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All Journal Media Statistika Ekonomi Bisnis Animal Production : Indonesian Journal of Animal Production Conference SENATIK STT Adisutjipto Yogyakarta Bisma: Jurnal Bisnis dan Manajemen Legal Standing : Jurnal Ilmu Hukum UPEJ Unnes Physics Education Journal eBA Journal: Journal Economic, Bussines and Accounting Diklat Review : Jurnal manajemen pendidikan dan pelatihan Jurnal Pendidikan dan Teknologi Indonesia ANGON: Journal of Animal Science and Technology Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Informatika Medis (J-INFORMED)
Agus Priyono
Bagian Klinik Hewan, Fakultas Kedokteran Hewan, Universitas Udayana, Bali
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Jurnal Informatika Medis (J-INFORMED)

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PREDIKSI JUMLAH PENYAKIT INFEKSI SALURAN PERNAPASAN AKUT (ISPA) MENGGUNAKAN SIMPLE MOVING AVERAGE Priyono, Agus; Shodiq, Muhammad; Ramanda, Febri
Jurnal Informatika Medis Vol. 1 No. 2 (2023): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v1i2.1646

Abstract

Health problems in Indonesia are still a topic that really needs to be developed and researched considering that disease problems in Indonesia are diverse and contribute to high death rates. Acute Respiratory Infection (ARI) is a disease that often occurs in society and is considered normal or not dangerous, but can cause death. A group of diseases included in ISPA are, Pneumonia, Influenza, and Respiratory Syncytial Virus (RSV). ISPA disease in Indonesia contributes to the highest number of deaths, so there is a need for action or policy that can control ISPA disease in the future. The aim of this research is to apply the simple moving average method to predict ARI disease. This method is simple in prediction but has optimal results in some use cases. This research uses annual data from 2007 to 2022 for the calculation method. The research results show that the simple moving average method provides accurate prediction results with a MAPE value of 11% for predicting the number of ISPA cases. It is hoped that the results of this research can determine policies for controlling ARI diseases Keywords: Acute Respiratory Infection, Prediction, Simple Moving Average
METODE RANDOM FOREST UNTUK MEMUDAHKAN KLASIFIKASI DIAGNOSIS PENYAKIT MENTAL Priyono, Agus; Shodiq, Muhammad; Alvinsyah, Dwi Putra; Hidayah, Septina Alfiani
Jurnal Informatika Medis Vol. 2 No. 1 (2024): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v2i1.2119

Abstract

Mental health is important in the development of every individual. A bad mentality can prevent a person from developing, making a person easily stressed, hopeless, and even attempt suicide and commit crimes. Currently there are quite a lot of case related to mental health which are caused by many factors such as economic, social and medical. Reflecting on this fact, there is a need for rapid mental health detection so that immediate intervention can be carried out. This needs to be done so that the patient's condition can improve. This research focuses on diagnosing mental illness by utilizing machine learning. The method used is random forest which in several studies has been proven to produce good accuracy. Random forest performs machine learning on the attributes contained in the dataset combined with K-Fold Cross Validation so that each patient can be evaluated. Next, a tuning process is also carried out to test the parameters contained in the method. After the tuning process was carried out, the best parameters obtained were n-estimator of 30, maximum depth of 4, minimum sample leaf of 2, and minimum sample split of 10. From the combination of these parameters, accuracy is 90.83%, recall is 90.83 %, and precision of 93.25%.
Co-Authors Abdul Muhsyi aditya permana putra Aditya, Dedy Yusuf Afif Nur Faozi Agustinah Setyaningrum, Agustinah Alif Mirzania Alvinsyah, Dwi Putra Bakar, Abdoel Bambang Susanto Destiana, Rea Dewi Prihatini Dian Ekowati Didit Budi Nugroho Gagal Rencana, Feri Susilo Hari Sukarno Hermin Purwaningsih Hidayah, Septina Alfiani Indra Indra Isa Akhlis Jadidyah, Havida Khanifatul Khusna Krismiwati Muatip Kusmayadi, Kusmayadi Lucie Setiana Marni Astuti, Marni Mochamad Sugiarto Muhammad Nuskhi Muhammad Shodiq, Muhammad N. Ari Subagio Novi Puspitasari Pambudi Yuwono Pusalia, Retno Ramadhan, Divan Janatra Ramanda, Febri Rosita, Andriani Salma Fauziyyah Subagio, Ari Subagio, N Ari Uyuunul Mauidzoh Wahyu Hardyanto Yovitantri, Maricia Yoyo Yoyo