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The purpose of writing this article is to determine the application of the nearest neighbor classification algorithm in diabetes detection. This nearest neighbor classification algorithm is a classification method often used to classify objects based on available data. This method works by searching for the closest objects in the dataset and classifying the new objects based on the closest object category. The application of the KNN algorithm can be carried out in various fields, such as analyzing the feasibility of credit granting, classifying online news materials or diagnosing diabetes. In this article, the researcher uses a literature review research method assisted by a descriptive analysis approach, to analyze the data and by describing the data that has been previously collected where the author describes data that has been obtained from various literary sources such as journals, data and others. The data obtained will be analyzed and interpreted in accordance with the objective of this research, which is to determine the application of nearest neighbor classification to detect diabetes.

Pabrik Roti Bakar Azhari adalah perushaan industri yang bergerak dibidang roti bakar. Perusahaan ini masih terdapat permasalahan dalam distribusinya, adanya pengiriman yang tidak terjadwal dan tidak memaksimalkan kapasitas angkut kendaraan mengakibatkan jarak pengiriman semakin panjang. Penentuan rute distribusi merupakan hal yang penting bagi perusahaan untuk meminimalkan biaya distribusi. Sebagai pabrik produsen roti bakar , pemilihan rute yang optimal perlu menjadi perhatian Pabrik Roti Bakar Azhari karena mempengaruhi biaya pengiriman produk tersebut. Saving Matrix dan Nearest Neighbor adalah kombinasi metode yang digunakan untuk penentuan rute distribusi yang optimal. Metode saving matrix dapat menentukan rute gabungan yang optimal dengan mempertimbangkan kapasitas kendaraan distribusi selanjutnya algoritma nearest neighbor dapat menentukan urutan jarak pengiriman terpendek pada kelompok rute yang terbentuk. Penelitian ini berhasil mendapatkan rute distribusi menggunakan metode saving matrix dan nearest neighbor yang lebih baik dari segi jarak tempuh pengiriman dan biaya dsitribusi. Hasil yang diperoleh adalah pengelompokan 4 rute distribusi dari jarak rute awal adalah 139,3 km dan jarak rute akhir adalah 109,2 km. Hasil  biaya distribusi rute awal sebesar Rp Rp 4.751.233 / bulan dan biaya distribusi rute akhir sebesar Rp 4.521.058 / bulan. Dengan metode saving matrix dan  algoritma nearest neighbor adalah penghematan jarak sebesar 21,61%, dan penghematan biaya distribusi sebesar 4,85%.

Scholarships are a form of assistance in the form of educational expenses provided by the government or foundations to students or students who are categorized as from underprivileged families. However, in datermining scholarship recipients, there are still many scholarship recipients who come from wealthy families, while those from less fortunate families do not receive this assistance. This may be due to calculations and data processing that still use manual methods, causing scholarship recipients to not be on target. The purpose of this research is to simplify and minimize calculation errors in determining scholarship recipients for the Smart Indonesia Program (PIP) at SMK Karya Medika. Therefore, for calculating and processing PIP scholarship recipients data, data mining techniques can use the calssification method using the K-NN algprithm. K-Nearest Neighbor is a data classification method that will be used for data objects based on learning data that is closer to the object. In this study using the Confusion Matrix test so as to obtain an accuracy value of 80.00%.     

The main problem of the Traveling Salesman Problem is that a salesman travels to several places to go with a known distance and then returns to his original place by using the shortest route from his journey, and all the places the salesman goes to are only allowed once. This research focuses on the problem of distributing goods at PT. The Medan Nugraha Ekakurir (JNE) route with the destination delivery address in the Medan area. The Cheapest Insertion Heuristic Algorithm is an algorithm used to form tours (travels) by gradually building the shortest path route with minimal weight, by adding new points one at a time. One. The Nearest Neighbor Algorithm is a simple and fast algorithm to build a feasible initial tour length from TSP where the technique takes the shortest distance from the initial position regardless of other distances. This study resulted in the conclusion that the application of the cheapest insertion heuristic and nearest neighbor algorithms in terms of finding the distance to the problem of shipping goods at PT. The Medan Nugraha Ekakurir (JNE) route starts with finding the distance between addresses with the help of google maps, then continues with the help of the WinQSB software. Based on the research results obtained using the cheapest insertion heuristic and nearest neighbor algorithms, it is obtained that the search for the shortest route distance for shipping goods at PT. The smaller Medan Nugraha Ekakurir (JNE) route is generated by the nearest neighbor algorithm. This shows that the nearest neighbor algorithm is more effective in terms of finding the traveling distance on the Traveling Salesman Problem problem of shipping goods at PT. Medan's Nugraha Ekakurir (JNE) Line.

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

Penelitian ini bertujuan untuk menerapkan algoritma K-Nearest Neighbor (KNN) dalam pengenalan pola tulisan tangan angka 0-9. Penelitian ini menggunakan data sekunder berupa gambar angka 0-9 dalam bentuk bitmap yang diunduh dari internet. Setiap gambar angka diubah menjadi fitur numerik menggunakan metode ekstraksi fitur Zoning. Selanjutnya, data fitur numerik tersebut diuji menggunakan metode KNN untuk memprediksi angka yang ditulis.