Article Per Year (5 Year)
p-Index From 2019 - 2024
3.687
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Fisika FLUX Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik Technologia: Jurnal Ilmiah G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Sistem Pendukung Keputusan dengan Aplikasi International Conference on Sciences Development and Technology Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat (JAPAMAS)
Armansyah Armansyah
Computer Science Study Program, Universitas Islam Negeri Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Literasi Digital : Pemanfaatan dan Penggunaan E-Library Menggunakan Software SLiMS" di Desa Denai Lama, Pantai Labu-Deli Serdang Ilka Zufria; Abdul Halim Hasugian; Suhardi; Muhammad Siddik Hasibuan; Aidil Halim Lubis; Armansyah
Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2022): Juni 2022
Publisher : Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Information technology has developed very rapidly and covers various fields. The field of education is one area that is influenced by information technology. Both in the formal learning process at school and non-formal in the form of training outside of school. The form of participation from universities, especially the Computer Science study program, FST UIN North Sumatra Medan, in this community service activity is to provide skills training in the field of information technology in the form of digital literacy and the use of SLiMS software to the Circle Community Reading Park (TBM), Denai Lama Village, Labu Beach. Deli Serdang, North Sumatra which was held with the theme "Digital Literacy: Utilization and Use of E-Library Using SLiMS Software".
Penerapan Algoritma C4.5 Pada Klasifikasi Status Gizi Balita Yusuf Ramadhan Nasution; Armansyah; Mhd Furqan; Toibatur Rahma Matondang
JURNAL FASILKOM Vol 14 No 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6941

Abstract

The study aims to classify the nutritional status of the child using the C4.5 algorithm. The secondary data used is derived from the assessment of the nutrition status of a child in Puskesmas Promji and Puksesmas Suka Makmur. A classification model is constructed using the C4.5 algorithm based on a number of predictor factors that have been determined. The research methodology includes data collection, data preprocessing, model development with C4.5 algorithms, model evaluation, and results analysis. Model evaluation is done using measurements such as accuracy. In addition, the significance of predictor variables in affecting the nutritional status of infants was also evaluated through data analysis. This research contributed to the development of a method of classifying the nutritional status of infants using the C4.5 algorithm approach. The implication of this study is that the classification model developed can be used as a tool to support early identification and intervention against nutritional problems in infants. Furthermore, based on testing using the confusion matrix technique with the 80:20 data division of a total of 502 datasets, consisting of 402 training data and 100 testing data, an accuracy rate of 80 percent was obtained.
Penerapan Metode Forward Chaining Dan Dempster-Shafer Pada Sistem Pakar Deteksi Dini Gangguan Kesehatan Mental Siti Khalizah; Ilka Zurfia; Armansyah
JURNAL FASILKOM Vol 14 No 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6942

Abstract

Early detection of mental health disorders is a major challenge in the field of health. The forward chaining method and the Dempster-Shafer theory are two approaches that can be used in the development of expert systems for early detection of mental health disorders. The forward chaining method is used to identify early symptoms that may indicate mental health disturbances, whereas the dampster-sshafer theories are used to manage uncertainty in the conclusion process, while the dampster-shafer theorem is used for managing uncertainties in the diagnosis process. The study combines both approaches in the development of an expert system for the early detection of mental health disorders. Furthermore, Dempster-Shafer's theory is used to combine evidence from various symptoms and take into account the uncertainty in diagnosis. This method is implemented in a computer-based expert system that can assist health professionals in the early detection of mental health disorders in patients. The system tests were conducted using information from a number of patients who had been clinically diagnosed, and the results suggested that this approach could provide accurate results in the early detection of mental health disorders. In conclusion, the combination of the forward chaining method and the Dempster-Shafer theory achieved 100% accuracy of the system with an average density of 73,496%.
Implementation of Naïve Bayes Method Diagnosing Diseases Nile Tilapia Ridho Wahyudi Pulungan; Sriani Sriani; Armansyah Armansyah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3834

Abstract

The Nile tilapia, also known as Oreochromis niloticus, was a freshwater fish species first produced in East Africa in 1969. It became a popular aquaculture fish in freshwater ponds across Indonesia. Besides its delicious taste, the Nile tilapia is rich in nutrients essential for human health. However, cultivating Nile tilapia was challenging due to frequent bacterial diseases. These diseases often led to mass fish deaths, causing financial losses, especially for new fish farmers. The rapid spread of diseases emphasized the need for prompt intervention to prevent further losses. Farmers needed adequate knowledge about Nile tilapia diseases, but often struggled to absorb information provided by the government. Hence, the presence of experts or veterinarians was crucial in assisting farmers to address these issues. Farmers of Nile tilapia sought assistance from experts or veterinarians, but this was not easy. It involved substantial costs and time, while quick intervention was necessary to mitigate losses. The solution proposed was the development of an expert system for diagnosing and treating Nile tilapia diseases. Thus, an expert system was built to assist fish farmers in identifying fish diseases and their treatments by implementing the naïve Bayes method. The expert system transferred human knowledge to computers, enabling them to solve problems like experts, thereby making expert knowledge accessible to non-experts. Naïve Bayes was implemented to determine the highest probability based on input symptoms. This research used five test data samples to apply the naïve Bayes method to diagnose Nile tilapia diseases, resulting in an accuracy rate of 80%. Therefore, the implementation of naïve Bayes in diagnosing Nile tilapia diseases is considered reasonably effective.
Co-Authors Abdul Halim Hasugian Adnan Buyung Nasution Aidil Halim Lubis Anggara AlFaridzi Tamba Cici Armayani Devi Apriani Dimas Kurniawan Dita Desliani Fitri Handayani Siregar Guntur Syahputra Heri Santoso Ilka Zufria Ilka Zurfia Lely Sahrani Lisma Autia Masthura, Masthura Maulana Habib Mhd Furqan Moch Iswan Perangin-angin Muhammad Dedi Irawan Muhammad Ihsan Nugraha Muhammad Ikhsan Muhammad Ikhsan Muhammad Siddik Hasibuan Nur Adillah Nurhasanah Nurhasanah Razzaq H. Nur Wijaya Ridho Wahyudi Pulungan Rizki Ananda Siti Khalizah Suhardi Suhardi Toibatur Rahma Matondang Yustria Handika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Zepi Yarti Simpa