Strategi Komunikasi Dompet Dhuafa Yogyakarta Dalam Peningkatan Ziswaf dan \nMempertahankan Kepercayaan Muzakki di Tengah Pandemi Covid-19
Bibliographic record
Abstract
This study analyzes the communication strategy of Dompet Dhuafa \nYogyakarta (Wallet for Incapable People in Yogyakarta) amid the socio-economic \ncrisis during the Covid-19 pandemi. In the first quarter of 2020, Indonesia's \neconomic growth slowed by 1.01 percent. This condition is a direct impact of the \ncessation of national economic activities during the implementation of the workfrom-home \n \npolicy. The highest slowdown in economic growth occurred in the \neducation services sector at -10.39% and the government administration sector at 8.54%. \n \nThe socio-economic crisis did not dampen the community's ethos to help \neach other. Based on the reality on the ground, the Islamic philanthropic movement \namong Indonesian Muslims has increased significantly. This is evidenced by one \nof the non-governmental organizations, namely Dompet Dhuafa which is engaged \nin the humanitarian sector, experiencing an increase in the collection of ZIS funds \n(Zakat, Infaq, and Sadaqah or Alms), Dompet Dhuafa funds grew 16.32% during \nthe Covid-19 storm. Therefore, the researchers sparked the curiosity of researchers \nto analyze the problems, namely (1) How was the communication strategy of \nDompet Dhuafa Yogyakarta in increasing Ziswaf's (Zakat, Infaq, Sadaqah or Alms \nand Waqaf or Waqf) income during the Covid-19 Pandemi; (2) How is Dompet \nDhuafa's communication efforts in maintaining Muzakki (A person who give zakat) \nduring the Covid-19 Pandemi; (3) Supporting factors for the Communication of \nDompet Dhuafa Yogyakarta in increasing Ziswaf and (4) Inhibiting factors for \nCommunication of Dompet Dhuafa Yogyakarta in increasing Ziswaf in the days of \nCovid-19? \nThe method used in this research is descriptive-qualitative with the research \nsubject: The Leader of Dompet Dhuafa Yogyakarta as a policymaker related to \nraising funds during the pandemi. Meanwhile, in the process of collecting data, \nresearchers used the methods of interview, observation, and documentation. As an \nanalysis, the researcher used an interactive analysis technique consisting of three \ncomponents, namely data reduction, data presentation, and conclusion testing. \nThe results of this study are (1) the communication strategy of Dompet \nDhuafa Yogyakarta in increasing and maintaining muzakki, namely through digital \nmarketing strategies, the use of social media, and the use of printing media, (2) in \nmaintaining the trust of muzakki using Case Relationship Management (CRM) and \nthe selection of communicators in delivering the message, (3) the supporting \nfactors, the number of volunteers, social media, and partners who joined in stopping \nthe spread of Covid-19, and (4) the inhibiting factors were Covid-19, media, and \nadaptation
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".