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All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Nuansa Informatika
Iwan Setiawan
Universitas Logistik dan Bisnis Internasional
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PENERAPAN KONSEP BIG DATA UNTUK OPTIMALISASI MANAJEMEN ASET DI PT POS INDONESIA Muhammad Ruslan Maulani; Iwan Setiawan; Marwanto Rahmatuloh
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 10 No. 2 (2024)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol10.iss2.2024.1528

Abstract

This research aims to apply the Big Data concept in order to optimize asset management at PT Pos Indonesia. PT Pos Indonesia needs to adopt Big Data technology to collect, manage, and analyze data generated by various sources. The research method used in this study is Scrum, a software development framework that focuses on flexibility and team collaboration. This research involves several stages. First, a needs analysis and identification of assets that need to be optimized were conducted. Next, data was collected from various sources such as asset management systems. The collected data is then processed and stored in a suitable Big Data infrastructure. Next, using the Scrum method, the research team and stakeholders were involved in the process of developing a Big Data solution. Short sprints were conducted to implement and test various components of the system, such as the data collection platform, analysis algorithms, and visualization of results. Regular iterations allowed for continuous adjustments and improvements according to feedback provided by the team and stakeholders. The result of this research is a prototype application and the application of the Big Data concept implemented in the asset management application at PT Pos Indonesia. By optimizing the use of data, PT Pos Indonesia can increase operational efficiency, improve decision making based on more accurate data analysis.
Predicting the Happiness Index Based on the HDI Indicator in Indonesia Using the Ensemble Learning Approach: Prediksi Indeks Kebahagiaan Berdasarkan Indikator IPM di Indonesia Menggunakan Pendekatan Ensemble Learning Syafrial Fachri Pane; Rofi Nafiis Zain; Iwan Setiawan; Virdiandry Putratama
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.410

Abstract

Machine Learning is used to analyze complex data in various fields of research. In this study, we applied an ensemble learning approach consisting of Random Forest Regression (RF), XGBoost Regression (XGB), Decision Tree Regression (DT) and Pearson correlation analysis as well as Shapley Additive Explanations (SHAP) to analyze the relationship between the HDI and Happiness indicators in Indonesia. Second, building a prediction model with an ensemble learning approach, namely stacking, which consists of several algorithms including RF, XGB, DT. The results of this study, one, based on the results of Pearson correlation analysis, Permutation Importance (PI), and SHAP, show that the happiness score of Indonesian people has a strong correlation with the Human Development Index variable. The Pearson correlation result shows a value of 0.88, which indicates a very strong positive relationship between HDI and happiness. In addition, the Permutation Importance and SHAP analysis also confirms that HDI is one of the most influential variables in predicting happiness scores in Indonesia. Second, the performance model for predicting happiness using stacking regressors with an R-Squared value of 97.68\%, MAE 0.002900, MSE 0.000021, and RMSE 0.004604.
Co-Authors Marwanto Rahmatuloh Muhammad Ruslan Maulani Rofi Nafiis Zain Syafrial Fachri Pane Virdiandry Putratama