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All Journal Jurnal Informatika JTET (Jurnal Teknik Elektro Terapan) Orbith : Majalah Ilmiah Pengembangan Rekayasa dan Sosial Indonesian Journal of Information System JCRS (Journal of Community Research and Service) Patria Artha Journal of Community Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Idhawati Hestiningsih
Program Studi Teknik Informatika Jurusan Teknik Elektro Politeknik Negeri Semarang
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Public Health Social Sciences

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Journal : Indonesian Journal of Information System

 1 
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.
Intelligent Prediction and Detection of Diabetes Mellitus Using Machine Learning Handoko, Slamet; Sukamto; Triyono, Liliek; Hestiningsih, Idhawati; Sato-Shimokarawa, Eri; Lavindi, Eri Eli
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.v6i1.7602

Abstract

One of the diseases with a fairly high number of sufferers today is Diabetes Mellitus. The increase in the number of people with diabetes is caused by delays in diagnosis and also difficulties in monitoring the blood sugar level. Therefore, a solution is needed to overcome this problem, namely a blood sugar level monitoring system to predict and detect. The blood sugar level monitoring system is an intelligent system that can monitor blood sugar levels in Diabetes Mellitus patients. This system aims to make it easier for patients to check blood sugar levels regularly, to minimize the occurrence of increased blood sugar levels that aggravate the disease. Moreover, machine learning algorithms are a viable method used in recent studies for analyzing, predicting, and classifying health data while improving the health conditions of telemonitoring and telediagnosis. The main purpose of this article is to employ machine learning algorithms for blood sugar level classification in real time. The results of this study indicate that the system can be used to monitor blood sugar levels. The results of the implementation of the system that can be used by users include monitoring the results of measuring blood sugar levels. Keywords: Monitoring Machine Learning, Prediction, Diabetes Mellitus, Data Mining
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.
Intelligent Prediction and Detection of Diabetes Mellitus Using Machine Learning Handoko, Slamet; Sukamto; Triyono, Liliek; Hestiningsih, Idhawati; Sato-Shimokarawa, Eri; Lavindi, Eri Eli
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.v6i1.7602

Abstract

One of the diseases with a fairly high number of sufferers today is Diabetes Mellitus. The increase in the number of people with diabetes is caused by delays in diagnosis and also difficulties in monitoring the blood sugar level. Therefore, a solution is needed to overcome this problem, namely a blood sugar level monitoring system to predict and detect. The blood sugar level monitoring system is an intelligent system that can monitor blood sugar levels in Diabetes Mellitus patients. This system aims to make it easier for patients to check blood sugar levels regularly, to minimize the occurrence of increased blood sugar levels that aggravate the disease. Moreover, machine learning algorithms are a viable method used in recent studies for analyzing, predicting, and classifying health data while improving the health conditions of telemonitoring and telediagnosis. The main purpose of this article is to employ machine learning algorithms for blood sugar level classification in real time. The results of this study indicate that the system can be used to monitor blood sugar levels. The results of the implementation of the system that can be used by users include monitoring the results of measuring blood sugar levels. Keywords: Monitoring Machine Learning, Prediction, Diabetes Mellitus, Data Mining
Co-Authors - Mardiyono, - - Sukamto, - Abdollah, Faizal Achmat Ainul Afif Adam Alamsyah, Juan Adi Wibowo, Nur Afandi Nur Aziz Thohari Ahmad Ilyas Aisyatul Karima Alifia Choirunnisa, Alifia Amran Yobioktabera Apriantoro, Roni Arif Bramantyo, Hutama Arif Bramantyo Budi Suyanto Dewi ANGGRAENI Dimas Adi Nugroho Fardana, Budi Firdausy, Fatkhiya Go Andre Widodo, Go Andre Hadwi Widi Sasono, Sindung Hery Setijasa, Hery ISWANTI Lavindi, Eri Eli Mardiyono Mardiyono Mardiyono, Mohammad Arif Mohammad Khambali, Mohammad Muhammad Muqorrobin, Muhammad N, Ari Sriyanto Nasekhah, Durrotun Nurmala, Nandya Parsumo Raharjo, Parsumo Rachman, Rizky Amanda Rian Prasetio Santoso, Kuwat Sato-Shimokarawa, Eri Slamet Handoko Slamet Widodo Suares, Roemaldo Sukamto Sukamto Sukamto suyamto, budi Tahan Prahara, Tahan Prahara Tri Raharjo Yudantoro Triyono, Liliek Wahyu Sulistiyo Wahyu Wibowo, Angga Wildan Arief Arrosyidi