The Effect of Prepaid Electricity System on Household Energy Consumption – The Case of Bogor, Indonesia

Nugroho, Sudarmanto Budi; Zusman, Eric; Nakano, Ryoko; Takahashi, Kentaro; Koakutsu, Kazuhisa; Kaswanto, Regan Leonardus; Arifin, Nurhayati Hadi Susilo; Munandar, Aris; Arifin, Hadi Susilo; Muchtar, Muchamad; Gomi, Kei; Fujita, Tsuyoshi

doi:10.1016/j.proeng.2017.07.117

Cited by 15 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Listrik Pra Bayar (LPB) dapat menjadi salah satu solusi penghematan energi pada ketenagalistrikan di negara maju dan berkembang [1] [2] [3]. Bagi pemerintah dan perusahaan listrik LPB diimplementasikan dengan tujuan penghematan energi [4] sehingga dijadikan LPB dijadikan media kampanye penghematan energi [5] atau untuk mengurangi tunggakan rekening listrik pelanggan dan pencurian listrik [6] [7]. Penggunaan LPB bagi pelanggan dianggap lebih mudah dalam mengontrol tagihan listrik, mudah dalam hal pembelian token dan juga tidak perlu mengantri [8], selain itu pelanggan beranggapan bahwa LPB lebih transparan dalam hal pembiayaan [9].…”

Section: Pendahuluanunclassified

Identifikasi Fitur untuk Prediksi Penerimaan Program Listrik Prabayar: Kasus di PLN Tahuna

Riduwan¹,

Purwitasari

Raharjo

2022

View full text Add to dashboard Cite

Listrik Prabayar (LPB) memberikan manfaat bagi perusahaan listrik dalam hal mengurangi piutang pelanggan dan memberikan kemudahan pengendalian pemakaian listrik bagi pelanggan. Perusahaan Listrik Negara (PLN) mempunyai program pemasaran untuk berpindah (migrasi) dari listrik pascabayar menjadi LPB. Pencapaian Key Performance Indicator (KPI) Program Pemasaran LPB PLN Tahuna pada 2021 hanya 1.185 pelanggan dari taget 2.261 pelanggan. Hal ini memberikan peluang perbaikan karena program pemasaran saat ini belum mengoptimalkan penggunaan data sebagai dasar penentuan prospek pelanggan. Penelitian ini mengajukan metode identifikasi fitur dan skenario pemilihan algoritma Pembelajaran Mesin yang tepat untuk memprediksi penerimaan pelanggan listrik pasca bayar terhadap program prabayar. Identifikasi fitur dilakukan dengan pengukuran korelasi Pearson. Kandidat algoritma Pembelajaran Mesin yang terpilih adalah Logistic Regression, Support Vector Machines, Decision Tree, dan Random Forest. Model-model yang dihasilkan dievaluasi menggunakan confusion matrix sehingga didapatkan model terbaik untuk studi kasus yang diajukan. Penelitian menunjukkan bahwa fitur tarif, daya, frekuensi terlambat membayar listrik, pemakaian rata-rata listrik bulanan (kWh) dan Kabupaten mempunyai korelasi signifikan dengan penerimaan LPB. Adapun model dengan algoritma Random Forest adalah model terbaik sesuai tujuan penelitian dengan F1-Measure tertinggi (95,17%).

show abstract

Section: Pendahuluanunclassified

Identifikasi Fitur untuk Prediksi Penerimaan Program Listrik Prabayar: Kasus di PLN Tahuna

Riduwan¹,

Purwitasari

Raharjo

2022

View full text Add to dashboard Cite

show abstract

“…Customers could use electrical energy as needed due to the prepaid electricity. In other words, customers will be more comfortable and their electricity consumption is under control by using the prepaid electricity (Fatin, 2014;Nugroho et al, 2017).…”

Section: Prepaid Electricity (Electricity Consumption Using Token)mentioning

confidence: 99%

“…Moreover, it was supported by their surrounding environment including their family that still utilized the postpaid electricity. Some studies, such as Nugroho et al, (2017) suggested that family, age, education, and income influenced decision to utilize prepaid electricity. Likewise, Bove and Mitzifiris (2007); Kasnaeny and Sudiro, (2013); Miranda (2009), stated that habitual buying affected consumers' decision to use a product.…”

Section: Determination Of the Number Of Inhibiting Factors To Switchmentioning

confidence: 99%

“…For example, an examination on a tangible product by Njite et al (2008); Aslam and Frooghi (2018); and Blut et al (2007) found that switching cost was an inhibiting factor for interest to switch both to a new brand, new firm, or other banks. Moreover, the influence of education and knowledge of prepaid electricity utilization was found by (Nugroho et al, 2017).…”

Section: Determination Of the Number Of Inhibiting Factors To Switchmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Consumer Switching Barriers to Use Prepaid Electricity Systems in the Household Sector in Makassar, Indonesia

Karim¹,

Tajibu²,

Akhmad

2020

IJEEP

View full text Add to dashboard Cite

A new alternative of electricity bill payment exists in Indonesia at present, namely prepaid systems. The system is intended to facilitate customers in electricity saving where the electricity utilization could be adjusted. In reality, however, post-paid electricity system utilization still receives great demand. As a consequence, it could not suppress electricity consumption. This research aims to learn factors inhibiting consumers to switch to prepaid electricity systems for their electricity consumption. A descriptive research carries out to answer the research problems. Sample consists of 100 people determined via a snowball sampling technique. Data collection is conducted using questionnaires and measured using Likert Scale of 1-5. Factory analysis is used as an analysis tool. The research results indicate that the main factor that becomes a barrier for electricity customers to switch to prepaid systems is habitual using factor. Switching cost factor, lack of prepaid system information, and relationship and reputation of post-paid products are the second, third, and fourth factors, respectively.

show abstract

“…For this reason, the government needs to take significant action to meet the availability of electricity (Romadhoni and Akhmad, 2020). One of the policies is to implement a prepaid system in reducing household electricity consumption (Karim et al, 2021;Nugroho et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Consumer Behavior in Using Prepaid Electricity Systems in the Covid-19 Pandemic Period in Makassar City, Indonesia

Karim

Tajibu

Fitrianti

et al. 2021

IJEEP

View full text Add to dashboard Cite

The Effect of Prepaid Electricity System on Household Energy Consumption – The Case of Bogor, Indonesia

Cited by 15 publications

References 21 publications

Identifikasi Fitur untuk Prediksi Penerimaan Program Listrik Prabayar: Kasus di PLN Tahuna

Identifikasi Fitur untuk Prediksi Penerimaan Program Listrik Prabayar: Kasus di PLN Tahuna

Determination of Consumer Switching Barriers to Use Prepaid Electricity Systems in the Household Sector in Makassar, Indonesia

Consumer Behavior in Using Prepaid Electricity Systems in the Covid-19 Pandemic Period in Makassar City, Indonesia

Contact Info

Product

Resources

About