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All Journal Jiko (Jurnal Informatika dan komputer) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Dwi Arman Prasetya
UPN "Veteran" Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Simple Sentiment Analysis Using LSTM and BERT Algoritmhs for Classifying Spam and Non-Spam Data Prismahardi Aji Riyantoko; Dwi Arman Prasetya; Tahta Dari Timur
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 2 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i2.40

Abstract

Sentiment analysis has become a useful tool for doing data analysis and classification based on words, phrases, or documents. Previously, researchers conducted extensive research on sentiment analysis using a variety of algorithms and models. Based on previous research, the results of the sentiment analysis have a negative impact on model performance and data type. At the moment, researchers are using the LSTM and BERT models to classify SMS data into spam and non-spam. The researcher using TD-IDF and GloVe algorithm to determine the weighting of the values represented in vectors in each word to optimize the results of value accuracy. Regardless of the results obtained, the methods BERT and LSTM have a value accuracy sensitivity of 99.35% and 98.22%, respectively. The results present that the completion of spam and non-spam dataset classification is very effective and efficient. Tests were also carried out using disaster twitter data, but the level of accuracy of the values decreased. Therefore, it can be supposed that the different types of datasets considerably affect the performance of the temptation model.
Penerapan Metode Decision Tree C4.5 untuk Klasifikasi Data Kandidat Tenaga Kerja pada Perusahaan Outsourcing Ahmad Ardhy Ansyah; Tresna Maulana Fahrudin; Dwi Arman Prasetya
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v6i1.12670

Abstract

Sebuah perusahaan biasanya melakukan screening menggunakan metode konvensional dalam mencari kandidat tenaga kerja. Hal tersebut berdampak pada proses pemilihan kandidat tenaga kerja yang menghabiskan waktu yang cukup lama dan ketidakkonsistenan dalam pengambilan keputusan. Oleh karena itu, dalam proses pemilihan kandidat tenaga kerja dapat menggunakan sebuah model machine learning yang dapat melakukan klasifikasi berdasarkan profil dan kompetensi kandidat tenaga kerja. Model machine learning yang digunakan salah satunya adalah Decision Tree C4.5 yang mampu menghasilkan sebuah keputusan pemilihan kandidat tenaga kerja secara otomatis berdasarkan data.  Hasil pengujian menunjukkan bahwa model ini memiliki akurasi dan precision yang tinggi, terutama pada validation model menggunakan holdout atau percentage split dengan proporsi data training dan testing sebesar 70:30 masing-masing yakni mencapai akurasi terbaik sebesar 0.99, dan precision sebesar 0.9. Dengan demikian, model ini dapat dimplementasikan dalam sistem seleksi kandidat untuk meningkatkan efisiensi proses seleksi dan pengelolaan data kandidat.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Adinda Aulia Rahmawati; Amri Muhaimin; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 1 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Co-Authors Adinda Aulia Rahmawati Ahmad Ardhy Ansyah Amri Muhaimin Prismahardi Aji Riyantoko Tahta Dari Timur Tresna Maulana Fahrudin