Sistem Referensi Pemilihan Smartphone Android Dengan Metode Fuzzy C-Means dan TOPSIS

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Giovan Meidy Susanto ◽  
Sandy Kosasi ◽  
David David ◽  
Gat Gat ◽  
Susanti M. Kuway
Keyword(s):  
Software Design ◽  
Euclidean Distance ◽  
Programming Models ◽  
Extreme Programming ◽  
Cluster Center ◽  
Acceptance Testing ◽  
Well Testing ◽  
Test Results ◽  
Fuzzy C Means ◽  
White Box Testing

Difficulties faced by STMIK Pontianak students while choosing an Android smartphone are the diversity of brands, types, series and specifications makes it hard to choose which is the best. Determine that matches to needs along with an appropriate budget also difficult. This research aims to design a reference system for selecting an Android smartphone to resolve the problem. This system was developed using the Fuzzy C-Means algorithm and TOPSIS. The research method used is survey. Software design uses agile with extreme programming models also White-Box Testing, cluster center testing, and acceptance testing. This research found a method to get an alternative group that matches to the user from existing cluster by Euclidean Distance. This research produces a system that can clustering smartphone data and provide references in the form of alternatives that matches to the user. The test results using White-Box Testing produce all functions running well. Testing the cluster center using MSE gets the central error values ​​C1: 1.8481, C2: 2.5316, and C3: 1.8214. Acceptance tesing results above 70%. Weaknesses in this system do not discuss lifestyle needs in choosing an Android smartphone. The criteria used in this research is still technical and not use non-technical criteria.

scholarly journals A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data

2021 ◽  
Vol 15 ◽  
pp. 1759-1765
Author(s):  
Souad Azzouzi ◽  
Amal Hjouji ◽  
Jaouad EL- Mekkaoui ◽  
Ahmed EL Khalfi
Keyword(s):  
Euclidean Distance ◽  
Dimensional Space ◽  
Noisy Data ◽  
High Accuracy ◽  
Cluster Center ◽  
Fuzzy C Means ◽  
Clustering And Classification ◽  
The Comparative Study ◽  
Fuzzy C Means Algorithm ◽  
Statistical Clustering

The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibilistic Fuzzy C-Means (ImPFCM) by combining the PFCM algorithm with a very powerful statistical method. The properties of this new ImPFCM algorithm show that it is not only applicable on clusters of spherical shapes, but also on clusters of different sizes and densities. The results of the comparative study with very recent algorithms indicate the performance and the superiority of the proposed approach to easily group the datasets in a large-dimensional space and to use not only the Euclidean distance but more sophisticated standards norms, capable to deal with much more complicated problems. On the other hand, we have demonstrated that the ImPFCM algorithm is also capable of detecting the cluster center with high accuracy and performing satisfactorily in multiple environments with noisy data and outliers.

Pengembangan Sistem Manajemen Event Pameran Karya Mahasiswa Menggunakan Metode Extreme Programming

2021 ◽  
Vol 5 (4) ◽  
pp. 809-819
Author(s):  
Ahmad Ali Mutezar ◽  
Umniy Salamah
Keyword(s):  
Development Process ◽  
System Development ◽  
Extreme Programming ◽  
Test Results ◽  
Event Data ◽  
Event Management ◽  
Soft Skill ◽  
Registration Process ◽  
User Expectations ◽  
Box Method

An event is a means for students to improve their soft skill and hard skill. In college, one kind of event that usually held regularly is an exhibition. It is usually held around the universities environtment, but in practice there are still some shortcomings, such as the registration process is done manually, attendance of participants that are not integrated with the system, and unavailability of certificates for participants who have attended the event. Since the outbreak of Covid-19, organizing the events must be done online, so we need a system that can accommodate this. Therefore, this study aims to create an event management system that can manage exhibition event data. Besides, the system is also equipped with a feature to generate an E-Certificate that has a QR Code embedded. The method used in this study is Extreme Programming, with its flexible nature toward changes to facilitate the process of system development. The testing in this study is using black box method, with the test results show that all functional in the system can run well in accordance with user expectations. The use of the Extreme Programming method produces a quality system, because users are involved during the system development process.  

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

scholarly journals Design and Build a Multilevel Mace Nutmeg Dryer to Improve the People's Economy

SinkrOn ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 107-112
Author(s):  
Ihsan Ihsan ◽  
Dirja Nur Ilham ◽  
Reza Ade Putra ◽  
Rudi Arif Candra ◽  
Arie Budiansyah
Keyword(s):  
Software Design ◽  
Economic Value ◽  
Research Work ◽  
Test Results ◽  
Drying Process ◽  
Wet Season ◽  
Drying Time ◽  
Agricultural Community ◽  
Design Software

Nutmeg is a source of income for some people in South Aceh, and some types of nutmeg, like mace nutmeg, are of better quality. Mace nutmeg is also an agricultural community with great economic value and benefits for humans, as it can be processed into spices and herbs. A range of products includes nutmeg oil and medicines. The harvesting and drying of mace nutmeg, which is still considered a problem by nutmeg growers, cannot be isolated from the processing of the basic ingredients. The natural process of drying mace nutmeg involves the use of sunshine, which necessitates a considerable drying time. Therefore nutmeg farmers frequently complain of erratic weather, especially during the wet season. The constant rain can cause the mace to rot, causing the nutmeg farming community's revenue to become unstable. Methods and steps of research work starting from the study of literature, determination of design specifications, hardware design, software design, toolmaking, tool testing.Good results were reached with the dryness of the mace nutmeg, which can be adjusted, and without putting into account the weather in the drying process, which is usually done with the heat of the sun, per the results of the testing of the designed tools.The average dryness of mace nutmeg is at a temperature of 45 percent with a time of 4 hours and a capacity of 100 grams; according to the test results of the automatic mace drying machine, it produces 50 grams of dry mace.  

scholarly journals Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

2021 ◽  
Vol 10 (2) ◽  
pp. 291
Author(s):  
Zuherman Rustam ◽  
Aldi Purwanto ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Lung Nodule ◽  
Image Data ◽  
Cluster Center ◽  
Support Vector ◽  
Fuzzy C Means ◽  
Kernel Distance ◽  
Abnormal Cells ◽  
Membership Matrix

<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

The Integrated Technology of Residual Reserves Localization and Profit Increase on Brownfields

2021 ◽  
Author(s):  
Artur Aslanyan ◽  
Bulat Ganiev ◽  
Azat Lutfullin ◽  
Ildar Z. Farhutdinov ◽  
Danila Gulyaev ◽  
...  
Keyword(s):  
Well Testing ◽  
Test Results ◽  
Reservoir Model ◽  
Digital Twin ◽  
Integrated Technology ◽  
History Analysis ◽  
Production History ◽  
Production Increase ◽  
Interference Tests ◽  
Advanced Production

Abstract Brown fields that are currently experiencing production decline can benefit a lot from production enhancement operations based on localization of residual reserves and geology clarification. The set of solutions includes targeted recommendations for additional well surveys followed by producers and injectors workovers, like whole wellbore or selective stimulation, polymer flow conformance, hydraulic fracturing and side tracking. As a result, previously poorly drained areas are involved in production, which increases current rates and ultimate recovery. The integrated technology of residual reserves localization and production increase includes: Primary analysis of the production history for reservoir blocks ranking by production increase potential. Advanced bottom-hole pressures and production history analysis by multiwell deconvolution for pressure maintenance system optimization and production enhancement. Advanced production logging for flow profile and production layer-by-layer allocation. Conducting pulse-code interference testing for average saturation between wells estimation. 3D reservoir dynamic model calibration on advanced tests findings. Multi-scenario development planning for the scenario with biggest NPV regarding surface infrastructure. The presented integrated technology is carried stage by stage. Based on the data analysis at the first stage (the Prime analysis) it is possible to get three types of results. The top-level assessment of the current development opportunities of the area, evaluation of current residual reserves on base of displacement sweep efficiency estimation, and evaluation of the potential production increase for various blocks of the field. Results of the second stage were obtained for the block deemed with the highest potential for production increase. Those results may reveal possible complications, and relevant workovers can be advised along with additional surveys that can further help to locate current reserves. The last stage of Prime analysis provides the most suitable choice was to perform an advanced logging and well-testing, as they include both single-well and multi-well tests. Pulse-code interference tests, multi-well retrospective tests and reservoir-oriented production logging make it possible to scan the reservoir laterally and vertically, which is especially important for multi-layered fields. The reservoir parameters obtained from the test results are used to calibrate the dynamic reservoir model. The effects of production enhancement operations are calculated from the 3D model. The set of possible activities is evaluated in terms of their financial efficiency based on the economic model of the operator company using multi-scenario approach on a specifically created digital twin of the field. The unique feature of this approach lies in an integrated usage of advanced production history analysis, advanced logging and well-testing technologies, as well as further calibration of the dynamic reservoir model based on test results and used-friendly interface for field digital twin interaction. This paper demonstrates on how to use the field tests results to calibrate the reservoir model and increase the accuracy of production forecasting by reducing the model uncertainty, with intent to increase profit of brownfields.

Using Extreme Programming in the Software Design Course

2002 ◽  
Vol 12 (3) ◽  
pp. 223-234 ◽  
Author(s):  
David H. Johnson ◽  
James Caristi
Keyword(s):  
Software Design ◽  
Extreme Programming

CSG well testing—Santos' experience in Queensland

2013 ◽  
Vol 53 (1) ◽  
pp. 227
Author(s):  
Czek Hoong Tan ◽  
Guncel Demircan ◽  
Mathias Satyagraha
Keyword(s):  
Radial Flow ◽  
Test Methods ◽  
Well Testing ◽  
Test Results ◽  
Well Test ◽  
Well Performance ◽  
Validity Of Results ◽  
Transient Behaviour ◽  
Key Factor ◽  
Primary Porosity

Permeability of the cleat system is a key factor controlling the productivity of CSG reservoirs and, therefore, the commerciality of development projects. Well testing is routinely used to provide representative values of coal permeability. The authors’ experience has shown pressure transient behaviour in coal reservoirs to be similar to those in primary porosity systems, with pseudo radial flow frequently observed, and the dual-porosity signature largely absent. Despite the authors’ best efforts in test design, large permeability variation and extremely high skin factors have been seen. The authors have run variations of drill stem tests (DSTs), injection tests, and wireline tests to understand the dependency of results to test methods, and the validity of results obtained. Pertinent examples of each type of test are discussed. Finally, recommendations to reconcile well test results to actual well performance are presented.

An Energy-Efficient Clustering Algorithm Using Fuzzy C-Means and Genetic Fuzzy System for Wireless Sensor Network

2016 ◽  
Vol 26 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Ayub Shokrollahi ◽  
Babak Mazloom-Nezhad Maybodi
Keyword(s):  
Energy Consumption ◽  
Fuzzy System ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Cluster Center ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Genetic Fuzzy System ◽  
Energy Efficient Clustering

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

Design of a Direct TPMS System Based on SP37

2013 ◽  
Vol 278-280 ◽  
pp. 1022-1026
Author(s):  
Dong Dong Hou ◽  
Dai Rong Hu ◽  
Yuan Yang Liu ◽  
Ming Qiang Li
Keyword(s):  
Software Design ◽  
Test Results ◽  
Wireless Data ◽  
Key Technology ◽  
System Composition ◽  
Actual Test

This paper introduces the TPMS installation method and system composition, and on this foundation, a kind of scheme of the TPMS emitter based on the SP37 chip is proposed. Monitor use the microprocessor and TDA5230 chip to realize the wireless data receiving, and this paper details the key technology in the hardware and software design. The actual test results show that, sensitivity of the wireless data receiving of the system not less than -105dBm, and the system works stability when speed of car is 80 ~100 km/h.

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