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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Informatika Ekonomi Bisnis MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informasi dan Komunikasi Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Komtekinfo Gotong Royong : Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat (J-P3KM) Jurnal Informatika Ekonomi Bisnis Jurnal Riset Sistem Informasi
Lawrence Adi Supriyono
Universitas Jakarta Internasional
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Mechanical Engineering Public Health Social Sciences Other

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Predictive Modeling of Covid-19 Spread with Machine Learning: A Focus on Decision Tree Accuracy Aldila, Amalia Shifa; Supriyono, Lawrence Adi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Virus Sars CoV-2 merupakan penyebab utama wabah Covid-19 yang pertama kali terdeteksi di Wuhan, Tiongkok, pada Desember 2019 dan dengan cepat menyebar ke seluruh dunia. Penelitian ini bertujuan untuk memprediksi jumlah kasus terkonfirmasi dan tingkat keparahan wabah dalam rentang 23 Januari hingga 10 Juni 2020. Data yang digunakan adalah dataset terbuka dari Kaggle berjudul "Global Forecasting Covid-19 Week 5”. Untuk menghasilkan prediksi yang optimal, penelitian ini menguji berbagai algoritma pembelajaran mesin dan pembelajaran mendalam, yaitu Random Forest, XGBoost, Polynomial Regression, Decision Tree, ANN, dan LSTM. Kinerja model dinilai melalui skor dan Root Mean Square Error (RMSE). Hasil terbaik dicapai oleh model Decision Tree dengan skor sebesar 0,97 dan RMSE 52,57, menunjukkan akurasi tinggi dalam prediksi kasus Covid-19. Penelitian ini mengindikasikan bahwa model Decision Tree unggul dalam prediksi Covid-19 dibandingkan algoritma lain dan menawarkan potensi signifikan untuk pengembangan strategi mitigasi yang lebih efektif di masa mendatang.
PERANCANGAN OTOMASI ALAT INFUS BERBASIS FUZZY LOGIC Lawrence Adi Supriyono; Arief Marwanto; Suryani Alifah
Elkom: Jurnal Elektronika dan Komputer Vol. 15 No. 1 (2022): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v15i1.785

Abstract

Starting from the development of medical technology that is increasingly sophisticated and rapidly growing,researchers conduct medical research, namely about patient infusion handling services. In handling patient infusion, currently it is still manual which is carried out by nurses / medical personnel. Infusion handling services for patients still have shortcomings, namely the process of monitoring and replacing infusion fluids which are often late. If the problem is not treated quickly, it can lead to problems, namely the presence of air embolism in the blood vessels (the entry of foreign objects into the blood vessels, for example air). From that problem, the researchers made a new innovation in medical technology in handling infusions automatically and based on IoT. In this study, the smart online infusion device that has been made has good features and is very effective in handling infusions. This device has 3 main functions, namely: it can monitor the remaining infusion, it can change the infusion fluid automatically and it can indicate a blocked patient's infusion. This device already has a method for processing data with fuzzy logic. Media monitoring has been supported by a website that can be controlled remotely and in real time. Tests have been carried out and the effectiveness of the system is found to have an error rate of 0.2% - 0.7% and has an accuracy of 98%. Thus this tool can be used in terms of handling patient infusion automatically.
Co-Authors A Andhika Agus Wibowo Agus Wibowo Amalia Shifa Aldila Andhika Andhika, A Andi Saryoko Andini, Siwi Putri Andy Febrian Wibowo Andy Febrian Wibowo Arief Marwanto Arief Marwanto Aziz, Faruq Candra Supriadi Daryati, Elisabeth Isti Dwi Setiawan Dwi Setiawan Dwiyogo, Santos Winarso Erna, Maria Agustina Gunawan, Stella Putri Habibie, Dedi Rahman Ida Ayu Putu Sri Widnyani Instianti Elyana, Instianti Juniarto, Antonius Marcus Gunadi Miranti Andhita Scantya Mohammad Alfian Mudzakir Nisrina, Safira Fegi Okafor, Ngozi Lilian Prasetyo Hartanto Previana, Cantika Nur Putranto, Kartiko Eko Ramadhan, Aditya Rian Saeda, Ida Safira Fegi Nisrina Sarah Khairunnisa Budiyanto Sujatmiko, Devlin Wen Suryani Alifah Susanto, Chandra Toar, Yandri Ardolof B.L. Wadu, Novita M Kana Yuli Fitrianto Zhafirah, Rafdah