Evaluasi Kinerja Pengelolaan Persampahan di Kota Palu berdasarkan Prespektif Masyarakat
Bibliographic record
Abstract
Abstract. Along with the increasing population, it results in an increase in the volume of waste every day, so that waste becomes an important issue in urban areas such as in Palu City. The purpose of this study is to evaluate the performance in implementing the waste management program in Palu City. This study uses a quantitative approach with descriptive analysis methods and a Likert scale. Based on the analysis carried out, from the volume of waste produced, the waste that can be transported from the waste source to the TPA is 55.41% of the amount of waste production produced. The factors that cause this are the limited number of waste facilities and infrastructure and the frequency of transportation carried out. The local government targets this waste management to be 100%, while the average achievement is 71.76%. From the assessment based on the community perspective, the performance of waste management in Palu City is low. Several things that can be recommended from the results of this study are first, increasing the capacity of facilities and infrastructure, increasing the frequency of waste transportation, and second, conducting intensive education and mentoring activities for the community to increase public awareness and knowledge regarding the importance of waste management. Abstrak. Seiring dengan peningkatan jumlah penduduk, mengakibatkan peningkatan volume sampah setiap harinya, sehingga persampahan menjadi isu penting di kawasan perkotaan sebagaimana halnya di Kota Palu. Tujuan dari penelitian ini yaitu untuk mengevaluasi kinerja dalam mengimplementasikan program pengelolaan sampah di Kota Palu. Penelitian ini menggunakan pendekatan kuantitatif dengan metode analisis deskriptif dan skala likert. Berdasarkan analisis yang dilakukan, dari volume sampah yang dihasilkan, sampah yang dapat terangkut dari sumber sampah ke TPA adalah 55,41% dari jumlah produksi sampah yang dihasilkan. Faktor yang menyebabkan hal ini dalah karena keterbatasan jumlah sarana dan prasarana persampahan dan frekuensi angkutan yang dilakukan. Pemerintah daerah mentargetkan untuk pengelolaan sampah ini adalah 100%, sedangkan rata-rata capaian 71,76%. Dari penilaian berdasarkan perspektif masyarakat bahwa kinerja pengelolaan sampah di Kota Palu adalah rendah. Beberapa hal yang dapat direkomendasikan dari hasil penelitian ini adalah pertama penambahan kapasitas sarana prasarana, peningkatan frekuensi angkutan sampah, dan yang kedua adalah melakukan kegiatan edukasi dan pendampingan secara intensif kepada masyarakat untuk meningkatkan kesadaran dan pengetahuan masyarakat terkait pentingnya pengelolaan sampah.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".