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Record W3135892849 · doi:10.52160/ejmm.v2i5.160

PENGARUH KUALITAS LAYANAN, HARGA, KEPUASAN PELANGGAN SERTA LOYALITAS PELANGGAN PADA PT KAI KOMMUTER JABODETABEK STASIUN DEPOK JAWA BARAT

2018· article· id· W3135892849 on OpenAlexaff
Syahrial Addin, Putie Maharani Basa, Nurullah Sururi Afif

Bibliographic record

VenueJurnal Mitra Manajemen · 2018
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsLoyalist College
Fundersnot available
KeywordsMathematicsBusiness administrationPhysicsHumanitiesBusinessArt

Abstract

fetched live from OpenAlex

Kebijakan pada kualitas pelayanan dan harga tiket dapat mempengaruhi loyalitas pelanggan jasa PT KAI Commuter Jabodetabek. Penelitian ini bertujuan menganalisis pengaruh kualitas pelayanan dan harga tiket terhadap loyalitas pelanggan jasa PT KAI Commuter Jabodetabek, baik secara simultan maupun parsial. Metode penelitian menggunakan metode kuantitatif yang bersifat deskriptif dan verifikatif dengan ukuran populasi 100 responden dengan teknik sampling. Pengolahan data menggunakan analisis regresi linear berganda dan koefisien determinasi pada taraf signifikan 5%. Teknik pengumpulan data menggunakan teknik observasi, wawancara dan kuesioner. Hasil penelitian menunjukkan besarnya pengaruh kualitas pelayanan dan harga tiket terhadap loyalitas pelanggan jasa PT KAI Commuter Jabodetabek sebesar 31.9%, model dapat dijalankan dan sisanya (68.1%) tidak dapat dijalankan oleh variabel lainnya yang tidak diteliti. Kualitas pelayanan memberikan pengaruh yang lebih besar dibandingkan dengan harga tiket terhadap loyalitas pelanggan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.258
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes1
Has abstractyes

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