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All Journal Buletin Al-Turas Teosofi: Jurnal Tasawuf dan Pemikiran Islam Hikmah : Journal of Islamic Studies Arabi : Journal of Arabic Studies Techne : Jurnal Ilmiah Elektroteknika IQRO: Journal of Islamic Education Journal of Computer System and Informatics (JoSYC) Jurnal Penelitian Agama Hindu An-Nawa : Jurnal Studi Islam TAJDID: Jurnal Pemikiran Keislaman dan Kemanusiaan Ma'alim: Jurnal Pendidikan Islam Jurnal Abdikarya : Jurnal Karya Pengabdian Dosen dan Mahasiswa Sistem Pendukung Keputusan dengan Aplikasi PEKERTI Journal Pendidikan Agama Islam & Budi Pekerti JURNAL PARADIGMA: Journal of Sociology Research and Education Mazalat: Jurnal Pemikiran Islam Proceeding Of International Conference On Education, Society And Humanity Jurnal Studi Islam dan Sosial Jurnal KACA Jurnal Informatika Medis (J-INFORMED)
Muhammad Shodiq, Muhammad
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Religion Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Veterinary Other

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Journal : Jurnal Informatika Medis (J-INFORMED)

 1 
IMPLEMENTASI GREY MODEL (1,N) UNTUK SISTEM PERAMALAN JUMLAH TANGKAPAN IKAN Shodiq, Muhammad; Ayuningsih, Ekatri; Ramanda, Febri
Jurnal Informatika Medis Vol. 1 No. 1 (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.v1i1.1194

Abstract

The increasing need for fish causes problems related to number of fish catches in the fisheries sector. In fish catches amount, all information related to fishing ground is well known, but on the other hand it is not easy to predict the number of fish catches due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply grey forecasting model GM (1.N) to forecast the number of fish catches. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data on monthly number of fish catches and wave height in the year of 2016 to 2018 to analyze calculations using the GM (1.N) models. The study was conducted with 36 time series data. The result showed that the MAPE on the GM (1.N) model of 57% in the experiment with 36 data.
SISTEM INFORMASI PENERIMAAN CALON TENAGA KERJA INDONESIA PDA PT DEWI PENGAYOM BANGSA KABUPATEN PATI Ramanda, Febri; Shodiq, Muhammad; Nur, Delyardi
Jurnal Informatika Medis Vol. 1 No. 1 (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.v1i1.1196

Abstract

One form of service in PT Dewi protector Nations Pati regency receive prospective workers to some of Taiwan, Hongkong, Singapore often have difficulty including data management is still done manually, the amount of data they accumulate, requiring a lot of space to save the files prospective migrants and the length of the search data. The system developed is expected to address the various issues of labor recruitment Indonesia. The system is designed with modeling UML (Unified Modeling Language) and programming languages PHP. The result of this design produces Candidate Information System Acceptance Indonesian Workers at PT Dewi protector Nation Pati regency.
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 Achmad Teguh Wibowo Agus Priyono Alvinsyah, Dwi Putra Amrullah, Abdul Karim Ayuningsih, Ekatri Celine, Devi Rofidah Charomaini, Ainussofi Barkatul Darsita Suparno Diana Islamy, Sarah Lady Eko Handoyo Hajar, Salwa Haq, Ahmad Yusronil Hasbillah, Ahmad Ubaydi Heri Ardiansyah Hidayah, Septina Alfiani Ishari, Nurhafid Jundillah, Muhammad Labib Junianto, Viki Kusnul Prianto, Kusnul Lukman Hakim Lutfiyah Lutfiyah M. Ari Prayogo Mahbubillah, M. Ainul Musyarofah, Umirul Nafahatus Sahariyah Al-Ulya, Naila Nisa', Mauidlotun Nisa, Mauidlotun Nur, Delyardi Nurlaili H., Musfiroh Patianansa, Sebena Gaos PS, Alaika M. Bagus Kurnia Rahman, Chicha Putri Ramanda, Febri Raziqy, Naufal Hanif Riendy, Riendy Rifa'i, Muh. Khoirul Rohmatin Agustina Roup, Mu'min Roup, Mumin Salwa, Septi Lailatis Sulanam Syafi’i, Ahmad Musta’in Toifur, M Ulul Absor, Firdaus Wati Nilamsari, Wati Wijayanti, Berly Zahra Setiawan, Autia