JARINGAN KOMUNIKASI DALAM MENINGKATKAN PRODUKTIVITAS PELAPAK (STUDI KASUS PADA KOMUNITAS BUKALAPAK WILAYAH JAKARTA)
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.
Bibliographic record
Abstract
In the last five years, the number of SMEs in Indonesia has continued to increase. However, only 4.9% made use of digital access as a sales medium, either through social media or e-commerce. In 2017, the number of SMEs going online increase to 8%, and is targeted to increase in 2018 by the Minister of Cooperatives and SMEs of the Republic of Indonesia. Increasing e-commerce is a challenge for Bukalapak. To continue to maintain its exixtance, Bukalapak formed the ‘Komunitas Pelapak’ as a communication network between ‘pelapak’ (seller) in each region. This community allows pelapak to share strategies to increase productivity. This study aims to describe the communication network of ‘Komunitas Pelapak’ in the Jakarta area and analyze it’s role in improving the productivity of pelapak in the community. This research method is qualitative descriptive using a case study spproach. The study, which used in-depth interview techniques with members of the Komunitas Bukalapak Jakarta area, found that within the Bukalapak community in Jakarta, there were four (4) individuals who were chosen by members of the community. The four individuals constitute opinion leaders who are active in providing information and motivation to other community members through social media channels, the WhatsApp Group of the Bukalapak community, and offline activities such as ‘kopdar’ and social activities. By communicating with information and motivation in every community activity, it can increase enthusiasm and innovation for the products and also the sales method. This will ultimately increase the productivity of community members in doing business in the world of e-commerce.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it