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All Journal International Journal for Applied Information Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Endahti, Les
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Sistem Informasi Pengajuan dan Persetujuan Domain untuk Instansi Pemerintah pada Diskominfo Purwakarta Berbasis Web Jalaludin, Jalaludin; Zahra, Denada Fatimah; Endahti, Les
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1568

Abstract

The advancement of information technology has significantly influenced how government agencies perform their duties and responsibilities, with information systems and computer technology becoming the backbone for data management and the provision of accurate information. In this context, it is essential to create systems that facilitate an integrated and efficient process for domain application and approval, The development technique uses the waterfall method, which depicts software development as a series of sequential and linear phases. This web-based system enables users to submit domain applications anytime and anywhere, and it enhances the transparency of the application and approval process through real-time monitoring by applicants. The research results indicate that this web-based domain application and approval information system can improve the efficiency and effectiveness of the domain application process at Diskominfo Purwakarta. With this system, the time required for domain application and approval is significantly reduced, and the accuracy and transparency of the process are increased. Additionally, the system enhances user satisfaction by providing ease of access and real-time process monitoring. The adoption of this system also reduces the workload of administrators by allowing organizational units to input data independently. Evaluation results show that the system is well-received by users and meets the operational needs of Diskominfo Purwakarta.
Evaluating the Performance of Random Forest Algorithm in Classifying Property Sale Amount Categories in Real Estate Data Endahti, Les; Faturahman, Muhammad Shihab
International Journal for Applied Information Management Vol. 5 No. 4 (2025): Regular Issue: December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i4.114

Abstract

This study explores the use of machine learning algorithms to classify property sale categories in real estate data, focusing on the performance of the Random Forest algorithm. The dataset, comprising over one million records of property sales from 2001 to 2022, includes features such as sale amount, assessed value, sales ratio, property type, and residential type. The primary objective is to determine which algorithm better predicts property sale categories and to assess how these predictions can aid in market segmentation and property valuation. After preprocessing the data by removing irrelevant columns and handling missing values, we applied the Random Forest classifier to predict five key property types: 'Single Family', 'Residential', 'Condo', 'Two Family', and 'Three Family'. The model achieved an accuracy of 82.98%, with high recall for categories like 'Single Family' and 'Condo', but struggled with 'Residential', which displayed a lower recall due to its diverse nature. The findings suggest that the Random Forest algorithm performs well in predicting certain property types, but improvements are needed for categories with more variation. The study highlights the importance of selecting relevant features such as sale amount and assessed value, which were found to be the most influential in determining property type. Real estate professionals can leverage these machine learning models for more accurate market segmentation, leading to better pricing and marketing strategies. However, the study also acknowledges limitations, such as the complexity of the 'Residential' category and potential data imbalance. Future research could focus on incorporating additional features, such as location-specific data or detailed property descriptions, and testing alternative algorithms to further enhance classification accuracy.
Co-Authors Chatarina Umbul Wahyuni Faturahman, Muhammad Shihab Zahra, Denada Fatimah