Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal International Journal of Integrative Sciences Scientica: Jurnal Ilmiah Sains dan Teknologi
Putri, Firda Salsabila
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences Other

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

Found 3 Documents
Search

 1 
PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI KUALITAS UDARA DI KOTA BANDUNG Pradiah, Adinda Rachma; Az-Zahra, Aisha Shinta; Lintang, Ananda Masayu; Suci, Meta Arfiola; Putri, Firda Salsabila
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 8 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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

Abstract

Polusi udara di kota-kota besar Indonesia menjadi masalah serius karena dapat menyebabkan berbagai penyakit pernapasan dan bahkan kematian. Teknologi data mining dapat dimanfaatkan untuk menganalisis data kualitas udara dan memprediksi tingkat polusi di masa depan, memungkinkan tindakan preventif yang lebih cepat. Penelitian ini membandingkan beberapa algoritma klasifikasi data mining untuk memprediksi kualitas udara di Kota Bandung, dengan fokus pada Naive Bayes, K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM). Data kualitas udara diperoleh dari berbagai sensor yang tersebar di seluruh Kota Bandung. Hasil penelitian menunjukkan bahwa algoritma SVM memberikan kinerja terbaik dengan akurasi prediksi sebesar 92,5%. Temuan ini diharapkan dapat membantu pemerintah dan pemangku kebijakan dalam mengambil keputusan yang tepat untuk mengurangi polusi udara dan meningkatkan kesehatan masyarakat di Kota Bandung.
Development of an Expert System for Diagnosing Respiratory Diseases in Animals Using the Bayesian Network and Rule-Based System Methods Farhan Stiady Syah; Rosyani, Perani; Suryaningrat; Putri, Firda Salsabila; Ashari, Idpan; Sofian, Kurnain
International Journal of Integrative Sciences Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v4i1.13481

Abstract

Respiratory diseases in animals are a common health issue affecting both livestock and pets. These conditions can lead to significant economic losses in the livestock sector and reduce the quality of life for pets if left untreated. Early and accurate diagnosis is crucial to identify diseases promptly, prevent further spread, and minimize negative impacts on animals and their owners. Therefore, a system capable of providing fast, accurate, and data-driven diagnoses is essential. This study aims to develop an expert system specifically designed to diagnose respiratory diseases in animals using two main approaches: Bayesian Network and Rule-Based System. The Bayesian Network models uncertainties by analyzing probabilistic relationships between observed symptoms and potential diseases, while the Rule-Based System supports decision-making based on predefined rules. The combination of these methods is expected to yield more accurate and informative diagnostic results. Symptom data for this study were obtained from various sources, including relevant medical literature and animal health databases. The system was developed using Python programming language, leveraging libraries such as pgmpy for constructing Bayesian Network models and experta for implementing the Rule-Based System. The development and testing processes were conducted on the Google Colab platform, enabling efficient data processing, simulation, and visualization. The expert system was evaluated using simulated symptom data, with performance parameters including diagnosis probability and overall accuracy. The results indicate that the expert system effectively provides diagnoses based on user-input symptoms. The probability information included in the diagnostic results aids veterinarians and livestock owners in making more precise, data-driven medical decisions
Liver Disease Classification Using Decision Tree and Random Forest Algorithms Cahyono, Yoyo; Rosyani, Perani; Syah, Farhan Stiady; Putri, Firda Salsabila; Ashari, Idpan; Sofian, Kurnain
International Journal of Integrative Sciences Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v4i1.13509

Abstract

Diagnosing diseases using technology is no longer uncommon. With advancements in healthcare technology, decision-making, particularly in detecting liver diseases, has become more efficient. Liver, an essential human organ, sees its functionality decline in patients with liver diseases. According to WHO data (2013), 28 million individuals in Indonesia suffer from liver diseases, marking it as one of the ten deadliest diseases. Early detection is crucial for effective treatment. This study aims to predict liver diseases using the Random Forest algorithm. Feature selection and classifier choice are pivotal for improving accuracy and computational efficiency. Using the Liver Disease Patient Dataset, the study involved data analysis, preprocessing, algorithm modeling, and visualization. Results show the Random Forest algorithm achieved an accuracy of 0.713326 with an F1 score of 81%
Co-Authors Ashari, Idpan Az-Zahra, Aisha Shinta Cahyono, Yoyo Farhan Stiady Syah Lintang, Ananda Masayu Perani Rosyani Pradiah, Adinda Rachma Sofian, Kurnain Suci, Meta Arfiola Suryaningrat Syah, Farhan Stiady