Predictive Analytic on Human Resource  Department Data Based on Uncertain Numeric  Features Classification

Huda, Asrul; Ardi, Noper

doi:10.3991/ijim.v15i08.20907

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Some optimization methods require derived information or even need to have a complete understanding of the structure of matter and variables. Genetic Algorithm does not require specific information about a problem so it is more flexible [13] [14]. It's just that in previous studies the genetic algorithm only considers the parameters for each intersection and has not considered the coordination between traffic intersections.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Genetic Algorithm for Urban Traffic Light Schedule Problem

Sartikha¹,

Ardi²,

Lubis³

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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Congestion is a major problem at urban traffic light intersections. One of the problems is the uneven distribution of vehicles at traffic light intersections, even though the portion of green duration at traffic light intersections is the same. Must present an adjusted deduction based on the number of vehicles at each intersection. In addition to optimizing the green duration, if you only consider one intersection, it will cause other problems such as congestion at the next connected intersection so that the required parameters are used for coordination between the initial intersection and the next connected intersection. In this research, optimization was carried out by giving weight to each parameter based on real vehicle data passing through the route at 11 intersections in Yogyakarta. At 11 intersections, 9 traffic light intersections were taken which will make the optimal green duration based on the parameters of the initial flow, destination flow and trip duration for traffic light coordination between the initial traffic light intersection and the next connected traffic light intersection. Next, the fitness function is formulated and then processed using a genetic algorithm. In the Genetic Algorithm optimization process, chromosomes are the green duration of 9 traffic light intersections which will then be processed with the genetic algorithm stages. The result is 9 optimal green durations based on the initial flow, the destination flow and the duration of the trip. The green duration adjusts the weights with maximum accuracy and reduces the vehicle travel duration from 44-64 seconds per one traffic light and a total of 419 seconds at 9 traffic intersections from the optimized data.

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Section: Introductionmentioning

confidence: 99%

Optimization of Genetic Algorithm for Urban Traffic Light Schedule Problem

Sartikha¹,

Ardi²,

Lubis³

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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“…By using this method, the endemic butterfly data that have been obtained are grouped into several clusters, and then literacy is carried out to get the grouping results that do not change. Furthermore, the researchers designed a data mining application for the distribution of butterflies using the Waterfall process model, which can provide information in the form of butterfly data input, K-Means calculations, butterfly distribution maps and butterfly searches based on family [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Application for the Spread of Endemic Butterfly Cenderawasih Bay using the K-Means Clustering Algorithm

Wattimena,

Abilliyo S. Mampioper,

Reni Koibur

et al. 2023

Int. J. Onl. Eng.

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The superfamily Papilionoidea day butterfly, which is endemic to the Cenderawasih Bay islands (Numfor, Supiori, Biak and Yapen), consists of 6 family species: the Papilionidae, Hesperiidae, Pieridae, Riodinidae, Lycaenidae and Nymphalidae families. This study aims to analyze the grouping of endemic butterflies of the Bay of Cendrawasih based on wings and colours in 4 Clusters, namely Numfor, Supiori, Biak and Yapen Islands, by applying the function of the K-Means Clustering algorithm data mining method. The grouping selection was carried out 7 times with the conclusion that Numfor had 13 types of Endemic Butterfly species, Biak had 7 Papuan Endemic Butterfly Species, Supiori had 9 Endemic Butterfly Species, and Yapen had 11 Endemic Butterfly Species. The analysis results were then retested in an application built using the Waterfall system development method and the PHP and MySQL programming languages. In addition to applying the K-Means Clustering algorithm for grouping endemic butterflies, the application created produces a butterfly distribution map that displays butterfly information based on family.

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“…The utilization of decision trees for mortality prediction in hemodialysis patients with diabetes offers several advantages. Firstly, decision trees provide a transparent and interpretable framework, allowing clinicians to understand the decision-making process and identify critical factors contributing to mortality risk (Huda and Ardi 2021). This interpretability is crucial in medical settings, where trust and understanding of the model's predictions are paramount.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree for Predicting the Mortality in Hemodialysis Patient with Diabetes

Ardi

Lubis

Isnayanti

2023

jmp

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Hemodialysis patients with diabetes face a significantly higher risk of mortality compared to those without diabetes. Accurate prediction of mortality in this patient population is crucial for guiding clinical decision-making, improving patient outcomes, and optimizing resource allocation. Hemodialysis is a procedure for cleaning the blood from the waste products of the body's metabolism. this is one of modality to treat end stage kidney disease. Diabetes mellitus is a significant contributor to the global burden of chronic kidney disease (CKD), and patients with diabetes undergoing hemodialysis are at a higher risk of mortality compared to those without diabetes. Identifying factors that influence mortality risk in this population can aid in clinical decision-making and improve patient outcomes. Dialysis is performed on patients with kidney failure, both acute kidney failure and chronic kidney failure. This study is aimed to predict the mortality risk of hemodialysis patients with diabetes. The Taiwanese hemodialysis center enrolled a total of 665 hemodialysis patients. The prediction is based on Decision Tree. Compared with K-Nearest Neighbor, linear discriminant, Logistic Regression, and Ensemble, Decission Tree performed better. As for related medical variables like parathyroid surgery, urea reduction ratio, etc., they play a much smaller role in mortality risk factors than diabetes and cardiovascular disease. Diabetes

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Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification

Cited by 6 publications

References 9 publications

Optimization of Genetic Algorithm for Urban Traffic Light Schedule Problem

Optimization of Genetic Algorithm for Urban Traffic Light Schedule Problem

Data Mining Application for the Spread of Endemic Butterfly Cenderawasih Bay using the K-Means Clustering Algorithm

Decision Tree for Predicting the Mortality in Hemodialysis Patient with Diabetes

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