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İŞLEYEN, Şakir. "Clustering Analysis of Employment Sectors According to OECD Countries Using the K-Average Method." International Journal of Contemporary Economics and Administrative Sciences 11, no. 1 (2021): 093–105. https://doi.org/10.5281/zenodo.5136506.
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<strong>Abstract</strong> In addition to being one of the important parameters showing the welfare level of the countries, employment shows the economic development of the countries in which sector is concentrated. The existence of industry-based employment in developed or developing countries supports this situation. In addition, the resources of countries direct the employment policy of that country. It is stated in the literature that there is generally employment in this field in countries with high agricultural resources. In this study, the employment data of 36 OECD countries between 1991 and 2019 were analysed using the Cluster analysis K-Average Method, which was obtained from the official web site of the World Bank. According to the employment data in Agriculture, Industry and Service sectors, it was analysed in which cluster OECD countries are located and whether the variables show a meaningful clustering.
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Angelina, Lisa Harsyiah, and Nur Asmita Purnamasari. "PERBANDINGAN METODE AVERAGE LINKAGE DAN K-MEANS DALAM MENGELOMPOKKAN PERSEBARAN PENYAKIT MULUT DAN KUKU DI INDONESIA." Fraction: Jurnal Teori dan Terapan Matematika 4, no. 2 (2024): 49–57. https://doi.org/10.33019/fraction.v4i2.63.
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The purpose of this study was to analyze the spread of Foot and Mouth Disease (FMD in Indonesia by using two different methods: average linkage and k-means. In addition, this study also aimed to determine the most effective method of classifying the distribution of FMD in Indonesia between the two methods used. The results of cluster validation showed that the optimal number of clusters formed in the average linkage method was 4, while in the k-means method, there were 3 clusters. The grouping with the average linkage method was better than the results of classifying with the k-means method, as the standard deviation ratio in the average linkage method was smaller at 0,035, compared to 0,258 in the k-means method. Therefore, it was concluded that the average linkage method was better than the k-means method in classifying the distribution of FMD in Indonesia.
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Voznyi, Yaroslav, Mariia Nazarkevych, Volodymyr Hrytsyk, Nataliia Lotoshynska, and Bohdana Havrysh. "DESIGN OF BIOMETRIC PROTECTION AUTHENTIFICATION SYSTEM BASED ON K-AVERAGE METHOD." Cybersecurity: Education, Science, Technique 12, no. 4 (2021): 85–95. http://dx.doi.org/10.28925/2663-4023.2021.12.8595.
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The method of biometric identification, designed to ensure the protection of confidential information, is considered. The method of classification of biometric prints by means of machine learning is offered. One of the variants of the solution of the problem of identification of biometric images on the basis of the k-means algorithm is given. Marked data samples were created for learning and testing processes. Biometric fingerprint data were used to establish identity. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. Experimental results indicate that the k-means method is a promising approach to the classification of fingerprints. The development of biometrics leads to the creation of security systems with a better degree of recognition and with fewer errors than the security system on traditional media. Machine learning was performed using a number of samples from a known biometric database, and verification / testing was performed with samples from the same database that were not included in the training data set. Biometric fingerprint data based on the freely available NIST Special Database 302 were used to establish identity, and the learning outcomes were shown. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. The machine learning system is built on a modular basis, by forming combinations of individual modules scikit-learn library in a python environment.
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Sihombing, Pardomuan Robinson. "Implementation of K-Means and K-Medians Clustering in Several Countries Based on Global Innovation Index (GII) 2018." Advance Sustainable Science, Engineering and Technology 3, no. 1 (2021): 0210107. http://dx.doi.org/10.26877/asset.v3i1.8461.
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The Global Innovation Index (GII) is an instrument to assess the ranking of innovation capabilities of all countries. The sub-index of the GII has seven enabler pillars: Institutions, Human Capital and Research, Infrastructure, Market sophistication, Business Sophistication, Knowledge and Technology Outputs, and Creative Outputs. The k-means method and k-medians method are methods for cluster countries based on GII. Cluster 1 in k-means method consists of 48 Countries, Cluster 2 consists of 45 Countries and Cluster 3 consists of 33 Countries and has the average value of seven variables are the highest. Cluster 1 in k-medians method consists of 33 Countries and has the average value of seven variables are the highest., Cluster 2 consists of 53 Countries and Cluster 3 consists of 40 Countries. The result clustering with using k-means method and k-medians method showed that k-medians is better than k-means method because the variance value of k-medians is smaller than k-means.
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Siregar, Hotmaida Lestari, Muhammad Zarlis, and Syahril Efendi. "Cluster Analysis using K-Means and K-Medoids Methods for Data Clustering of Amil Zakat Institutions Donor." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 2 (2023): 668. http://dx.doi.org/10.30865/mib.v7i2.5315.
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Cluster analysis is a multivariate analysis method whose purpose is to classify an object into a group based on certain characteristics. In cluster analysis, determining the number of initial clusters is very important so that the resulting clusters are also optimal. In this study, an analysis of the most optimal number of clusters for data classification will be carried out using the K-Means and K-Medoids methods. The data were analyzed using the RFM model and a comparative analysis was carried out based on the DBI value and cluster compactness which was assessed from the average silhouette score. The K-Means method produces the smallest DBI value of 0.485 and the highest average silhouette score value of 0.781 at k=6, while the K-Medoids method produces the smallest DBI value of 1.096 and the highest average silhouette score value of 0.517 at k=3. The results show that the best method for data clustering donations Amil Zakat Institutions is using the K-Means method with an optimal number of clusters of 6 clusters.
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Dhanabal, S., and S. Chandramathi. "Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method." International Journal of Information and Communication Technology 11, no. 2 (2017): 260. http://dx.doi.org/10.1504/ijict.2017.086252.
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Dhanabal, S., and S. Chandramathi. "Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method." International Journal of Information and Communication Technology 11, no. 2 (2017): 260. http://dx.doi.org/10.1504/ijict.2017.10007027.
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Gabrovšek, Boštjan, Tina Novak, Janez Povh, Darja Rupnik Poklukar, and Janez Žerovnik. "Multiple Hungarian Method for k-Assignment Problem." Mathematics 8, no. 11 (2020): 2050. http://dx.doi.org/10.3390/math8112050.
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The k-assignment problem (or, the k-matching problem) on k-partite graphs is an NP-hard problem for k≥3. In this paper we introduce five new heuristics. Two algorithms, Bm and Cm, arise as natural improvements of Algorithm Am from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms, Dm, Em, and Fm, incorporate randomization. Algorithm Dm can be considered as a greedy version of Bm, whereas Em and Fm are versions of local search algorithm, specialized for the k-matching problem. The algorithms are implemented in Python and are run on three datasets. On the datasets available, all the algorithms clearly outperform Algorithm Am in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm Am) to 0.08% over optimum (algorithm Em). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm Am) to 0.45% over optimum (algorithm Fm). Better quality of solutions demands higher computation times, thus the new algorithms provide a good compromise between quality of solutions and computation time.
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Safitri, Elsa Maulida. "Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods." Jurnal Statistika dan Komputasi 4, no. 2 (2024): 92–102. https://doi.org/10.32665/statkom.v4i2.3592.
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Background: Hospitals are institutions that provide inpatient care for the sick. In Bojonegoro, hospital services are considered adequate. However, a shortage of nurses often requires patients' families to assist with care. Objective: This research aims to compare clustering methods to find the best method that can be applied to cluster hospitals based on the type of health workers. Methods: This study uses two clustering methods, namely K-Means and K-Medoids Clustering, which are compared to determine the best method. The data source used is secondary data, which consists of the number of medical staff for each medical position, obtained from the Satu Data Bojonegoro website in 2020. Results: The K-means method proved to be the best for grouping healthcare workforce data. Its average within-cluster distance value is -6.763, the closest to zero. The K-means method resulted in 4 clusters. These include cluster_0 (3 hospitals), cluster_1 (1 hospital), cluster_2 (1 hospital), and cluster_3 (5 hospitals). Conclusion: The clustering results show that K-Means with 4 clusters is the best method, with Cluster_0 and Cluster_3 having below-average health workers, and Cluster_1 and Cluster_2 having above-average health workers, with Cluster_2 having the highest and Cluster_3 the lowest number of health workers in Bojonegoro.
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Safitri, Elsa Maulida. "Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods." Jurnal Statistika dan Komputasi 3, no. 2 (2024): 92–102. https://doi.org/10.32665/statkom.v3i2.3592.
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Background: Hospitals are institutions that provide inpatient care for the sick. In Bojonegoro, hospital services are considered adequate. However, a shortage of nurses often requires patients' families to assist with care. Objective: This research aims to compare clustering methods to find the best method that can be applied to cluster hospitals based on the type of health workers. Methods: This study uses two clustering methods, namely K-Means and K-Medoids Clustering, which are compared to determine the best method. The data source used is secondary data, which consists of the number of medical staff for each medical position, obtained from the Satu Data Bojonegoro website in 2020. Results: The K-means method proved to be the best for grouping healthcare workforce data. Its average within-cluster distance value is -6.763, the closest to zero. The K-means method resulted in 4 clusters. These include cluster_0 (3 hospitals), cluster_1 (1 hospital), cluster_2 (1 hospital), and cluster_3 (5 hospitals). Conclusion: The clustering results show that K-Means with 4 clusters is the best method, with Cluster_0 and Cluster_3 having below-average health workers, and Cluster_1 and Cluster_2 having above-average health workers, with Cluster_2 having the highest and Cluster_3 the lowest number of health workers in Bojonegoro.
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Fahriya, Andina, Febryna Sembiring, and Budi Susetyo. "Penggerombolan provinsi di Indonesia berdasarkan instrumen akreditasi satuan pendidikan jenjang SMK menggunakan K-means dan average linkage." Majalah Ilmiah Matematika dan Statistika 24, no. 2 (2024): 174. http://dx.doi.org/10.19184/mims.v24i2.40822.
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Improvement and updates need to be done in order to maintain the existence of a school. Accreditation is one of the references to assess the excellence of a school. There are several components used in the accreditation assessment included in the IASP, namely Graduate Quality, Learning Process, Teacher Quality, and School Management. Additionally, to determine which provinces have low, medium, or high IASP scores, clustering is performed on the IASP scores of those provinces. Cluster analysis is a method used to group research objects based on similarities in their characteristics. In this study, clustering was performed using the K-means and average linkage methods on the average IASP scores of vocational high schools (SMK) in 34 provinces in Indonesia. With the Elbow Criterion approach, four clusters were formed for each method. The results of Dunn Index showed that the average linkage method performed better in clustering compared to the K-Means method. Keywords: IASP, Cluster Analysis, K-Means, Average LinkageMSC2020: 62H30
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Karimi Sabet, Javad, Cyrus Ghotbi, and Farid Dorkoosh. "Application of Response Surface Methodology for Optimization of Paracetamol Particles Formation by RESS Method." Journal of Nanomaterials 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/340379.
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Ultrafine particles of paracetamol were produced by Rapid Expansion of Supercritical Solution (RESS). The experiments were conducted to investigate the effects of extraction temperature (313–353 K), extraction pressure (10–18 MPa), preexpansion temperature (363–403 K), and postexpansion temperature (273–323 K) on particles size and morphology of paracetamol particles. The characterization of the particles was determined by Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Liquid Chromatography/Mass Spectrometry (LC-MS) analysis. The average particle size of the original paracetamol was 20.8 μm, while the average particle size of paracetamol after nanonization via the RESS process was 0.46 μm depending on the experimental conditions used. Moreover, the morphology of the processed particles changed to spherical and regular while the virgin particles of paracetamol were needle-shape and irregular. Response surface methodology (RSM) was used to optimize the process parameters. The extraction temperature, 347 K; extraction pressure, 12 MPa; preexpansion temperature, 403 K; and postexpansion temperature, 322 K was found to be the optimum conditions to achieve the minimum average particle size of paracetamol.
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Apridayanti, Annisa Zuhri, M. Fathurahman, and Surya Prangga. "Clustreing of Province in Indonesia Based on Education Indicators Using K-Medoids." Jurnal Varian 7, no. 2 (2024): 199–206. http://dx.doi.org/10.30812/varian.v7i2.3205.
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Data mining is searching for interesting patterns or information by selecting data using specific techniques or methods. One method that can be used in data mining is K-Medoids. K-Medoids is a method used to group objects into a cluster. This research aimed to obtain the optimal number of clusters using the K-Medoids method based on Davies-Bouldin Index (DBI) validity on education indicators data by province in Indonesia in 2021. The results showed that the optimal number of clusters using the K-Medoids method based on DBI validity is 5 clusters. Cluster 1 consists of 1 province with a higher average dropout rate, average length of schooling, and well-owned classrooms compared to other clusters. Cluster 2 consists of 15 provinces with an average proportion of school libraries lower than Clusters 3 and 4 and higher than Clusters 1 and 5. Cluster 3 consists of 9 provinces with an average proportion of school libraries, proportions of school laboratories, net enrollment rates, and higher school enrollment rates than other clusters. Cluster 4 consists of 8 provinces with a higher average enrollment rate than the other clusters. Cluster 5 consists of 1 province with a higher average repetition rate and student-per-teacher ratio than other clusters.
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Maltamo, Matti, and Annika Kangas. "Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution." Canadian Journal of Forest Research 28, no. 8 (1998): 1107–15. http://dx.doi.org/10.1139/x98-085.
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In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.
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Anita Desiani, Annisa Aulia Lestari, and Lucy Chania Agatha. "Analisis Perbandingan Klasifikasi Penyakit Jantung Menggunakan Algoritma Nai ̈ve Bayes dan Algoritma Logistic Regression." Jurnal Rekayasa Elektro Sriwijaya 5, no. 2 (2024): 82–90. http://dx.doi.org/10.36706/jres.v5i2.104.
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Heart disease is a condition where parts of the heart are damaged. Thus, early detection is needed. One of them is by doing data mining classification using the Naïve Bayes and Logistic Regression algorithms. This research will compare Naïve Bayes and Logistic Regression algorithms through the training percentage split and k-fold cross validation methods to get the best classification results in detecting heart disease by calculating the average value of precision, recall, and accuracy. The Naïve Bayes algorithm with the training percentage split method produces average values for precision, recall, and accuracy of 83%, 82.5% and 81%, while the Naïve Bayes algorithm with k-fold cross validation provides average values for precision, recall, and accuracy of 83.5%, 85.5% and 83%. Logistic Regression algorithm with percentage split training method produces average values for precision, recall, and accuracy of 73.5%, 73.5% and 73%, while Logistic Regression algorithm using k-fold cross validation produces average values for precision, recall, and accuracy of 84%, 83.5% and 84%. This shows that the Naïve Bayes algorithm using percentage split is better than Logistic Regression, but when using the k-fold cross validation method, the Logistic Regression algorithm has a significant increase compared to Naïve Bayes. So that to classify heart disease is better with the Logistic Regression Algorithm with the k-fold cross validation method.
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Liu, Wei, and Qiong Hua Zhou. "Properties of Nanocrystalline Aluminium Fabricated by Warm-Vacuum-Compaction Method." Advanced Materials Research 299-300 (July 2011): 82–85. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.82.
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Nanocrystalline aluminium bulk material with average grain size of 25.2 nm was prepared by warm-vacuum-compaction method. The as-prepared nanocrystalline aluminium was characterized by X-ray diffraction (XRD), differential scanning calarmeutry analysis (DSC), thermogravimetric analysis (TG), and Microhardness test, respectively. The experimental results show that the average grain size and microstrain of the nanocrystalline aluminium are 25.2 nm and 0.018%, respectively. The melting point of as-prepared nanocrystalline aluminium is 918.9 K, which is lower than that of coarse-grained aluminium by 14 K. The endothermic value of nanocrystalline aluminium is 196.3J/g. The average microhardness of the as-prepared nanocrystalline aluminium is 1.65 GPa, which is 11 times higher than that of coarse-grained aluminium.
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Geetika, Borah, K. Gogoi Pradip, Borah Aicharjya, and Sharma Ponchami. "Sol-gel method of synthesis and characterization of some mixed-oxide nanocomposites." Journal of India Chemical Society Vol. 87, Nov 2010 (2010): 1345–49. https://doi.org/10.5281/zenodo.5805686.
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Department of Chemistry, Dibrugarh University, Dibrugarh-786 004, Assam, India <em>E-mail :</em> geetikachem@yahoo.co.in Coal Chemistry Division, NEIST, Jorhat-785 006, Assam, India <em>Manuscript received 6 October 2009, accepted 31 May 2010</em> Nanosized crystallites of the mixed oxides BaCuO<sub>2</sub> (1), BaNiO<sub>2</sub> (2), Ba<sub>2</sub>Co<sub>4</sub>O<sub>7.8</sub> (3) and Ba<sub>2</sub>CuCe<sub>2</sub>O<sub>7</sub>( 4) were synthesized by comparatively inexpensive route : Sol-Gel method. The particle size and morphology were studied by Scanning Electron Microscopy (SEM). The crystallinity, average crystalline size and phase purity or the prepared nanocomposites were analyzed by powder X-ray diffraction technique (XRD). The results indicated that all the samples had good crystallinity and composed or multiple phases with the exception of BaNiO<sub>2</sub>. The average crystalline sizes were found to be In the range 29-44 nm. Impedance measurements In the temperature range 303-363 K reveal decrease or bulk resistance (R<sub>h</sub>) with increase or temperature upto 333 K, Indicating semiconducting behaviour In this temperature range. The energy gap (<em>E</em><sub>g</sub>) measurements via Four-Probe setup instrument reveal that they fall in the range 0.15-2.15 eV.
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Indrajaya, Denny, Adi Setiawan, and Bambang Susanto. "Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 1 (2022): 149–64. http://dx.doi.org/10.30812/matrik.v22i1.1758.
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In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distributions of training datasets and testing datasets. In this research, the selection of SNP locations for the classification process was carried out by correlation analysis. The average accuracy obtained for the k-Nearest Neighbor method with the value of k=31 is 98.38% where the average F1 score is 98.39% while the Naive Bayes method obtained the average accuracy of 96.74% and the average F1 score of 96.63%. In this case, the k-Nearest Neighbor method is better than the Naive Bayes method in classifying SNP data to determine the origin of a person's ancestor tends to be from CEU or YRI.
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Sudarthio, Arthian Terry Sammatha, Bagus Mulyawan, and Darius Andana Haris. "APLIKASI E-COMMERCE BERBASIS WEB MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE DAN K-MEDOIDS." Jurnal Ilmu Komputer dan Sistem Informasi 8, no. 1 (2020): 31. http://dx.doi.org/10.24912/jiksi.v8i1.11461.
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Sugi Jaya Mandiri, Ltd. is a company specializing in sales of industrial equipments, such as cables, pipes, construction materials, machine spareparts et cetera. The company processes data manually by using Microsoft Excel. The process complicates data recapitulation, especially concerning large amounts of data. Another issue is the lack of advertising towards customer. This application aims to solve the problems by implementing data processing feature with Weighted Moving Average timeseries method and favourite product feature to better market products to prospective customers using k-Medoids clustering method.Preliminary evaluations using k-Medoids method shows two products fit to advertise by considering attributes such as sold amount, mean sold amount and transaction amount. Data evaluation using Weighted Moving Average method acquires sales forecast with Accuracy rate of 92.456%.
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Ginting, Salomo, Syahril Efendi, and Saib Suwilo. "Performance Improvement of Grid Mapping K-Means with the Average Value at Grid Point." IOP Conference Series: Earth and Environmental Science 1083, no. 1 (2022): 012082. http://dx.doi.org/10.1088/1755-1315/1083/1/012082.
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Abstract K-Means is a method that is well-known for its ability to handle large datasets, but is often stuck in a local optima state. This issue happens because K-Means generally uses random numbers to serve as the center point (centroid) of each cluster, and places each instance based on the proximity of the distance using Euclidean Distance. Hence, the concept of density parameter was developed, which tries to determine the ideal centroid based on the determination of several grid points in each existing cluster. The concept is known as Grid Mapping. In simple terms, Grid Mapping K-Means is a method in which each cluster is divided into several grid points, and then the centroid is randomly determined from each existing grid point. Further, the grid point with the largest number of instance is used as the initial centroid of each cluster. However, this method certainly has a weakness, since there is a possibility that the initial centroid on the generated grid point is not the best initial centroid. Therefore, this study was conducted to test the determination of initial centroid by Grid Mapping K-Means.
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Jusman, Moh, Nur’eni Nur’eni, and Lilies Handayani. "Ensemble K-Nearest Neighbors Method to Predict Composite Stock Price Index (CSPI) in Indonesia." Jurnal Matematika, Statistika dan Komputasi 18, no. 3 (2022): 423–33. http://dx.doi.org/10.20956/j.v18i3.19641.
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The Composite Stock Price Index (CSPI) is a guide for investors to see the movement of stock prices as a whole from time to time. These movements always change from time to time, so it is necessary to use analytical methods to make predictions. The method that can be used to examine this is the K-Nearest Neighbor method. The combination of the results of several K-NN predictions is an effective way to get one final prediction result, namely the method ensemble K-NN. The response variable used in this study is the Composite Stock Price Index (CSPI), while the predictor variables are the gold price, the rupiah exchange rate against the dollar, and the Dow Jones Industrial Average (DJIA) index. The data used are 52 periods. The data used for training are 39 periods and the data used for testing is 13 periods. The prediction results from the ensemble have better results than the K-NN. The prediction results from the ensemble have better results than the single K-NN. The prediction results from the method are ensemble K-NN average of 6078, 634 with a MAPE value of 7,16% including high accuracy
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Eliza Agustia, Dewi, Noer Fadhly, and Cut Zukhrina Oktaviani. "Clustering of Districts Based on Infrastructure Indicators Using K-Means And Average Linkage Methods." International Journal of Science, Technology & Management 6, no. 1 (2025): 54–60. https://doi.org/10.46729/ijstm.v6i1.1193.
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Aceh Jaya is one of the districts in Aceh Province that has great potential in various sectors, such as agriculture, plantations, and tourism. In the Aceh Jaya District Development Plan (RPK) Book 2023-2026, it is stated that the unequal distribution of infrastructure development in Aceh Jaya District in 2023-2026 has resulted in low investment competitiveness and decreased economic performance. Because of these conditions, this study clusters regions in Aceh Jaya District based on infrastructure indicators so that groups of sub-districts are obtained based on the level of infrastructure development. The method used in this study is clustering analysis by comparing it with two clustering methods, namely K-means and average linkage, which are validated based on the silhouette coefficient value to see which cluster is the best. The results obtained are the Kaiser-Mayer-Olkin (KMO) value of 0.611, which indicates that the sample is representative of a total of eight variables; for the zscore value, there is no outlier data, which means that the data of the eight infrastructure variables can be used entirely. Meanwhile, the clustering results using the K-Means method resulted in 3 clusters, and with the Average Lingkage method resulted in 2 clusters. This means that Aceh Jaya District can be clustered into 2 clusters, as seen from the silhouette coefficient value of 0.736. The conclusion that can be drawn from this research is that infrastructure development has not been evenly distributed in the 9 existing sub-districts, so that strategies and priorities are needed in infrastructure development in Aceh Jaya Regency.
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KURNIA, RAHMADI, MELIA ASMITA, ROZAKY IHSAN, IKHWANA ELFITRI, and DANANG KUMARA HADI. "Perbandingan Metoda Klasifikasi K-Nearest Neighbor dan Support Vector Machine pada Pengenalan Benda Terhalang berbasis Kode Rantai." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 3 (2024): 823. http://dx.doi.org/10.26760/elkomika.v12i3.823.
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ABSTRAKBenda yang terhalang oleh benda lain memiliki bentuk yang tidak sempurna karena sebagian sisinya tidak terlihat. Untuk mengatasi permasalahan tersebut, digunakan metoda yang dapat mengenali bentuk pada benda pada sisi yang masih nampak. Penelitian ini membandingkan metoda klasifikasi K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) berbasis kode rantai untuk mendeteksi bentuk benda terhalang. Terdapat 15 sampel untuk lima bentuk bangun datar pada 2 jenis citra benda. Hasil untuk dua jenis citra, metoda KNN memiliki rata-rata ketepatan sebesar 89,6% sedangkan metoda SVM sebesar 88.4%. Waktu komputasi citra animasi menggunakan metoda SVM lebih cepat 0,044 detik dari pada metoda KNN dan lebih cepat 0,034 detik untuk citra riil. Rata-rata memori yang digunakan dengan metoda SVM pada citra animasi lebih sedikit 0,32 Mb dari pada metoda K-NN Pada citra riil rata-rata memori yang digunakan dengan metoda SVM lebih sedikit 0,44 Mb dari metoda K-NN.Kata kunci: transformasi Hough, kode rantai, bentuk benda, KNN, SVM ABSTRACTObject that are blocked by other objects have an imperfect shape because some of their side are not visible. To overcome this problem, we propose a comparison the K Nearest Neighbor classification (K-NN) and the Support Vector Machine (SVM) method based on chain code algorithm. We used 15 samples for each shape of the object for two kind of images. The result of KNN method classification has an average accuracy of 89,6%. The SVM method has an average accuracy of 88.4%. The average computing time for the SVM method is 0,044 seconds faster than KNN method for drawing image and 0,0034 seconds faster for real images, The average memory for drawing image using the SVM method is 0,32Mb less than K-NN. In the real images the average memory used with the SVM method is 0,44 Mb less than the K-NN.Keywords: hough transform, chain code, shape object, KNN, SVM
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Kurniawan, Muchamad, Rani Rotul Muhima, and Siti Agustini. "Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 2 (2020): 34. http://dx.doi.org/10.25139/ijair.v2i2.3030.
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One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method. The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.
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Leu, Jai-Houng, Chih-Yao Lo, and Chi-Hau Liu. "Development and Test of Fixed Average K-means Base Decision Trees Grouping Method by Improving Decision Tree Clustering Method." Journal of Applied Sciences 9, no. 3 (2009): 528–34. http://dx.doi.org/10.3923/jas.2009.528.534.
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Priambodo, Bagus, Azlina Ahmad, and Rabiah Abdul Kadir. "Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 01 (2020): 18. http://dx.doi.org/10.3991/ijoe.v16i01.11671.
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For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments. Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.
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Setiyorini, Tyas, and Rizky Tri Asmono. "PENERAPAN METODE K-NEAREST NEIGHBOR DAN GINI INDEX PADA KLASIFIKASI KINERJA SISWA." Jurnal Techno Nusa Mandiri 16, no. 2 (2019): 121–26. http://dx.doi.org/10.33480/techno.v16i2.747.
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Predicting student academic performance is one of the important applications in data mining in education. However, existing work is not enough to identify which factors will affect student performance. Information on academic values or progress on student learning is not enough to be a factor in predicting student performance and helps students and educators to make improvements in learning and teaching. K-Nearest Neighbor is a simple method for classifying student performance, but K-Nearest Neighbor has problems in terms of high feature dimensions. To solve this problem, we need a method of selecting the Gini Index feature in reducing the high feature dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with values of k (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the highest average accuracy of 74.068 while the K-Nearest Neighbor and Gini Index methods showed the highest average accuracy of 76.516. From the results of these tests it can be concluded that the Gini Index is able to overcome the problem of high feature dimensions in K-Nearest Neighbor, so the application of the K-Nearest Neighbor and Gini Index can improve the accuracy of student performance classification better than using the K-Nearest Neighbor method.
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Sebko, V. V., Ye V. Pyrozhenko, N. M. Zashchepkina, V. G. Zdorenko, and O. M. Markina. "Four-parameter electromagnetic method for determining the parameters of brewery effluents." Journal of Achievements in Materials and Manufacturing Engineering 113, no. 2 (2022): 49–64. http://dx.doi.org/10.5604/01.3001.0016.1405.
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of the article is to study a four-parameter electromagnetic method for joint measurements of electrical resistivity k, relative permittivity εr, temperature t and density ρ of samples of acidic, alkaline and average effluents from a microbrewery based on a magnetic flux probe (MFP), which considers the influence of informative parameters of beer effluents on the components of the amplitude and phase signals of a multiparameter device. The implementation of the four-parameter method is carried out on the basis of the dependences G1 = f (A1) and G2 = f (A2) at two frequencies of the electromagnetic field f0 and f1 for acid, alkaline and average effluent and allows you to jointly determine the four parameters of effluent samples with the same converter in the same control area. The proposed method makes it possible to improve the accuracy of identifying effluent samples since the obtained multiparameter information makes it possible to determine the nature and properties of effluent samples using only one transducer with certain physical characteristics. The research results lead to the expansion of the technical capabilities of electromagnetic measurement methods, as well as to an increase in the metrological characteristics of electromagnetic transducers and an increase in the accuracy of measuring the parameters of effluent samples compared to reference methods and measuring instruments. Thus, the implementation of this approach contributes to the prediction and prevention of the reasons for the deviation of beer effluent samples from the specified indicators of environmental safety. The universal conversion functions MFP have been established, connecting the amplitude and phase components of the converter signals with the parameters k, εr, t and ρ of acidic, alkaline and average effluents. Based on the universal transformation functions G1 = f (A1) and G2 = f (A2), a four-parameter electromagnetic method for joint measurements of electrical resistivity k, relative permittivity εr, temperature t and density ρ of acidic, alkaline, and average effluents from breweries has been developed. When conducting research at two close frequencies of the electromagnetic field f0 = 20.3 MHz and f1 = 22 MHz, algorithms were obtained for measuring and calculating procedures for determining k, εr, t and ρ for samples of acidic, alkaline and average effluents from the brewing industry. Research perspectives consist in the creation of automated systems for multiparameter measuring control of the physicochemical characteristics of acidic and alkaline effluent from food and processing industries based on the immersed electromagnetic transducer. Based on the data obtained using informative methods to measure the parameters of effluent samples, an integrated method for treating beer effluents of various compositions will be proposed. At the same time, the scheme of the integrated treatment method should include a filter that provides the introduction of a magnetic fluid and a separation device that allows us to remove a fraction, including pollution in itself. Is that the proposed four-parameter electromagnetic method makes it possible to determine to what composition the controlled samples of wastewater should be attributed (acidic or alkaline). It, in turn, makes it possible to choose a rational method for treating beer effluents and to prevent the reasons for the deviation of effluent samples from the environmental safety indicators set by the standards. of the article is the research related to the expansion of the functional and technical capabilities of the electromagnetic two-frequency transducer MFP through the implementation of a four-parameter electromagnetic method of joint measurements of electrical resistivity k, relative permittivity εr, temperature t and density ρ of acidic, alkaline and average effluents from breweries. The universal transformation functions G1 = f (A1) and G2 = f (A2) found in the work at two close magnetic field frequencies, f0 = 20.3 MHz and f1 = 22 MHz, make it possible to control four physicochemical parameters of acidic, alkaline and average wastewater at the same time by the same MFP. An algorithm has been developed for determining the signal components of a two-frequency thermal MFP, the ranges of which correspond to the ranges of changes in electrical resistivity k, relative permittivity εr, temperature t and density ρ of acidic, alkaline, and average brewery effluents. The basic relations that describe the two-frequency four-parameter electromagnetic method of joint measurements of the physicochemical parameters of acidic, alkaline and averaged beer effluents have been obtained.
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Sobol, I. M., and B. V. Shukhman. "Quasi-Monte Carlo method for solving Fredholm equations." Monte Carlo Methods and Applications 25, no. 3 (2019): 253–57. http://dx.doi.org/10.1515/mcma-2019-2045.
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Abstract A Monte Carlo method used for the estimation of convergent von Neumann series solutions of a Fredholm equation of second kind is considered. The sum {z^{(d)}(x)} of d initial terms of the von Neumann series estimating the solution {z(x)} of the equation is represented as a d-dimensional integral over the unit cube {H_{d}} . This note presents three examples calculating {z^{(d)}(x)} for different kernels with norms {\lVert K\rVert<1} . We found that {z^{(d)}(x)} calculated using a quasi-Monte Carlo (QMC) method converges significantly faster than the corresponding Monte Carlo (MC) estimates in the entire range of {\lVert K\rVert} values. We also found that the average dimension {\hat{d}} of the integrand in all our examples is small, less than 3. We suggest that the average dimensions {\hat{d}} of our d-dimensional integrands are bounded as {d\to\infty} .
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Setiyorini, Tyas, and Rizky Tri Asmono. "PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 5, no. 1 (2019): 7–14. http://dx.doi.org/10.33480/jitk.v5i1.613.
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Education is a very important problem in the development of a country. One way to reach the level of quality of education is to predict student academic performance. The method used is still using an ineffective way because evaluation is based solely on the educator's assessment of information on the progress of student learning. Information on the progress of student learning is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. K-Nearest Neighbor is an effective method for classifying student performance, but K-Nearest Neighbor has problems in terms of large vector dimensions. This study aims to predict the academic performance of students using the K-Nearest Neighbor algorithm with the Information Gain feature selection method to reduce vector dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with k values (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the largest average accuracy of 74.068 while the K-Nearest Neighbor and Information Gain methods obtained the highest average accuracy of 76.553. From the results of these tests it can be concluded that Information Gain can reduce vector dimensions, so that the application of K-Nearest Neighbor and Information Gain can improve the accuracy of the classification of student performance better than using the K-Nearest Neighbor method.
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LIU, YONGSHENG, YUNBO ZHONG, JINCANG ZHANG, et al. "STRUCTURE AND MAGNETIC PROPERTIES OF NANOCRYSTALLINE MnZn FERRITES BY A PHASE TRANSFORMATION METHOD." International Journal of Modern Physics B 22, no. 20 (2008): 3433–38. http://dx.doi.org/10.1142/s0217979208039861.
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Nanocrystalline Mn 0.6 Zn 0.4 Fe 2 O 4 particles are synthesized by a phase transformation method. The crystal structure of these particles is that of spinel MnZn ferrite. The average particle size is about 50 nm and the grain size is about 11 nm. Magnetic measurements show that the saturation magnetization at 120 K is ~80% larger than that at 300 K, and imply that the majority of the nanoparticles are superparamagnetic from 65 to 300 K.
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Petrova, Lidia V. "Investigations on the Source Material of Oat Seeds, Avena sativa through the Multidimensional Ranking Method in Natural Conditions of Yakutia, Siberia, Russia." Bioscience Biotechnology Research Communications 14, no. 4 (2021): 1666–72. http://dx.doi.org/10.21786/bbrc/14.4.44.
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Eighty-three collection samples of oat seeds (Avena sativa L.) of various ecological, geographical, and breeding origins were studied in the conditions of Central Yakutia during 2017-2019 by the method of multidimensional ranking according to six economically valuable characteristics, namely, the duration of the growing season, grain yield, grain weight from the plant and panicles, the weight of 1.000 grains, and yielding tillering capacity. According to the results of the multidimensional ranking, the varieties were divided into three groups: the best, average, and worst. At that, from the data entered for 83 samples, the program determined priorities based on a combination of the duration of the growing season and yield. The group of best samples, based on a combination of economically valuable features, included 63% of samples from Europe, 30% from Russia, and 7% from Asia. The main share in the average group was made up of samples from Europe (63%), Russia (33%), and Asia (4%). The local zoned variety – Pokrovsky standard is included in the average group with a rank limit of 118.8. The worst group included the most samples from Europe (41%), Russia (26%), America (26%), Africa (3.7%), and Asia (3.7%). According to the precocity, 11 samples were identified that ripened earlier than the standard by 7-11 days. These are K-15062 (Omsk Region), K-15108 (USA), K-15111 (Colombia), K-15184 (Kemerovo Region), K-15191 (Slovakia), K-15357 (Norway), K-15375, K-15416, K-15418 (Germany), K-15392 (Sweden), and K-15408 (Belarus). Samples with high grain yield were included in the group of the best varieties. Among the selected varieties, cultivars K-15293 from Poland and K-15415 from Germany had the most stable yield over the years
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Wu, Yewen, Shi Zeng, Bin Wu, Bin Yang, and Xianyi Chen. "Quantitative Weighted Visual Cryptographic (k, m, n) Method." Security and Communication Networks 2021 (May 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/9968652.
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The weighted visual cryptographic scheme (WVCS) is a secret sharing technology, where weights are assigned to each shadow (participant) according to its importance. Among WVCS, the random grid-based WVCS (RGWVCS) is a frequently visited subject. It considers the premise of equality of all participants, without taking into account the existence of privileged people in reality. To address this problem of RGWVCS, this paper designs a new model, named as (k, m, n)-RGWVCS (where m < k < n ), in which the secret is encrypted into n shares and sent to k participants. In the recovery end, the secret could be reconstructed by minimum m shares when the privileged join in; otherwise, k shares are needed. The experimental results show that our method has the advantage of no pixel expansion and no codebook design by means of random grid. Moreover, the contrast of our model increased by 32.85% on average compared with that of other WVCS.
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Iswanto, Iswanto, Tulus Tulus, and Poltak Sihombing. "Comparison of Distance Models on K-Nearest Neighbor Algorithm in Stroke Disease Detection." Applied Technology and Computing Science Journal 4, no. 1 (2021): 63–68. http://dx.doi.org/10.33086/atcsj.v4i1.2097.
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Stroke is a cardiovascular (CVD) disease caused by the failure of brain cells to get oxygen supply to pose a risk of ischemic damage and result in death. This Disease can detect based on the similarity of symptoms experienced by the sufferer so that early steps can be taking with appropriate counseling and treatment. Stroke detecting requires a machine learning method. In this research, the author used one of the supervised learning classification methods, namely K-Nearest Neighbor (K-NN). K-NN is a classification method based on calculating the distance to training data. This research compares the Euclidean, Minkowski, Manhattan, Chebyshev distance models to obtain optimal results. The distance models have been tested using the stroke dataset sourced from the Kaggle repository. Based on the test results, the Chebyshev model has the highest levels of accuracy compared to the other three distance models with an average accuracy value of 95.49%, the highest accuracy of 96.03%, at K = 10. The Euclidean and Minkowski distance models have the same level of accuracy at each K value with an average accuracy value of 95.45%, the highest accuracy of 95.93% at K = 10. Meanwhile, Manhattan has the lowest average compared to the other distance models, which is 95.42% but has the highest accuracy of 96.03% at the value of K = 6
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Chang, Te Jen, Ping Sheng Huang, Shan Jen Cheng, Ching Yin Chen, and I. Hui Pan. "Low-Complexity Multiplication Using Complement and Signed-Digit Recoding Methods." Applied Mechanics and Materials 619 (August 2014): 342–46. http://dx.doi.org/10.4028/www.scientific.net/amm.619.342.
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In this paper, a fast multiplication computing method utilizing the complement representation method and canonical recoding technique is proposed. By performing complements and canonical recoding technique, the number of partial products can be reduced. Based on these techniques, we propose algorithm provides an efficient multiplication method. On average, our proposed algorithm to reduce the number of k-bit additions from (0.25k+logk/k+2.5) to (k/6 +logk/k+2.5), where k is the bit-length of the multiplicand A and multiplier B. We can therefore efficiently speed up the overall performance of the multiplication. Moreover, if we use the new proposes to compute common-multiplicand multiplication, the computational complexity can be reduced from (0.5 k+2 logk/k+5) to (k/3+2 logk/k+5) k-bit additions.
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黄, 蕊. "Stock Price Forecasting Method Based on CAE and GRU Models of K-Line and Moving Average." Advances in Applied Mathematics 12, no. 01 (2023): 373–85. http://dx.doi.org/10.12677/aam.2023.121041.
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Dogan, Yahya. "A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max-Pooling." Traitement du Signal 40, no. 2 (2023): 577–87. http://dx.doi.org/10.18280/ts.400216.
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Panggabean, Supriadi, Windu Gata, and Tri Agus Setiawan. "Analysis of Twitter Sentiment Towards Madrasahs Using Classification Methods." Journal of Applied Engineering and Technological Science (JAETS) 4, no. 1 (2022): 375–89. http://dx.doi.org/10.37385/jaets.v4i1.1088.
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In today's digital era, the influence and use of the internet has become a necessity, especially in Indonesia itself, internet users in early 2021 reached 202.6 million people. The most widely used internet use by Indonesians is social media. Several incidents of sexual violence that occurred in the madrasa environment as reported in the media, the emergence of radical Islamic issues which he said were the fruit of thoughts from the madrasa environment, terrorism which was also said to come from misinterpreting knowledge from madrasahs, intolerance to different religions, changes in the character of madrasah students and so on will cause negative thoughts towards madrasah. To find out how the sentiment of social media users towards madrasahs, a study was conducted on analisis twitter sentiment towards madrasah using the classification method. The methods used are Naïve Bayes (NB), Decision Tree (DT) and K – Nearest Neighbor (K-NN). Toimprove the performance of the classification method is carried out using the Particle Swarm Optimization (PSO) selection feature. On the other hand, tools gataframework, execute Python script dan rapidminer diguna kan jug a dalam penelitian this to membantu preprocessi ng dan cleansing pa da datasethingga membantu menciptaka n corpus dan sentiment ana lysis. Acuration obtained from the Naïve Bayes algorithm accuracy: 76.86% +/- 5.24% (micro average: 76.86%), Decision Tree accuracy: 61.38% +/- 5.46% (micro average: 61.35%), K-NN accuracy: 74.70% +/- 4.83% (micro average: 74.67%), Naïve Bayes PSO accuracy: 80.80% +/- 4.86% (micro average: 80.79%, Decision Tree PSO accuracy: 65.27% +/- 5.26% (micro average: 65.28%), and K-NN PSO accuracy: 67.24% +/- 7.92% (micro average: 67.25%). The results showed that the Naïve Bayes PSO algorithm got the best and accurate results. This study succeeded in obtaining an effective and best algorithm in classifying positive comments and negative comments related to sentiment analysis towards madrasahs by classification method.
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Marques, Valter, Marcos Ceddia, Mauro Antunes, et al. "USLE K-Factor Method Selection for a Tropical Catchment." Sustainability 11, no. 7 (2019): 1840. http://dx.doi.org/10.3390/su11071840.
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The use of the Universal Soil Loss Equation (USLE) and the Sediment Delivery Ratio (SDR) facilitates sediment yield (SY) estimates in watersheds. However, the soil loss predictions are frequently unrealistic because of the methods used to estimate the USLE’s factors. Here, we evaluated the performance of methods to estimate the soil erodibility (K-factor) and the influence of its estimation in the SY predictions. K-factor values were obtained from three widely used equations and using a portable rainfall simulator. These values were used to compute annual average soil loss and SY in a tropical watershed. We compared SY estimates with a 15-month observed sediment discharge dataset sampled in the catchment outlet. The most reliable method for the K-factor estimating was the USLE nomograph. Furthermore, our results indicate that the use of a portable rainfall simulator to estimate the K-factor tends to underestimate soil loss and sediment delivery.
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Jo, Yong Gil, Yohan Lee, Joonha Lee, Kee Jeong Bae, Min Bom Kim, and Young Ho Lee. "Screw Fixation Method through Temporary Kirschner Wire Hole for Coronal Hamate Fracture." Archives of Hand and Microsurgery 26, no. 4 (2021): 245–53. http://dx.doi.org/10.12790/ahm.21.0121.
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Purpose: Hamate coronal body fracture is a rare injury and often associated with dislocation of the carpometacarpal joint. For preserving the carpometacarpal joint, open reduction and rigid internal fixation is needed to displaced fracture. The purpose of this study was to evaluate the outcome of treating hamate coronal fracture with the screw fixation method through a temporary Kirschner wire (K-wire) fixation hole.Methods: From August 2016 to January 2021, eight patients who had displaced coronal hamate body fractures were enrolled. All patients were performed open reduction and multiple K-wires fixations. After that, the cortical screws were then inserted directly into the holes made by removing the K-wires one by one. The outcome measures were Disabilities of the Arm, Shoulder and Hand (DASH) scores and visual analogue scale (VAS) scores.Results: The average follow-up period was 11.5 months (range, 5–8 months) after surgery, and the bone union was observed at the 8 weeks after surgery. We confirmed that bone union had been completed for all the patients, and functional tests showed that the average DASH score was 3.95 (range, 0–8.3) and VAS score was 0.8 (range, 0–3).Conclusion: In coronal hamate body fractures, open reduction and screw fixation method through temporary K-wire fixation hole is simple and effective treatment technique.
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Gavrilović, Tamara, Vesna Đorđević, Jovana Periša, et al. "Luminescence Thermometry with Eu3+-Doped Y2Mo3O12: Comparison of Performance of Intensity Ratio and Machine Learning Temperature Read-Outs." Materials 17, no. 21 (2024): 5354. http://dx.doi.org/10.3390/ma17215354.
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Accurate temperature measurement is critical across various scientific and industrial applications, necessitating advancements in thermometry techniques. This study explores luminescence thermometry, specifically utilizing machine learning methodologies to enhance temperature sensitivity and accuracy. We investigate the performance of principal component analysis (PCA) on the Eu3+-doped Y2Mo3O12 luminescent probe, contrasting it with the traditional luminescence intensity ratio (LIR) method. By employing PCA to analyze the full emission spectra collected at varying temperatures, we achieve an average accuracy (ΔT) of 0.9 K and a resolution (δT) of 1.0 K, significantly outperforming the LIR method, which yielded an average accuracy of 2.3 K and a resolution of 2.9 K. Our findings demonstrate that while the LIR method offers a maximum sensitivity (Sr) of 5‰ K⁻1 at 472 K, PCA’s systematic approach enhances the reliability of temperature measurements, marking a crucial advancement in luminescence thermometry. This innovative approach not only enriches the dataset analysis but also sets a new standard for temperature measurement precision.
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Robiati, Silfi, Dina Fitria, Dodi Vionanda, and Dwi Sulistiowati. "Comparison of K-Means and K-Medoids in Clustering Regency/City in West Sumatra Province Based on Environmental Indicators." Indonesian Journal of Statistics and Its Applications 8, no. 2 (2024): 191–201. https://doi.org/10.29244/ijsa.v8i2p191-201.
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The Environmental Quality Index is an index that describes the condition of environmental management results nationally, and generalises from all regencies/cities and provinces in Indonesia. Although the Environmental Quality Index of West Sumatra Province has increased, there are still regencies/cities in West Sumatra Province have decreasing Environmental Quality Index. Therefore, it is necessary to conduct further analysis, one of which is to form a group of regencies/cities into a group according to their similarities or characteristics. This study aims to compare the K-Means and K-Medoids methods in grouping regencies/cities in West Sumatra Province based on environmental quality indicators in 2023. The data used in this research is secondary data, which is orginally the publication of Central Bureau of Statistics namely Sumatera Barat Dalam Angka in 2024. The research compares the K-Means cluster method and the K-Medoids cluster method. It concludes K-Means better than K-Medoids methods based on DB index with three clusters. First cluster has 12 regencies/cities with a high average air quality index, the second cluster has 6 regencies/cities that have small amounts of waste, and the third cluster has 1 city with a high average water quality index and land quality index, but a large amount of waste. Keywords: Cluster, Comparison, Environmental, K-Means, K-Medoids
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Tao, Dan. "Dynamic Web Page Graphic Design Method for Internet Big Data Information System." Mathematical Problems in Engineering 2022 (August 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/6753671.
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With the rapid development of the Internet, the page design technology of PC search engine tends to be mature. The content displayed in front of users not only pays attention to the practicability of information, but also takes into account the beauty of page layout. However, the web page design of the Internet mobile terminal is slightly backward, so we study the multimodal method based on the feature of large data density to extract the text, and use the K-means clustering algorithm to classify and recognize the text. The results show that the comprehensive evaluation of the design search engine on multiple platforms is higher than 0.89; In fuzzy keyword retrieval, the average accuracy of dynamic k-value clustering algorithm is 87.5%, while the average accuracy of traditional K-means clustering algorithm is 78.5%. Finally, in terms of user evaluation, the satisfaction of search pages increased by 5%–10%. Experiments show that the optimized algorithm and page design not only improve the accuracy and applicability in function, but also optimize the layout of text and pictures on the page.
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Yanti, Christina Purnama, Ni Wayan Eva Agustini, Ni Luh Wiwik Sri Rahayu Ginantra, and Dewa Ayu Putri Wulandari. "Perbandingan Metode K-NN Dan Metode Random Forest Untuk Analisis Sentimen pada Tweet Isu Minyak Goreng di Indonesia." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 2 (2023): 756. http://dx.doi.org/10.30865/mib.v7i2.5900.
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Along with the development of technological advances, a lot of social media is used by humans, one of which is Twitter social media. On Twitter social media, we can find a lot of text data, opinions and public opinion, as the issue of cooking oil is currently hot in Indonesia. In this study, the K-NN and Random Forest methods were used, and the purpose of this study was to compare the two methods in sentiment analysis on the issue of cooking oil. The results of the accuracy of these two methods are not too far apart. Each of the two methods used will be divided into three research scenarios, the first is scenario 1, a collection of 500 data, scenario 2, a collection of 800 data, and scenario 3, a collection of 1,000 data, where the ratio of training data and test data is 80:20. The test results for the K-NN method in scenario 2 are superior with an accuracy presentation of 74.58%, 56.75% precision and 44.57% recall and the lowest result is the K-NN method scenario 1 with an accuracy presentation of 71. 50%, 47.83% precision and 37.45% recall. The average test results for the K-NN method are 72.86% accuracy, 52.26% precision and 41.04% recall. While the average results of the random forest method are 73.37% accuracy, 52.26% precision and 34.28% recall
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Meilina, Lely, Nyoman Putra Sastra, and Dewa Made Wiharta. "LECTURERS ADMISSIONS SELECTIONS MODEL USING FUZZY K-NEAREST NEIGHBOR METHOD." Jurnal Teknik Informatika (Jutif) 4, no. 2 (2023): 449–56. http://dx.doi.org/10.52436/1.jutif.2023.4.2.740.
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Higher Education, or tertiary education, is the final stage which is optional in formal education. It is usually organized in the form of a university, academy, seminary, high school, or institute. Every tertiary institution needs qualified and professional educators because they have an important role in the process of implementing the Tri Dharma of Higher Education. Recruitment for teaching staff usually has several stages and standardization of assessment in selection proces. In order for the process of selecting educators to be carried out objectively, a support system is need to carry out the assessment process. This study applies the Fuzzy K-Nearest Neighbor (FK-NN) method for the classification process in determining prospective educators who pass or not. Data classification is a new data or object grouping into classes or labels based on certain attributes. The application of the FK-NN method has several stages, namely weighting the criteria, then calculating the closeness of the test data and training data, finding the value of k-nearest neighbors between the training data and testing data and determining the membership of each data. Tests were carried out using the Confusion matrix method on several variations of the k value where the highest percentage was obtained from the value of k = 5. The test results for all k values obtained an average accuracy rate of 89.22%, 89.22% precision and 82.45% recall with 114 training data and 50 test data. Based on the average value of the test results, it can be concluded that the FK-NN method is feasible and good to use for the selection of educators with the classification of pass or not.
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KIM, EUNG-KYEU, JIAN-TONG WU, SHINICHI TAMURA, et al. "COMPARISON OF NEURAL NETWORK AND K-NN CLASSIFICATION METHODS IN VOWEL AND PATELLAR SUBLUXATION IMAGE RECOGNITIONS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (1993): 775–82. http://dx.doi.org/10.1142/s0218001493000388.
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We make a comparision of classification ability between BPN (BackPropagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method has a long learning time but a very short recognition time. Especially if the number of dimensions of the samples is large, it can be said that BPN is better than k-NN in classification ability.
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Fujimoto, Kenjiro, Mamoru Watanabe, Toshiyuki Mori, and Shigeru Ito. "Synthesis of hollandite-type KxGaxSn8−xO16 fine particles by the sol-gel method." Journal of Materials Research 13, no. 4 (1998): 926–29. http://dx.doi.org/10.1557/jmr.1998.0127.
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KxGaxSn8−xO16 (x ≤ 2) powders with hollandite structure were prepared by the sol-gel method using metal alkoxides. Dried gels, when being annealed at 973 K, changed to well-crystallized hollandite powders with about 22 m2/g in BET value which consisted of needle-like crystallites 25 nm wide and 65 nm long in average. The specific surface area was nearly 100 times larger than that of the hollandite produced at 1648 K by the conventional method, and the preparation temperature was lowered by 500 to 700 K. The powders obtained at 973 K were characterized as an attractive porous material showing a pore size distribution profile sharply monodispersed at 10.7 nm in the mesopore range.
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Pamungkas, Lanjar, Nur Aela Dewi, and Nessia Alfadila Putri. "Classification of Student Grade Data Using the K-Means Clustering Method." Jurnal Sisfokom (Sistem Informasi dan Komputer) 13, no. 1 (2024): 86–91. http://dx.doi.org/10.32736/sisfokom.v13i1.1983.
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The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: "High Achievers," "Average Performance," and "Needs Improvement." Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.
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Gohel, Mukesh, Jemil S. Makadia, and Chandan Chakrabarti. "Effect of Hypoproteinemia on Electrolyte Measurement by Direct and Indirect Ion Selective Electrode Methods." Journal of Laboratory Physicians 13, no. 02 (2021): 144–47. http://dx.doi.org/10.1055/s-0041-1730821.
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Abstract Objective The aim of this study was to see the effect of hypoproteinemia on electrolyte measurement by two different techniques, that is, direct ion selective electrode (ISE) and indirect ISE. Material and Method It was an observational study in which 90 serum samples with normal protein content (Group-1) were subjected to sodium (Na+) and potassium (K+) measurements by direct and indirect ISE methods. In the same way, 90 serum samples with total protein < 5 g/dL (Group-2) were subjected to Na+ and K+ measurements by direct and indirect ISE methods. Result In samples from Group-1 patients, average Na+ was 138.1 ± 4.764 mmol/L by direct ISE method and 139.3 ± 3.887 mmol/L by indirect ISE method while average K+ was 4.41 ± 0.644 mmol/L by direct ISE method and 4.40 ± 0.592 mmol/L by indirect ISE method. There was no statistically significant difference in Na+ and K+ values measured by different methods. In samples from Group-2 patients, measured value of Na+ by direct ISE and indirect ISE was 134.57 ± 5.520 mmol/L and 138.64 ± 5.401 mmol/L, respectively. Difference between these two values was statistically significant with p-value of < 0.0001, but direct ISE and indirect ISE measured values of K+ was 4.146 ± 0.9639 mmol/L and 4.186 ± 0.8989, respectively, with no significant difference. Conclusion Direct and indirect ISE methods are not comparable and showing significantly different results for Na+ in case of hypoproteinemia. So, it is recommended that setups like intensive care unit or emergency department, where electrolyte values have significant treatment outcome, should follow direct ISE method and should compare its previous result with the same method. Both the methods should not be used interchangeably.
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He, Jiaji, Bingxin Lin, Qizhi Zhang, and Yiqiang Zhao. "Gate-Level Hardware Trojan Detection Method Based on K-Hypergraph." Electronics 14, no. 9 (2025): 1902. https://doi.org/10.3390/electronics14091902.
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To shorten the development cycle of integrated circuit (IC) chips, third-party IP cores (3PIPs) are widely used in the design phase; however, these 3PIPs may be untrusted, creating potential vulnerabilities. Attackers may insert hardware Trojans (HTs) into 3PIPs, resulting in the leakage of critical information, alteration of circuit functions, or even physical damage to circuits. This has attracted considerable attention, leading to increased research efforts focusing on detection methods for HTs. This paper proposes a K-Hypergraph model construction methodology oriented towards the abstraction of HT characteristics, aiming at detecting HTs. This method employs the K-nearest neighbors (K-NN) algorithm to construct a hypergraph model of gate-level netlists based on the extracted features. To ensure data balance, the SMOTE algorithm is employed before constructing the K-Hypergraph model. Then, the K-Hypergraph model is trained, and the weights of the K-Hypergraph are updated to accomplish the classification task of distinguishing between Trojan nodes and normal nodes. The experimental results demonstrate that, when evaluating Trust-Hub benchmark performance indicators, the proposed method has average balanced accuracy of 91.18% in classifying Trojan nodes, with a true positive rate (TPR) of 92.12%.