Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments

Ariyati, Indah; Rosyida, Susy; Ramanda, Kresna; Riyanto, Verry; Faizah, Siti; Ridwansyah,

doi:10.1088/1742-6596/1641/1/012090

Cited by 11 publications

(10 citation statements)

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“…Particle swarm optimization generally chooses the maximum number of iterations or meets a predefined minimum classification condition. The specific algorithm flow is as follows [11][12] (1) Begin. Input the initial data of the distribution network, obtain the information of each node and branch, and determine the upper and lower limits of the voltage of each node.…”

Section: Algorithm Processmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

2022

DPS

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Distributed power generation has unparalleled advantages in energy substitution, environmental protection, improving system power supply reliability and reducing power supply costs. The purpose of this paper is to study the application of the improved particle swarm optimization algorithm(IPSO) in the location selection and sizing of distributed power generation. This paper first briefly introduces the characteristics of today's main distributed energy resources, and empirically analyzes the impact of distributed energy access on the distribution network. On this basis, a single-objective optimization model considering several constraints is established for the minimum investment cost and net loss of distributed generation. By examining the shortcomings of the standard particle swarm optimization algorithm, combining the optimization algorithm with various optimization techniques, a hybrid particle swarm optimization algorithm is proposed, and the effectiveness of the algorithm is studied. The optimization model is determined through the algorithm improvement, which further confirms the feasibility and effectiveness of the model and algorithm improvement. Then, in view of the environmental problems most concerned by modern society, advanced multi-objective algorithms are used to realize the overall optimization of power station location and power for distributed generation, and to provide decision makers with a variety of solutions. Experiments have shown that the overall optimization scheme in this paper can save about 20% of the cost, and through optimization, the network loss voltage can be reduced by about 30%.

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Section: Algorithm Processmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

2022

DPS

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“…Algoritma Decision Tree C4.5 seperti dengan namanya merupakan metode klasifikasi pohon keputusan. Decision Tree C4.5 merupakan proses klasifikasi memilih atribut dari dataset sebagai root node dan membuat cabang pada setiap nilai atribut yang digunakan dalam membagi kasus pada setiap cabang, kemudian melakukan proses yang sama pada setiap cabang sampai didapatkan kelas yang sama untuk semua kasus pada cabang (Ariyati et al, 2020). Algoritma ini mempunyai masukan terdiri dari data latih dan sampel.…”

Section: Decision Tree C45unclassified

“…*) Penulis korespondensi: dafizadi77@gmail.com Salah satu metode klasifikasi yang sering digunakan dalam membuat klasifikasi pada data adalah Decision Tree C4.5. C4.5 merupakan proses klasifikasi memilih atribut dari dataset sebagai root node dan membuat cabang pada setiap nilai atribut yang digunakan dalam membagi kasus pada setiap cabang, kemudian melakukan proses yang sama pada setiap cabang sampai didapatkan kelas yang sama untuk semua kasus pada cabang (Ariyati et al, 2020). Klasifikasi menggunakan Decision Tree C4.5 telah banyak digunakan di berbagai penelitian.…”

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“…PSO merupakan algoritma pencarian berbasis populasi yang pada prosesnya dilakukan inisialisasi populasi solusi secara acak untuk dapat menyelesaikan masalah optimasi (Grosan et al, 2006). Penggunaan optimasi PSO pada Decision Tree C4.5 sudah pernah dilakukan dalam penelitian klasifikasi penggunaan pembayaran digital yang dapat meningkatkan nilai akurasi pada klasifikasi (Ariyati et al, 2020).…”

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Kombinasi Analytical Hierarchy Process, C4.5, dan Particle Swarm Optimization pada Klasifikasi Pegawai

Nugroho

Widodo

Gernowo

2022

J. Sistem Info. Bisnis

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Decision Tree C4.5 is widely implemented in various research fields in determining classification, but there are still weaknesses in Decision Tree C4.5, one of which is that it cannot rank each alternative. In this study, to overcome the weakness of Decision Tree C4.5, a combination of Analytical Hierarchy Process (AHP) methods, Decision Tree C4.5, and Particle Swarm Optimization (PSO) methods is proposed in the case study of employee classification for promotion recommendations. The research begins by determining the criteria and weighting criteria from the interview results which are then processed with AHP to produce employee ratings and eligibility labels for the classification process. The classification process uses the Decision Tree C4.5 method which is optimized with the PSO algorithm so as to produce employee eligibility data for promotions. The results of the combined research of AHP, Decision Tree C4.5, and PSO methods show that AHP can produce employee ratings based on performance and potential criteria, and Decision Tree C4.5 classification and optimization with PSO have better accuracy results, namely 95.80% compared to Decision Tree C4.5 method without PSO optimization is 93.40%. Based on the results of the ranking and classification of this research can be used as a basis for promotion of employees.

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“…Another study that uses the C4.5 algorithm is also shown good performance for classifying the eligibility value of new prospective debtors [12] and exceeding Naïve Bayes in predicting the future behavior of the customers [13]. The C4.5 algorithm can also be optimized with PSO to select parameters/features [14]- [18]. This feature selection is done to avoid unrelated features that can reduce the performance of the classifier model.…”

Section: Introductionmentioning

confidence: 99%

Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization

Nazir

Akhyar

Yusra

et al. 2022

SJI

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Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).

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Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments

Cited by 11 publications

References 4 publications

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

Kombinasi Analytical Hierarchy Process, C4.5, dan Particle Swarm Optimization pada Klasifikasi Pegawai

Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization

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