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All Journal Jurnal Informatika dan Rekayasa Elektronik Journal of Scientific Research, Education, and Technology The Indonesian Journal of Computer Science
Muhammad Zakariyah
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Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Sentiment Analysis of Hotel Reviews Using Support Vector Machine Simarmata, Alexander Romian; Muhammad Zakariyah
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3405

Abstract

With technology nowadays, everyone can leave their review about a hotel on the internet. This creates a new issue for the hotel itself because the reviews can come in in thousands amount. This will consume a lot of time to handle these reviews manually. In this study, a sentiment analysis model will be made to overcome the issue. The data in this study is collected from Kaggle website. This data contains 20,491 reviews about a hotel. The data will then be preprocessed and given a label for each data point. Then, the model is trained using the clean data. The model will use Naïve-Bayes, Logistic Regression, and Support Vector Machine algorithm. From the result performed, it's concluded that Support Vector Machine performed more accurately with 94% rate.
IMPLEMENTASI APLIKASI DISKUSI DAN PEMBELAJARAN PASAR MODAL BERBASIS ANDROID Eusebia Nawang Ari; Muhammad Zakariyah
Jurnal Informatika dan Rekayasa Elektronik Vol. 7 No. 2 (2024): JIRE NOPEMBER 2024
Publisher : LPPM STMIK Lombok

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

Abstract

Penggunaan media pembelajaran konvensional seperti presentasi dan dokumen sering kali tidak memadai untuk memenuhi kebutuhan pembelajaran yang dinamis dan interaktif di bidang pasar modal. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan aplikasi berbasis Android sebagai media diskusi dan pembelajaran pasar modal. Aplikasi ini dirancang untuk meningkatkan aksesibilitas materi pembelajaran, serta menyediakan fitur diskusi dan kuis untuk evaluasi pemahaman. Dengan menggunakan metode pengembangan sistem Agile, aplikasi ini dikembangkan dan diuji pada Kelompok Studi Pasar Modal Universitas Teknologi Yogyakarta (KSPM UTY). Hasil pengujian menunjukkan bahwa aplikasi ini mampu meningkatkan interaksi antara anggota dan pengurus KSPM UTY dalam proses pembelajaran pasar modal. Aplikasi ini juga membantu dalam mengelola materi secara lebih efektif dan mendukung evaluasi pembelajaran melalui fitur kuis.
Development of an Android Mobile Application for Capital Market Education A Case Study at KSPM UTY Eusebia Nawang Ari; Muhammad Zakariyah
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 3 No. 4 (2024): Vol. 3 No. 4 2024
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v3i4.518

Abstract

This study explores the development of an Android mobile application to enhance capital market education within the Capital Market Study Group (KSPM) at Yogyakarta Technology University (UTY). The application aims to provide members with accessible and structured learning resources, including downloadable materials, interactive quizzes, and discussion forums. The system defines three main user roles that is: members, management, and super admin. Members can see and download capital market materials and quizzes to evaluate their understanding, while management oversees manage of capital market materials. The super admin role is responsible for overall user management and monitoring bug reports. The system utilizes Firebase as its database to enable real-time data management and synchronization, ensuring reliability. The implementation of this application has demonstrated its effectiveness in fostering better understanding of capital market concepts among KSPM members. This development represents a significant step toward more interactive and efficient learning methods, contributing to advancements in educational technology.
A Deep Learning Approach for Faded Road Marking Detection Using YOLOv8 Prasetyo, Damar Galih Jati; Muhammad Zakariyah
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.929

Abstract

Faded road markings can reduce driver visibility and increase the risk of traffic accidents. This study aims to develop an automatic detection system to identify the level of road marking fading using a deep learning approach based on YOLOv8. The dataset consists of 2,049 road marking images categorized into two classes clear and faded and trained with four data augmentation variations: no augmentation, horizontal flip, saturation + exposure, and a combination of 90° rotation, grayscale, saturation, and brightness. The research process includes data collection, image preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results show that the model trained with saturation + exposure augmentation achieved the best performance, with an accuracy of 86%, precision of 86%, recall of 86%, and an F1-score of 85%. These findings demonstrate that illumination- and color-based augmentation variations are effective in improving the model’s generalization capability under diverse environmental conditions. This study is expected to serve as a foundation for developing automatic road marking monitoring systems to enhance transportation safety and efficiency.
Co-Authors Alexander Romian Simarmata Eusebia Nawang Ari Prasetyo, Damar Galih Jati Simarmata, Alexander Romian