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All Journal Jurnal Teknik Informatika STMIK Antar Bangsa Indonesian Journal of Networking and Security - IJNS
Jefi, Jefi
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Prediksi Peminatan Pelanggan dalam Penjualan Produk Sepatu Menggunakan Metode Decision Tree Berbasis Particle Swarm Optimization pada PT. Baskara Cipta Pratama Puspita, Ari; Jefi, Jefi; Fahmi, Muhammad
Jurnal Teknik Informatika Vol. 5 No. 1 (2019): JTI Periode Februari 2019
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v5i1.296

Abstract

Abstract—The PSO-based optimization C4.5 model gives a higher value of 78.16% compared to the C4.5 algorithm model that is 73.88. The results obtained differences between the two models by 4.28%. While for evaluation using ROC curve for second model that is, for model of algorithm C4.5 value of AUC is 0,764 with level of diagnosis classification fair, and for model of algorithm C4.5 based on PSO AUC is 0,780 with level of diagnosis of fair classification. It s concluded that ROC curvesmodels shows C4.5 algorithm based on PSO is larger. It can be inferred that C4.5 algorithm based on particle swam optimization is more accurate in predicting the customers’ interest for buying shoes.         Intisari— Analisis optimasi model algoritma C4.5 berbasis PSO memberikan nilai akurasi yang lebih tinggi yaitu 78.16% dibandingkan dengan model algoritma C4.5 yaitu 73.88%. Dari hasil tersebut didapatkan selisih antara kedua model yaitu 4,28%. Sementara untuk evalusai menggunakan ROC curve untuk kedua model yaitu, untuk model algoritma C4.5 nilai AUC adalah 0.764 dengan tingkat diagnosa Fair classification, dan untuk model algoritma C4.5 berbasis PSO nilai AUC adalah 0.780 dengan tingkat diagnosa Fair classification. Dari evaluasi ROC curve tersebut terlihat bahwa model algoritma C4.5 berbasis PSO lebih besar  Sehingga dapat disimpulkan bahwa algoritma C4.5 berbasis particle swarm optimization lebih akurat dalam memprediksi minat beli produk sepatu.  Kata Kunci — C4.5, Produk, Sepatu PSO 
Prediksi Peminatan Pelanggan dalam Penjualan Produk Sepatu Menggunakan Metode Decision Tree Berbasis Particle Swarm Optimization pada PT. Baskara Cipta Pratama Puspita, Ari; Jefi, Jefi; Fahmi, Muhammad
Jurnal Teknik Informatika Vol 5 No 1 (2019): JTI Periode Februari 2019
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v5i1.296

Abstract

Abstract—The PSO-based optimization C4.5 model gives a higher value of 78.16% compared to the C4.5 algorithm model that is 73.88. The results obtained differences between the two models by 4.28%. While for evaluation using ROC curve for second model that is, for model of algorithm C4.5 value of AUC is 0,764 with level of diagnosis classification fair, and for model of algorithm C4.5 based on PSO AUC is 0,780 with level of diagnosis of fair classification. It s concluded that ROC curvesmodels shows C4.5 algorithm based on PSO is larger. It can be inferred that C4.5 algorithm based on particle swam optimization is more accurate in predicting the customers’ interest for buying shoes.         Intisari— Analisis optimasi model algoritma C4.5 berbasis PSO memberikan nilai akurasi yang lebih tinggi yaitu 78.16% dibandingkan dengan model algoritma C4.5 yaitu 73.88%. Dari hasil tersebut didapatkan selisih antara kedua model yaitu 4,28%. Sementara untuk evalusai menggunakan ROC curve untuk kedua model yaitu, untuk model algoritma C4.5 nilai AUC adalah 0.764 dengan tingkat diagnosa Fair classification, dan untuk model algoritma C4.5 berbasis PSO nilai AUC adalah 0.780 dengan tingkat diagnosa Fair classification. Dari evaluasi ROC curve tersebut terlihat bahwa model algoritma C4.5 berbasis PSO lebih besar  Sehingga dapat disimpulkan bahwa algoritma C4.5 berbasis particle swarm optimization lebih akurat dalam memprediksi minat beli produk sepatu.  Kata Kunci — C4.5, Produk, Sepatu PSO 
Prediksi Bayi Lahir Secara Prematur Dengan Menggunakan Metode C.45 Berbasis Particle Swarm Optimization Pada Klinik Umi Jefi, Jefi
Indonesian Journal of Networking and Security (IJNS) Vol 8, No 3 (2019): IJNS Juli 2019
Publisher : APMMI - Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (421.917 KB) | DOI: 10.55181/ijns.v8i3.1590

Abstract

Abstract - Babies born prematurely can occur when the pregnancy has not reached the mature gestational age. Pregnancy usually lasts about 40 weeks. Some risk factors for preterm birth include having given birth to a premature baby before and becoming pregnant with twins. The complications associated with preterm birth include immature lungs, difficulty regulating body temperature, difficulty eating, and slow weight gain. Premature babies can require longer or more intense nursery care, medication, and sometimes surgery. Until now, there have been several cases of preterm labor that have no known cause. There are a number of factors and health problems that can trigger premature labor, which are unhealthy mothers, smoking, a history of pregnancy, fetal conditions, psychological conditions. For this reason, the author intends to make a study on how to predict a patient who will deliver prematurely. In this study C4.5 algorithm model and C4.5 algorithm model based on particle swarm optimization are used to get the rule in predicting premature births of babies and provide accuracy values. more accurate. After testing with two models, namely C4.5 Algorithm and C4.5 Algorithm based on particle swarm optimization, the results obtained are C4.5 Algorithm produces an accuracy value of 94.30% and AUC value of 0.986 with a diagnosis level of Excellent Classification, but after The addition is C4.5 algorithm based on particle swarm optimization, the accuracy value is 97.91% and the AUC value is 0.997 with the diagnosis level of Excellent Classification. So that both methods have different levels of accuracy which is equal to 3.61%. Keywords: gestational age, premature, C4.5 algorithm
Co-Authors Agus Salim Ari Puspita Baginda Oloan Lubis Muhammad Fahmi