Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal SisInfo : Jurnal Sistem Informasi dan Informatika
Nurul Gusriani
Departemen Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Padjadjaran
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penerapan Model Support Vector Machine Pada Kasus Klasifikasi Teks Berdasarkan Tujuan SDGS Ke Tiga, Empat, Dan Enam Saprilian Hidayat; Herlina Napitupulu; Nurul Gusriani
SisInfo Vol 6 No 2 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i2.893

Abstract

Text classification is a branch of Natural Language Processing (NLP) that enables computers to understand, interpret, and respond to text in a comprehensible language. Classifying texts based on the Sustainable Development Goals (SDGs) is crucial because monitoring the progress of SDGs remains a challenge. Previous studies have shown that text classification techniques using the BERT model have proven effective in classifying texts based on SDG goals. This research utilizes data sourced from the OSDG community website. The method employed is the Support Vector Machine Multiclass (SVM) model and TF-IDF word representation. This research aims to classify texts based on the Sustainable Development Goals (SDGs), specifically focusing on goals three, four, and six., evaluate the model's performance based on the F1-Score metric, and determine the optimal values for the hyperparameters regularized constant and gamma in the RBF kernel. The results of this research yielded a default F1-Score of 97.95% and a post-tuning F1-Score of 97.95%, with the optimal values of C=1, gamma=1, and kernel=rbf.
Model Gerak Brown Fraksional Geometrik dalam Peramalan Harga Saham PT Indofood Sukses Makmur Tbk Menggunakan Pemrograman Python Nurhadini Putri; Firdaniza Firdaniza; Nurul Gusriani
SisInfo Vol 6 No 1 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i1.798

Abstract

Peramalan harga saham yang tepat diperlukan oleh para investor. Beberapa metode dapat dilakukan dalam peramalan harga saham, seperti model trend, Autoregressive Integrated Moving Average, Double Moving Average, dan Exponential Smoothing. Selain itu, terdapat pula model yang lebih kompleks, seperti model Gerak Brown Geometrik (GBG) dan model Gerak Brown Fraksional Geometrik (GBFG). Model GBG dan GBFG memiliki beberapa keunggulan, diantaranya dapat meramalkan harga saham dengan periode waktu pendek, kesesuaian model dengan pergerakan harga saham yang selalu bernilai positif dan tidak memerlukan banyak pengujian data. Selain itu, model GBFG juga dapat mengatasi masalah data aktual saham yang sebagian besar tidak saling bebas. Penelitian ini bertujuan melakukan peramalan harga saham PT Indofood Sukses Makmur Tbk (INDF) mengunakan model Gerak Brown Fraksional Geometrik (GBFG). Indeks Hurst pada model GBFG diestimasi menggunakan Rescaled Range (R/S) dengan bantuan pemrograman Python. Hasil dari peramalan pergerakan harga saham PT Indofood Sukses Makmur Tbk (INDF) menggunakan model GBFG memberikan nilai yang sangat akurat berdasarkan nilai MAPE.
Co-Authors Firdaniza Firdaniza Herlina Napitupulu Nurhadini Putri Saprilian Hidayat