scholarly journals Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour

2021 ◽  
Author(s):  
Raemon Syaljumairi ◽  
Sarjon Defit ◽  
S Sumijan ◽  
Yusma Elda
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Signal Strength ◽  
Access Point ◽  
Receive Signal ◽  
Receive Signal Strength ◽  
Nearest Neighbour ◽  
K Nearest Neighbor

Teknologi wireless saat ini bisa dimanfaatkan untuk menentukan posisi pengguna di dalam ruangan. Pemanfaatan sinyal strength WiFi dari Access Point (AP) bisa memberikan informasi posisi pengguna yang berada di dalam ruangan. Alternatif penentuan posisi pengguna di dalam ruangan menggunakan Receive Signal Strength (RSS) WiFi. Penelitian ini dilakukan untuk mengkalasifikasian jarak Euclidean Distance antara data training dengan data testing pengguna terhadap hotspot dengan mengukur tingkat akurasi pengklasifikasian jarak pengguna dengan hotspot menggunakan metode K-Nearest Neighbour. Penelitian ini dilakukan dengan membandingkan jarak antar pengguna terhadap 2 atau lebih AP menggunakan Teknik Euclidean Distance. Teknik Euclidean Distance digunakan sebagai kalkulator jarak dimana ada dua titik dalam bidang 3 dimensi dengan mengukur panjang segmen yang menghubungkan dua titik. Teknik ini paling baik untuk merepresentasikan jarak antara pengguna terhadap AP. Pengumpulan data RSS menggunakan teknik Fingerprinting. Data RSS tersebut dikumpulkan dari 20 AP yang terdeteksi menggunakan aplikasi wifi analizer, dari hasil scanning tersebut didapatkan data RSS sebanyak 709 data RSS. Nilai RSS tersebut dijadikan sebagai data training. K-Nearest Neighbor (KNN) saat mengelompokkan data uji yang baru yang digunakan adalah neighbourhood clasification sehingga K-NN mampu mengklasifikasikan jarak terdekat dari data uji yang baru dengan nilai data training yang ada. Berdasarkan hasil pengujian diperoleh tingkat akurasi sebesar 95% dengan K adalah 3. Berdasarkan hasil penelitian yang telah dilakukan bahwa dengan menggunakan metode K-NN diperoleh persentase tertinggi pada k = 3 sebesar 95% dan nilai error minimum sebesar 5%

scholarly journals OPTIMALISASI JARINGAN NIRKABEL DENGAN METODE RSSI DI AIKOM TERNATE

2021 ◽  
Vol 9 (1) ◽  
pp. 108-115
Author(s):  
Laroma Larumbia ◽  
Susanti H Hasan ◽  
Seh Turuy
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Blind Spot ◽  
Data Rate ◽  
Receive Signal ◽  
Receive Signal Strength ◽  
Receive Signal Strength Indicator ◽  
Wireless Monitoring

Penelitian ini bertujuan untuk optimalisasi jaringan nirkabel dari titik buta atau blind spot di lingkungan kampus AIKOM Ternate. Area-area titik buta ini membuat pengguna jaringan nirkabel (dosen, staf, dan mahasiswa) tidak nyaman dikarenakan harus mendekat ke sumber jaringan (access point (AP)) terdekat agar dapat mengakses jaringan internet ataupun intranet. Dengan optimalisasi jaringan nirkabel ini, area jangkauan jaringan nirkabel disesuikan dengan kebutuhan agar tidak ada lagi titik buta. Penggunaan aplikasi Wireless Monitoring (Wirelessmon) untuk mendeteksi area jangkauan dari setiap AP yang dipasang, yang diukur adalah receive signal strength indicator (RSSI), termasuk penentuan penggunaan kanal dari setiap AP agar tidak tumpang tindih atau overlapping dalam penggunaan kanal pada setiap AP dan data rate yang mengalami peningkatan dan penurunan menyesuaikan dengan kualitas sinyal yang diterima. Hasil penelitian ini menunjukkan setelah dilakukannya optimalisasi dengan memasang AP pada lima titik. Optimalisasi jaringan nirkabel pada kampus AIKOM Ternate berhasil dengan RSSI -61dBm sampai dengan -44dBm, RSSI termasuk dalam kategori sangat bagus (very good). Disarankan pengukuran RSSI menggunakan software lebih dari satu sehingga dilakukan perbandingan, melakukan perbandingan RSSI yang diterima dengan throughput yang dihasilkan pada perangkat yang berbeda, dan pengelola jaringan dapat menggunakan hasil penelitian ini akan tetapi penggunaan perangkat dengan spesifikasi yang berbeda dengan yang digunakan dalam penelitian ini maka disarankan untuk melakukan pengambilan data ulang agar hasilnya maksimal.

scholarly journals An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2117
Author(s):  
Xuesheng Peng ◽  
Ruizhi Chen ◽  
Kegen Yu ◽  
Feng Ye ◽  
Weixing Xue
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Physical Distance ◽  
Spatial Distance ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

scholarly journals Design and Implementation of a Wireless System to Locate a User in Indoor Environments

2020 ◽  
Vol 38 (11A) ◽  
pp. 1640-1651
Author(s):  
Amnah A. Kareem ◽  
Wissam H. Ali ◽  
Manal H. Jasim
Keyword(s):  
Nearest Neighbor ◽  
Detection System ◽  
Signal Strength ◽  
Access Point ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Access Points ◽  
Training Stage ◽  
Testing Stage ◽  
Preparation Phase

The technology of indoor positioning has pulled in the consideration of researchers the expanding capability of smartphones and the advancement of sensor innovation, alongside the increase the time people spend working inside the building or being indoors. Sensor innovation, which is one of the most generally utilized information hotspots for indoor setting, has a favorable position that sensors can receive information from a cell phone without introducing any additional device. The idea of the proposed system is to use the Wi-Fi access points, inside the building, together with a Smartphone Wi-Fi sensor which lets the building administrator locate those carrying smartphones, wherever they exist inside the building. The proposed system consists of two-stage the testing stage (or preparation phase) and, the second stage is the training stage (or positioning phase). The data is collected and selected for accurate readings; a router is used, which is the Mikrotik access point type from which we can read the RSS value. The RSS value represents the Wi-Fi signal strength of the target device. The proposed IPS detection system is independent and can work in unconstrained environments. The database used to measure the performance of the proposed IPS detection system is collected from 14 locations (different in size). The number of readings obtained from the collected database is 1199 readings consist of received signal strength value from five access points. The proposed IPS accuracy is 96.8595% and the mean error is about 1.2 meters are achieved when using, K-Nearest Neighbor (K-NN), used the...

scholarly journals Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour

2021 ◽  
pp. 128-133
Author(s):  
Raemon S Saljumairi ◽  
Sarjon Defit ◽  
S Sumijan ◽  
Yusma Elda
Keyword(s):  
Test Data ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Access Point ◽  
Training Data ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
3 Dimensional

The Current wireless technology is used to find out where the user is in the room. Utilization of WiFi strength signal from the Access Point (AP) can provide information on the user position in a room. Alternative determination of the user's position in the room using WiFi Receive Signal Strength (RSS). This research was conducted by comparing the distance between users to 2 or more APs using the euclidean distance technique. The Euclidean distance technique is used as a distance calculator where there are two points in a 3-dimensional plane or space by measuring the length of the segment connecting two points. This technique is best for representing the distance between the users and the AP. The collection of RSS data uses the Fingerprinting technique. The RSS data was collected from 20 APs detected using the wifi analyzer application, from the results of the scanning, 709 RSS data were obtained. The RSS value is used as training data. K-Nearest Neighbor (K-NN) uses the Neighborhood Classification as the predictive value of the new test data so that K-NN can classify the closest distance from the new test data to the value of the existing training data. Based on the test results obtained an accuracy rate of 95% with K is 3. Based on the results of research that has been done that using the K-NN method obtained excellent results, with the highest accuracy rate of 95% with a minimum error value of 5%

scholarly journals Efficient Shared Execution Processing of k-Nearest Neighbor Joins in Road Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Ju Cho
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Real Life ◽  
Road Networks ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Primitive Operation ◽  
Nested Loop

We investigate the k-nearest neighbor (kNN) join in road networks to determine the k-nearest neighbors (NNs) from a dataset S to every object in another dataset R. The kNN join is a primitive operation and is widely used in many data mining applications. However, it is an expensive operation because it combines the kNN query and the join operation, whereas most existing methods assume the use of the Euclidean distance metric. We alternatively consider the problem of processing kNN joins in road networks where the distance between two points is the length of the shortest path connecting them. We propose a shared execution-based approach called the group-nested loop (GNL) method that can efficiently evaluate kNN joins in road networks by exploiting grouping and shared execution. The GNL method can be easily implemented using existing kNN query algorithms. Extensive experiments using several real-life roadmaps confirm the superior performance and effectiveness of the proposed method in a wide range of problem settings.

scholarly journals Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qing Yang ◽  
Shijue Zheng ◽  
Ming Liu ◽  
Yawen Zhang
Keyword(s):  
Nearest Neighbor ◽  
Science And Technology ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Experimental Simulation ◽  
K Nearest Neighbor ◽  
Improvement Strategy ◽  
Fingerprint Algorithm ◽  
Received Signal Strength Indication

AbstractTo improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

scholarly journals Prediksi Klasifikasi Pembangunan Merek Kosmetik Dengan Metode Enbag K-Logres Berdasarkan Keterlibatan Pengguna Facebook

2020 ◽  
Vol 6 (1) ◽  
pp. 116-125
Author(s):  
Fajar Sarasati ◽  
Lia Dwi Cahyanti ◽  
Annida Purnamawati ◽  
Riyan Latifahul Hasanah
Keyword(s):  
Logistic Regression ◽  
Market Research ◽  
Nearest Neighbor ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Logistics Regression ◽  
Performance Metric ◽  
Brand Development ◽  
Svm Algorithm ◽  
Measurement Results

Abstract: Building a brand new company that starts a business by conducting market research is intended to introduce new products and maintain existing businesses. But the market survey actually requires quite a lot of costs for transportation costs, brochure printing costs, more employee salaries and so forth. Surveys conducted offline also reach a less extensive market, less maximum results and less detail, and require more time. Based on the description above, the researchers conducted a study using Facebook performance metric data that assessed the construction of cosmetics brands using the K-Nearest Neighbor and Logistics Regression (SVM) algorithm by classifying which posts were the most desirable and less desirable by consumers, as well as measuring by the EnBag method K-LoGres of the two algorithms to improve the performance of the two proposed algorithms. Bagging technique was chosen because it has the advantage of being able to improve the measurement results and improve the accuracy of classification measurements by combining two or more algorithms. Based on the measurement results of Facebook metric data which assesses the development of cosmetic brands with the K-NN algorithm it gets an accuracy of 68.67% and a Logistic Regression (SVM) of 72.67% then the two algorithms are processed using the EnBag K-LoGres method getting an accuracy of 73.91%. Based on the results of measurements with the EnBag K-LoGres method the results increased by 1.24%.Keywords: Brand Development, Cosmetics, K-Nearest-Neighbour, Logistic (SVM), EnBag K-LogresAbstrak: Membangun merek perusahaan yang baru memulai usaha dengan melakukan riset pasar dimaksudkan untuk memperkenalkan produk baru serta mempertahankan usaha yang sudah ada. Namun survei pasar justru membutuhkan biaya yang cukup banyak untuk biaya transportasi, biaya cetak brosur, gaji karyawan lebih banyak dan lain sebagainya. Survei yang dilakukan secara offline juga menjangkau pasar kurang luas, hasil kurang maksimal dan kurang merinci, serta membutuhkan waktu yang lebih lama. Berdasarkan uraian diatas maka peneliti melakukan penelitian dengan memanfaatkan data metrik kinerja facebook yang menilai pembangunan merk kosmetik dengan menggunakan algoritma K-Nearest Neighbourdan Logistic Regreesion (SVM) dengan mengklasifikasikan postingan mana yang paling diminati dan kurang diminati oleh konsumen, serta melakukan pengukuran dengan metode EnBag K-LoGres dari kedua algoritma untuk meningkatkan kinerja kedua algoritma yang diusulkan. Teknik bagging dipilih karena memiliki kelebihan dapat memperbaiki hasil pengukuran serta meningkatkan akurasi dari pengukuran klasifikasi dengan menggabungkan dua atau lebih algoritma. Berdasarkan hasil pengukuran data metrik facebook yang menilai pembangunan merek kosmetik denganalgoritma K-NN memperoleh akurasi sebesar 68.67% dan Logistic Regression (SVM) sebesar 72.67% selanjutnya kedua algoritma diproses dengan metode EnBag K-LoGres mendapat akurasi sebesar 73.91%. Berdasarkan hasil pengukuran dengan metode EnBag K-LoGreshasilnya mengalami kenaikan sebesar 1.24 %.Kata kunci: Pembangunan Merek, Kosmetik, K-Nearest Neighbour, Logistic Regression (SVM), EnBag K-LoGres

scholarly journals ANALISA SENTIMEN PADA TINJAUAN BUKU DENGAN ALGORITMA K-NEAREST NEIGHBOUR

KONVERGENSI ◽  
2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Luvia Friska Narulita
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Term Frequency

Analisa sentimen pada tinjauan buku dapat digunakan untuk pengklasifikasian dokumen tinjauan sehingga pembagian sentimen positif dan negatif dapat dilakukan secara sistemis. Penggunaan metode k-nearest neighbor dan digabungkan dengan metode pembobotan istilah dan penghitungan tingkat kemiripan memberikan hasil yang cukup baik pada penelitian yang telah dilakukan. Kata Kunci: analisa sentimen, similarity, k nearest neighbor, term frequency

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