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All Journal Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura Jurnal Komputer dan Teknologi (JUKOMTEK)
Cindi Wulandari
Universitas Bina Insan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Other

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ANALISIS DAN PREDIKSI TINGKAT KERENTANAN PMKS MENGGUNAKAN CHI-SQUARE FEATURE SELECTION DAN RANDOM FOREST PADA DINAS SOSIAL KOTA LUBUKLINGAU Lovhura Anaphalys Sabryna; Andri Anto Tri Susilo; Cindi Wulandari
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

People with Social Welfare Problems (PMKS) are a part of society that faces social, economic, and environmental challenges, thus needing proper support and action from local governments. However, identifying and assessing the vulnerability of PMKS at the Lubuklinggau City Social Service still encounters obstacles, such as the numerous social factors involved and the risk of bias in evaluations. Thus, this study intends to create a predictive model for PMKS vulnerability levels using a machine learning method based on Chi-Square feature selection and the Random Forest algorithm. The research starts with gathering and prepping PMKS data, which includes socioeconomic factors, family situations, and access to public services. The Chi-Square method is used to identify the most impactful features related to PMKS status. The findings show that access to public services, children’s education status, home ownership, and monthly income are the most important features, supported by the highest Chi-Square scores and very low p-values. These chosen features are then used as inputs for the Random Forest classification model. The experimental results reveal exceptional model performance, achieving accuracy, precision, recall, and F1-score values of 100% for both categories, specifically PMKS and Non-PMKS. These results suggest that combining Chi-Square feature selection and the Random Forest algorithm can yield a precise and reliable predictive model for classifying PMKS vulnerability levels. Therefore, the proposed model has significant potential as an objective and data-based support tool for the Lubuklinggau City Social Service in developing policies and ensuring better-targeted distribution of social welfare programs.
SISTEM INFORMASI E-LIBRARY MENGGUNAKAN METODE WEB ENGINEERING Ihsan Alvindra; Cindi Wulandari; Bunga Intan
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.454

Abstract

A digital library information system (E-Library) offers a solution to the challenges of library data management—previously handled manually—which often resulted in disorganized records and difficulties in retrieving information regarding books and members. This study aims to design and develop an E-Library information system for SMP Negeri Purwodadi using the Web Engineering method, encompassing user requirements analysis, system design, implementation, and testing. The Laravel framework serves as the core technology for building a robust and efficient backend system, while Black Box Testing ensures the system operates in accordance with user needs. The results demonstrate that the system enables students and staff to access book data, search collections, and manage member information digitally via a user-friendly interface. Testing confirms that the system meets functionality and reliability standards. This development fosters a more integrated, efficient, and secure library management process, thereby enhancing the quality of school library services and supporting digital transformation in education.
ANALISIS DAN PREDIKSI TINGKAT KERENTANAN PMKS MENGGUNAKAN CHI-SQUARE FEATURE SELECTION DAN RANDOM FOREST PADA DINAS SOSIAL KOTA LUBUKLINGAU Lovhura Anaphalys Sabryna; Andri Anto Tri Susilo; Cindi Wulandari
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

People with Social Welfare Problems (PMKS) are a part of society that faces social, economic, and environmental challenges, thus needing proper support and action from local governments. However, identifying and assessing the vulnerability of PMKS at the Lubuklinggau City Social Service still encounters obstacles, such as the numerous social factors involved and the risk of bias in evaluations. Thus, this study intends to create a predictive model for PMKS vulnerability levels using a machine learning method based on Chi-Square feature selection and the Random Forest algorithm. The research starts with gathering and prepping PMKS data, which includes socioeconomic factors, family situations, and access to public services. The Chi-Square method is used to identify the most impactful features related to PMKS status. The findings show that access to public services, children’s education status, home ownership, and monthly income are the most important features, supported by the highest Chi-Square scores and very low p-values. These chosen features are then used as inputs for the Random Forest classification model. The experimental results reveal exceptional model performance, achieving accuracy, precision, recall, and F1-score values of 100% for both categories, specifically PMKS and Non-PMKS. These results suggest that combining Chi-Square feature selection and the Random Forest algorithm can yield a precise and reliable predictive model for classifying PMKS vulnerability levels. Therefore, the proposed model has significant potential as an objective and data-based support tool for the Lubuklinggau City Social Service in developing policies and ensuring better-targeted distribution of social welfare programs.
Co-Authors Andri Anto Tri Susilo Bunga Intan Ihsan Alvindra Lovhura Anaphalys Sabryna