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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal on Education Prosiding National Conference for Community Service Project Journal of Information System and Technology (JOINT) Conference on Business, Social Sciences and Technology (CoNeScINTech)
Suwarno Liang
Universitas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Analisis Perbandingan SVM, XGBoost dan Neural Network pada Klasifikasi Ujaran Kebencian Suwarno Liang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (394.423 KB) | DOI: 10.29207/resti.v5i5.3506

Abstract

In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classification
MobileNetV3-based Handwritten Chinese Recognition Towards the Effectiveness of Learning Hanzi Suwarno Liang; Tony Tan; Jonathan Jonathan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5505

Abstract

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool for writing Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both the application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, were involved in a User Acceptance Test (UAT). Data was gathered through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing the user’s handwriting, due to comparable accuracy size, rapid inference time, and smallest model size. While the application was well-received, several participants provided constructive feedback, suggesting potential improvements.
Co-Authors Jon Susanto Jonathan Jonathan Kevin Gautama Roma Sebet Manurung Tony Tan Violen Anjeli Anggraini Violin Anjeli Anggraini