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All Journal Elektrika Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Agus Supriyanto
Departemen Teknik Elektro, Fakultas Teknik, Universitas Diponegoro
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Desain Telemedicine Asam Urat Berbasis Internet of Things (IoT) Agus Supriyanto; Andi Kurniawan Nugroho; Sri Heranurweni
Elektrika Vol 15, No 1 (2023): April 2023
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/elektrika.v15i1.6004

Abstract

Gout is the final metabolite of purines. Purines are part of the nucleic acids found in the nuclei of body cells. Increased gout can cause rheumatic pain in the joint area and is often associated with extreme pain for those exposed to the disease. Doctors need to monitor so that they can assist patients in monitoring and treatment. Gout detection devices are only available in hospitals, clinics, health centers, laboratories, and equipment that was previously portable but could not be controlled directly by a doctor. The purpose of this study is to help make it easier for doctors to monitor patients with gout remotely or telemedicine via the internet. The research method uses blood to determine gout levels using the Internet of Things. Data acquisition is carried out with a resistance sensor (Authocheck) which is processed by the Arduino microcontroller. The processed data is then sent to the ESP8266 web server via WiFi. The use of the Internet of Things as a data transmission method for online use does not require human-to-human interaction. The sensor resistance value in the analog range obtained is 441.03 to 782.32 with a sensor voltage of 1.91 to 3.82 volts. The measured gout level is between 4 mg/dL and 8 mg/dL. The percentage of measured data with an average accuracy of 95.74% and an average error rate of 4.26% for the seven test data. Data is displayed directly on the device's LCD screen and on a web server that sends data from the ESP8266. Keywords: gout, Internet of Things, resistance sensor (Autocheck), telemedicine, web server ABSTRAKAsam urat merupakan metabolit akhir dari purin. Purin adalah bagian dari asam nukleat yang ditemukan dalam inti sel tubuh. Asam urat yang meningkat dapat menyebabkan nyeri rematik di area persendian dan sering dikaitkan dengan rasa sakit yang luar biasa bagi yang terpapar penyakit. Dokter perlu memantau agar dapat membantu pasien dalam pemantauan dan pengobatan. Alat pendeteksi asam urat hanya terdapat di rumah sakit, klinik, puskesmas, laboratorium dan alat-alat yang sebelumnya portable namun tidak dapat dikontrol langsung oleh dokter. Tujuan dari penelitian ini membantu memudahkan dokter untuk memantau pasien dengan penyakit asam urat secara jarak jauh atau telemedicine melalui internet. Metode penelitian menggunakan darah untuk menentukan kadar asam urat menggunakan Internet of Things. Akuisisi data dilakukan dengan sensor resistansi (Authocheck) yang diproses oleh mikrokontroler Arduino. Data yang telah diproses kemudian dikirim ke web server ESP8266 melalui  WiFi. Penggunaan Internet of Things sebagai metode transmisi data untuk penggunaan secara online tidak memerlukan interaksi manusia ke  manusia. Nilai  resistansi sensor pada rentang analog yang diperoleh adalah 441,03 hingga 782,32 dengan tegangan sensor 1,91 hingga 3,82 volt. Kadar asam urat yang diukur adalah antara 4 mg/dL dan 8 mg/dL. Persentase data terukur dengan akurasi rata-rata 95,74% dan rata-rata tingkat kesalahan  4,26% untuk tujuh data uji. Data ditampilkan langsung di layar LCD perangkat dan di web server yang mengirimkan data dari ESP8266.
Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM Agus Supriyanto; R. Rizal Isnanto; Oky Dwi Nurhayati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i4.8044

Abstract

Many farmers in Indonesia derive their income from coffee plants, which also play a crucial role in the country’s foreign exchange earnings. However, coffee plant production may decrease due to pests and disease attacks. Leaf diseases, such as leaf spot (Cercospora coffeicola) and leaf rust (Hemileia vastatrix), are among the most common diseases to occur in coffee plants. This research seeks to identify leaf diseases in robusta coffee leaves and determine the classification. The application of machine learning-based image processing using the support vector machine (SVM) classification method based on the gray-level co-occurrence matrix (GLCM) feature extraction can be the proposed solution. The preprocessing must precede the processing stage for easier analysis of the image’s quality. Then, the k-means clustering segmentation process was conducted to distinguish leaf parts affected by leaf spot and rust from those unaffected. The GLCM method was employed as the feature extraction based on the angular second moment (ASM) or energy features, contrasts, correlations, inverse different moment (IDM) or homogeneities, and entropy with angles of 0°, 45°, 90°, and 135°, as well as inter-pixel distances of 1 until 3. The classification was done with the SVM method using the linear, polynomial, and radial basis function (RBF) Gaussian kernels. This research used leaf spot and rust images, with training and test data of 320 and 80 images, respectively. The RBF Gaussian achieved the best test results with the best accuracy of 97.5%, precision of 95.24%, recall of 100%, and F1-score of 97.56%.
Co-Authors Andi Kurniawan Oky Dwi Nurhayati R. Rizal Isnanto Sri Heranurweni