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Record W4401375693 · doi:10.31539/intecoms.v7i4.10718

Rancang Bangun Rekomendasi Tempat Wisata Di Kabupaten Rembang Berbasis Website Menggunakan Metode Content Based Filtering

2024· article· id· W4401375693 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINTECOMS Journal of Information Technology and Computer Science · 2024
Typearticle
Languageid
FieldComputer Science
TopicInformation Retrieval and Data Mining
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHumanitiesArtGeographyCartography

Abstract

fetched live from OpenAlex

Kabupaten Rembang merupakan salah satu daerah yang berada di wilayah Jawa Tengah bagian utara Kabuaten Rembang juga memiliki banyak objek wisata, kuliner, rumah adat dan sebagainya. Tempat wisata di kawasan Kabupaten Rembang sangat beragam .Oleh karena itu penulis akan membuat sistem rekomendasi tempat wisata berbasis website di Kabupaten Rembang. Sistem rekomendasi ini bertujuan untuk membantu wisatawan mendapatkan informasi tempat-tempat wisata yang berada di Kabupaten Rembang dan sekitarnya. Dengan menggunakan metode Content-based Filtering, sistem akan melihat tempat wisata yang wisatawan pilih sebelumnya dan memberikan rekomendasi tempat wisata menggunakan metode tersebut. Sistem yang dibuat juga telah dilakukan uji coba menggunakan black box testing dan pengujian usability dari 24 responden dan menghasilkan nilai keseluruhan 89%. Dengan adanya sistem ini diharapkan dapat membantu wisatawan untuk menentukan tempat wisata lebih cepat dan akurat.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.015
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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