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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi dan Sistem Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka Jurnal Nasional Komputasi dan Teknologi Informasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Eksakta : Berkala Ilmiah Bidang MIPA Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis Science, Technology, and Communication Journal
Surya Agustian
UNIVERSITAS ISLAM NEGERI SULTAN SYARIF KASIM RIAU
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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia Saputra, Nugroho Wahyu; Insani, Fitri; Agustian, Surya; Sanjaya, Suwanto
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3552

Abstract

Crude oil is a much-needed energy for the whole world. Each country is inseparable from the use of crude oil for use in various sectors, such as transportation, so that the price of world crude oil is the most important variable for the world. Fluctuations in oil prices will cause various problems, such as inflation, changes in market prices, and others. Therefore, the prediction of world crude oil prices is very important as a consideration for decision making. This study implements deep learning using the Gated Recurrent unit model. The data used is the price of Brent crude oil with a total of 5834 data, starting from January 4, 2000 to December 19, 2022. The parameters used are the number of GRU units, batch size, and lookback. The best model produced in this study is the GRU model with hyperparameters consisting of 30 lookbacks, 50 GRU units, and 256 batch sizes with the lowest MAPE value among the other models, which is 2.25%. The MAPE value states that predictions using the GRU model are said to be very good at predicting world crude oil prices
Klasifikasi Sentimen Terhadap Pengangkatan Kaesang Sebagai Ketua Umum Partai PSI Menggunakan Metode Support Vector Machine .Safrizal, Safrizal; Agustian, Surya; Nazir, Alwis; Yusra, Yusra
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5340

Abstract

The appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) has sparked various responses on social media, particularly on Twitter. This research aims to classify public sentiment regarding the appointment using the Support Vector Machine (SVM) algorithm with FastText feature representation. The data used for classification involves a small training dataset. The text preprocessing process includes cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. FastText word embedding is used to convert words into vectors, and an SVM model with Grid Search is used for parameter tuning to obtain the optimal model. The use of external datasets to expand the initially limited training dataset enhances data representation and improves the model's performance in sentiment classification. The Covid dataset was expanded by adding 100, 200, and 300 tweets for each negative, positive, and neutral label. From the experiments conducted, the best accuracy on the test data was found in experiment ID C2 with an F1-Score of 53.59% and an accuracy of 62.73%. In experiment ID C3 with the same dataset, the F1-Score was 50.46% and the accuracy was 60.46%. Finally, in experiment ID C7 with the same dataset, the F1-Score was 47.19% and the accuracy was 53.09%.
Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas Pranata, Joni; Agustian, Surya; Jasril, Jasril; Haerani, Elin
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6335

Abstract

Masalah keterbatasan data latih menjadi tantangan utama dalam klasifikasi sentimen di berbagai bahasa, termasuk bahasa Indonesia, terutama untuk analisis sentimen terkait topik tertentu. Hal ini disebabkan oleh berbagai faktor, dan umumnya adalah kebutuhan untuk mengetahui dengan segera bagaimana sentimen terhadap suatu isu, sehingga tidak mungkin menghabiskan waktu untuk memberi label yang cukup pada data untuk proses pelatihan. Penelitian ini mengusulkan model klasifikasi sentimen dengan sumber data pelatihan yang sedikit, pada studi kasus pengangkatan Kaesang Pangarep sebagai ketua umum PSI. Algoritma Random Forest digunakan sebagai model dasar (baseline) yang dioptimasi dengan penambahan data eksternal untuk training, pemrosesan teks (text preprocessing) dan parameter tuning. Fitur input yang digunakan adalah model bahasa IndoBERT sebagai embedding kata untuk menghasilkan representasi teks yang lebih kontekstual. Hasil penelitian menunjukkan bahwa metode IndoBERT dengan Random Forest yang dioptimasi memberikan peningkatan performa yang signifikan dibandingkan baseline, sebesar 6%. Hasil klasifikasi model yang paling optimal sebesar 54% unutk F1-score dan 63% akurasi. Temuan ini menegaskan bahwa penambahan data eksternal dan optimasi parameter dapat meningkatkan kemampuan generalisasi model dalam klasifikasi sentimen bahasa Indonesia. Penelitian ini diharapkan dapat menjadi referensi metodologis bagi studi klasifikasi sentimen serupa yang menghadapi kendala ukuran dataset.
Klasifikasi Sentimen Menggunakan Metode Passive Aggressive dengan Menggunakan Model Bahasa BERT pada Dataset Kecil Subhi, Yazid Abdullah; Agustian, Surya; Irsyad, Muhammad; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6389

Abstract

Text classification is one of the most popular tasks in natural language processing, especially in the context of sentiment classification. Insufficient training data poses a significant challenge in many text classification studies. This research focuses on optimizing classification performance using the Passive Aggressive (PA) algorithm, leveraging limited training data. It compares conventional text representation methods like TF-IDF with modern approaches employing word embeddings such as FastText and BERT. The primary dataset encompasses sentiment issues related to Kaesang Pangarep's appointment as the chairman of PSI, gathered through Twitter crawling, and classified into positive, negative, and neutral sentiment labels. Two versions of the training data, each containing only 300 balanced tweets for positive, negative, and neutral classes, were used. The data was split 80% for training and 20% for validation in the search for an optimal model. External data with different issues and pre-existing sentiment labels was used to augment the training data. Experimental results demonstrated that the BERT language model, used as input features for the Passive Aggressive method with hyperparameter tuning, outperformed TF-IDF features. Evaluation on the test data revealed that BERT features with Passive Aggressive achieved an F1-score of 0.52, surpassing the conventional TF-IDF representation with an F1-score of 0.42. The utilization of the BERT language model significantly contributed to improving text classification performance in the field of natural language processing, particularly for the Passive Aggressive method.
Co-Authors .Safrizal, Safrizal Afdhal Zikri Afriyanti, Liza Aftari, Dhea Putri AGUNG SUCIPTO Ahmad, Rizmah Zakiah Nur Alfitra Salam Arasy, Abdurrahman Ash Shiddicky Aulia Ramadhani Ayu Fransiska Baehaqi Delifah, Nur Dermawan, Jozu Dzaky Abdillah Salafy Eka Pandu Cynthia El Saputra, Yoga Elin Haerani Elvia Budianita Fahrezy, Irgi Faizah Husniah Fauzan Ray T Fauzi Ihsan Febi Yanto Febrian Rizki Adi Sutiyo Fitri Insani Fitri Insani Fitri Wulandari Fitri, Dina Deswara Fuji Astuti Gusti, Siska Kurnia Habib Hakim Sinaga Hadi, Mukhlis Halimah Hasibuan, Ilham Habibi Heru Wibowo Idhafi, Zaky Iffa, Marwika Rifattul Ihsan, Miftahul Iis Afrianty Iis Afrianty Illahi, Ridho Iman Fauzi Aditya Sayogo Indri Pangestuti Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Lestari Handayani Lubis, Anggun Tri Utami BR. Miftah Farid Muhammad Fikry Muhammad Fikry Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Irsyad Muhammad Ravil Muktar Sahbuddin Mukti M Kusairi Mulyadi, Syahrul Nadila Handayani Putri naldi, Afri Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Novriyanto Nurul Fatiara Oktavia, Lola Pangestu, Yoga Pizaini Pizaini Pranata, Joni Prima Yohana Putri Zahwa Putri, Adilah Atikah Putri, Atika Rahmad Abdillah Rahmad Kurniawan Ramadhani, Siti Reski Mai Candra Reski Mai Candra Rizqa Raaiqa Bintana Safrizal, Afri Naldi Salam Kurniawan Saputra, Ikhsan Dwi Saputra, M Ridho Saputra, Nugroho Wahyu Sinaga, Habib Hakim Siti Ramadhani Siti Ramadhani Siti Ramadhani Sri Puji Utami A. Subhi, Yazid Abdullah Suci Rahayu Sulistia Ningsih, Sulistia Suwanto Sanjaya Syaiful Azhar Trya Ayu Pratiwi Utari, Roid Fitrah Yusra Yusra Yusra, Yusra