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All Journal TEKNIK INFORMATIKA ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal Teknik Komputer AMIK BSI JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jurnal Informatika dan Rekayasa Elektronik Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Journal of Informatics and Communication Technology (JICT) Jurnal Informatika Teknologi dan Sains (Jinteks) Industrial Research Workshop and National Seminar SisInfo : Jurnal Sistem Informasi dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Riwayat: Educational Journal of History and Humanities Journal of Informatics and Communication Technology (JICT)
Kasyidi, Fatan
Program Studi Teknik Informatika, Universitas Jenderal Achmad Yani, Cimahi
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Penyakit Jantung Tipe Kardiovaskular Menggunakan Adaptive Synthetic Sampling dan Algoritma Extreme Gradient Boosting Permana, Acep Handika; Umbara, Fajri Rakhmat; Kasyidi, Fatan
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.v6i1.5421

Abstract

Cardiovascular diseases are conditions that commonly affect the cardiovascular system, such as heart disease and stroke. According to data from the World Health Organization (WHO), 17.9 million deaths worldwide in 2019 were attributable to cardiovascular disease. Early detection is crucial, but diagnosing heart disease is complex in developing countries due to the limited availability of diagnostic tools and medical personnel. This study uses the Heart Disease Dataset from Kaggle, consisting of 15 attributes and 4238 records, to develop a heart disease classification model using XGBoost. The research stages include data imputation, data transformation using LabelEncoder, data balancing using ADASYN, data splitting (80% training data, 20% testing data), and hyperparameter tuning with Bayesian Optimization. The results show that the XGBoost model with ADASYN performs better, with a ROC-AUC of 0.971 and an accuracy of 0.916, compared to the model without ADASYN, which has a ROC-AUC of 0.698 and an accuracy of 0.841. Based on the research results, ADASYN has proven effective in improving model performance on imbalanced datasets. Additionally, Bayesian Optimization plays an important role in finding the optimal parameter combination, which can further enhance model performance. With this research, the impact is quite significant in the development of early detection methods for cardiovascular heart disease, particularly through the application of the XGBoost classification algorithm
Klasifikasi Sentimen Untuk Mengetahui Kecenderungan Politik Pengguna X Pada Calon Presiden Indonesia 2024 Menggunakan Metode IndoBert Oktariansyah, Indro Abri; Umbara, Fajri Rakhmat; Kasyidi, Fatan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5435

Abstract

X has evolved into one of the most popular social media platforms in the world. In Indonesia, the use of X is quite widespread, especially in discussions about the presidential election, which is currently a hot topic. Everyone has different views on the candidates, both positive and negative. With a large amount of tweet data from users, this information can serve as a data source for processing and analysis. Various methods can be used to analyze and classify sentiment from this data, one of which is using BERT. This research conducts sentiment classification using BERT with the IndoBert model. The research aims to classify sentiments towards tweets related to the 2024 Indonesian presidential election to understand the political inclinations of X users, evaluate the performance of the IndoBert model in sentiment classification, and assess the extent to which back translation augmentation and synonym augmentation techniques can enhance the model's performance. Data was collected using crawling techniques for seven days leading up to the election and manually labeled by annotators. Synonym augmentation and back translation techniques were used to balance data in minority classes. The data was divided into 80% training data, 10% test data, and 10% validation data. The classification process was conducted using the IndoBert model that had been fine-tuned. The research results show that IndoBert with synonym augmentation achieved the highest accuracy, which was 82% in the first experiment and 81% in the second experiment. On the other hand, back translation only reached an accuracy of 78% in the first experiment and 74% in the second experiment. This indicates that synonym augmentation proved to be more effective in increasing data variation and model performance on the dataset used in this research.
Co-Authors Ade Kania Ningsih Ade Kania Ningsih Afifah, Hira Nur AGIEL FADILLAH HERMAWAN Agung Besti Agus Komarudin Agya Java Maulidin Aldi Sidik Permana Anbiya, Dhika Rizki Anggoro, Sigit Anshori, Siddiq Ahmad Armandika, Fladio Besti, Agung Djamal, Esmeralda Contesa Djuniardi Suhardinata Dwifani, Bella Melati Wiranur Eddie Khrisna Putra Erna Sesarliana* Esmeralda Contessa Djamal Faiza Renaldi Fajri Rakhmat Umbara Fajri Rakhmat Umbara FAJRI RAKHMAT UMBARA FAJRI RAKHMAT UMBARA FERDIANSYAH, ALDOVA fery bayu aji Fikri Nugraha FIQRI FAKHRUL GUNAWAN Fladio Armandika Hadiana, Asep Id Insan Kamil Nurhikmat Irma Santikarama Janjan Nurjaman Kukuh Yulion Setia Prakoso LIONITAMA, VENA MEILINDA Maulidina Norick Eriyadi Melina Melina Muhamad Rizal Firmansyah NIDA MUTHI ANNISA Nugraha, Fikri Nur Shabrina, Nariswari Nurrokhimah, Siti Oktariansyah, Indro Abri Permana, Acep Handika Permana, Aldi Sidik Puspita Nurul Sabrina Rafli Firdaus Reza Dwi Putra Rezky Yuniarti Ridwan Ilyas Rizky Ryan Dwi Yudistira Seffiera Fauziah SENJAWATI, RINDU TEGAR Sopian, Annisa Mufidah Sukma, Rifaz Muhammad Szalfa Saadiatus Sakinah Umbara, Fajri Rakhmat Wildan Pratama Wina Witanti Yasmina Azzahra Yoga Esa Mahendra Yulison Herry Chrisnanto Yustiana Fauziyah