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All Journal CESS (Journal of Computer Engineering, System and Science) SemanTIK : Teknik Informasi INTECOMS: Journal of Information Technology and Computer Science IJID (International Journal on Informatics for Development) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Djtechno: Jurnal Teknologi Informasi Bulletin of Computer Science Research Jurnal Info Sains : Informatika dan Sains Journal of Scientific Research, Education, and Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Abdi Rakyat
Farida Ardiani, Farida
Magister Teknik Informatika, Universitas Islam Indonesia, Yogyakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Implementation of NLP in Groupin Implementation of NLP in Grouping Social Assistance Recipients: A Case Study of Girisubo District Panggalih, Mahmud Isti; Ardiani, Farida
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 07 (2026): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i07.2333

Abstract

This research aims to examine and implement text analysis of social assistance recipients data using Natural Language Processing (NLP) methods as a solution to accelerate and improve efficiency in the process of categorizing aid recipients. Based on the design and testing processes carried out, the developed system is capable of automatically processing descriptive data of aid recipients and grouping them into appropriate categories, such as the elderly, school children, pregnant women, the disabled, and minors. The research design uses System Development Research. The results of the NLP system implementation in the case study of categorizing social assistance recipients in Girisubo District show a significant improvement in operational efficiency and classification accuracy. Quantitatively, the trained NLP model successfully achieved high performance metrics. The average classification accuracy exceeds 90%, with data clustering computation time much faster compared to the manual process that takes hours. The main conclusion of this study indicates that the implementation of Natural Language Processing (NLP) in Girisubo District is effective in classifying categories of aid recipients. The model shows the strongest performance in the Pregnant Women group (95% Accuracy) and Persons with Disabilities (97% Accuracy) with perfect Precision of 100%. The performance for the Elderly and School Children is also very good (minimum 89% Accuracy). Overall, the system has proven reliable and is capable of automatically grouping descriptive data into the appropriate categories.
Co-Authors Abadi, Adelano Nur Agus Suhendar, Agus Alanshari, Muhajir Alfath, Muhammad Arifin, Naufal Hafizh Diwandari, Saucha Edoardo Yanuar Putra Fegiyanto, Rizki Gaviota, Alvero Halim, William Hardiansah, Gian Kalifia, Anna Dina Khanifah, Haesti Nur Lukfi, Ikbal Sohiburido Murti Retnowo Nasmah Nur Amiroh Nugroho, Iqbal Satrio Panggalih, Mahmud Isti Rodhiyah Mardhiyyah Romadhon, Nur Aditya Rony, Zahara Tussoleha Rumalutur, Muhammad Ifdhal Sutarman Syahalam, Izaaz Azaam Titi, Niken Trengginas, Way Lilo Tandang