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All Journal Indonesian Journal of Multidisciplinary Science Mutiara: Multidiciplinary Scientifict Journal
Tarsini, Iin
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Arts Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Mendeteksi Sarkasme Di Komentar Twitter Menggunakan Bilstm Dengan Penyetelan Hiperparameter Tarsini, Iin
Mutiara: Multidiciplinary Scientifict Journal Vol. 1 No. 12 (2023): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v1i12.433

Abstract

Di era digital, sarkasme sering digunakan dalam komunikasi online, terutama di platform media sosial seperti Twitter. Namun, deteksi sarkasme otomatis merupakan tantangan utama dalam Natural Language Processing (NLP), karena sarkasme bergantung pada konteks yang lebih luas yang sulit diidentifikasi dengan teknik tradisional. Oleh karena itu, metode yang dapat memahami konteks lebih dalam dan mendalam diperlukan untuk meningkatkan akurasi deteksi sarkasme. Penelitian ini bertujuan untuk mengembangkan dan menguji model deteksi sarkasme menggunakan Bidirectional Long Short-Term Memory (BiLSTM) yang dioptimalkan dengan hyperparameter tuning, untuk meningkatkan kinerja deteksi sarkasme dalam teks Twitter. Penelitian ini menggunakan dua dataset utama, yaitu Twitter Sarcasm Data dan SST-2 Sarcasm Dataset, yang telah diberi label secara manual. Model BiLSTM dibangun menggunakan pustaka Keras dan TensorFlow, dan penyetelan hiperparameter dilakukan dengan Pencarian Grid dan Pencarian Acak untuk meningkatkan kinerja model. Model BiLSTM menunjukkan performa terbaik dengan akurasi 85%, akurasi 82%, dan skor F1 80,5%. Penyusunan hiperparameter yang tepat juga meningkatkan penarikan dan mengurangi overfitting dibandingkan dengan model lain, seperti LSTM, CNN, dan SVM.
Explore flowchart and pseudocode concepts in algorithms and programming Tarsini, Iin; Anggraeni, Riska
Indonesian Journal of Multidisciplinary Science Vol. 3 No. 5 (2024): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v3i5.807

Abstract

Programming algorithms are a class of mathematical algorithms that are widely used in computer science and engineering. They are used to solve problems involving mathematical calculations, but in their implementation, algorithms do not always handle complex calculations, especially when genetic algorithms are often used. This article uses the method of literature study or literature review to evaluate several studies related to the topic discussed, especially in introducing the basic concepts of programming algorithms, especially for beginners, focusing on the basic understanding required. The research includes a review of algorithmic concepts, algorithm notation, characteristics, properties, and the basic structure of algorithm learning, along with the steps of implementing programming algorithms. The conclusion of this article is that learning basic programming algorithms is an essential necessity for anyone who wants to get started in the world of programming.
Co-Authors Riska Anggraeni