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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA)
Rizal Tjut Adek
Malikussaleh University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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PENERAPAN RELEVANCE-AUGMENTED GENERATION LARGE LANGUAGE MODELS UNTUK SUMMARISE BERITA PADA PORTAL BERITA DETIKNEWS Khananda Raihansyah; Rizal Tjut Adek; Nunsina
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4021

Abstract

In today's digital era, easy access to information is characterized by the rise of online news portals that present hundreds of articles every day. However, the high volume of news available often makes it difficult for readers to identify the essence of various articles on similar topics. Reading articles one by one becomes a time-consuming activity and risks causing information overload. To address these issues, this research develops an automated system that is able to summarize news based on certain topics by utilizing the Relevance-Augmented Generation (RAG) approach reinforced by the use of Large Language Models (LLMs). The system is built using Python programming language and integrated with Flask framework as a web interface. Data collection is done through a scraping process from the Detiknews portal using a special API. The articles obtained were then analyzed using natural language processing (NLP) techniques, including evaluation of sentence length, sentence position in the article, as well as the frequency of keyword occurrence to determine the most relevant sentences. The initial summary generated is further refined with the help of the LLMs model through the Groq API. The implementation results show that the system is able to present information-dense, accurate, and efficient summaries. The summary makes it easy for users to get the gist of the news quickly without losing the main context. Thus, this system provides a solution to the challenges in online news information processing while increasing the ease of access to information for readers.
Clustering of Indonesian Provinces based on Demographic Characteristics using DBSCAN Algorithm Sasmita Sasmita; Rizal Tjut Adek; Lidya Rosnita
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6309

Abstract

This research aims to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering provinces in Indonesia based on demographic characteristics using a Web GIS system. The study utilizes demographic data of 34 provinces from 2020–2023, covering population size, population growth rate, poverty percentage, population density, and gender ratio. DBSCAN was applied with parameters epsilon (ε) = 0.50 and minimum samples = 2, evaluated using the Davies-Bouldin Index (DBI). The results show that DBSCAN successfully identified two consistent main clusters throughout the period: the High Cluster consisting of three provinces (West Java, Central Java, East Java), the Medium Cluster including 29–30 provinces, and DKI Jakarta as a single noise point.. Clustering quality varied with DBI values ranging from 0.3839 to 0.4123, with the best quality in 2020 and the highest internal diversification in 2023. Riau Islands fluctuated as a noise point in 2021 before returning to stability. The developed Web GIS system successfully integrated interactive map visualization using OpenStreetMap and Leaflet, providing a comprehensive spatio-temporal analysis dashboard. This study demonstrates the effectiveness of DBSCAN in detecting demographic patterns and regional anomalies, while producing a visualization tool that supports regional development planning and national demographic policies in Indonesia.
Co-Authors Khananda Raihansyah Lidya Rosnita Nunsina Sasmita Sasmita