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Management Strategies for Upgrading MSMEs in Facing Economic Uncertainty in Indonesia Sabil, Sabil; Rosento, Rosento; Lahat, Mohammad Amas; Marthanti, Amas Sari; Suratriadi, Panji; Hi Lawu, Suparman
Dinasti International Journal of Economics, Finance & Accounting Vol. 6 No. 5 (2025): Dinasti International Journal of Economics, Finance & Accounting (November - De
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijefa.v6i5.5483

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in supporting Indonesia's economic stability and growth, yet this sector faces serious challenges due to increasingly complex global and domestic economic uncertainties. In this context, understanding effective management strategies is crucial for MSMEs seeking to upgrade—that is, to transform into more structured, competitive business entities. This study aims to identify and synthesize the management strategies employed by MSMEs in responding to uncertain economic dynamics. Using a systematic literature review approach, this research analyzes 25 relevant open-access scholarly articles published over the past five years, thematically organized based on Porter's generic strategy framework, business resilience theory, and the MSME growth-stage model. The findings reveal that the primary strategies adopted by MSMEs include product differentiation, business process digitalization, and cross-sector collaboration through training, business incubation, and community partnerships. These approaches significantly contribute to enhancing the adaptive capacity and competitiveness of MSMEs amid economic pressures. This article offers conceptual contributions by integrating strategic management theory with the local MSME context in Indonesia, while also providing practical recommendations for policymakers and business practitioners to strengthen systemic and sustainable MSME development models moving forward.
Perancangan Sistem Informasi Penggajian Karyawan pada PT. Probesco Disatama : Penelitian Dessy Suryani; Amas Sari Marthanti; Adika May Sari; Rosmita Rosmita; Ahmad Rafik
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5403

Abstract

PT. Probesco Disatama merupakan salah satu perusahaan yang bergerak dibidang industri alat berat. Sistem penggajian karyawan yang berjalan saat ini masih menggunakan Microsoft word atau Excell, sehingga hal ini membuat penyampaian laporan perihal gaji karyawan menjadi lambat sehingga menyebabkan keputusan yang akan diambil oleh pihak manajemen terlalu lama diputuskan dan kemajuan yang diharapkan untuk perusahaan belum terlaksana. Sistem penggajian karyawan secara manual mempunyai resiko human error dalam hal efisiensi, keakuratan data , ketepatan waktu, perhitungan data dan pencarian data. Berdasarkan permasalahan tersebut, penulis mencoba memberikan solusi dengan merancang Sistem Penggajian Karyawan berbasis web untuk menanggulangi permasalahan yang ada. Adapun yang digunakan adalah metode waterfall, yang terdiri dari tahapan analisa kebutuhan, desain sistem, penulisan kode, pengujian, dan pemeliharaan program. Dengan adanya sistem penggajian ini, diharapkan dapat meminimalkan masalah yang terjadi pada sistem sebelumnya.
INPATIENT CLUSTER ANALYSIS FOR MEDICAL RESOURCE OPTIMIZATION USING K-MEANS CLUSTERING Adika May Sari; Amas Sari Marthanti; Rosmita; Dessy Suryani; Ahmad Rafik
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 11 No. 2 (2026): Mei
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The objective of this study is to analyze inpatient data in order to segment patients based on specific characteristics such as age, type of illness, medical procedures, and length of stay. The K-Means Clustering method is applied to identify patterns or patient segments that can be utilized in decision-making related to bed management and medical staff allocation more efficiently. The analysis was conducted using the Python programming language for data processing and result visualization. The findings indicate the existence of several groups of patients with distinct characteristics, which can serve as a strategic reference for improving service quality and the operational effectiveness of the hospital.