Data Mining Classification Models for Industrial Planning

Bragança, Ricardo; Portela, Filipe; Vale, A.; Guimarães, Tiago; Santos, Manuel Filipe

doi:10.1007/978-981-10-5427-3_60

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…This transformation of the variable type reduced the pre-existing dependence relationship with the O-D pairs and permitted formulation of scenarios with different travel times and numbers of jobs. This alteration of the type of variable was also used in the modeling carried out by Bragança et al ( 66 ) and Legg et al ( 67 ).…”

Section: Resultsmentioning

confidence: 99%

“…The results of the disaggregated model (Table 8) show that despite the unbalanced characteristic of the variables (see Figure 4, Tables 2 and 3), the application of the Sturges rule ( 66 , 67 ) in numerical datum allowing the specific category classification, followed by the Laplace smoothing ( 44 , 45 , 47 ), mitigate the sampling bias problem. However, the inconclusive results with regard to the aggregation level in the case of the city of Rio de Janeiro, when compared with the results of the aggregated models, may suggest that they were not sensitive to the spatial resolution schemes adopted.…”

Section: Discussionmentioning

confidence: 99%

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Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

Lemos

Nascimento

Silva

2023

Transportation Research Record: Journal of the Transportation R

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This article analyzes the influence of spatial distribution of jobs, which represents intervening opportunities, in aggregated and disaggregated trip distribution models that generated eight origin–destination matrices. The observed trip matrix was obtained from the Urban Transport Master Plan (PDTU) of the Rio de Janeiro Metropolitan Region in Brazil. The estimated matrices were calculated using the gravity, intervening opportunities and Naïve Bayes models, organized at two aggregation levels. These models’ performance was evaluated considering the aggregation level, the model approach adopted (aggregated or disaggregated), and the spatial distribution of the intervening opportunities. The results indicated that the intervening opportunities exert a positive influence on all models, under any level of zone size (subdistricts or neighborhoods). In the aggregate models, the variable “jobs” insertion improved the models’ performance when applied at a higher aggregation level (subdistricts). The best-case scenario showed a coefficient of determination (R2) equal to 0.8705, obtained in a disaggregated model with intervening opportunities applied in subdistricts.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

Lemos

Nascimento

Silva

2023

Transportation Research Record: Journal of the Transportation R

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“…Kekurangan WEKA dapat terjadi overloading data jika database yang dimasukkan melebihi kuota yang ditentukan di dalam WEKA. [11].…”

Section: 86unclassified

Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary

Pramudiansyah¹

2021

JIKOM

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Pelanggan merupakan sumber keuntungan bagi perusahaan seperti halnya pada bisnis di bidang ritel Pelanggan merupakan aset bagi perusahaan. Saat ini, perusahaan berlomba-lomba dalam memenangkan hati para pelanggan. Karena masing-masing pelanggan tidak hanya memiliki kebutuhan, preferensi harapan dan perilaku yang berbeda-beda namun juga profil pendapatan dan biaya yang berbeda maka sebuah perusahaan perlu memetakan tingkat loyalitas pelanggannya. Namun tidak adanya pengolahan data pelanggan yang telah dimiliki menjadikan perusahaan tidak mengetahui valuable dari pelanggan yang ada. Penetapan rencana strategi pemasaran seharusnya dapat dilakukan salah satunya dengan memanfaatkan valuable customer yang dimiliki. Pemanfaatan valuable customer dilakukan dengan menggunakan data mining, penerapan model Recency Frequency Monetary pada tahapan data preparation dapat membantu untuk melihat valuable dari masing-masing pelanggan. hasil pengujian menggunakan Elbow Method didapatkan titik siku terbaik yaitu sebanyak 4 klaster dan setelah dilakukan perhitungan menggunakan algoritma k-means maka dihasilkan sebanyak 38,4% atau berjumalah 557 pelanggan masuk kedalam kelompok kategori Platinum, 12,6% atau berjumlah 184 pelanggan kategori Gold, 22,7% atau berjumlah 330 pelanggan kategori Silver, dan 26.% atau berjumlah 378 pelanggan kategori Bronze.

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Data Mining Classification Models for Industrial Planning

Cited by 2 publications

References 16 publications

Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary

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