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.

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Microprocessor and microcontroller laboratories are type III laboratories that are in the electronics engineering study program majoring in electrical engineering, this laboratory serves the activities of student practices for microprocessor system courses, microcontroller systems and interfaces I and II and very large scale intergration (VLSI) programmable logic devices and programmable electronics. Laboran as manager and person in charge of the laboratory, every month submits reports on equipment support facilities and practice modules made manually to the head of the laboratory. Every day the laboratory staff must examine all equipment activities one by one such as computers and practice modules to find out the latest conditions. Seeing the problems above, we need a system solution that can provide fast, accurate, complete and integrated information as a whole. The purpose of this study is to produce an information system design for maintenance and repairs to microprocessor and microcontroller laboratories. The method used in this study analyzes the current system to create a conceptual model so as to produce a database diagram. Next, the design of the display includes the design of the application interface such as the reporter display, laboratory performance reports etc. To further maximize laboratory use and quality teaching and learning is expected that students, lecturers and staff will also participate by reporting damage to supporting facilities at the microprocessor and microcontroller laboratory.

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%