Metode Fuzzy Inference System untuk Penilaian Kinerja Pegawai Perpustakaan dan Pustakawan

Alamsyah, Alamsyah; Muna, Izza Hasanul

doi:10.15294/sji.v3i1.6136

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…From table 6, the author membership is obtained as follows: Cluster 1= {B}, Cluster 2= {A,E,G,H}, Cluster 3= {C,D,F} In this step, the ratio between the BCV (Between Cluster Variation) and WCV (Within Cluster Variation) is also calculated: BCV= d(m1,m2) + d(m1,m3) + d(m2,m3) = 6,741 WCV=1.275 2 +0 2 +0.471 2 +0.745 2 +0.354 2 +0.745 2 +0.791 2 +1.064 2 = 4.833 So big ratio BCV/WCV = 6.741/4.833 = 1.394 When compared, the current ratio (1,394) is greater than the previous ratio (0,950) therefore the algorithm continues to the next step.…”

Section: Table 4 Distance Between Data and Centroidmentioning

confidence: 99%

“…Higher Education is a scientific institution that has the task of organizing education and teaching above the secondary level in tertiary institutions and that provides education and teaching based on Indonesian national culture in a scientific way. According to [1], performance appraisal is a very important aspect in order to improve the quality of human resources (HR). This is one way to find out the condition of performance results, including the performance of a lecturer in publishing the results of his scientific work in the form of national and international journals.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited Sinta

Dahnial

2023

J. Electr. Eng. Comput. Sci.

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Lecturer research or scientific results published both nationally and with Sinta accreditation. This is done to determine the level of publication of lecturer research results that have been published so that they can be easily disseminated so that the knowledge gained influences knowledge in general and has an impact on people's lives. The problem in this research is how to apply the K-Means clustering method with reference to the results of lecturer research publications based on national journals and sinta accredited journals. In this study, the researchers used the K-Means method, which has the goal of minimizing the objective function that has been set in the clustering process and maximizing the variation of data in other clusters. So researchers will look for the best number of clusters in the K-means method. The discussion is carried out based on the data that has been collected in the form of national journal weights and sinta accredited journal weights by utilizing Matlab R2013b software. From the results of system testing, lecturer clustering was obtained related to the results of journal weights, namely lecturers with high national scores and no sinta accreditation weights, lecturers with quite high national weights and high sinta accreditation weights, moderate national weights and low sinta accreditation weights, low national weights and high weights with low Sinta accreditation. This study resulted in the conclusion that the data was processed through several stages, starting with calculating the weight value of each issue of the national journal and sinta accredited journal. Then the results of these weights are recapitulated based on each lecturer so that it can be seen the total value of the lecturers' weights in publishing research results in the form of journals that have been published both in the form of national publications and sinta publications.

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Section: Table 4 Distance Between Data and Centroidmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited Sinta

Dahnial

2023

J. Electr. Eng. Comput. Sci.

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“…This phase clarifies the description of how input and output is processed to produce the system [20]. It is build with a waterfall model, which consist of requirement analysis, design, coding, and testing [21].…”

Section: Expert System Design and Developmentmentioning

confidence: 99%

Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs

Perbawawati

Sugiharti

Muslim

2019

SJI

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The development of technology capable to imitating the process of human thinking and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs. Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy

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“…In the subdivision of information technology there are several methods that can be used to help everyday life, one of which is to diagnose an disease. One way of diagnosing disease, fuzzy logic can be used to present the linguistic truth values of a set of members [1] and alternative ways of thinking that can model complex systems using knowledge and experience that have logic starting as a study of language in argumentation and persuasion [2] . One of the fuzzy methods used in diagnosing an disease is Neuro Fuzzy.…”

Section: Introductionmentioning

confidence: 99%

Optimization Neuro Fuzzy Using Genetic Algorithm For Diagnose Typhoid Fever

Fata

Arifudin

Prasetiyo

2019

SJI

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Neuro Fuzzy is one method in the field of information technology used in diagnosing an disease. The application of Neuro Fuzzy is to identify disease. Genetic algorithms can be used to find solutions without paying attention to the subject matter specifically, one of which is an optimization problem. Typhoid or typhoid fever is a disease caused by Salmonella enterica bacteria, especially its derivatives. The diagnosis of typhoid fever is not an easy thing to do. This is because some of the indications experienced by patients also appear in other diseases. The number of patients with typhoid fever that requires accuracy in diagnosing typhoid fever based on indications caused. Based on this background this study aims to assist in the diagnosis of typhoid fever with 11 indication variables. This study uses medical record data for typhoid fever in 2017 Tidar Magelang Hospital. The method used is Neuro Fuzzy which optimizes the value of the degree of membership with genetic Algorithms. Then the value of the degree of neuro fuzzy membership is more optimal. The results of this optimization are the diagnosis of typhoid fever based on the variable of indications entered. From the research results obtained from the neuro fuzzy method get an 80% accuracy value and neuro fuzzy optimization results with genetic algorithms with a value of pc 0.5, pm 0.2 and max generation 25 the value of accuracy increases to 90%. Suggestions from this study, need to add more specific indication variables.

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Metode Fuzzy Inference System untuk Penilaian Kinerja Pegawai Perpustakaan dan Pustakawan

Cited by 3 publications

References 1 publication

Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited Sinta

Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited Sinta

Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs

Optimization Neuro Fuzzy Using Genetic Algorithm For Diagnose Typhoid Fever

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