario is done by giving disturbance to the RSS value received at several anchor nodes. In testing using a simulation dataset, position prediction results are obtained with an accuracy value of 100% for without interference scenarios. Meanwhile, in the scenario with interference, the accuracy of position prediction is 86.66%. The results of the position prediction test using direct measurement data obtained an accuracy value of 96.22%, for the scenario without interference and 92.45%. Based on the results of the study using simulation data and actual measurement data in the implementation, it was concluded that the use of the DeepFi-LoRaIn technique was able to overcome the problem of the variability of the RSS value in the room and was able to maintain the accuracy of position prediction in case of disturbances caused by changes in environmental conditions.