Judul: Kalibrasi Alat Ukur Kadar Glukosa Darah Non-Invasive Berbasis Raspberry Pi
Abstrak: Glukosa darah menjadi parameter dalam kesehatan sebagai pendeteksi penyakit diabetes melitus. Bagi penderita DM, pengukuran glukosa darah sangat penting untuk memantau status glikemik. Namun saat ini rata-rata pengukuran kadar glukosa darah masih menggunakan teknik invasive (mengambil sampel darah dari pasien), yang bisa saja menyebabkan trauma. Sehingga dibutuhkan alat yang mampu bekerja secara non-invasive yang sudah terkalibrasi dengan baik. Berdasarkan hal tersebut, penelitian ini bertujuan untuk meng-upgrade dan mengetahui tingkat akurasi alat ukur kadar glukosa darah non-invasive berbasis Raspberry Pi hasil upgrade penelitian sebelumnya. Penelitian ini memanfaatkan prinsip reflektansi, dimana LED pada probe menembakkan cahaya. Selanjutnya sensor membaca tegangan yang diterima photodiode, yang kemudian dikonversi menjadi data digital oleh ADS1115. Data yang didapatkan kemudian diolah menggunakan regresi linear berganda untuk mendapatkan persamaan. Persamaan ini yang kemudian dimasukkan pada alat, guna proses kalibrasi. Didapatkan nilai Root Mean Squared Error (RMSE) sebesar 7,836, menandakan akurasi yang baik dengan toleransi kesalahan < 10 berdasarkan standar ISO 15197:2013., Blood glucose is a parameter in health as a detector for diabetes mellitus. For people with DM, blood glucose measurement is very important to monitor glycemic status. However, currently the average blood glucose level measurement is still using invasive techniques (taking blood samples from patients), which can cause trauma. So we need a tool that is able to work non-invasively that is well calibrated. Based on this, this study aims to upgrade and determine the level of accuracy of non-invasive blood glucose level measuring devices based on Raspberry Pi, the results of previous research upgrades. This research utilizes the reflectance principle, where the LED on the probe shoots light. Furthermore, the sensor reads the voltage received by the photodiode, which is then converted into digital data by ADS1115. The data obtained were then processed using multiple linear regression to obtain the equation. This equation is then entered in the tool, for the calibration process. The Root Mean Squared Error (RMSE) value is 7,836, indicating good accuracy with an error tolerance < 10 based on ISO standards 15197:2013.
Keyword: blood glucose, non-invasive, Raspberry Pi

Judul: Non-Invasive Hemoglobin Measurement Tool Calibration
Abstrak: Di seluruh dunia prosentase penderita anemia, yaitu suatu keadaan rendahnya kadar hemoglobin dalam darah, masih tinggi. Oleh karena itu alat ukur hemoglobin sangat diperlukan. Namun sampai sekarang kebanyakan alat ukur hemoglobin masih menggunakan metoda invasive yang mengambil sampel darah dengan melukai pasien. Walaupun memiliki ketelitian yang sangat tinggi namun metoda invasive bisa menyebabkan ketidaknyamanan atau trauma bagi sebagian orang. Oleh karena itu dibutuhkan alat ukur hemoglobin non-invasive yang mudah digunakan dan hasilnya bisa diandalkan. Penelitian ini melakukan kalibrasi sekaligus pengembangan terhadap alat ukur hemoglobin non-invasive hasil penelitan sebelumnya. Alat ini bekerja menggunakan prinsip reflektansi dimana LED pada probe memancarkan cahaya yang menembus kulit hingga mencapai pembuluh vena yang berisi darah yang mengandung hemoglobin. Pantulan cahaya dari vena diterima sensor photodiode, yang kemudian dikonversi menjadi data digital oleh ADS1115 dan disimpan dalam bentuk database di dalam Raspberry Pi. Data kemudian diolah menggunakan regresi linear berganda untuk kalibrasi dengan data pengukuran hemoglobin metoda SLS sebagai ground truth sehingga didapatkan persamaan linear berganda. Nilai Root Mean Squared Error (RMSE) pada tahap kalibrasi didapatkan sebesar 1,332, yang berarti memenuhi syarat karena berdasarkan Jurnal Biomedical and Health Informatics persyaratan alat ukur hemoglobin nilai maksimal RMSE sebesar 1,352. Selanjutnya persamaan ini kemudian dimasukkan pada alat untuk proses validasi. Hasil validasi yang mendapatkan nilai RSME sebesar 1,264 yang berarti cukup rendah, atau alatnya cukup teliti., Throughout the world the percentage of people with anemia, which is a condition of low levels of hemoglobin in the blood, is still high. Therefore a hemoglobin measuring tool is needed. However, until now most hemoglobin measuring tools still use invasive methods which take blood samples by injuring the patient. Even though it has very high accuracy, the invasive method can cause discomfort or trauma for some people. Therefore, a non-invasive hemoglobin measuring tool is needed that is easy to use and has reliable results. This study calibrated as well as developed a non-invasive hemoglobin measuring tools resulting from previous research. This tool works using the principle of reflectance where the LED on the probe emits light that penetrates the skin until it reaches the veins which contain blood containing hemoglobin. The light reflected from the veins is received by the photodiode sensor, which is then converted into digital data by the ADS1115 and stored in a database on the Raspberry Pi. The data was then processed using multiple linear regression for calibration with hemoglobin measurement data using the SLS method as the ground truth so that a multiple linear equation was obtained. The Root Mean Squared Error (RMSE) value at the calibration stage was 1.332, which means that it meets the requirements because based on the Biomedical and Health Informatics Journal the maximum RMSE value for the hemoglobin measuring instrument is 1.352. Furthermore, this equation is then entered into the tool for the validation process. The validation results obtained an RSME value of 1,264 which means it is quite low, or the tool is quite thorough.
Keyword: Anemia, hemoglobin, non-invasive, Raspberry Pi

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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