The implementation of Markov chain to predict market share smartphone customers in Surabaya during pandemic COVID-19 / Hilyatun Nuha ... [et al.]
Bibliographic record
Abstract
In the second quarter of 2019, smartphone shipments in Indonesia reached the highest figure in history, which was 9.7 million units according to a market research Indonesia Digital Conference (IDC). The smartphone competition in Indonesia continues to increase drastically in 2018. Samsung survived on the top position with a market share of 25.4% followed by Xiaomi 20.5%, Oppo 19.5% and Vivo 15.9%. The four smartphone brands are the biggest market share smartphone in Indonesia. In this summary, this research will propose market share prediction for each smartphone brand in Surabaya up to 2023 using Markov Chain. This research will identify factors in the selection of smartphone brands. Then we will determine the weight of each factor using Analytical Hierarchy process (AHP). Finding the right marketing strategy with the expectation that smartphone vendor can maintain and increase the volume of sales of its products so that it can reach the desired market share. The purpose of this research is to be able to provide suggestion for smartphone businesses in Surabaya.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".