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All Journal Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Global Science Society Jurnal GAMMA-PI
Ulya Nabila
Universitas Samudra
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Public Health Social Sciences

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PENGGUNAAN GEOGEBRA 6.0.462.0 PADA PERMASALAHAN PROGRAM LINIER BAGI GURU MATEMATIKA SMA SE-ACEH TAMIANG Ulya Nabila; Nurviana Nurviana; Amelia Amelia; Fitra Muliani
Global Science Society Vol 2 No 2 (2020): Global Science Society (GSS) Jurnal Ilmiah Pengabdian Kepada Masyaraka
Publisher : LPPM dan PM Universitas Samudra

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

Abstract

Teachers as educators are required to be able to develop and apply various learning methods, through media, tools and resources in learning. The use of software, such as GeoGebra 6.0.462.0, is an attempt to make the learning process more varied and interesting. The purpose of this activity is to provide training to high school teachers to solve problems related to linear programs. A direct approach method, namely through lectures and practices are used in this activity. The lecture method is carried out by providing information to the teacher about the advantages and benefits of GeoGebra 6.0.462.0 as one of the powerful mathematical software in helping to solve problems related to linear programs. While the practice method is carried out by providing GeoGebra 6.0.462.0 software training. The results of this activity were that the teachers were able to use GeoGebra 6.0.462.0 to solve linear program problems.
E-LEARNING TRAINING TO OPTIMIZE TEACHING LEARNING PROCESSES FOR TEACHER MATHEMATICS SMK KOTA LANGSA Fitra Muliani; Amelia Amelia; Ulya Nabila; Nurviana Nurviana; Riezky Purnamasari
Global Science Society Vol 3 No 1 (2021): Global Science Society (GSS) Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : LPPM dan PM Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/gss.v3i1.2739

Abstract

The education system, from elementary to tertiary levels, has implemented sophisticated technology-based learning methods, which requires us to survive. IT-based learning is needed in this regard, especially during the current Covid-19 pandemic. In addition, based on an appeal from the government, the learning process is carried out by applying the distance method, which can be done by utilizing technology, namely E-Learning media such as google classroom and zoom. As for the purpose of service through training for Mathematics teachers of Langsa City Vocational High School, namely to help in problems related to distance learning. The method used is the direct approach method, lectures and practicum. The approach method is carried out by observing teacher problems related to the difficulties and lack of use of E-learning media. The lecture method is carried out by providing tutorials to the teacher on the use of E-learning media, then followed by practicum. The results of this service are very good, it can be seen from the participation and enthusiasm of the teacher in participating in this activity then the teacher is able to use online learning media. This service is expected to contribute to mathematics teachers, especially the Langsa City Vocational School teachers, in the distance learning process by utilizing google classroom and zoom.
PENGARUH PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DAN TINGKAT PENGANGGURAN TERBUKA TERHADAP INDEKS PEMBANGUNAN MANUSIA DI KABUPATEN LANGKAT Mariana Mariana Naibaho; Ulya Nabila
JURNAL GAMMA-PI Vol 3 No 2 (2021): Jurnal Gamma-Pi (Matematika dan Pendidikan Matematika)
Publisher : Program Studi Matematika, Fakultas Teknik, Universitas Samudra. Langsa, Aceh.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/jgp.v3i2.3684

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh PDRB dan Tingkat Pengangguran Terbuka terhadap IPM di Kabupaten Langkat. Adapun metode yang digunakan adalah metode kuantitatif dengan pendekatan deskriptif. Teknik analisis data menggunakan model regresi liniear berganda yang didukung dengan uji asumsi klasik. Penelitisn ini menggunakan data sekunder yaitu data time series 9 tahun dari tahun 2011-2019 yang diperoleh dari kantor BAPPEDA Langkat. Variabel PDRB berpengaruh negatif dan signifikan terhadap IPM di Kabupaten Langkat sedangkan variabel TPT berpengaruh negatif dan signifikan terhadap IPM di Kabupaten Langkat.Kata Kunci: Indeks Pertumbuhan Manusia, PDRB, Tingkat Pengangguran Terbuka
Penerapan Backpropagation Neural Network pada Prediksi Curah Hujan di Sumatera Utara Ulya Nabila; Indah Ramadhani; Fazrina Saumi
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 1 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v4i1.10503

Abstract

Indonesia is located on the equator with a latitude of 6°N to 11°S. This latitude causes a high intensity of solar radiation so that the air temperature becomes high. This causes the evaporation of water from the surface of the sea and land to form clouds and rain. One of the provinces in Indonesia that has high rainfall is North Sumatera. Due to the high rainfall, the province often experiences floods and landslides. In order to minimize the impact of floods and landslides, the first thing that can be done is to predict the rainfall in North Sumatera. The method used is Backpropagation Neural Network. This method is a kind of artificial intelligence designed to process information by imitating the nervous system of the human brain. This research aimed to predict rainfall in North Sumatera using Backpropagation Neural Network and determine the accuracy of this method. The data came from the Badan Pusat Statistik (BPS) of North Sumatera, namely rainfall data from January 2020 to December 2022. The results implied that rainfall in 2023 for each month is 190; 266; 197; 178; 290; 182; 299; 350; 213; 485; 357; and 389 (mm/month) and has an accuracy of Mean Absolute Percentage Error (MAPE) of 35.55%, so this method is categorized as suitable for predicting rainfall.
Co-Authors Amelia Fazrina Saumi Fitra Muliani Indah Ramadhani Mariana Mariana Naibaho Nurviana Riezky Purnamasari