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Pengaruh Penguasaan Kompetensi Teknis Terhadap Kinerja Pegawai Yang Dimoderasi Oleh Kompetensi SMART ASN Pada Jabatan Pelaksana Penyusun Bahan Publikasi Dan Kehumasan Febrina, Rina; Aisyah, Shally Alpriany
Jurnal Ilmiah Sumber Daya Manusia Vol 5 No 1 (2021): JENIUS (Jurnal Ilmiah Manajemen Sumber Daya Manusia)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/JJSDM.v5i1.13141

Abstract

Gagasan SMART ASN merupakan langkah menghadapi era revolusi industri 4.0 mengharuskan ASN beradaptasi terhadap transformasi teknologi agar pelayanan publik lebih efektif, efisien, ditunjang dengan kinerja pegawai secara keseluruhan. Hal tersebut berkaitan dengan penguasaan kompetensi teknis yang didukung dengan penguasaan kompetensi penunjang SMART ASN. Jabatan penyusun bahan publikasi dan kehumasan merupakan jabatan yang diperlukan di setiap unit instansi untuk kelancaran informasi. Penelitian ini mengujikan pengaruh penguasaan kompetensi teknis dan kompetensi penunjang SMART ASN serta moderasi SMART ASN terhadap kinerja pegawai penyusun bahan publikasi kehumasan di Kementerian Perhubungan. Hasil analisis menunjukkan penguasaan kompetensi teknis memiliki pengaruh positif terhadap kinerja pegawai, sementara penguasaan kompetensi penunjang SMART ASN berperan sebagai pure moderator yang memperkuat pengaruh penguasaan kompetensi teknis oleh pegawai terhadap kinerjanya. Analisis instrumen menunjukkan bahwa pegawai masih mengalami kesulitan jika ranah kehumasan menjangkau hubungan luar lebih luas seperti media dan redaksi. Pegawai dengan penempatan kantor pusat dan area ibukota provinsi lebih fasih dalam menguasai teknologi sosial media dan kemajuan informasi. Motivasi pegawai untuk melakukan pekerjaan lebih kolaboratif, up to date, dan kreatif masih perlu ditingkatkan.
Optimization of Environmentally Friendly Material Selection for Automotive Mechatronics Components Using LCA Data and Multi‑Criteria Decision Making (MCDM) Ibrahim, Fauzi; Marjuni, Teuku; Febrina, Rina; Oktarina, Devi; Natalina, Natalina; Ergantara, Rani Ismiarti; Sulistyaningrum, Diah Ayu Wulandari
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.8

Abstract

The automotive industry faces an increasing demand for sustainable material selection as mechatronic components become more widespread in electrified vehicles. However, data-driven material selection approaches that simultaneously integrate environmental, economic, and technical criteria without laboratory experiments remain underdeveloped. This study addresses this gap by developing a computational framework that combines Life Cycle Assessment (LCA) with a Multi-Criteria Decision-Making (MCDM) approach, specifically the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, using Analytical Hierarchy Process (AHP)–based weights. The framework enables a transparent and reproducible evaluation of environmentally friendly materials for automotive mechatronic components. A case study on an actuator housing evaluates seven material alternatives: Al 6061 (die-cast), recycled Al (die-cast), Mg AZ91 (die-cast), PA6-GF30 (injection), PBT-GF30 (injection), PA12 (SLS 3D print), and bio-based PBT-GF30 (injection). The criteria include total global warming potential (GWP), cumulative energy demand (CED), water use, recyclability, cost, mass, stiffness index, thermal conductivity, and supply risk. Results show that recycled aluminum achieves the highest ranking (closeness coefficient = 0.939), followed by Al 6061 (0.727) and Mg AZ91 (0.547). A Monte Carlo analysis with 1,000 iterations confirms that recycled aluminum consistently remains the best option with 100% robustness under varying weighting conditions. The proposed workflow is replication-ready and can be directly integrated with established LCA databases such as GREET, Ecoinvent, or EPD, enabling engineers to perform sustainable and quantitative material decisions using only data and computational analysis.