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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Madaniya Jurnal Teknik Informatika (JUTIF) Makara Journal of Science Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics

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Analysis of the Effect of Feature Extraction on Sentiment Analysis using BiLSTM: Monkeypox Case Study on X/Twitter Noryasminda; Triando Hamonangan Saragih; Rudy Herteno; Mohammad Reza Faisal; Andi Farmadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.73

Abstract

The monkeypox outbreak has again become a global concern due to its widespread spread in various countries. Information related to the disease is widely shared through social media, especially Twitter which is a major source of public opinion. However, the complexity of language and the diverse viewpoints of users often pose challenges in accurately analyzing sentiment. Therefore, sentiment analysis of tweets about monkeypox is important to understand public perception and its impact on the dissemination of health information. This research contributes to identifying the most effective word embedding-based feature extraction method for sentiment analysis of health issues on social media. The purpose of this study is to compare the performance of word embedding methods namely Word2Vec, GloVe, and FastText in sentiment analysis of tweets about monkeypox using the BiLSTM model. Data totaling 1511 tweets were collected through a crawling process using the Twitter API. After the data is collected, manual labeling is done into three sentiment categories, namely positive, negative, and neutral. Furthermore, the data is processed through a preprocessing stage which includes data cleaning, case folding, tokenization, stopword removal, and stemming. The evaluation results show that FastText with BiLSTM produces the highest accuracy of 90%, followed by Word2Vec at 89%, and GloVe at 87%. FastText proved to be more effective in reducing classification errors, especially in distinguishing between negative and positive sentiments due to its ability to capture subword information and broader context. These findings suggest that the use of FastText can improve the accuracy of sentiment analysis, especially on health issues that develop on social media, so that it can support data-driven decision making by relevant parties in handling information dissemination. 
A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Dodon Turianto Nugrahadi; Rudy Herteno; Mohammad Reza Faisal; Nursyifa Azizah; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5126

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate characteristics. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for future studies in developing new methods to enhance identification accuracy.
Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Indriani, Fatma; Nugroho, Radityo Adi; Faisal, Mohammad Reza; Kartini, Dwi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6100

Abstract

Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments.
Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models Agustina, Winda; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Saragih, Triando Hamonangan; Farmadi, Andi; Budiman, Irwan; Parenreng, Jumadi Mabe; Alkaff, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5098

Abstract

Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.
Application of Random Forest Method Classification for Glycosylation in Lysine Protein Sequences Fitriyana, Silfia; Syarif, Admi; Rossyking, Favorisen; Faisal, Mohammad Reza
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241218

Abstract

Grouping glycosylated lysine proteins into groups according to the type of glycosylation seen in the lysine protein sequence is known as glycosylation in the lysine protein sequence. In this work, the sensitivity, specificity, accuracy, and Matthew’s correlation coefficient (MCC) of the random forest approach for classifying the glycosylation of lysine protein sequences were examined. With 214 positive and 406 negative data, the lysine protein dataset derived from benchmark data contains 620 total proteins with a protein length of 15 sequences. 90% of the dataset is used for training, while 10% is used for testing. Using the R package BioSeqClass version 1.44.0, feature extraction employed protein descriptors, specifically AA Index, CTD, and PseAAC, with a total of 60 features. The Random Forest classification algorithm was used to reprocess the results with Mtry values of 4, 8, and 16. The number of trees (ntree) was randomly set to 250, 500, 750, and 1000. The best results were achieved with a dataset split of 90% training data and 10% test data, using Mtry of 42 and 1000 trees, resulting in 89.97% sensitivity, 92.79% specificity, 80.76% MCC, and 90.42% accuracy. These results demonstrate that the combination of feature extraction and the Random Forest algorithm is effective in classifying lysine proteins.
Seleksi Fitur Hybrid Grey Wolf Optimization dan Particle Swarm Optimization pada Distance Biased Naive Bayes untuk Klasifikasi Kanker Payudara Ratna Septia Devi; Triando Hamonangan Saragih; Mohammad Reza Faisal
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.4737

Abstract

Kanker payudara adalah penyebab utama kematian akibat kanker tertinggi kedua di dunia. Pasien Kanker payudara terus mengalami peningkatan dan menjadi masalah kesehatan yang cukup serius di seluruh dunia, termasuk juga di Indonesia. Diagnosis dini adalah salah satu pendekatan terbaik untuk mencegah penyakit ini semakin meningkat dan berkembang. Machine learning dapat melakukan penambangan data menggunakan serangkaian fitur pada sebuah data. Penelitian ini menggunakan dataset public dari UCI machine learning repository yaitu Breast Cancer Wisconsin (Diagnostic). Pada dataset ini memiliki atribut sebanyak 32 fitur, namun banyaknya fitur pada sebuah data juga akan memperlambat waktu komputasi dari metode klasifikasi yang digunakan. Pada penelian ini, akan dilakukan seleksi fitur menggunakan metode Hybrid Grey Wolf Optimization dan Particle Swarm Optimization (HGWOPSO) untuk memilih fitur yang paling informatif dan signifikan untuk digunakan pada klasifikasi. Metode klasifikasi yang digunakan adalah Distance Biased Naive Bayes (DBNB) yang terdiri dari dua modul yaitu Weighted Naïve Bayes Module (WNBM) dan Distance Reinforcement Module (DRM). Dari penelitian ini, didapatkan performa akurasi tertinggi pada model DBNB tanpa seleksi fitur sebesar 94,90%, DBNB dengan GWO sebesar 95,08%, DBNB dengan PSO sebesar 95,25%, dan DBNB dengan HGWOPSO sebesar 96,13%. Dapat disimpulkan bahwa model DBNB dengan seleksi fitur HGWOPSO mengalami peningkatan dibandingkan dengan DBNB tanpa seleksi fitur maupun dengan seleksi fitur individualnya.
Co-Authors Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Admi Syarif Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Ghinaya, Helma Hanif Rahardian Herteno, Rudy Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Karlina Elreine Fitriani Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Mahmud Mahmud Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Ngo, Luu Duc Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Rahmina Ulfah Aflaha Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reza Rendian Septiawan Riadi, Putri Agustina Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizki, M. Alfi Rizky, Muhammad Hevny Rossyking, Favorisen Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Wildan Panji Tresna Winda Agustina Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi