Efektivitas Pengelolaan Parkir Di Kelurahan Handil Bakti Kecamatan Alalak Oleh Dinas Perhubungan Kabupaten Barito Kuala
Bibliographic record
Abstract
This study aims to: (1) describe the effectiveness of parking management in Handil Bakti Village, Alalak District, by the Transportation Department of Barito Kuala Regency, and (2) identify the inhibiting factors. The research employs a descriptive qualitative approach, with data collected through interviews, observations, and documentation. Data analysis follows the qualitative stages proposed by Miles and Huberman, including data reduction, data display, and conclusion drawing. The results indicate that parking management in Handil Bakti Village is effective, as evidenced by the achievement of parking retribution targets in 2022 (101.4%) and 2023 (120%). However, several inhibiting factors were identified, such as the presence of illegal parking attendants at certain locations and times, which disrupts the performance of authorized attendants. Additionally, the lack of strict enforcement by the Transportation Department against illegal parking attendants contributes to the problem, partly due to insufficient monitoring infrastructure. These findings suggest the need for improved supervision and regulation enforcement to optimize parking management in the area. ABSTRAK Penelitian ini bertujuan untuk: (1) mendeskripsikan efektivitas pengelolaan parkir di Kelurahan Handil Bakti, Kecamatan Alalak, oleh Dinas Perhubungan Kabupaten Barito Kuala, dan (2) mengidentifikasi faktor penghambatnya. Metode penelitian menggunakan pendekatan kualitatif deskriptif dengan teknik pengumpulan data melalui wawancara, observasi, dan dokumentasi. Data dianalisis secara kualitatif mengikuti tahapan Miles dan Huberman, meliputi reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa pengelolaan parkir di Kelurahan Handil Bakti dinilai efektif, ditandai dengan tercapainya target retribusi parkir pada tahun 2022 (101,4%) dan 2023 (120%). Namun, terdapat faktor penghambat, yaitu keberadaan juru parkir liar di lokasi dan waktu tertentu yang mengganggu kinerja juru parkir resmi. Selain itu, kurang tegasnya tindakan Dinas Perhubungan dalam menertibkan juru parkir liar turut menjadi kendala, yang disebabkan oleh minimnya sarana dan prasarana untuk pemantauan dan pengawasan. Temuan ini mengindikasikan perlunya peningkatan pengawasan dan penegakan aturan guna mengoptimalkan pengelolaan parkir di wilayah tersebut.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".