Kajian Kinerja Pembangunan Smart Governance dengan Pendekatan Importance Performance Analysis
Bibliographic record
Abstract
Abstract. Bogor City as a hinterland city is one of the cities included in the 'First 100 Cities Movement Towards a Smart City in Indonesia' as a pilot project since 2017-2021. The construction plan is stated in the Bogor City Smart City Masterplan document for 2017-2021. One of the important factors in the success of smart cities is smart governance. Smart governance is government governance that aims to improve government performance through the use of ICT. This study focuses on governance problems that occur in Bogor City, especially in Central Bogor subdistrict. Where there are several problems such as inactive sub-district government websites, publication of government agency performance reports (LAKIP) that are not updated, and so on. So that this study was prepared with the aim of measuring the achievements of smart governance development based on 14 indicators synthesized from journal sources and smart city masterplan documents. This research uses a mix method approach method with a descriptive research method. The analysis method used is an IPA (Importance Performance Analysis) analysis to measure the level of performance and the level of community expectations for the development of smart governance and a descriptive analysis to explain the results of the IPA analysis. Based on the analysis, it is known that development achievements based on the level of suitability of smart governance development in Central Bogor subdistrict reached a score of 84.19%, which means that the government's performance in general is considered not in accordance with community expectations so improvements need to be made to improve government performance. Abstrak. Kota Bogor sebagai kota penyangga ibu kota negara merupakan salah satu kota yang termasuk dalam ‘Gerakan 100 Kota Pertama Menuju Smart City di Indonesia’ sebagai pilot project sejak tahun 2017-2021. Rencana pembangunannya tercantum dalam dokumen Masterplan Smart City Kota Bogor Tahun 2017-2021. Salah satu faktor penting dalam keberhasilan smart city adalah smart governance. Smart governance adalah tata kelola pemerintah yang bertujuan untuk meningkatkan kinerja pemerintah melalui pemanfaatan TIK. Kajian ini berfokus pada permasalahan tata kelola pemerintahan yang terjadi di Kota Bogor khususnya di Kecamatan Bogor Tengah. Dimana terdapat beberapa permasalahan seperti website pemerintah kecamatan yang tidak aktif, publikasi laporan kinerja instansi pemerintah (LAKIP) yang tidak diperbarui, dan sebagainya. Sehingga penelitian ini disusun dengan tujuan untuk mengukur capaian pembangunan smart governance yang didasarkan pada 14 indikator yang disintesis dari sumber jurnal dan dokumen masterplan smart city. Penelitian ini menggunakan metode pendekatan mix method dengan metode penelitian deskriptif. Adapun metode analisis yang digunakan adalah analisis IPA (Importance Performance Analysis) untuk mengukur tingkat kinerja dan tingkat harapan masyarakat terhadap pembangunan smart governance dan analisis deskriptif untuk menjelaskan hasil dari analisis IPA. Berdasarkan analisis IPA, diketahui bahwa capaian pembangunan berdasarkan tingkat kesesuaian pembangunan smart governance di Kecamatan Bogor Tengah mencapai skor 84,19% yang artinya kinerja pemerintah secara umum dianggap belum sesuai dengan harapan masyarakat sehingga perlu dilakukan perbaikan untuk meningkatkan kinerja pemerintah.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".