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IMPLEMENTATION ANALYSIS OF SERVICES BASED ON SOCIAL INCLUSION IN THE COMMUNITY OF THE REPUBLIK GUBUK Inawati, Inawati; Dewi, Amalia Nurma; Martutik, Martutik; Setiawan, Setiawan
Jurnal Diskursus Islam Vol 10 No 1 (2022): April
Publisher : Program Pascasarjana, UIN Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jdi.v10i1.24538

Abstract

Abstract: This study aims to analyze the form of implementation of social inclusion-based services in the Republic Gubuk Community which is an association of Gubuk Baca in the Jabung area, Malang Regency. The research was conducted using a descriptive qualitative data analysis approach. data sources used primary and secondary data sources obtained through interviews, observation, and literature review. The results of the research show that the activities that are part of the program at the Gubuk Republik Community are a form of social inclusion-based library service which is in line with the concept of social inclusion-based library services developed by Utami and Prasetyo which describes several aspects, namely reading huts as facilitators in developing growth potential. economy, reading huts as a vehicle for problem-solving, reading huts as a center for community activities, and reading huts play an active role in the literacy movement through various activities such as BUMI Gubuk (Hut-Owned Enterprise), teaching thug, People's Campus, mobile library movement, and others. etc. 
TOPIC MODELING OF UNDERGRADUATE THESES METADATA USING LDA FOR TREND ANALYSIS IN LIS Sherly Rosa Anggraeni; Moh Safii; Rani Auliawati Rachman; Amalia Nurma Dewi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9330

Abstract

Uncovering latent trends in academic metadata is critical for under-standing research dynamics in information systems. This study em-ploys Latent Dirichlet Allocation (LDA), a probabilistic machine learning algorithm in artificial intelligence, to analyze metadata from 180 undergraduate theses (2020–2024) at Universitas Negeri Malang, using Library and Information Science (LIS) as a case study. The da-taset, comprising titles and abstracts, underwent preprocessing (tokeni-zation, lowercasing, stopword removal, and domain-specific term fil-tering) to create a clean text corpus. Using Gensim’s variational Bayes, LDA models with K=5–15 topics were tested, selecting K=8 based on optimal C_v coherence (0.65) and perplexity (~150). Topics, labeled via top keywords, include Digital Libraries, User Behavior, and Ar-chive Management. Annual topic distributions, visualized via stacked bar charts, revealed a surge in digital topics during 2021–2022 (pan-demic-driven) and rising user behavior focus in 2022–2023. This LDA framework demonstrates scalability for text mining in academic data-bases, offering a replicable pipeline for trend analysis across domains like education or social media. Findings complement global studies, providing insights for curriculum alignment. Limitations include small dataset size and metadata-only analysis. Future work could integrate full-text data or advanced models like BERTopic for enhanced seman-tic discovery.