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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Linguistik Komputasional Jurnal Informatika dan Rekayasa Elektronik Journal of Computer System and Informatics (JoSYC) Automata
Ahmad Fathan Hidayatullah, Ahmad Fathan
Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Pemodelan Klasifikasi Gaji Menggunakan Support Vector Machine Anas Satria Lombu; Syarif Hidayat; Ahmad Fathan Hidayatullah
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2137

Abstract

It is known that there are currently many types of work in the field. Creativity of the community and economic pressure that is felt makes people have to work hard to be able to meet the needs of life. One way that must be done to be able to continue to survive by working. By working someone can produce wages or salaries so that the necessities of life of a person can be met. Various work that exists raises a problem. In determining the salary or wages of a job. The salary given to someone must be in accordance with the criteria of the worker. Then we need a Machine Learning model to predict a person's salary. In this study, a classification model was made to determine a person to be categorized into salaries above 7 million and salaries below 7 million based on suitable criteria or attributes. This study uses the Python programming language and took 1000 samples from the dataset obtained from Kaggle. The Machine Learning method used is the Support Vector Machine. Then compared to the K-Nearest Neighbors method. In the SVM model the model accuracy was obtained of 87% and 86% for the KNN model. From the results of accuracy, it was found that the SVM model was better than the KNN model in conducting salary classifications based on existing jobs.
Named Entity Recognition on Tourist Destinations Reviews in the Indonesian Language Ahmad Fathan Hidayatullah; Muhammad Fakhri Despawida Aulia Putra; Adityo Permana Wibowo; Kartika Rizqi Nastiti
Jurnal Linguistik Komputasional Vol 6 No 1 (2023): Vol. 6, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v6i1.89

Abstract

To find information about tourist destinations, tourists usually search the reviews about the destinations they want to visit. However, many studies made it hard for them to see the desired information. Named Entity Recognition (NER) is one of the techniques to detect entities in a text. The objective of this research was to make a NER model using BiLSTM to detect and evaluate entities on tourism destination reviews. This research used 2010 reviews of several tourism destinations in Indonesia and chunked them into 116.564 tokens of words. Those tokens were labeled according to their categories: the name of the tourism destination, locations, and facilities. If the tokens could not be classified according to the existing categories, the tokens would be labeled as O (outside). The model has been tested and gives 94,3% as the maximum average of F1-Score.
Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation Kartika Rizqi Nastiti; Ahmad Fathan Hidayatullah; Ahmad Rafie Pratama
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.636

Abstract

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation Kartika Rizqi Nastiti; Ahmad Fathan Hidayatullah; Ahmad Rafie Pratama
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.636

Abstract

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
Co-Authors Adityo Permana Wibowo Ahmad Rafie Pratama Amiin Majiid Nugroho Anas Satria Lombu Annisa Zahra Azmiardhy Zulkifli Farmadiansyah Cahyaningtyas, Siwi Chairullah Naury Chanifah Indah Ratnasari Dantik Puspita Sari, Dantik Puspita Dhomas Hatta Fudholi Dimas Bintang Prasetyo Fayruz Rahma Febrianto Eko Saputro Fudholi, Dhomas Hatta Habib Faizal Fadli Kartika Rizqi Nastiti Kartika Rizqi Nastiti Muhammad Fakhri Despawida Aulia Putra Muhammad Fakhri Despawida Aulia Putra Muhammad Rifqi Ma'arif Nassharih Abdulloh Naury, Chairullah Nuli Giarsyani Pamungkas, Rheza Daffa Prasetyo, Alan Dwi PRATIWI, INTAN Rheza Daffa Pamungkas Ridho Rahmadi Satrio Wisnugroho Septia Rani Siwi Cahyaningtyas Yurio Windiatmoko