Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sistem Informasi dan Informatika (SIMIKA)
Rizal Tjut Adek
Malikussaleh University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Decision Tree-Based Web Expert System for Preliminary Screening of Refractive Eye Disorders Dini Fariha; Rizal Tjut Adek; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13213

Abstract

Refractive eye disorders remain one of the leading causes of visual impairment worldwide, while limited access to ophthalmology services often delays early diagnosis. This study investigates the effectiveness of the Decision Tree algorithm for symptom-based classification of refractive eye disorders and its implementation within a web-based expert system for preliminary eye health screening. Data were collected from 505 respondents using a structured Google Forms questionnaire. After preprocessing and labeling, the dataset was divided into 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix together with accuracy, precision, recall, and F1-score metrics to provide a comprehensive assessment under an imbalanced class distribution. Experimental results showed that the proposed Decision Tree model achieved an overall accuracy of 89.11% and demonstrated satisfactory performance in identifying dominant diagnostic classes while maintaining transparent and interpretable decision rules. The developed web-based system provides symptom-based diagnosis, examination history management, and PDF report generation to support preliminary eye health assessment. These findings indicate that the proposed approach is suitable as an accessible decision-support tool for preliminary refractive eye disorder screening, particularly in areas with limited access to ophthalmology services.
Co-Authors Dini Fariha Khananda Raihansyah Lidya Rosnita Nunsina Rizki Suwanda Sasmita Sasmita