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All Journal Indonesian Journal of Informatic Research and Software Engineering PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Adelia, Qaula
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Perancangan Sistem Informasi Open Recruitment Asisten Laboratorium Prodi Sistem Informasi UIN Suska Riau: Information System Design For Open Recruitment of Laboratory Assistants for Information Systems Study Program at UIN Suska Riau Harmade, Dani; Ananda Putri Aulia; Ash-Shiddiqi, Ikhwan; Adelia, Qaula
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 4 No. 2 (2024): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v4i2.1820

Abstract

Laboratorium komputer saat ini memiliki peran yang penting, melalui eksperimen langsung dengan komputer pelajar lebih memahami teori yang mereka dapatkan dengan lebih baik. Dalam menggunakan laboratorium dibutuhkan pendampingan khusus. Selain kepala laboratorium, asisten laboratorium juga turut serta mendampingi proses praktikum. Laboratorium Sistem Informasi di Universitas Islam Negeri Sultan Syarif Kasim Riau belum mengadopsi sistem pendaftaran asisten laboratorium secara online. Proses pendaftaran masih bersifat manual, yang seringkali memakan waktu lama dan berpotensi menyebabkan kesalahan pencatatan data serta risiko kehilangan informasi yang penting. Tujuan dari penelitian ini adalah untuk membangun sistem informasi open recruitment asisten laboratorium di Laboratorium Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau. Metode yang digunakan dalam membangun sistem ini adalah metode Object-Oriented Analysis and Design (OOAD). Hasil dari penelitian ini adalah sistem informasi open recruitment asisten laboratorium di Laboratorium menggunakan model prototyping.
Lung Disease Risk Prediction Using Machine Learning Algorithms Aulia, Ananda Putri; Adelia, Qaula; Mubarak, Haykal Alya; Fatan, Mohd. Adzka; Sudarno, Sudarno
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

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Abstract

Lung diseases, including lung cancer, are one of the leading causes of death in the world. Early detection is essential to increase patients' chances of recovery and reduce healthcare costs. The utilization of machine learning algorithms can be used to solve this problem. This study evaluates five machine learning algorithms, namely K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), for lung disease prediction using a dataset of 30,000 data with 11 attributes from Kaggle. The dataset was processed through data preprocessing and divided into training and test data with a ratio of 70%:30% and 80%:20%. The algorithm performance was evaluated using precision, recall, F1-score, and accuracy metrics. The results show that RF, SVM, and DT algorithms have the highest performance, with accuracy reaching 94.72% at 70%:30% ratio. The DT algorithm, which previously showed low performance in heart disease classification, provided competitive results in lung disease prediction. This research focuses on the importance of proper algorithm selection and data organization to improve the effectiveness of disease prediction. The findings contribute to the development of artificial intelligence technology for medical applications, particularly in supporting early diagnosis of lung diseases.
Co-Authors Ananda Putri Aulia Ash-Shiddiqi, Ikhwan Aulia, Ananda Putri Fatan, Mohd. Adzka Harmade, Dani Mubarak, Haykal Alya Sudarno Sudarno