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Record W4410220378 · doi:10.62951/repeater.v3i2.407

Rancang Bangun Pengembangan Robot Pembersih Sampah Berbasis Internet of Thing (IOT) Untuk Pemantauan dan Pengontrolan Jarak Jauh.

2025· article· en· W4410220378 on OpenAlexaff
MHD Micho Januar Prananta, Akim Manaor Hara Pardede, Melda Pita Uli Sitompul

Bibliographic record

VenueRepeater · 2025
Typearticle
Languageen
FieldComputer Science
TopicIoT-based Control Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceInternet of ThingsWorld Wide Web

Abstract

fetched live from OpenAlex

The increasing waste problem requires innovative solutions that are efficient and sustainable. This study aims to design and build an Internet of Things (IoT) based garbage cleaning robot that can be monitored and controlled remotely. This system is designed by utilizing a microcontroller as the main brain, sensors to detect the presence of garbage, and an IoT-based communication module that allows monitoring and control of the robot via mobile devices or the web. Based on the results of the analysis and testing carried out, this study shows that the use of ESP8266 in the RC Trans Robot motor control with the Blynk application has succeeded in significantly increasing system efficiency. The system successfully responds to user input via a mobile application and can control the movement of the robot in real-time. The performance of the robot system controlled via a WiFi network shows good stability in various test scenarios. The robot can operate effectively within a distance that matches the range of the WiFi network, with fast control response and reliable communication.

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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
Published2025
Admission routes1
Has abstractyes

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