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All Journal Indonesian Journal of Information System International Journal of Artificial Intelligence and Science
Wahyu Wibowo, Angga
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Computer Science & IT

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Heart Rate and Body Temperature Tracking Application Based on Fuzzy Logic Nur Aziz Thohari, Afandi; Wahyu Wibowo, Angga; Karima, Aisyatul; Hestiningsih, Idhawati; Santoso, Kuwat; Abdollah, Faizal
Indonesian Journal of Information Systems Vol. 6 No. 1 (2023): August 2023
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v6i1.7481

Abstract

Temperature and heart rate are indicators of health. It is necessary to monitor heart rate and body temperature to prevent the spread of a virus or disease. In most cases, heart rate and body temperature are monitored independently, and the patient cannot view a record of previous examinations. This study uses fuzzy logic to develop an application for monitoring heart rate and body temperature. It can monitor heart rate and body temperature in real-time, store a history of previous examinations, and use fuzzy logic to diagnose body conditions based on heart rate and body temperature data. Based on test results, the sensor reading error rate for heart rate is minimal at 0.68 and for body temperature at 0.18. The accuracy of fuzzy diagnosis of the patient's body condition is one hundred percent. The performance indicator for the application is excellent, the completion rate is 100 percent, and the time-based efficiency is 93%. The results of the user satisfaction test indicate that most users are pleased with the application's usability. The average value for measuring user satisfaction is 80%, with the highest result of the five measurement criteria being 89.6% for the ease-of-use criterion.
Heart Rate and Body Temperature Tracking Application Based on Fuzzy Logic Nur Aziz Thohari, Afandi; Wahyu Wibowo, Angga; Karima, Aisyatul; Hestiningsih, Idhawati; Santoso, Kuwat; Abdollah, Faizal
Indonesian Journal of Information Systems Vol. 6 No. 1 (2023): August 2023
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v6i1.7481

Abstract

Temperature and heart rate are indicators of health. It is necessary to monitor heart rate and body temperature to prevent the spread of a virus or disease. In most cases, heart rate and body temperature are monitored independently, and the patient cannot view a record of previous examinations. This study uses fuzzy logic to develop an application for monitoring heart rate and body temperature. It can monitor heart rate and body temperature in real-time, store a history of previous examinations, and use fuzzy logic to diagnose body conditions based on heart rate and body temperature data. Based on test results, the sensor reading error rate for heart rate is minimal at 0.68 and for body temperature at 0.18. The accuracy of fuzzy diagnosis of the patient's body condition is one hundred percent. The performance indicator for the application is excellent, the completion rate is 100 percent, and the time-based efficiency is 93%. The results of the user satisfaction test indicate that most users are pleased with the application's usability. The average value for measuring user satisfaction is 80%, with the highest result of the five measurement criteria being 89.6% for the ease-of-use criterion.
Classification of Aquatic Species in Cultivation Ponds via Image Processing and Machine Learning Setiawan, Arif; Wahyu Wibowo, Angga; Setiaji, Pratomo; Agus Triyanto, Wiwit; Arifin, Muhammad
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.9

Abstract

Fish cultivation is a vital economic activity for coastal communities, yet traditional farming methods often face challenges such as environmental instability, feeding inefficiencies, and water pollution. Effective monitoring of underwater environments is essential to improve fish quality and farming efficiency. A crucial part of this process is the accurate classification of fish and non-fish objects. This study proposes a method for underwater classification using morphometric feature extraction and machine learning techniques. The research process involves six main steps: (1) preparation of Region of Interest (ROI) detection data, (2) extraction of morphometric features—length (L) and width (W), (3) feature computation, (4) data partitioning for training and testing, (5) classification using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN), and (6) evaluation using a confusion matrix. Among all models tested, the Random Forest algorithm yielded the highest accuracy at 93%, with classification results showing True Positive = 349, False Positive = 28, True Negative = 223, and False Negative = 0. The findings highlight RF’s potential for enhancing automated fish monitoring in smart aquaculture systems.
Co-Authors Abdollah, Faizal Afandi Nur Aziz Thohari Aisyatul Karima Arif Setiawan Idhawati Hestiningsih Muhammad Arifin Pratomo Setiaji Santoso, Kuwat Wiwit Agus Triyanto