Article Per Year (5 Year)
p-Index From 2021 - 2026
1.556
P-Index
This Author published in this journals
All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL INSTEK (Informatika Sains dan Teknologi) KOMPUTIKA - Jurnal Sistem Komputer TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research
Fitri Insani
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : jurnal computer science and information technology coscitech

 1 
Pencarian adverse event yang timbul akibat penggunaan obat dexamethasone menggunakan algoritma apriori Nuradha Liza Utami; Alwis Nazir; Pizaini; Elvia Budianita; Fitri Insani
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Inflammation is the body's response to infection, irritation, or injury characterized by redness, increased temperature, swelling, and pain. Dexamethasone is one of the drugs from the corticosteroid group that is commonly used, dexamethasone has a wide indication in medicine is often considered a drug that can save lives, causing many people to then buy dexamethasone drugs without medical indications and prescriptions assuming dexamethasone drugs can treat various diseases. The use of dexamethasone can result in side effects including decreased immunity, diabetes, hypertension, moon face, osteoporosis, and cataracts. In addition to frequent side effects, adverse events may also occur. This study aims to find the relationship of adverse events that arise as a result of using dexamethasone drugs, by applying the data mining technique of association rule method with apriori algorithm. The dataset used in the research is sourced from the FDA Adverse event Reporting System (FAERS) database which is managed using the KDD stages which include data selection, cleaning, transformation, and data mining. the results of the research are implemented into the apriori algorithm data mining system and tested using the lift ratio value. The rules generated in this study have a lift ratio value of more than 1, which means that the rules generated are valid and show the benefits of these rules.
Model Prediksi Jumlah Penjualan Pelumas Mesin Di PT. X Dengan Algoritma Naïve Bayes Purnama, Nilam; Fitri Insani; Elin Haerani; Iis Afrianty
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8250

Abstract

Machine lubricants are essential materials used to reduce friction between two moving surfaces, improve machine efficiency, and extend the lifespan of components. This study aims to predict the sales volume of machine lubricants at PT. X using the Naïve Bayes algorithm. The data used includes attributes such as year, month, material description, total allocation, realization, and remaining allocation, with a total of 3,006 data points obtained from PT. X's Warehouse Management System (WMS). The model was tested using the 10-Fold Cross Validation method and testsing without such validation. The test results show an accuracy of 71% with 10-Fold Cross Validation, compared to 14% without validation. Additional testing showed an accuracy of 5%, with RMSE of 124.71 and MAPE of 0.95. Based on these results, it is recommended to optimize data preprocessing, such as handling data imbalance and feature normalization, to improve prediction accuracy. Furthermore, using more diverse validation techniques, such as stratified cross-validation, can provide more stable evaluations. Given that predictions are influenced solely by historical data, it is recommended to periodically update the data to keep the model relevant and accurate. This research is expected to assist PT. X in planning sales strategies and managing lubricant stock more effectively.
Implementasi Algoritma Improve Apriori Terhadap Keluarga Beresiko Stunting Muhammad Habib Nazlis; Fitri Insani; Alwis Nazir; Iis Afrianty
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting is a serious health issue in Indonesia, particularly among families with low socio-economic conditions. However, the lack of precise criteria or measurements of social conditions contributing to at-risk families makes prediction challenging. This study aims to identify patterns of relationships among 17 criteria influencing stunting risk, such as maternal age, number of children, type of flooring in the house, and access to clean water, by enhancing the efficiency of the Apriori algorithm through hash-based techniques. Data were obtained from families in Tuah Madani District, Pekanbaru, and analyzed using data preprocessing and transformation methods. The implementation of this algorithm within a web-based information system enables rapid and efficient analysis to identify stunting risks based on relevant combinations of criteria. The analysis results indicate that certain criteria, such as maternal age above 35 years, status as a couple of childbearing age (PUS), and having more than three children, are significantly associated with stunting risk, with a support value of 37.54% and a confidence level of 83.16%. This study contributes to the development of efficient methods for stunting risk analysis and provides a foundation for more targeted health interventions. Future researchers are advised to expand the data scope by including additional regions and different time periods to improve result generalization. Furthermore, incorporating other variables, such as maternal nutritional status or the education level of household heads, may offer deeper insights into understanding stunting risk patterns.
Co-Authors Alwis Nazir Damayanti, Elok Eggy P Elin Haerani Elvia Budianita Frica A Ambarwati Iis Afrianty Iwan Iskandar Jasril Jasril M Fadhli Ihsan MHD Ade Setiawan Muhammad Habib Nazlis Muhammad Hasbi Assidiqqi Novi Yanti Nuradha Liza Utami Okfalisa Okfalisa Okfalisa saktioto Pizaini Pizaini Purnama, Nilam Rahmad Abdillah SURYA ADITYA GD Suwanto Sanjaya Syarifatun Nissa Wenny Tarisa Oktaviany Yelfi Vitriani Yelvi Fitriani Yusra