Aplikasi Prediksi Kesehatan Menggunakan Machine Learning

Based on data stated by the Ministry of Communication and Information, the level of use of mobile devices and the internet in Indonesia is very high. One application of mobile device technology is in the body health monitoring system. This health care monitoring system will help people monitor their health conditions to prevent and control chronic diseases and allow the medical team to monitor and assist when needed. In this study, a health prediction model and mhealth application were developed using one of the machine learning techniques, namely Naïve Bayes. Mhealth which has been integrated with machine learning can determine the possibility of individuals being healthy, moderately healthy, less healthy, and unhealthy. The body health prediction information data sent are in the form of body temperature, heart rate, systolic and diastolic blood pressure, respiration, and saturation, which are then displayed on a smartphone. With the help of the Health detection app, an individual can find out and deal with it at an early stage, to prevent the situation from getting worseAbstrak   Berdasarkan data yang dinyatakan oleh Kementerian Komunikasi dan Informasi yang menilai tingkat penggunaan perangkat seluler dan internet di Indonesia sangatlah tinggi. Salah satu  penerapan teknologi perangkat seluler ada pada sistem monitoring kesehatan tubuh. Sistem pemantauan perawatan kesehatan ini akan membantu orang memantau kondisi kesehatan mereka untuk mencegah dan mengendalikan penyakit kronis dan memungkinkan tim medis untuk memantau dan membantu saat dibutuhkan. Pada penelitian ini, sebuah model prediksi kesehatan dan aplikasi mhealth dikembangkan dengan menggunakan salah satu teknik machine learning yaitu Naïve Bayes. Mhealth yang telah diintegarsikan dengan machine learning dapat menentukan kemungkinan individu menjadi sehat, cukup sehat, kurang sehat, dan tidak sehat. Data informasi prediksi kesehatan tubuh yang dikirimkan berupa berupa suhu tubuh, detak jantung, tekanan darah sistolik dan diastolik, respirasi, dan saturasi, yang kemudian ditampilkan pada smartphone. Dengan bantuan aplikasi deteksi Kesehatan, seorang individu dapat mengetahui dan mengatasi pada tahap awal, sehingga dapat mencegah situasi menjadi lebih buruk.  Kata Kunci— mHealth, Machine Learning, Classification, Naïve Baye

Perbandingan Kinerja Sistem Logika Fuzzy Tipe-1 dan Interval Tipe-2 pada Aplikasi Mobile Robot

This paper presents differences of type-1 with interval type-2 fuzzy logic systems. T1FLS contains three main processes which are fuzzifier, inference engine, and defuzzifier. Whereas in IT2FLS has five contains which are fuzzifier, inference engine, typereduction, and defuzzifier. The significant difference is on type-reduction, which makes more complex than T1FLS. Each advantages and disadvantages also affect to efficiency and performance of Fuzzy Logic Systems

Perbedaan Sistem Logika Fuzzy Tipe-1 dan Interval Tipe-2 pada Aplikasi Mobile Robot

This paper presents differences of type-1 with interval type-2 fuzzy logic systems. T1FLS contains three main processes which are fuzzifier, inference engine, and defuzzifier. Whereas in IT2FLS has five contains which are fuzzifier, inference engine, type-reduction, and defuzzifier. The significant difference is on type-reduction, which makes more complex than T1FLS. Each advantages and disadvantages also affect to efficiency and performance of Fuzzy Logic System