Pendeteksian Penggunaan Masker Untuk Pencegahan Penyebaran Covid-19 Menggunakan Algoritma K-nearest neighbor

Penyebaran Corona Virus Disease (COVID-19) telah menjadi perhatian dunia sejak tahun 2019 sampai sekarang. Berbagai upaya dalam pencegahan penyebaran penyakit tersebut telah dilakukan, seperti penggunaan masker yang sangat diwajibkan di berbagai negara termasuk Indonesia. Banyak masyarakat yang mengabaikan peraturan tersebut, sehingga penularannya semakin cepat dan berdampak pada melemahnya perekonomian. Para ahli telah banyak melakukan penelitian, seperti pendeteksian terhadap penggunaan masker sebagai upaya pencegahan penularan penyakit tersebut. Hal ini dilakukan untuk digunakan pada perangkat yang mampu melakukan pendeteksian secara otomatis dan mempermudah pemerintah dalam melakukan pengawasan terhadap penggunaan masker tersebut. Salah satunya teknik tersebut adalah pendeteksian yang dilakukan dengan teknik machine learning dalam penerapan computer vision. Dalam penelitian ini, sebuah algoritma klasifikasi yaitu k-nearest neighbor (kNN) akan digunakan dalam melakukan pendeteksian penggunaan masker berbasis pengolahan citra. Citra yang telah dikumpulkan akan dilakukan tahap ekstraksi terlebih dahulu sebelum proses klasifikasi dilakukan. Tahap selanjutnya akan dilakukan pendeteksian terhadap citra dengan hasil klasifikasi sebanyak 3 kelas, yaitu: menggunakan masker, tidak menggunakan masker, dan menggunakan masker hanya sebagian. Berdasarkan pengujian yang telah dilakukan untuk pendeteksian penggunaan masker menggunakan algoritma kNN dan ekstraksi ciri tekstur (GLCM dan LBP), memperoleh hasil akurasi sebesar 91,04%, sensitivity sebesar 81,38%, dan specificity sebesar 94,49%. Hasil yang diperoleh dari pengujian tersebut mendapatkan kinerja yang baik dalam melakukan pendeteksian penggunaan masker

Pendeteksian Penggunaan Masker Untuk Pencegahan Penyebaran Covid-19 Menggunakan Algoritma K-nearest neighbor

Penyebaran Corona Virus Disease (COVID-19) telah menjadi perhatian dunia sejak tahun 2019 sampai sekarang. Berbagai upaya dalam pencegahan penyebaran penyakit tersebut telah dilakukan, seperti penggunaan masker yang sangat diwajibkan di berbagai negara termasuk Indonesia. Banyak masyarakat yang mengabaikan peraturan tersebut, sehingga penularannya semakin cepat dan berdampak pada melemahnya perekonomian. Para ahli telah banyak melakukan penelitian, seperti pendeteksian terhadap penggunaan masker sebagai upaya pencegahan penularan penyakit tersebut. Hal ini dilakukan untuk digunakan pada perangkat yang mampu melakukan pendeteksian secara otomatis dan mempermudah pemerintah dalam melakukan pengawasan terhadap penggunaan masker tersebut. Salah satunya teknik tersebut adalah pendeteksian yang dilakukan dengan teknik machine learning dalam penerapan computer vision. Dalam penelitian ini, sebuah algoritma klasifikasi yaitu k-nearest neighbor (kNN) akan digunakan dalam melakukan pendeteksian penggunaan masker berbasis pengolahan citra. Citra yang telah dikumpulkan akan dilakukan tahap ekstraksi terlebih dahulu sebelum proses klasifikasi dilakukan. Tahap selanjutnya akan dilakukan pendeteksian terhadap citra dengan hasil klasifikasi sebanyak 3 kelas, yaitu: menggunakan masker, tidak menggunakan masker, dan menggunakan masker hanya sebagian. Berdasarkan pengujian yang telah dilakukan untuk pendeteksian penggunaan masker menggunakan algoritma kNN dan ekstraksi ciri tekstur (GLCM dan LBP), memperoleh hasil akurasi sebesar 91,04%, sensitivity sebesar 81,38%, dan specificity sebesar 94,49%. Hasil yang diperoleh dari pengujian tersebut mendapatkan kinerja yang baik dalam melakukan pendeteksian penggunaan masker

Identify The Authenticity of Rupiah Currency Using K Nearest Neighbor (K-NN) Algorithm

The rupiah currency is a valid exchange rate used in transactions in the Republic of Indonesia. The Rupiah is often falsified as paper currency. Rupiah paper has a unique texture characteristic so that if processed digitally, it will be easy to distinguish from fake ones.  Designing the authenticity of Rupiah currency system using the K-NN method aims to facilitate the authenticity of the currency and test the accuracy of the method used. The method used in this research is the method of Gray Level Co-occurrence Matrix (GLCM) as a method of feature extraction and K-Nearest Neighbor (K-NN) algorithm used in the identification process. The testing phase uses data for 18 currency images. The results showed an accuracy rate of 100% for the value k = 1, 77.78% for the value k = 3, and 55.56% for the value k = 5. The highest level of accuracy in a currency authenticity identification system occurs when the value of k = 1 is 100%. The value of k on the classification input using the K-NN can determine the level of accuracy of the classification process

IEEE 802.11 as a passive sensor for crowd density counting in closed areas

Crowd density counting obtained popularity in recent years with COVID-19 and the social separation constraints that have to be enforced in public areas. Many methods and techniques can be utilized for crowd density counting. However, these techniques depend on expensive equipment and massive deployment of different sensors in the targeted area. In this work, a simple crowd density counting framework based on measuring the received signal strength (RSS) of IEEE802.11, known as, WIFI in closed areas is leveraged. An access point (AP) and a Raspberry PI kit has been located in a closed area to harvest the RSS value when people pass through the area. K-NN machine learning algorithm has been trained with different features extracted from the RSS to predict the number of people in the area. Finally, an Android smartphone App has been written to monitor the counted number to enforce the counting constraint in the closed areas. The model has been deployed in the engineering faculty. Our results show that K-NN with RSS features for passively crowd density counting achieved 88% accuracy. However, this accuracy dropped to 75% with people running scenario

Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa

Klasifikasi adalah proses menemukan model atau fungsi yang menggambarkan dan membedakan kelas atau konsep data. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma klasifikasi berdasarkan pembelajaran dari data yang sudah terklasifiasi sebelumnya. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma yang sangat bagus dalam menangani beberapa kasus, salah satu kelebihan k-NN diantaranya adalah tangguh terhadap data training yang noisy dan sangat efektif apabila data trainingnya besar. Namun terdapat beberapa masalah pada algoritma k-NN diantaranya adalah penentuan nilai k untuk pemilihan jumlah tetangga terdekatnya sangat sulit, karena nilai k sangat peka atau sensitif terhadap hasil klasifikasi. Pada penelitian ini, akan dilakukan pemodelan klasifiasi dengan menggunakan algoritma k-NN yang difokuskan pada proses penentuan nilai k terbaik pada dataset IKG (Indeks Kesulitas Geografis) desa. Pada penelitian ini akan melakukan integrasi algoritma k-NN dengan menentukan nilai k optimal dengan optimize parameters berdasar algoritma genetika

ck-NN: A Clustered k-Nearest Neighbours Approach for Large-Scale Classification

k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity

CLASSIFICATION OF BENTHIC HABITAT BASED ON OBJECT IN SHALLOW WATERS OF KARANG LEBAR AND LANCANG ISLAND

Teknik klasifikasi berbasis objek (OBIA) merupakan salah satu teknik pemetaan habitat bentik selain metode konvensional (berbasis piksel). Pemetaan metode OBIA dengan memanfaatkan algoritma machine learning terbatas pada perairan Karang Lebar dan Pulau Lancang. Penelitian ini bertujuan untuk mengetahui performa algoritma machine learning (support vector machine (SVM), decision tree (DT), random forest (RF), dan k-nearest neighbour (KNN) dalam mengklasifikasikan habitat bentik perairan dangkal berdasarkan objek menggunakan data satelit Sentinel-2. Metode klasifikasi yang digunakan adalah metode OBIA dengan dua tingkatan analisis. Hasil analisis Agglomerative Hierarchial Clustering diperoleh sebanyak 6 kelas habitat bentik yaitu karang, patahan karang (rubble), lamun, pasir rubble, dan pasir. Tingkat pertama adalah memisahkan darat, laut dangkal dan laut lebih dalam. Tingkat kedua adalah klasifikasi menggunakan algoritma machine learning, hasil klasifikasi menunjukkan alogritma SVM mendapatkan nilai akurasi yang lebih tinggi dibandingkan algoritma lainnya dengan akurasi sebesar 84% di perairan Karang Lebar, kemudian pada perairan Pulau Lancang mendapatkan akurasi sebesar 80% dengan algoritma SVM. Habitat dasar perairan dangkal Karang Lebar dan Pulau Lancang mampu dipetakan dengan baik menggunakan metode OBIA. Perbedaan tingkat akurasi antara perairan Karang Lebar dan Pulau Lancang disebabkan oleh tingkat kekeruhan perairan.The object-based classification technique (OBIA) is one of the benthic habitat mapping techniques besides the conventional (pixel-based) method. The mapping of the OBIA method using machine learning algorithms is limited to the waters of Karang Lebar and Lancang Island. This study aims to determine the performance of machine learning algorithms (support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN)) in classifying shallow water benthic habitats based on objects using Sentinel satellite data. -2. The classification method used is the OBIA method with two levels of analysis. A total of 6 benthic habitat classes were obtained from field observations and Agglomerative Hierarchial Clustering analysis, namely coral, rubble, seagrass, rubble sand, and sand. The results obtained include the first level separating land, shallow sea and deeper sea. The second level is classification using a machine learning algorithm, the results of the classification show that the SVM algorithm gets a higher accuracy value than other algorithms with an accuracy of 84% in Karang Lebar waters, then in Lancang Island waters it gets an accuracy of 80% with the SVM algorithm. The bottom habitat of the shallow waters of Karang Lebar and Lancang Island can be well mapped using the OBIA method. The difference in the level of accuracy between the waters of Karang Lebar and Pulau Lancang is caused by the level of turbidity of the waters

Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction

[EN] Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to success or failure of a project. The risk should be predicted earlier to make a software project successful. A Model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. Also, a comparison is done between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/ Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) to achieve best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew¿s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB and CDT achieve better results.This work was supported by by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224)Naseem, R.; Shaukat, Z.; Irfan, M.; Shah, MA.; Ahmad, A.; Muhammad, F.; Glowacz, A.... (2021). Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics. 10(2):1-19. https://doi.org/10.3390/electronics1002016811910