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All Journal Seminar Nasional Teknologi Informasi Komunikasi dan Industri Applied Information System and Management Jurnal Informasi dan Komputer TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research
Alwis Nazir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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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

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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.
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

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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 Aminuyati Elin Haerani Elvia Budianita Fadhilah Syafria Fitri Insani Iis Afrianty Iwan Iskandar Jasril Jasril Lia Anggraini M Fikry Muhammad Alvin Muhammad Fikry Muhammad Habib Nazlis Muhammad Hasbi Assidiqqi Muhammad Ichsanul Bukhari Nailatul Fadhilah Nuradha Liza Utami Pizaini Pizaini Putri Mardatillah Rahmad Kurniawan Rio Fernando Silvi Ana Sukma Evadini Teddie Darmizal Wenny Tarisa Oktaviany