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Record W4297808282 · doi:10.29313/bcsurp.v2i2.3175

Kajian Kinerja Pembangunan Smart Governance dengan Pendekatan Importance Performance Analysis

2022· article· en· W4297808282 on OpenAlexaff
Avina Husna, Ernady Syaodih

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGovernment (linguistics)Corporate governanceSmart cityAgency (philosophy)Descriptive statisticsBusinessPlan (archaeology)Local governmentInformation and Communications TechnologyDescriptive researchPolitical scienceComputer sciencePublic administrationGeographyFinanceSociologyComputer securityInternet of ThingsMathematicsWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.262
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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