Review of financials, sales, and marketing strategy for micro, small, and medium enterprises in Palangka Raya City
DOI:
https://doi.org/10.52300/grow.v10i2.18993Keywords:
MSME, operational efficiency, marketing strategy, data-based decision makingAbstract
This academic investigation is meticulously designed with the primary objective of categorizing and analyzing the performance metrics of Micro, Small, and Medium Enterprises (MSMEs) situated within the urban confines of Palangka Raya City, with a particular emphasis on financial dimensions, product sales figures, and the efficacy of marketing strategies employed by these enterprises. Through the utilization of this systematic methodology, it becomes feasible to discern and identify groups of MSMEs that exhibit homogeneity in their operational characteristics, thereby facilitating the development of more precise and tailored strategies aimed at fostering their growth and development. To gather relevant primary data that would underpin this analysis, a comprehensive survey was administered to a total of 100 respondents, utilizing a well-structured questionnaire designed to elicit detailed information regarding their business operations. The analytical process employed in this study incorporated descriptive statistical tests, which were instrumental in not only processing the collected data but also in effectively visualizing the results, thus enhancing the interpretability of the findings. The outcomes derived from the clustering analysis revealed significant patterns that can be leveraged to inform management strategies for MSMEs, particularly in relation to enhancing operational efficiency and augmenting the effectiveness of their marketing endeavors. It is the aspiration of this scholarly work that the insights garnered herein will serve as a valuable resource for government entities and various stakeholders, enabling them to make informed decisions that are grounded in empirical data and that contribute positively to the economic growth of the local community. Furthermore, by providing a clearer understanding of the operational landscape of MSMEs, this study aims to encourage the implementation of supportive policies that are conducive to sustainable development within the region. Ultimately, the research endeavors to bridge the gap between academic inquiry and practical application, thereby fostering a collaborative environment in which both theory and practice can thrive in the pursuit of economic advancement. In conclusion, the findings of this study hold the potential to significantly influence the strategic direction of MSMEs in Palangka Raya City, ultimately contributing to a more robust local economy.
Downloads
References
Abdullah, D., Susilo, S., Ahmar, A. S., Rusli, R., & Hidayat, R. (2022). The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data. Quality & Quantity, 56(3), 1283–1291.
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.
Akhmad, A., Khalid, I., & Asdar, A. (2023). Strategy for Development of Micro, Small and Medium Enterprises in Gowa Regency, Indonesia. European Journal of Development Studies, 3(5), 7–15.
Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. The Knowledge Engineering Review, 34, e1.
Anwar, G., & Abdullah, N. N. (2021). The impact of Human resource management practice on Organizational performance. International Journal of Engineering, Business and Management (IJEBM), 5.
Arraniri, I., Firmansyah, H., Wiliana, E., Setyaningsih, D., Susiati, A., Megaster, T., Rachmawati, E., Wardhana, A., Yuliatmo, W., & Purwaningsih, N. (2021). Manajemen sumber daya manusia. Penerbit Insania.
Bank Indonesia. (2022). Laporan Perekonomian Provinsi Kalimantan Tengah. https://www.bi.go.id/id/publikasi/laporan/lpp/Documents/Laporan-Perekonomian-Provinsi-Kalimantan-Tengah-Mei-2022.pdf
Buckley, P. J., Pass, C. L., & Prescott, K. (1988). Measures of international competitiveness: a critical survey. Journal of Marketing Management, 4(2), 175–200.
Chen, Y. T., & Witten, D. M. (2022). Selective inference for k-means clustering. ArXiv Preprint ArXiv:2203.15267.
Darsana, I. M., & Sukaarnawa, I. G. M. (2023). Manajemen sumber daya manusia. Mafy Media Literasi Indonesia.
Dinas Koperasi dan UMKM. (2023). Pertumbuhan Usaha Mikro, Kecil Dan Menengah Provinsi Kalimantan Tengah. https://diskopukm.kalteng.go.id/dataukm
Ghazal, T. M. (2021). Performances of k-means clustering algorithm with different distance metrics. Intelligent Automation & Soft Computing, 30(2), 735–742.
Haddoud, M. Y., Beynon, M. J., Jones, P., & Newbery, R. (2018). SMEs’ export propensity in North Africa: a fuzzy c-means cluster analysis. Journal of Small Business and Enterprise Development, 25(5), 769–790.
Heryati, A., & Herdiansyah, M. I. (2020). The Application of Data Mining by using K-Means Clustering Method in Determining New Students’ Admission Promotion Strategy. International Journal of Engineering and Advanced Technology, 9(3), 824–833.
Ichsan, R. N., SE, M. M., Lukman Nasution, S. E. I., & Sarman Sinaga, S. E. (2021). Bahan Ajar Manajemen Sumber Daya Manusia (MSDM). CV. Sentosa Deli Mandiri.
Lilis Sulastri, L. (2016). Manajemen Usaha Kecil Menengah. LGM-LaGood’s Publishing.
Lubis, F. A., Harisudin, M., & Fajarningsih, R. U. (2019). Strategi Pengembangan Agribisnis Cabai Merah di Kabupaten Sleman dengan Metode Analytical Hierarchy Process. AGRARIS: Journal of Agribusiness and Rural Development Research, 5(2), 119–128.
Marcelina, D., Kurnia, A., & Terttiaavini, T. (2023). Analisis Klaster Kinerja Usaha Kecil dan Menengah Menggunakan Algoritma K-Means Clustering: Cluster Analysis of Small Medium Enterprise Performance with K-Means Clustering Algorithm. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 293–301.
Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology, 9(1), 15–24.
Mustaniroh, S. A., Santoso, I., & Permatasari, M. T. Y. K. (2019). Analisis klaster industri enting geti berdasarkan kinerja UKM dan kualitas produk menggunakan k-means clustering. Jurnal Teknologi Pertanian, 20(2), 103–114.
Mutegi, H. K., Njeru, P. W., & Ongesa, N. T. (2015). Financial literacy and its impact on loan repayment by small and medium entrepreneurs.
Norfai, S. K. M. (2022). Analisis data penelitian (Analisis Univariat, Bivariat dan Multivariat). Penerbit Qiara Media.
Perdana, A., Lee, H. H., Koh, S., & Arisandi, D. (2022). Data analytics in small and mid-size enterprises: Enablers and inhibitors for business value and firm performance. International Journal of Accounting Information Systems, 44, 100547.
Pratika, A., Budiarto, B., & Senjawati, N. D. (2022). Alternatif Strategi Pemasaran Susu Almond Menggunakan Metode Analytical Hierarchy Process (AHP) Pada UMKM Ralalii. Jurnal Dinamika Sosial Ekonomi, 23(2), 159–175.
Rahmanto, B. T., Nurjanah, S., & Darmo, I. S. (2018). Peran Komunitas Dalam Meningkatkan Kinerja Ukm (Ditinjau Dari Faktor Internal). Jurnal Riset Manajemen Dan Bisnis (JRMB) Fakultas Ekonomi UNIAT, 3(1), 18.
Rustiyan, R., & Mustakim, M. (2018). Penerapan Algoritma Fuzzy C Means untuk Analisis Permasalahan Simpanan Wajib Anggota Koperasi. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(2), 171–176.
Sari, A. F., Sampurna, R. H., & Meigawati, D. (2022). Stategi Dinas Koperasi, UKM, Perdagangan dan Perindustrian dalam Pemberdayaan UMKM di Kota Sukabumi. Jurnal Inovasi Penelitian, 2(10), 3353–3360.
Sari, D. K., & Budiharsono, S. (2023). Strategi Pemerintah Kota Bekasi Dalam Pengembangan UMKM Makanan Minuman Pada Era Digital. Management Studies and Entrepreneurship Journal (MSEJ), 4(4), 3603–3612.
Stone, R. J., Cox, A., & Gavin, M. (2020). Human resource management. John Wiley & Sons.
Surya, B., Menne, F., Sabhan, H., Suriani, S., Abubakar, H., & Idris, M. (2021). Economic growth, increasing productivity of SMEs, and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 20.
Tamba, S. P., Batubara, M. D., Purba, W., Sihombing, M., Siregar, V. M. M., & Banjarnahor, J. (2019). Book data grouping in libraries using the k-means clustering method. Journal of Physics: Conference Series, 1230(1), 12074.
Terttiaavini, T., Marnisah, L., Yulius, Y., & Saputra, T. S. (2019). Pengembangan Kewirausahaan †œKemplang Tunu†Sebagai Produk Cemilan Khas Kota Palembang. Jurnal Abdimas Mandiri, 3(1).
Terttiaavini, T., Sofian, S., & Saputra, T. S. (2021). Pendampingan Penyusunan Program Rencana Kerja Badan Usaha Milik Desa Dalam Rangka Optimalisasi Potensi Desa Serijabo Ogan Ilir Sumatera Selatan. JMM (Jurnal Masyarakat Mandiri), 5(6), 3536–3546.
Tian, W., Zheng, Y., Yang, R., Ji, S., & Wang, J. (2014). Research on clustering based meteorological data mining methods. Adv Sci Technol Lett, 79, 106–112.
Wahyudi, E. N., Utomo, A. P., & Mariana, N. (2019). Pengelompokan Jenis Usaha Umkm Kota Semarang Dalam Rangka Proses Pembinaan Dan Pendampingan Untuk Pengembangan Usaha Dengan Teknik Data Mining. Dinamik, 24(1), 13–20.
Yacob, S., Erida, E., Machpuddin, A., & Alamsyah, D. (2021). A model for the business performance of micro, small and medium enterprises: Perspective of social commerce and the uniqueness of resource capability in Indonesia. Management Science Letters, 11(1), 101–110.