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All Journal Jurnal Ilmiah Ecosystem International Journal of Engineering, Science and Information Technology Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Ilmu Komputer Journal of Information System, Technology and Engineering Journal of International Multidisciplinary Research
Handayani, Tri Pratiwi
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Jurnal Ilmu Komputer

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SISTEM INFORMASI PENCARIAN JASA TUKANG BANGUNAN Polapa, Risman; Abas, Mohamad Ilyas; Handayani, Tri Pratiwi; Lasarudin, Alter
Jurnal Ilmu Komputer (JUIK) Vol 4, No 3 (2024): October 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i3.3477

Abstract

This research aims to design a Web-based Building Services Search Information System and implement a Web-Based Building Builder Services Search Information System. This research was developed using the Rapid Application Development (RAD) method. In RAD, users and analysts participate in three stages. The three stages are implementation, RAD design and requirements planning. With these stages, the application of the RAD method is very appropriate and suitable for developing website-based systems. The results of this research were that the researcher had previously conducted an interview with one of the head builders. Craftsmen in their profession as builders are to get work that suits their skills, only certain people know and understand the workman's performance, what field the craftsman is skilled in, so if there is a special job that must be done by a craftsman that suits his skills. So the employer must meet the craftsman directly and ask about the craftsman's skills. Conclusion: We have succeeded in designing a web-based information system for searching for construction services, able to make it easier for customers (employers) to search for construction services according to the skills required by customers (employers), as well as making it easier for builders to find work that suits the craftsman's skills. With this information system, it can help builders and customers (employers) in the process of searching for builders according to their expertise in the form of the Web or other web browsers.
Comparative Analysis of CNN-LSTM and LSTM Models for Cyberbullying Detection with Increasing Dataset Sizes Handayani, Tri Pratiwi; Abas, Mohamad Ilyas
Jurnal Ilmu Komputer (JUIK) Vol 4, No 2 (2024): JUNE 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i2.3185

Abstract

This study compares the performance of two deep learning models, CNN-LSTM and LSTM, for identifying cyberbullying in social media text. Three distinct dataset sizes are used for our evaluation and comparison: 1,000, 5,000, and 10,000 samples. The results indicate that the CNN-LSTM model outperforms the LSTM-only model (Ablation model) for the largest dataset size, exhibiting substantial enhancements in accuracy, precision, recall, and F1-Score as the dataset size increases. The Ablation model exhibits competitive performance and slightly superior results on the mid-sized dataset. However, it inevitably falls behind the CNN-LSTM model when trained on 10,000 samples. These findings imply that increasing the complexity of the CNN layer in the CNN-LSTM model improves its ability to collect significant features in bigger datasets, making it more successful for cyberbullying detection. 
Co-Authors Alter Lasarudin Gani, Hilmansyah Gazali, Al Ghani, Hilmasyah Ibrahim, Irawan Mohamad Ilyas Abas Nursetiawati Nursetiawati Poha, Rahmad A Polapa, Risman Pratiwi I Wantu Wahyudin Hasyim