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All Journal Jurnal Teknik Mesin, Industri, Elektro dan Informatika DBESTI: Journal of Digital Business and Technology Innovation
Rusmanto Rusmanto
Sekolah Tinggi Teknologi Terpadu Nurul Fikri
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science

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Strategi Pemasaran Digital Melalui Instagram pada Usaha DEF Laundry: Perancangan dan Implementasi Faizah Yusriyah; Shelly Pramudiawardani; Rusmanto Rusmanto
DBESTI: Journal of Digital Business and Technology Innovation Vol 3 No 1 (2026): Mei, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v3i1.2141

Abstract

DEF Laundry is a laundry service business located in Bojonggede. This business faces challenges in implementing digital marketing, particularly in utilizing social media. This study aims to design and implement a digital marketing strategy for DEF Laundry using Instagram, as well as to identify the types of promotional content that can attract customer interest in choosing DEF Laundry's services. Using a qualitative-correlational and descriptive approach, data were collected through interviews and validated through data triangulation. The results show that the digital marketing strategy designed using the STP approach and SWOT analysis successfully identified a relevant market segment. The implementation of promotional content was considered effective, as it conveyed service information clearly and attracted audience attention. Moreover, customer decisions regarding the use of DEF Laundry's services were influenced by three main factors: service quality, access to clear information, and appealing digital promotional strategies. The digital marketing strategy implemented through Instagram proved effective in increasing customer interest. The informative and interactive promotional content supported the customer's decision-making process. Thus, this strategy can serve as a solution to support the digitalization of DEF Laundry's promotional efforts in the social media era.
Analisis Prediktif Arus Kas Bersih Perusahaan Multifinance Menggunakan Machine Learning Linear Regression Amalia Amalia; Lulu Aprilia Wijayanti; Rusmanto Rusmanto
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 5 No. 2 (2026): Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v5i2.6234

Abstract

Cash flow forecasting is important because changes in cash availability can affect liquidity management, funding decisions, and operational stability. This study aims to develop and evaluate an interpretable predictive model for next-period Net Cash Flow using machine learning Linear Regression. Quarterly panel data were collected from 11 multifinance issuers listed on the Indonesia Stock Exchange during 2021–2025. The predictors include Operating Cash Flow, Investing Cash Flow, Financing Cash Flow, and company identity, while the target is next-period Net Cash Flow. Microsoft Excel was used for initial data preparation, checking, and verification, while Google Colab with Python, Pandas, NumPy, scikit-learn, and Matplotlib was used for data processing, modeling, evaluation, charts, and dashboard visualization. From 220 raw observations, 209 model-ready observations were obtained, consisting of 176 training and 33 testing observations. Linear Regression produced MAE of 5,175,010,760.53 and RMSE of 11,439,892,766.99, lower than naive forecasting with MAE of 7,569,470,101.77 and RMSE of 16,555,565,343.09. However, the negative testing R-squared indicates limited performance under heterogeneous and extreme cash-flow movements. These findings show that Linear Regression can provide a measurable and interpretable initial basis for cash flow prediction, although broader data, additional variables, and stronger models are still required.
Co-Authors Amalia Amalia Faizah Yusriyah Lulu Aprilia Wijayanti Pramudiawardani, Shelly