The implementation of learning by teachers can measure the quality of schools and students. Schools with diverse student backgrounds need to take strategic steps in managing learning to get optimal learning outcomes. Good learning designs and techniques can motivate students' interest in learning. The teacher's role is very important in managing learning to create an effective teaching and learning process. Data Mining or also known as Knowledge Discovery in Database (KDD) is the process of extracting knowledge from large data to find new patterns to get new knowledge and information. Data Mining technology is used to explore existing knowledge in the database. One of the methods used in data mining is clustering with the K-Means algorithm. This study aims to conduct student clustering to obtain a balanced class composition in order to improve the quality and student learning outcomes as seen in the increasing in the class average score. The data processed in this study came from the main school data as many as 90 students of the XI class of Computer Network Engineering Skills Competency at SMKN Negeri 2 Padang Panjang in the 2020/2021 school year. The variables used in data processing are student scores, parents' income and the distance from where students live to school. The student clustering calculation using K-Means succeeded in grouping 90 students into 3 clusters where cluster 1 totaled 47 students, cluster 2 totaled 10 students and cluster 3 totaled 33 students. Each member of the cluster will be divided evenly into 3 groups studying to get a balanced class composition. This research can be used as a basis for decision making by schools in clustering student placements to improve learning outcomes. By the increasing in the grade point average, the school average score will also increased.

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%

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%