Analisis Penerapan Optimasi Perbandingan Kinerja Algoritma C4.5 Dan Naïve Bayes Berbasis Particle Swarm Optimization (Pso) Untuk

Siswa, Taghfirul Azhima Yoga

doi:10.52771/bangkitindonesia.v7i2.48

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…This is of course done in line with the economic principle of always trying to minimize costs while still achieving the highest results. This optimization is also important because competition in all fields is very tight (Sistem et al, 2018). Another alternative form of the Vehicle Routing Problem problem is the Capacitated Vehicle Routing Problem , which imposes a limit on the number of vehicles used by limiting their capacity.…”

Section: Methodsmentioning

confidence: 99%

“…In the Particle Swarm optimization framework, flocs are referred to as groups while particles are referred to as individuals. Each individual moves at a speed that is adjusted to the scope of the search and keeps a record of the most optimal position reached until then, as stated by (Sistem et al, 2018). (Naseem & Razzak, 2020) have outlined the various phases involved in the Particle Swarm optimization algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Capacitated Vehicle Routing Problem In Optimization Of Waste Truck Routes Using The Particle Swarm Optimization Algorithm

Rambe,

Rakhmawati

2023

SinkrOn

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In this research process, the aim is to determine the shortest route for transporting waste with the shortest or minimum total distance. In this case, what must be considered is the transportation of waste, regarding the distance between the location of the waste source to the landfills. The problem of waste transportation routes can be made in a mathematical method. The method used in waste transportation is the Capacitated Vehicle Routing Problem (CVRP) method. In solving the problem of transporting waste using the CVRP method, distance must be minimized or optimized using the Particle Swarm Optimization Algorithm. On the initial route of work area 1 there is the o-p-q-r-s-o route which has a total distance of 133.5 Km, in which there are several villages such as the Mambang Muda TPA - Belungihit Village - Aek Hitetoras Village - Brussel Plantation Village - Aek Tapa Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance is 132.5 Km with the o-s-q-r-p-o route where the village is Mambang Muda TPA - Aek Tapa Village - Aek Hitetoras Village - Brussels Plantation Village - Belungihit Village - Mambang Muda Landfill. In WK 2 there is an initial route o-p-q-r-s-o which has a total distance of 153.9 Km, in which the route has several TPA Mambang Muda villages - Marbau Village - Lobu Rampah Village - Simpang 4 Village - Pernantian Plantation Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance and route is 127.7 Km. In work area 3 with the initial route and distance of 150 and after using the PSO algorithm, the optimal route and distance is 137.1 Km. In work area 4 with a route and distance of 127.8 Km, the optimal route and distance using the PSO algorithm is 149.8 Km. Work area 4 has a route and initial distance of 157.8 Km and the optimal PSO route and distance is 149.8. The 5th working area has an initial distance of 180.3 and the PSO route is 134.3 Km. The working area of the 5 initial routes is 152.4 Km and the PSO route is 152.4.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Capacitated Vehicle Routing Problem In Optimization Of Waste Truck Routes Using The Particle Swarm Optimization Algorithm

Rambe,

Rakhmawati

2023

SinkrOn

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“…According to the research conducted by Siswa, it has been determined that the best classification algorithms for detecting the type of breast cancer are Logistic Regression and SVM. These two algorithms have demonstrated the highest accuracy values, equivalent to 96.8% [3]. Research by Chen et al [4] on breast cancer classification using Logistic Regression proved that using Logistic Regression can obtain a good accuracy with a value of 94%.…”

Section: Introductionmentioning

confidence: 96%

Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms

Farahdiba,

Kartini,

Nugroho

et al. 2023

Indonesian J. Comput. Cybern. Syst.

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Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.

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Analisis Penerapan Optimasi Perbandingan Kinerja Algoritma C4.5 Dan Naïve Bayes Berbasis Particle Swarm Optimization (Pso) Untuk

Cited by 2 publications

References 6 publications

Capacitated Vehicle Routing Problem In Optimization Of Waste Truck Routes Using The Particle Swarm Optimization Algorithm

Capacitated Vehicle Routing Problem In Optimization Of Waste Truck Routes Using The Particle Swarm Optimization Algorithm

Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms

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