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All Journal Jurnal Ilmu dan Teknologi Kesehatan Journal of Computer Networks, Architecture and High Performance Computing
Azi, Amanda
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Computer Science & IT Education Health Professions Nursing Public Health

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RANCANG BANGUN ALAT KALIBRASI SPHYGMOMANOMETER Nugroho, Agung Satrio; Viridianti, Vivi Vira; Azi, Amanda
Jurnal Ilmu dan Teknologi Kesehatan Vol 12, No 2 (2021)
Publisher : STIKES Widya Husada Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33660/jitk.v12i2.410

Abstract

Peralatan kesehatan pada umumnya membutuhkan tingkat akurasi yang baik, sehingga perlu dilakukan kalibrasi. Salah satunya adalah alat sphygmomanometer yang digunakan untuk mengukur tekanan darah. Alat kalibrasi yang telah ada digunakan dengan membandingkan nilai tekanan raksa dengan nilai tekanan alat menggunakan pandangan mata. Cara ini berpotensi terjadi kesalahan pembacaan. Dari masalah tersebut, dibuatlah sebuah modifikasi alat sphygmomanometer yang dapat mengurangi kesalahan pembacaan tekanan. Alat ini digunakan dengan cara menekan tombol untuk menyimpan tekanan hasil kalibrasi, sementara pengguna hanya perlu melihat skala tekanan pada sphygmomanometer raksa. Alat ini menggunakan sensor tekanan MPX5500DP dengan jangkauan tekanan 0 – 500 kPa. Output sensor diolah menggunakan mikrokontroler Atmega328 dan hasil pengukuran tekanan disimpan di dalam EEPROM. Hasil tersebut dipanggil kembali dan ditampilkan pada LCD Selain itu alat ini mampu menghitung kebocoran tensimeter dan menampilkan kondisi tensimeter ”layak” atau “tidak layak”. Untuk mengetahui akurasi alat kalibrator ini dilakukan pengujian di PT. Sinergi Indocal Sejahtera. Metode yang dilakukan adalah mengukur tekanan shygmomanometer air raksa menggunakan alat kalibrasi yang telah dibuat dan FLUKE DPM4 yang dilakukan secara bersamaan. Dari hasil perbandingan didapatkan hasil selisih pengukuran terhadap alat kalibrasi maksimal 0,5 mmHg, nilai tersebut masih di bawah nilai toleransi sebesar 1 mmHg. Persentase rata rata error pembacaan tekanan terhadap alat kalibrasi sebesar 0,3 %. Kata kunci : tekanan, kalibrasi, sphygmomanometer, kebocoran, akurasi.
Flood Prediction Using Support Vector Regression (Case Study of Floodgates in Jakarta) Azi, Amanda; Saleh, Robby Febrianur; Ardana, Wildan Muhammmad; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4360

Abstract

Flood can be interpreted as an event that occurs suddenly and quickly enough where the water discharge in the drainage channel cannot be accommodated, so that the blocked area causes the water discharge in the drainage channel in several surrounding areas to overflow and is one of the natural disasters that occurs at an unexpected time and cannot be prevented, because of this, a prediction must be made to detect floods for the next day. Flood prediction is a crucial aspect of disaster management and mitigation, particularly in flood-prone areas such as Jakarta, Indonesia. This study aims to leverage Support Vector Regression (SVR) to predict flood events by analyzing various environmental and hydrological factors that influence flooding. The primary data sources include historical wheater data, river water levels, floodgate positions in Jakarta. The data preprocessing involved cleaning, handling missing values, and normalizing the datasets to ensure compatibility with the SVR model. Feature selection was conducted to identify the most relevant predictors of flooding, such as wheater data, and river water levels. The dataset was then split into training and testing sets, maintaining an 80-20 ratio to ensure robust model validation. An SVR model with a radial basis function (RBF) kernel was trained on the standardized training data. The model's performance was evaluated using Root Mean Squared Error (RMSE) as the primary metric. The RMSE produced in this study was 0.112 with an R Square accuracy of 0.977. The results indicated that the SVR model could effectively predict flood events with a reasonable degree of accuracy, demonstrating its potential as a valuable tool in flood forecasting.
Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory Azi, Amanda; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Forthcoming: Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5506

Abstract

Jayapura, one of Indonesia’s major fishing cities, relies heavily on accurate weather predictions to ensure the safety of its fishermen, particularly due to its significant tuna and skipjack production. This study aims to improve rainfall forecasting in Jayapura using a Long Short-Term Memory (LSTM) model, a type of artificial neural network designed for time series prediction. Accurate rainfall forecasts are crucial for reducing the risks fishermen face at sea due to sudden weather changes. Daily data from the Meteorological Station in Dok II Jayapura was collected and processed to train the LSTM model, incorporating variables such as TAVG (average temperature), RH_AVG (average relative humidity), FF_AVG (average wind speed), Pressure (air pressure), and Wind_Gust (wind gust). The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), yielding low values of 0.0542 and 0.0847, respectively, indicating high prediction accuracy. The MAE reflects the average magnitude of errors, while the RMSE highlights the model’s sensitivity to larger deviations, both supporting the reliability of the LSTM approach. The findings demonstrate that LSTM models can effectively forecast rainfall in Jayapura, providing valuable information that helps fishermen plan their activities more safely and efficiently. The study concludes that LSTM is a robust tool for rainfall prediction, and the inclusion of additional meteorological variables has proven to enhance accuracy. Further research is recommended to explore other factors to improve prediction reliability.
Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory Azi, Amanda; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5506

Abstract

Jayapura, one of Indonesia’s major fishing cities, relies heavily on accurate weather predictions to ensure the safety of its fishermen, particularly due to its significant tuna and skipjack production. This study aims to improve rainfall forecasting in Jayapura using a Long Short-Term Memory (LSTM) model, a type of artificial neural network designed for time series prediction. Accurate rainfall forecasts are crucial for reducing the risks fishermen face at sea due to sudden weather changes. Daily data from the Meteorological Station in Dok II Jayapura was collected and processed to train the LSTM model, incorporating variables such as TAVG (average temperature), RH_AVG (average relative humidity), FF_AVG (average wind speed), Pressure (air pressure), and Wind_Gust (wind gust). The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), yielding low values of 0.0542 and 0.0847, respectively, indicating high prediction accuracy. The MAE reflects the average magnitude of errors, while the RMSE highlights the model’s sensitivity to larger deviations, both supporting the reliability of the LSTM approach. The findings demonstrate that LSTM models can effectively forecast rainfall in Jayapura, providing valuable information that helps fishermen plan their activities more safely and efficiently. The study concludes that LSTM is a robust tool for rainfall prediction, and the inclusion of additional meteorological variables has proven to enhance accuracy. Further research is recommended to explore other factors to improve prediction reliability.
Co-Authors Agung Satrio Nugroho Ardana, Wildan Muhammmad Kusrini, Kusrini Saleh, Robby Febrianur Viridianti, Vivi Vira