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All Journal Inferensi Journal of Artificial Intelligence and Digital Business
Wijaya, Ferdian Bangkit
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Social Sciences

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Applied Machine Learning DBSCAN for Identifying Clusters of Micro and Small Industries Wijaya, Ferdian Bangkit; Budiaji, Weksi; Wicaksono, Agung Satrio
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.515

Abstract

This study aims to identify clustering patterns of sub-districts in Serang District based on village participation in Micro and Small Industry (MSI) activities using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, a machine learning method in Unsupervised Learning. Secondary data from the Statistics Indonesia (BPS) on Potentials of Villages in Serang District for 2024 was used, covering 29 sub-districts and 15 MSI sector variables. Data preprocessing involved Min-Max Scaler normalization and Principal Component Analysis (PCA) to address sparsity and multicollinearity. DBSCAN parameter optimization was done through simulations of epsilon values (0–1) and MinPts (1–10), validated with the Silhouette Score and Davies-Bouldin Index. The optimal configuration of epsilon=0.3 and MinPts=1 resulted in seven clusters with no noise, and a Davies-Bouldin Index of 0.620, indicating good separation. Spatial analysis revealed meaningful cluster distribution, with comprehensive industry clusters in the central region and specialized clusters in peripheral areas. These findings provide a basis for formulating MSI development policies in Serang District, highlighting the importance of data preprocessing techniques in sparse data analysis for evidence-based decision-making.
Web Scraping Analysis of Job Platform Adoption in Banten's Manufacturing Sector Wijaya, Ferdian Bangkit; Budiaji, Weksi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2282

Abstract

This study addresses the critical disconnect between Indonesia's manufacturing sector and the digitally native Gen Z workforce, focusing on Banten Province, a region with high youth unemployment despite its industrial concentration. We quantitatively assess the digital presence of manufacturing companies on key recruitment platforms, including LinkedIn, Jobstreet, and the government's Karirhub portal, to quantify this gap. Using web scraping techniques with Python, company profile data was systematically collected and analyzed. The findings reveal a limited digital footprint, with overall company presence recorded at 43.24% on LinkedIn, 42.70% on Karirhub, and a notably lower 32.97% on Jobstreet. Significant disparities exist across subsectors; consumer-facing industries like Food, Beverage, and Automotive show high digital engagement, while sectors such as Textiles, Electronics, and Non-metallic Minerals lag considerably. Notably, the Tobacco industry was found exclusively on the government's Karirhub platform. This confirmed digital divide hinders effective talent acquisition and limits job seekers' access to credible information. We conclude that a strategic imperative exists for manufacturers to enhance their digital recruitment strategies. This is crucial not only for attracting Gen Z talent but also for aligning with Indonesia’s national digitalization agenda to reduce unemployment.
Forecasting Indonesia's Non-Oil and Gas Exports Using Facebook Prophet: A Seasonal and Trend Analysis Erfiani, Erfiani; Wijaya, Ferdian Bangkit
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23337

Abstract

This study aims to analyze and predict the trend of Indonesia's non-oil and gas exports using the Facebook Prophet model, focusing on identifying seasonal patterns, trends, and volatility present in the export data. Monthly export data from 2015 to 2025, sourced from the Statistics Indonesia (BPS), were used as the basis for analysis. The dataset revealed notable seasonal patterns and substantial volatility, particularly in the period following 2020. To model these dynamics, three Prophet model configurations were tested: one considering only annual seasonality, combining both annual and monthly seasonality, and another incorporating only monthly seasonality. The evaluation of these models showed with an initial Mean Absolute Percentage Error (MAPE) of 8.70%. This model was then optimized through hyperparameter tuning. The optimal parameter configuration (changepoint_prior_scale = 0.5, seasonality_prior_scale = 0.01, fourier_order = 3) resulted in a significant improvement, reducing the MAPE to 4.73%. This optimized model demonstrated its capacity to more precisely capture the complex patterns. Furthermore, the study projected Indonesia’s non-oil and gas exports for the period from April 2025 to December 2026. The projections indicate a relatively stable export trend within the range of 20,000 to 22,000 million USD per month, with consistent seasonal patterns.
Co-Authors Budiaji, Weksi Erfiani Erfiani WICAKSONO, AGUNG SATRIO