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Asep Erlan Maulana
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Ruang Gugus Mutu Fakultas Ilmu Komputer Universitas Pamulang - Kampus Viktor Lt. 3 Jalan Raya Puspitek No. 46 Buaran, Serpong, Tangerang Selatan, Banten, Indonesia
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INDONESIA
Jurnal Informatika Universitas Pamulang
Published by Universitas Pamulang
ISSN : 25411004     EISSN : 26224615     DOI : https://doi.org/10.32493
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Universitas Pamulang is a periodical scientific journal that contains research results in the field of computer science from all aspects of theory, practice and application. Papers can be in the form of technical papers or surveys of recent developments research (state-of-the-art). Topics cover the following areas (but are not limited to): Artificial Intelligence Big Data Business Intelligence Data mining Decision Support Systems Intelligent Systems Machine Learning Network and Computer Security Optimization Pattern Recognition Soft Computing Software Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol 10 No 2 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG" : 2 Documents clear
Penerapan Metode Preference Selection Index untuk Penerima Program Keluarga Harapan di Desa Gunungsari Kabupaten Pandeglang Hays, Riyan Naufal; Siswanto; Djaksana, Yan Mitha
Jurnal Informatika Universitas Pamulang Vol 10 No 2 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i2.49023

Abstract

The Program Keluarga Harapan (PKH) aims to alleviate poverty and improve access to education and healthcare for underprivileged families in Indonesia. In Gunungsari Village, Mandalawangi Subdistrict, Pandeglang Regency, where most residents are low-income farmers and breeders, PKH faces challenges in accurately identifying eligible recipients due to the limitations of manual selection, which is prone to subjectivity and human error. This study explores the application of the Preference Selection Index (PSI) method to improve the objectivity and accuracy of beneficiary targeting. From 973 households, five were selected as a representative sample for manual validation. Twelve socio-economic indicators were used to assess eligibility. The PSI method systematically calculates preference weights across multiple criteria, including income, employment, housing, assets, and infrastructure access, generating a ranking of the most eligible candidates. The highest score obtained was 3.188, identifying Family C as the top candidate. The method showed strong agreement with expert judgment, as evidenced by a Spearman Rank Correlation of 0.9. These findings suggest that PSI enhances the fairness and efficiency of the PKH selection process and supports its digital transformation. This study offers a replicable model for improving governance and targeting in rural social assistance programs.
Evaluasi Modern Model Pembelajaran Mesin pada Dataset SEERA untuk Estimasi Upaya Perangkat Lunak Nufus, Fina Sifaul; Subekti, Agus
Jurnal Informatika Universitas Pamulang Vol 10 No 2 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i2.51687

Abstract

Estimating software development effort is crucial in project planning and management, especially in resource-constrained environments. This study piloted four modern regression models: Random Forest, Support Vector Machine (SVM), Lasso Regression, and Ridge Regression, chosen because they represent different approaches: ensemble, margin-based, and L1 and L2 regularization. Experiments were conducted using the SEERA (Software Effort Estimation with Real Attributes) dataset, consisting of 99 entries, with a modern Python pipeline including preprocessing, feature selection, Z-score normalization, data splitting (80:20), and cross-validation (5-Fold Cross Validation). Models were evaluated using MAE, RMSE, and R². Results showed that Random Forest outperformed both the 80:20 split (R² = 0.740, MAE = 3981.53) and K-Fold (R² = 0.715, MAE = 3152.03), while SVM performed the worst with a negative R². Lasso and Ridge are only competitive at 80:20 but significantly decrease on K-Fold, indicating less stability. This research contributes by providing an in-depth evaluation based on a single dataset and demonstrating that the transparent Python pipeline based on K-Fold can be replicated to improve estimation accuracy. Future research could be conducted using advanced ensemble methods (e.g., XGBoost) and evaluated on larger datasets to generalize the results.

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