Evaluasi Situs Jejaring Sosial Warga.id Menggunakan Model Kesuksesan Sistem Informasi Delone & McLean Terhadap User Satisfaction Berdasarkan Persepsi Pengguna Warga.id
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Abstract
Bagi masyarakat Indonesia situs jejaring sosial memang sudah tidak asing lagi. Setiap orang pasti punya akun di salah satu sosial media. Menurut “We Are Social” mengungkap bahwa, pengguna internet di negara Indonesia bertambah dari tahun ke tahun. Berdasarkan hasil survey tersebut tentunya menjadi tantangan untuk developer mengembangkan situs jejaring sosial lokal, salah satunya Warga.id. Di negara Indonesia situs jejaring sosial lokal kalah populer dengan situs jejaring sosial seperti Facebook, Path, Twitter, Instagram, Google Plus dan lain sebagainya. Hal ini tidak memutuskan harapan bagi tim Warga.id yang masih terus melakukan inovasi fitur-fitur agar lebih disukai masyarakat. Selain itu Warga.id ingin mengajak pengguna menjadi lebih aktif. Penelitian ini dilakukan untuk mengetahui faktor-faktor kualitas situs warga.id berdasarkan model Delone & McLean apa saja yang memiliki pengaruh signifikan terhadap kepuasan pengguna dan untuk menjawab kendala yang dialami Warga.id. Hasil penelitian ini menunjukkan bahwa seluruh faktor kualitas berdasarkan model Delone & McLean memiliki pengaruh signifikan terhadap kepuasan pengguna dan menjawab kendala Warga.id bahwa situs tersebut membutuhkan pengembangan dalam hal kualitas layanan. Kata Kunci: Situs Jejaring Sosial, Model Delone & McLean, Kepuasan Pengguna, Kualitas Layanan. For Indonesian society, social networking sites it’s not something odd. Every people of course have account in one of social media ccount. “We Are Social” said that, internet users in Indonesia have increased every year. Based on survey result, there is a challenge for developer to expand social networking sites, including Warga.id. In Indonesia, local social networking sites it’s not as populer as Facebook, Path, Twitter, Instagram, Google Plus, etc. But Warga.id’s team still make a lot of inovation in build features that is preferred for society. Besides that, Warga.id want to make unactived user become active user. The purpose of this research is to identify factors of sites quality based on Delone & McLean that have significance effects on user satisfaction and to answer Warga.id problems. The results of this research shown that all DeLone & McLean quality factors have significance impact on user satisfaction and the impact from user satisfaction and answer Warga.id problems that Warga.id need to improve their service quality. Key Words: Delone & McLean, User Satisfaction, Sense of Belonging, Positive Word-of-mouth, Intention to Use, Knowledge Sharing. Daftar Pustaka 10TopTenReviews. (2014, February 1). Why Social Networking . Dipetik April 22, 2015, dari toptenreviews.com: http://social-networking-websites-review.toptenreviews.com Ali H. Al-Badi, M. O. (2013). Improving Usability of Social Networking Systems: A Case Study of LinkedIn. JISNVC: Journal of Internet Social Networking & Virtual Communities , 1-23. 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H. R, Pewawancara) Ermaniah, R. (2014, April 21). 6 Strategi Pemasaran Sukses dengan Cara yang Lebih Mudah . Dipetik April 20, 2015, dari mediabisnisonline.com: http://mediabisnisonline.com/6-strategi-pemasaran-sukses-dengan-cara-yang-lebih-mudah/ Goyette, I. R. (2010). e-WOM Scale: Word-of-Mouth Measurement Scale for e-Service Context. Canadian Journal of Administrative Sciences, 27 (1), 5-23. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/cjas.129/pdf Gruen, T. O. (2006). The Impact of Custome to Customer Online Know - How To Exchange on Customer Value and Loyalty. Journal of Business Research, 59 (4), 449-456. Retrieved from https://ideas.repec.org/a/eee/jbrese/v59y2006i4p449-456.html Han, M.-C. (2014). How Social Network Characteristics Affect Users’ Trust and Purchase Intention. International Journal of Business and Management, 9 (8), 122-132. Retrieved from http://www.ccsenet.org/journal/index.php/ijbm/article/view/35136 Indra, A. (2015, July 2). 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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it