FORECASTING JUMLAH MAHASISWA BARU MENGGUNAKAN METODE AUTOMATIC CLUSTERING AND FUZZY LOGIC RELATIONSHIP MARKOV CHAIN (STUDI KASUS : FAKULTAS TEKNOLOGI INFORMASI DAN KOMUNIKASI VISUAL INSTITUT SAINS DAN TEKNOLOGI AL-KAMAL)

Abstraction Forecasting or forecasting is a calculation analysis technique that is done by carrying out qualitative and quantitative approaches to think about future events using reference data in the past. The purpose of this study is to predict the number of new students at the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021. Prediction of the number of new students in the Faculty of Information and Visual Communication Technology of the Al-Kamal Institute of Science in the future accurately is very important to do, because many decisions can be taken by the Leaders of the Al-Kamal Institute of Science and Technology from these predictions. Markov Chain Automatic Clustering and Fuzzy Logic Relationship Method was chosen because it has a better level of accuracy among other Fuzzy Logic methods. The data used in this study are secondary data obtained from the Academic Information System of the Al-Kamal Institute of Science and Technology. Based on this research it was found that the predicted results of the number of new students of the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021 amounted to 64 with a MAPE of 8.25%

F-neuron in the clustering problems

We propose a new type of neuron based on the use of Fourier transform properties. This new type of neuron, called Fourier neuron (F-neuron), simplifies solving of a range of problems belonging to the class of problems of creating self-organizing networks using teacherless learning. The application of such F-neuron improves the quality and efficiency of automatic clustering of objects. We described the basic principles and approaches that allow to consider the properties vector as a parametric piecewise linear function, which provides the possibility to switch to Fourier-images operation both for input objects and for learning weights. The reasons for transferring information processing to Fourier space are justified, automatic orthogonalization and ranking of the Fourier image of the feature vector is explained. The advantages of the statistical approach to neuron training and construction of the refined neuron state function based on the parameters of the normal distribution are analyzed. We describe the procedure of training and pre-training the F-neuron that uses a statistical model based on the use of parameters of a normal distribution to calculate the confidence interval. We described an algorithm for recalculating normal distribution parameters when a new sample is added to the cluster. We reviewed some results of F-neuron technology and compared it with a traditional perceptron. A list of references and citations to the author’s previous works are given below.

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

Preliminary Study on Sapphire Color Grading Method Based on Automatic Clustering Algorithm of Color Space Features

Traditionally, the color grading of sapphire is mainly based on the naked eye judgment of the appraiser. This judgment standard is not clear enough, and the judgment result has a greater subjective influence, which affects the accuracy of the classification. In this study, the GEM-3000 ultraviolet-visible spectrophotometer was selected, and the color features of 180 sapphire samples were extracted and classified using the CIE1976 color space of the device. The Kmeans algorithm was used to cluster analysis of 140 samples, and the separability of the color space features of different color levels was verified, and the center sample of each color level was obtained. The Euclidean distance between the centers of the remaining 40 samples is calculated, and each color grade prediction label is determined, and the sapphire color is automatically classified based on this. The experimental results show that the accuracy of sapphire color classification using the above method is 97.5%, which confirms the effect and accuracy of the artificial intelligence method in sapphire color classification.

A New Clustering Approach for Automatic Oscillographic Records Segmentation

The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is so high that it makes human analysis infeasible. An alternative way of solving this problem is to group events in similar classes so that it is no longer necessary to analyze all the events, but only the most representative of each class. Several automatic clustering algorithms have been proposed in the literature. Most of these algorithms use validation indexes to rank the partitioning quality and, consequently, find the optimal number of clusters. However, this issue remains open, as each index has its own performance highly dependent on the data spatial distribution. The main contribution of this paper is the development of a methodology that optimizes the results of any clustering algorithm, regardless of data spatial distribution. The proposal is to evaluate the internal correlation of each cluster to proceed or not in a new partitioning round. In summary, the traditional validation indexes will continue to be used in the cluster’s partition process, but it is the internal correlation measure of each one that will define the stopping splitting criteria. This approach was tested in a real waveforms database using the K-means algorithm with the Silhouette and also the Davies–Bouldin validation indexes. The results were compared with a specific methodology for that database and were shown to be totally consistent.

Intelligent and Adaptive Web Page Recommender System

In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.

Indonesian Online News Extraction and Clustering Using Evolving Clustering

43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.