Identifikasi Elemen Citra Kota di Pusat Kota Tasikmalaya pada Media Sosial Instagram
Bibliographic record
Abstract
Abstract. Tasikmalaya City was a division of Tasikmalaya Regency in 1967. Before the expansion, Tasikmalaya City was the regional capital which could be recognized from the trading area on Jalan Cihideng. The Tasikmalaya city area experienced many changes after the expansion. So it was reported in the news that HZ Mustafa had now lost his identity. Therefore, it is necessary to identify the city image in the Tasikmalaya city center area which aims to determine the image of the Tasikmalaya city center area according to public opinion, through qualitative methods and GSM (Geocode Social Media) analysis image classification. The research results show that the city image element is H.Z. Mustofa and Cihideung Streets as path, H.Z, Mustofa and Cihideung shops as edges, trade areas as districts, city parks as nodes, and the Great Mosque of Tasikmalaya City as landmarks. Abstrak. Kota Tasikmalaya merupakan pemekaran dari Kabupaten Tasikmalaya pada tahun 1967. Sebelum pemekaran, Kota Tasikmalaya merupakan ibu kota daerah yang dapat dikenali dari kawasan perdagangan di Jalan Cihideng. Kawasan kota Tasikmalaya banyak mengalami perubahan pasca pemekaran. Sehingga diberitakan di pemberitaan kawasan HZ Mustafa kini kehilangan jati diri. Oleh karena itu, diperlukan identifikasi citra kota di kawasan Pusat kota Tasikmalaya yang bertujuan untuk mengetahui citra kawasan pusat kota Tasikmalaya menurut opini masyarakat, melalui metode kualitatif dan analisis GSM (Geocode Social Media) yaitu klasifikasi gambar. Hasil penelitian menunjukkan bahwa elemen citra kota adalah jalan H.Z. Mustofa dan Jalan Cihideung sebagai path, pertokoan H.Z, Mustofa dan Cihideung sebagai edges, kawasan perdagangan sebagai district, taman kota sebagai node, dan Masjid Agung Kota Tasikmalaya sebagai landmark.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".