Particle Swarm Optimization Untuk Sistem Informasi Penjadwalan Resource Di Perguruan Tinggi

Mansur, Mansur; Prahasto, Toni; Farikhin, Farikhin

doi:10.21456/vol4iss1pp11-19

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Swarm intelligence systems deploy innovative intelligence in solving optimization problems by taking inspiration from biological examples, such as the phenomenon of groups (swarms) in animals, where each group has individual behavior in carrying out joint actions to achieve the same goal. (Mansur et al, 2014) The Rapid Miner Framework process in Figure 7.…”

Section: Resultsmentioning

confidence: 99%

Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes

Sulistyo,

Palupi

2023

J. Ris. Inform.

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Classification of Blighted Ovum Factors or undeveloped fetuses is carried out considering that many cases occur in pregnant women. Blighted Ovum is 60% of the causes of miscarriage. In Indonesia, it is found in 37% of every 100 pregnancies. Classification uses Naïve Bayes based on Particle Swarm Optimization (PSO), which only requires small training data to determine the parameter estimates needed in the classification process, and the use of Particle Swarm Optimization can increase accuracy and solve optimization problems with the process of selecting variable data and attribute data to create a questionnaire as a data collection method. The results of the classification of blighted Ovum in pregnant women using the Naïve Bayes algorithm with the Rapid Miner framework obtained an accuracy value of 71.56% with an Area Under Curve (AUC) of 0.674 included in the excellent classification category. After using the PSO optimization, the accuracy value rose to 79.82% with an Area Under the Curve of 0.764, including a good classification category. Naïve Bayes is a suitable method for classification, and PSO can improve the accuracy and AUC values .

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Section: Resultsmentioning

confidence: 99%

Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes

Sulistyo,

Palupi

2023

J. Ris. Inform.

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“…Particle Swarm Optimization (PSO) has parameters such as position, maximum speed, acceleration constant, and weight of inertia. In the PSO technique, there are several ways to optimize, including increasing the attribute weight of all attributes or variables used, selecting attributes (attribute selection), and selecting features (feature selection) (Mansur, Prahasto, and Farikhin, 2014).…”

Section: Methodsmentioning

confidence: 99%

Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

Anwar

2021

IJRICE

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The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.

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“…Model ini akan disimulasikan dalam ruang dengan dimensi tertentu dengan sejumlah iterasi sehingga di setiap iterasi, posisi partikel akan semakin mengarah ke target yang dituju (minimasi atau maksimasi fungsi). Ini dilakukan hingga maksimum iterasi dicapai atau bisa juga digunakan kriteria penghentian yang lain [13], [16], [17].…”

Section: Pendahuluanunclassified

Perbandingan Metode Optimasi Non-Linear Partical Swarm Optimization (PSO) Dengan Metode Interior Point Untuk Optimasi Daya Pada Turbin Angin Dengan Menentukan Nilai Optimum Pitch Angle

Prasetyo

Rafinda

Nugroho

2022

kilat

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Along with the increasing development of technology, demand for electricity is also increasing. To overcome the problem of reducing electricity raw materials originating from fossil energy, new renewable energi is the solution. One of the new renewable energi sources that have high efficiency values is wind. Wind power plants require wind speed to produce efficiency and output power in the wind turbine. The optimization problem of a wind turbine is to determine the angle of placement of the wind turbine in order to produce the desired optimum power. This journal is determining the wind turbine pitch angle to determine the optimum power by comparing two optimization methods, namely Partical Swarm Optimization (PSO) and the Interior Point optimization method. The data to be obtained is the optimal distance between the turbines and the comparison of the efficiency between the two optimization methods in producing the optimal solution for the problem of placing wind turbines in the wind turbine field. Seiring dengan meningkatnya perkembangan teknologi, kebutuhan listrik juga semakin meningkat. Untuk mengatasi permasalahan berkurangnya bahan baku listrik yang berasal dari energi fosil, maka energi baru terbarukan adalah solusinya. Sumber energi baru terbarukan yang memiliki nilai efisiensi yang tinggi salah satunya adalah angin. Pembangkit listrik tenaga angin membutuhkan kecepatan angin untuk menghasilkan efisiensi dan daya keluaran pada turbin angin tersebut. Permasalahan optimisasi dari suatu turbin angin adalah menentukan sudut peletakan turbin angin agar menghasilkan daya optimum yang diinginkan. Pada jurnal ini bertujuan menentukan pitch angle wind turbine unutk menentukan daya optimum dengan membandingkan dua metode optimasi yaitu Partical Swarm Optimization (PSO) dan metode optimasi Interior Point. Data yang akan diperoleh adalah jarak optimal antar turbin dan perbandingan efisiensi antara kedua metode optimasi tersebut dalam menghasilkan solusi optimal untuk masalah penempatan turbin angin di lapangan turbin angin.

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Particle Swarm Optimization Untuk Sistem Informasi Penjadwalan Resource Di Perguruan Tinggi

Cited by 6 publications

References 13 publications

Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes

Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes

Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

Perbandingan Metode Optimasi Non-Linear Partical Swarm Optimization (PSO) Dengan Metode Interior Point Untuk Optimasi Daya Pada Turbin Angin Dengan Menentukan Nilai Optimum Pitch Angle

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