ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE)

Sang Nur Cahya Widiutama; Budi Warsito; Sudarno Sudarno

doi:10.14710/j.gauss.v11i1.34000

DOI: https://doi.org/10.14710/j.gauss.v11i1.34000

ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE)

Sang Nur Cahya Widiutama - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

*Budi Warsito - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Sudarno Sudarno - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

BibTex Citation Data :

@article{J.Gauss34000,
    author = {Sang Widiutama and Budi Warsito and Sudarno Sudarno},
    title = {ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE)},
    journal = {Jurnal  Gaussian},
  volume = {11},
    number = {1},
    year = {2022},
    keywords = {binary classification tree, recovery time, QUEST algorithm.},
    abstract = { Every company must have a way to solve the problems faced by its customers, PT. PLN Persero, the Indonesian national energy utility, must have a method to handle consumer complaints. PT. PLN Persero has a recovery time strategy for resolving consumer concerns, but it is not always effective in doing so. The QUEST algorithm (Quick, Unbiased, and Efficient Statistical Tree) approach is used to classify the problem of the recovery time policy failing on specific complaints. Classification of complaint data in order to obtain characteristics and factors as the main influence on the complaints and be able to provide new opinions for PT. PLN to address customer complaints. The QUEST method is a classification tree technique with two nodes per split that yields an unbiased variable. The QUEST method may be used with both category and numerical data. QUEST uses three stages to create a classification tree: picking the splitting variable, identifying the split point, and pausing the split. The classification tree generated has a tree depth of four layers and obtained three essential factors in the classification, namely weather, the number of customers experiencing the same event, and distance from the site. The classification tree accuracy level is 0.851 (or 85.1%), with a prediction error rate of 0.149 (or 14.9%).   Keywords:   binary classification tree, recovery time, QUEST algorithm.  },
   issn = {2339-2541},   pages = {45--55}  doi = {10.14710/j.gauss.v11i1.34000},
    url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/34000}
}

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Abstract

Every company must have a way to solve the problems faced by its customers, PT. PLN Persero, the Indonesian national energy utility, must have a method to handle consumer complaints. PT. PLN Persero has a recovery time strategy for resolving consumer concerns, but it is not always effective in doing so. The QUEST algorithm (Quick, Unbiased, and Efficient Statistical Tree) approach is used to classify the problem of the recovery time policy failing on specific complaints. Classification of complaint data in order to obtain characteristics and factors as the main influence on the complaints and be able to provide new opinions for PT. PLN to address customer complaints. The QUEST method is a classification tree technique with two nodes per split that yields an unbiased variable. The QUEST method may be used with both category and numerical data. QUEST uses three stages to create a classification tree: picking the splitting variable, identifying the split point, and pausing the split. The classification tree generated has a tree depth of four layers and obtained three essential factors in the classification, namely weather, the number of customers experiencing the same event, and distance from the site. The classification tree accuracy level is 0.851 (or 85.1%), with a prediction error rate of 0.149 (or 14.9%).

Keywords: binary classification tree, recovery time, QUEST algorithm.

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Keywords: binary classification tree, recovery time, QUEST algorithm.

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In Vol 11, No 1 (2022): Jurnal Gaussian

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