Article Per Year (5 Year)
p-Index From 2020 - 2025
2.127
P-Index
This Author published in this journals
All Journal Riau Journal of Computer Science JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Indonesian Journal of Electrical Engineering and Computer Science ARSY : Jurnal Aplikasi Riset kepada Masyarakat Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya International Journal of Science and Society (IJSOC) Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Electronic Integrated Computer Algorithm Journal JGEN : Jurnal Pengabdian Kepada Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Journal of Computer Science Advancements
Yuyun Yusnida Lase
Politeknik Negeri Medan
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Perbandingan TextRank Berbasis TF-IDF dan Word2Vec dalam Peringkasan Teks Berita Bahasa Indonesia Gurning, Yohannes Christian; Saragih, Samuel Cristian; Lase, Yuyun Yusnida; Julham, Julham
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.552

Abstract

Automatic text summarization has become an essential solution for processing massive textual information, particularly in lengthy news articles. This study compares two variants of the TextRank algorithm using different weighting schemes: TF-IDF and Word2Vec, for summarizing Indonesian news texts. The dataset comprises 160 news articles from Kompas.com, which underwent preprocessing. Evaluation was conducted using ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L), manual readability assessment, and execution runtime. The results indicate that TextRank with Word2Vec outperforms TF-IDF in both ROUGE scores (ROUGE-1 F1: 0.7033 vs 0.6454) and processing speed. These findings suggest that incorporating semantic representations into graph-based algorithms like TextRank significantly improves summary quality and runtime efficiency.
Classification Analysis of Product Sales Results at Alfamart Using the Naïve Bayes Method Lase, Yuyun Yusnida; Silaban, Citra Wasti; Sitepu, Alex Sander; Telaumbanua, Reza Kavarin
Electronic Integrated Computer Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: APRIL 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v1i2.18

Abstract

This research focuses on the analysis of the number of products sold, especially stock items from the distribution center to Alfamart stores. The main problem discussed in this study is the result of the number of unsold and sold products, which causes overstocking in the warehouse area. To overcome this problem, it will be solved using the Naive Bayes classification method. This research uses sample data of 100 products and uses data collection techniques such as observation and interviews. The collected data is analysed through a classification approach. This research aims to predict goods that sell and do not sell using Rapidminer using the NaïveBayes method. And to produce more accurate data for the product sales process. The reason for using this naïve bayes algorithm in the process of processing and analysing data is because the way this algorithm works uses statistical methods and probability in predicting future results. The validation results show that the Naive Bayes classification method implemented through Rapidminer provides a significant explanation with a fairly high accuracy and a positive effect on the prediction of sales of goods based on consumer demand and needs.
The Application of Artificial Intelligence in Processing Health Data in Biomedical Information Prayudani, Santi; Lase, Yuyun Yusnida; Husna, Meryatul; Adam, Hikmah Adwin
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2245

Abstract

The increasing complexity and volume of health data in modern biomedical systems have necessitated advanced technologies for effective data processing and analysis. Traditional methods often fall short in managing real-time, multidimensional data generated from various biomedical sources, such as electronic health records (EHRs), wearable devices, and genomic data. This research investigates the application of artificial intelligence (AI) in optimizing the processing and interpretation of biomedical health data. The objective of this study is to explore how AI-based technologies, including machine learning and deep learning algorithms, enhance the efficiency, accuracy, and predictive capabilities in biomedical information systems. By identifying patterns, anomalies, and correlations in large datasets, AI offers potential improvements in disease diagnosis, patient monitoring, and treatment personalization. This research employs a qualitative systematic review method, analyzing peer-reviewed literature published between 2015 and 2024 from major databases such as PubMed, IEEE Xplore, and Scopus. The analysis focuses on case studies, comparative evaluations, and implementation outcomes of AI in various biomedical domains. The findings reveal that AI applications significantly improve data processing speed and accuracy, enable early diagnosis of diseases such as cancer and diabetes, and support predictive analytics for patient outcomes. However, challenges remain in areas such as data privacy, ethical compliance, and algorithm transparency. In conclusion, the integration of AI into biomedical data systems holds transformative potential for healthcare delivery, though further interdisciplinary collaboration is required to address its limitations and ensure equitable access and ethical use.
Analysis of Regression and Neural Network Models in Predicting Patient Visit Volume Harizahahyu; Friendly; Fathoni, Muhammad; Lase, Yuyun Yusnida; Prayudani, Santi; Harfita, Nur Laily
International Journal of Science and Society Vol 7 No 4 (2025): International Journal of Science and Society (IJSOC)
Publisher : GoAcademica Research & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/ijsoc.v7i4.1561

Abstract

Predicting patient visit volume plays a crucial role in supporting decision-making and resource allocation in healthcare services. This study aims to compare the performance of Multiple Linear Regression and an Artificial Neural Network (ANN) in forecasting patient visits at a dental clinic, using daily patient visit data and predictor variables such as holidays and promotional activities. Multiple regression was used to capture the linear relationship between the predictor and response variables, while ANN was applied to explore potential non-linear relationships. The results indicate that multiple regression outperformed the ANN, demonstrated by lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values, and provided clearer interpretability, making it more beneficial for healthcare practitioners, particularly in the context of a limited dataset. In contrast, the ANN tended to produce overestimates and was less responsive to short-term variations. Therefore, multiple regression can still be considered a reliable, efficient, and interpretable prediction method for clinical data with a moderate sample size, while future research is recommended to use larger datasets and test other machine learning algorithms to improve the accuracy and generalizability of the results.
Co-Authors Adam, Hikmah Adwin Adwin Adam , Hikmah Al Khowarizmi Alex Sander Sitepu Citra Wasti Silaban Fatmi, Yulia Fildzah Nadiah Friendly Friendly, Friendly Gabriel Ardi Hutagalung Gurning, Yohannes Christian Harfita, Nur Laily Harizahahyu Harizahayu, Harizahayu Harris Aminuddin Haryadi - Hikmah Adwin Adam Husna, Meryatul Julham Julham Lubis, Arif Ridho Mahulae, Doni Ganda Marbungaran Muhammad Fathoni Muhammad Luthfi Hamzah Muharman Lubis Nauli, Arif Aryaguna Prayudani, Santi Ramdani Safitri, Habibi Reza Kavarin Telaumbanua Saragih, Samuel Cristian Silaban, Citra Wasti Sitepu, Alex Sander Syafli, Sekar Arini Syamsul Arifin Telaumbanua, Reza Kavarin Yulia Fatmi Yulia Fatmi