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Record W4384573777 · doi:10.59697/jsik.v6i2.197

PENERAPAN METODE CLUSTERING UNTUK PENGELOMPOKAN DATA PESERTA DIDIK BARU (PPDB) DI SMP SWASTA GOTONG ROYONG KUALA

2022· article· id· W4384573777 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

VenueJurnal Sistem Informasi Kaputama (JSIK) · 2022
Typearticle
Languageid
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Sekolah SMP Swasta Gotong Royong Kuala merupakan suatu yayasan atau lembaga pendidikan yang menerima siswa siswi baru setiap ajaran baru telah tiba. Siswa siswi yang mendaftar di sekolah SMP Swasta Gotong Royong Kuala bersumber dari lulusan sekolah dasar yang ada di sekitar Kecamatan Kuala hingga luar Kecamatan Kuala. Banyaknya jumlah pendaftar dari tahun ketahun, hal ini menyebabkan data atau berkas yang bertumpukan yang hanya disimpan pada lemari penyimpanan berkas yang ada disekolah. Berkas tersebut terkadang hanya dibuka ketika ingin mencari sebuah informasi saja. dengan kata lain berkas yang disimpan kurang memiliki manfaat yang lebih. Misalnya data tersebut dapat diolah dan dapat dijadikan sumber informasi yang baru dengan menggunakan teknik data mining. Data mining dapat membantu Sekolah dalam menggali pengetahuan baru dengan cara memproses data yang ada dengan metode clustering dan menggunakan algoritma K-Means.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0050.000
Scholarly communication0.0030.004
Open science0.0110.018
Research integrity0.0000.003
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 head0.039
GPT teacher head0.286
Teacher spread0.247 · 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