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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) INFOKUM Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Ramen, Sethu
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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SALES PREDICTION AT PT. GILANG PRATAMA USING THE MONTE CARLO SIMULATION METHOD Manurung, Jonson; Sihotang, Amran; Ramen, Sethu; Logaraj, Logaraj
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1159

Abstract

Simulation can help solve everyday problems such as problems that exist at PT. Gilang Primary. With a simulation application that can estimate the number of sales is very important for the company. If the manager can predict the number of sales, the cost of procuring and storing goods can be minimized. One approach that can be taken to estimate the number of sales is by way of simulation. This study uses the Monte Carlo method in managing data and analyzing inventory or determining the amount of goods to be sold in the next period at PT. Gilang Pratama with sampling from the process of random numbers (Additive Random Number). Data processing uses sample data based on sales history data in the previous year
Deteksi Tepi Citra Dengan Metode Laplacian of Gaussian Dan Metode Canny Sinaga, Bosker; Manurung, Jonson; Silalahi, Monalisa Hotmauli; Ramen, Sethu
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.401

Abstract

The research conducted is testing the accuracy of the level of similarity of the management of STMIK Pelita Nusantara. The facial images tested were 17 images and 136 tests in each method (Laplacian of Gaussian (LoG), Canny, and the combination of LoG + Canny). Tests were carried out using Matlab R2017b. From the test results, the researchers concluded that the accuracy of the highest level of similarity is the Laplacian of Gaussian method, namely images 12 and 17 with a percentage of 99.85%, then the Canny method, namely images 4 and 7 with a percentage of 99.53% and the lowest is the combination of the two methods. (LoG + Canny) namely images 6 and 13 with a percentage of 98.14%. And the highest average accuracy of the similarity window is the Laplacian of Gaussian method with a percentage of 49.91%, then the Canny method with a percentage of 38.19% and the lowest is the combination of the two methods (LoG + Canny) with a percentage of 37.81%.
Deteksi Tepi Citra Dengan Metode Laplacian of Gaussian Dan Metode Canny Sinaga, Bosker; Manurung, Jonson; Silalahi, Monalisa Hotmauli; Ramen, Sethu
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.401

Abstract

The research conducted is testing the accuracy of the level of similarity of the management of STMIK Pelita Nusantara. The facial images tested were 17 images and 136 tests in each method (Laplacian of Gaussian (LoG), Canny, and the combination of LoG + Canny). Tests were carried out using Matlab R2017b. From the test results, the researchers concluded that the accuracy of the highest level of similarity is the Laplacian of Gaussian method, namely images 12 and 17 with a percentage of 99.85%, then the Canny method, namely images 4 and 7 with a percentage of 99.53% and the lowest is the combination of the two methods. (LoG + Canny) namely images 6 and 13 with a percentage of 98.14%. And the highest average accuracy of the similarity window is the Laplacian of Gaussian method with a percentage of 49.91%, then the Canny method with a percentage of 38.19% and the lowest is the combination of the two methods (LoG + Canny) with a percentage of 37.81%.
Prediksi Keberhasilan Penanganan Stunting Menggunakan Seleksi Fitur PSO Dengan SaaS Cloud Computing Sinaga, Anita Sindar; Ramen, Sethu; Mulyani, Sri
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 23 No 1 (2024): Februari 2024
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v23i1.9561

Abstract

Permasalahan stunting merupakan tugas pokok setiap pemerintahan dari perkotaan sampai desa-desa. Deep Learning dapat mengenal pola rumit yan ada pada gambar, dokumen, video, dan data lain untuk menghasilkan prediksi yang akurat. Pengolahan data tidak terstruktur seperti kata, kalimat dapat diekstrak menerapkan Particle Swarm Optimization (PSO). Pengolahan data tidak terstruktur pada kata dan kalimat bersumber dari media online diekstrak menerapkan Particle Swarm Optimization (PSO) mencakup swarm, partikel, Pbest, Gbest, dan Velocity. Melalui empat tahapan algoritma PSO dimulai dari Inisialisasi, Evaluation fungsi fitness, update dan Termination. Prediksi capaian penanganan program stunting berdasarkan dampak, pencegahan, dan penyebab stunting yang diekstrak dari berbagai media online menggunakan Neural Network Particle Swarm Optimization (PSO) yang dibangun pada layanan perangkat lunak SaaS Cloud menghasilkan persentase baik akurasi Seleksi Fitur PSO sebesar 85.36%. Aplikasi SaaS dapat menginformasikan tingkat keberhasilan penanganan program stunting dari pencarian kalimat tidak terstruktur yang terhubung dengan internet
Co-Authors Bosker Sinaga Logaraj, Logaraj Manurung, Jonson Sihotang, Amran Silalahi, Monalisa Hotmauli Sinaga, Anita Sindar Sri Mulyani