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

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Rancang Bangun Sistem Informasi Manajemen Aset Berbasis Web untuk Menghitung Penyusutan Fiskal Nasrul Nasrul; Henry Saptono; Edi Wibowo; Amalia Amalia
Jurnal Informatika Terpadu Vol 10 No 1 (2024): Maret, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i1.1086

Abstract

Effective asset management is a critical component in maintaining the financial and operational stability of an institution. At STT Terpadu Nurul Fikri, asset management still relies on manual processes, resulting in inefficiency and increased risk of errors. One crucial aspect of asset management is fiscal depreciation calculation, a process that considers the reduction in asset value over its useful life for fiscal or taxation purposes. Despite its importance, the fiscal depreciation calculation process at STT Terpadu Nurul Fikri still depends on manual Excel spreadsheet usage. This approach is prone to errors, difficult to track, and time-consuming. This research aims to design and develop a web-based asset management information system using the Laravel Framework, employing the Extreme Programming (XP) software development methodology. XP provides flexibility for developers to adapt to changing needs and ensures the quality of the resulting product. The conclusion of this research is that by employing the XP approach and leveraging the Laravel Framework, the development of this system is expected to provide an effective solution to the challenges faced in asset management. This will ease the management of assets for STT Terpadu Nurul Fikri and simplify the fiscal depreciation calculation process for its assets.
Analisis Kuantitatif Strategi Pemasaran Tiktok dalam Meningkatkan Brand Awareness UMKM Swara Annisa Aprilia; Amalia Amalia
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.v3i1.2247

Abstract

TikTok is one of the most engaging digital platforms for marketing, particularly among the younger generation. UMKM Swara, a small business in the custom fashion sector, has utilized TikTok as a platform for digital marketing. However, in practice, it still faces challenges in attracting audience attention and maintaining consistency in content planning. This study aims to implement and recommend effective TikTok marketing strategies for UMKM Swara. A quantitative approach was used, with data collected through observation, interviews, and questionnaires distributed to UMKM Swara's TikTok audience. The analysis employed simple linear regression. The results show that TikTok marketing strategies have a significant impact on increasing UMKM Swara's brand awareness, with a regression coefficient of 0.432 and a significance value of 0.001. Classical assumption testing also confirmed that the regression model used is valid and free from bias. The R² value of 0.441 indicates that 44.1% of the variation in brand awareness can be explained by the TikTok marketing strategy. Therefore, creative and interactive content-based marketing strategies on TikTok are proven effective in enhancing UMKM Swara’s brand awareness.
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 Annisa Aprilia Edi Wibowo Lulu Aprilia Wijayanti Nasrul Rusmanto Rusmanto Saptono, Henry