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Record W4404578733 · doi:10.62951/modem.v2i4.236

Rancang Bangun Alat Pengukur Tinggi Badan Otomatis Menggunakan IoT

2024· article· en· W4404578733 on OpenAlexaff
Ajisro Siringoringo, Relita Buaton, K Husnul

Bibliographic record

VenueModem · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputer Science and Engineering
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsInternet of ThingsComputer scienceOperating systemEmbedded system

Abstract

fetched live from OpenAlex

The development of Internet of Things (IoT) technology provides opportunities to automate various devices, including height measuring devices. This research aims to design and build an automatic height measuring device that is integrated with IoT technology. This tool is designed to measure body height automatically and send the measurement data to a cloud-based platform, making it easier for users to monitor data in real-time via smart devices. This system uses an ultrasonic sensor to detect body height, a microcontroller as a data processor, and a Wi-Fi module to send data to the server. Test results show that this tool is capable of measuring body height with a good level of accuracy and provides convenience in storing and monitoring measurement data remotely. Thus, this IoT-based height measuring device can be an innovative solution for the need for height measurement that is more efficient and integrated with digital systems.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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