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All Journal Jurnal Pendidikan Sosiologi dan Humaniora Jurnal CoreIT JURNAL ILMIAH MATEMATIKA DAN TERAPAN JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL EDUCATION AND DEVELOPMENT JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Kepariwisataan: Jurnal Ilmiah International Journal of Economics (IJEC) International Journal of Educational Research Excellence (IJERE) Innovative: Journal Of Social Science Research Journal Emerging Technologies in Education Journal of Rural Tourism (JoRT) Jurnal Teknologi Cerdas Ulil Albab Jurnal Teknokes Journal of Computer Science Advancements Journal of Social Entrepreneurship and Creative Technology
Nugroho, Agung Yuliyanto
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Journal : JURNAL ILMIAH MATEMATIKA DAN TERAPAN

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Spatial Statistical Analysis for Poverty Mapping Using Machine Learning: Spatial Statistical Analysis for Poverty Mapping Using Machine Learning Nugroho, Agung Yuliyanto; Puji Sarwono
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17883

Abstract

Poverty is a multidimensional problem influenced not only by economic factors but also by spatial dimensions such as geographic location, accessibility, and environmental characteristics. This study aims to analyze spatial patterns of poverty and develop a poverty prediction model using a geospatial data-based machine learning approach. The data used comes from a combination of open sources such as the Central Statistics Agency (BPS), Landsat satellite imagery, and regional infrastructure data. The methods used include spatial autocorrelation analysis (Moran's I) to identify poverty clustering patterns, Local Indicators of Spatial Association (LISA) to detect poverty hotspots, and Random Forest and Gradient Boosting models to predict poverty levels based on environmental, social, and economic variables. The results show that poverty has a significant spatial pattern, where areas with high poverty rates tend to cluster in areas with low infrastructure access and high population density. The machine learning model demonstrated better prediction accuracy than the traditional linear regression approach, with an R² value reaching 0.87 and a lower prediction error rate (RMSE). These findings emphasize the importance of integrating spatial analysis and machine learning technology in understanding the dynamics of poverty geographically. This research contributes to the development of spatial data analysis methods in the context of public policy, particularly in supporting more targeted poverty alleviation intervention planning. The mapping results can serve as a basis for local governments in identifying priority areas, allocating resources, and designing data-driven development policies. Thus, this approach offers an innovative solution towards more efficient and evidence-based decision-making in poverty alleviation in Indonesia.
Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis: Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis Nugroho, Agung Yuliyanto
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17884

Abstract

In the era of digital transformation, e-commerce platforms have become a major driver of economic activity, generating vast amounts of transaction data every day. Analyzing these data can provide valuable insights into customer behavior, purchasing trends, and business performance. This study aims to apply the K-Means clustering algorithm to identify and analyze transaction patterns in e-commerce systems. The research focuses on developing an efficient data-driven approach to segment customers based on their transactional attributes, such as purchase frequency, transaction value, and product category preferences. The methodology involves several stages: data preprocessing, including cleaning and normalization; feature selection based on relevant transactional indicators; and the application of the K-Means clustering algorithm to group customers into clusters with similar characteristics. The Elbow Method was used to determine the optimal number of clusters. Data were processed using the Python programming language and libraries such as Scikit-learn and Pandas. The results reveal that K-Means effectively segments e-commerce customers into distinct groups that reflect their purchasing patterns—ranging from high-value loyal customers to occasional buyers. Each cluster presents unique behavioral profiles that can be interpreted for targeted marketing strategies. The clustering outcome provides useful insights for customer relationship management (CRM), inventory optimization, and personalized product recommendations. In conclusion, the application of the K-Means algorithm demonstrates significant potential in uncovering hidden patterns within large-scale e-commerce transaction data. The findings support the use of mathematical and computational models in improving decision-making processes in digital commerce. Future research is recommended to enhance cluster accuracy by integrating hybrid algorithms or deep learning-based segmentation approaches.
Co-Authors Abdul Malik Akbar, Riolandi Albadri, Albadri Anshoriyah, Sofiyatul Apriyanto, Apriyanto Aurelius Fredimento Betty Yel, Mesra Christine Widilestari Dina Destari Firdaus, M. Surya Maulana Flora Grace Putrianti Hamka Hamka, Hamka Herryani, Herra Hidayat, Noura Rizqyaannisa Ignatius Septo Pramesworo Joko Triyono Kristanto, Ferat Larisu, Zulfiah Liyan, Sur masriah masriah, masriah Musa Hefer Smas Nasim, Abu Sahman Nugroho, Dwi Yoso Nur Rohman Prasetio, Rachmat Puji Sarwono Purwati Purwati Rao, Ananya Raule, Jean Henry Rosalina Andriyani Ebang Pareira Saidah, Andi Sanjaya, Lalu Rudi Shimbun, Annisa Fikria Sisca Cletus Lamatokan Sitti Hasnah Sri Yulianto Fajar Pradapa Sudiro, Syawal Sukoco, Hendro Suswanto Suswanto, Suswanto Sutopo, Joko Syamsu, Moch. Nur Titik Haryanti Tundo, Tundo Waahib, Achmad Nashrul Wajnah, Wajnah Widiyanto, Nur Wong, Lucas Wulandari, Lastiani Warih Yulianto, Dwi Hery Yuniati, Nining