Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Pendidikan Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Alfin Syarifuddin Syahab
Universitas Teknologi Yogyakarta
Computer Science & IT Control & Systems Engineering Education Engineering

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

Found 2 Documents
Search

 1 
User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm Ilham Fannani; Enggar Novianto; Alfin Syarifuddin Syahab
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.42

Abstract

On the Google Play Store, users often read other users' app reviews and reputations, before downloading an app. This makes the analysis of user reviews very interesting for app owners to make future decisions. This study aims to analyze user reviews of the Info BMKG application on the Google Play Store, using sentiment analysis. This user review analysis uses the Support Vector Machine (SVM) method. The evaluation proposal was made from more than 3,000 user reviews collected from the INFOBMKG application on the Google Play Store. The results of the analysis using the Support Vector Machine produce an accuracy of 85.54% and the most frequently reviewed positive review results are "Good", while the most frequently reviewed negative reviews are "Error". Which indicates a complaint against INFOBMKG users, and from the negative words that appear most often, there are two combinations of the two words that appear most often together, namely the word "very helpful" and the word "less accurate", which indicates that user often complain about problems related to application performance. The results of the sentiment analysis process of testing 3000 review data using the fold = 5 test value in the Support Vector Machine (SVM) method obtained an accuracy of 85.54% which produces predictions on data testing, namely 1500 positive reviews and 1500 negative reviews 1500 reviews.
Komparasi Algoritma Machine Laearning untuk Klasifikasi Indeks Ultraviolet Alfin Syarifuddin Syahab; Andriyan Widiyanto; Luky Rafi Anuggilarso; Anugerah Bagus Wijaya
Jurnal Teknologi Informasi dan Pendidikan Vol 15 No 2 (2022): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v15i2.692

Abstract

Indonesia is a tropical climate country which has the potential for high intensity of sunlight exposure. Several types of exposure that receives are ultraviolet rays. According to the National Agency for Meteorology, Climatology and Geophysics, it provides information regarding the impact on human activities in an ultraviolet index which has a risk scale. The aim of this research was creating a recommender system based on the ultraviolet index category in providing daily activity advice to users. The methods used the K-nearest Neighbor algorithm and Support Vector Machine with a Collaborative Filtering Model-Based approach that could recommend items based on the results of a model trained to identify input data patterns. The stages carried out in this study included data collection, data pre-processing, data division into test data and train data, dataset testing, analysis of the results of models that had been trained in the accuracy values using the algorithm tested. The results of the confusion matrix calculation produced test evaluations in accuracy values, precision values, and recall values. The comparison of result had the highest performance in K-nearest Neighbor with an accuracy value of 99.69%, a precision value of 99.00%, and a recall value of 96.20%. In research used the Support Vector Machine showed the lowest performance with an accuracy value of 97.91%, a precision value of 93.20%, and a recall value of 86.40%.
Co-Authors Andriyan Widiyanto Anugerah Bagus Wijaya Enggar Novianto Ilham Fannani Luky Rafi Anuggilarso