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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : INTERNAL (Information System Journal)

 1 
PREDIKSI KESEHATAN MASYARAKAT INDONESIA MENGGUNAKAN RECURENT NEURAL NETWORK Amril Mutoi Siregar; Jajam Haerul Jaman; Abdul Mufti
INTERNAL (Information System Journal) Vol. 4 No. 1 (2021)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v4i1.285

Abstract

Health is very important for all human beings, especially in Indonesia, because human health can do activities properly and have high performance for both work and other social life. The task of predicting the future values of a time series is a problem that applications have in areas such as sales, engineering, epidemiology, etc. Much research effort has been made in the development of predictive models and performance improvement. The level of public health in Indonesia from 1995 to 2018 varied with the percentage of the population who experienced health complaints. The purpose of this study is to predict the future health of the Indonesian public so that it can be used as a tool to determine government policies in the health sector. The method used in predicting is the Recurent Neural Network (RNN) with secondary data sourced from the Central Statistics Agency (BPS) in the form of data sets, and dividing the data sets into training data and test data. Before the data is used as training data, we clean and tidy up the data first so that when it is implemented there are no errors either during training or testing. The results showed that at the beginning of the method RNN, the prediction results were far from the data, after an interval of 7 and above the predicted results were actually the same. Based on Figures 5 and 6, it can be said that the RNN method is very good for the prediction method.
PENERAPAN ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DAERAH RAWAN BENCANA DI INDONESIA Amril Mutoi Siregar
INTERNAL (Information System Journal) Vol. 1 No. 2 (2018)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v1i2.322

Abstract

Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm. The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.
Co-Authors Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Andri Juliyanto Anton Romadoni Junior Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Farkhina Dwi Utari Fitri Nur Masruriyah, Anis Hanny Hikmayanti Hanny Hikmayanti H Hanny Hikmayanti Handayani Indi Nurul Hassanah Indra, Jamaludin Jaman, Jajam Haerul Juwita, Ayu Ratna Lestari, Santi Arum Puspita Lutfiah Adeliana Maulana Abdur Rofik Mulya Cahya Ramadanty Murniasih Nahrowi Nahrowi Nahrowi Nofita Sari Nursela Basuni Permana, Tedi Priyatna, Bayu Rahmad Nahar Siregar Rizqi Fahrozi Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sutan Faisal Sutan Faisal Sutan Faisal Tia Astiyah Hasan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin