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Record W4387932141 · doi:10.55340/jiu.v12i1.1299

Peringatan Dini Banjir Menggunakan Multi Sensor Pada Prototype Aliran Sungai Berbasis Internet of Things

2023· article· id· W4387932141 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 INFORMATIKA · 2023
Typearticle
Languageid
FieldComputer Science
TopicIoT-based Control Systems
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsOperating systemForestryComputer scienceGeography

Abstract

fetched live from OpenAlex

Salah satu dampak nyata dari perubahan iklim adalah banjir yang telah terjadi lebih sering di banyak wilayah padat penduduk dan menyebabkan dampak pada kehidupan manusia dan mata pencaharian. Untuk mendapatkan ketinggian permukaan air kanal, dengan cara memanfaatkan rambatan gelombang suara ultrasonik yang dipantulkan pada obyek. Dengan diketahuinya jarak obyek, maka dapat dilakukan komputasi untuk mengetahui ketinggian permukaan air kanal. Nilai ketinggian permukaan air kanal dikirim melalui jaringan internet menuju Internet of Thing (IoT) cloud server yang dapat di monitor oleh pengguna. Penelitian ini bertujuan untuk merancang bangun alat mengukur ketinggian air sungai sehingga dapat mendeteksi apabila akan terjadi banjir, alat ini menggunakan multi level sensor sebagai sensor yang dapat mendeteksi ketinggian air dan memanfaatkan teknologi IoT untuk monitoring langsung. Hasil yang dicapai dalam penelitian ini alat prototype peringkatan dini banjir dapat yang dikontrol menggunakan aplikasi android untuk mengetahui ketinggian air melalui sensor yang dipasang pada aliran sungai, sehingga dapat dijadikan sebagai system peringatan dini banjir.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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