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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Altius: Jurnal Ilmu Olahraga dan Kesehatan Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Firdaus Firdaus
Universitas Sriwijaya
Computer Science & IT Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Nutrition Therapy System untuk Penderita Diabetes Melitus 2 Burman Bagaskara; Firdaus Firdaus; Eni Triningsih
Generic Vol 11 No 1 (2019): Vol 11, No 1 (2019)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes Militus tipe 2 merupakan penyakit yang di sebabkan oleh hormon insulin yang tidak berkerja dengan normal sehingga gula di dalam darah menjadi tidak stabil. Penyebab utama dari penyakit ini adalah pola hidup yang tidak sehat yang di ikuti dengan cara makan yang tidak teratur dan tidak sesuai kebutuhan nutrisi tubuh. Untuk mencegah atau mengurangi dari gejala penyakit ini dapat di lakukan dengan cara mengetahui kebutuhan nutrisi yang di butuhkan tubuh dan mampu mencatat segala aktifitas konsumsi nutrisi setiap harinya. Tentunya hal tersebut sangat sulit untuk di terapkan apa bila tidak ada sistem yang mampu mengelolanya. Dan juga untuk menambah preferensi makanan di butuhkan data saran makanan yang cocok untuk di konsumsi sesuai batas kebutuhan nutrisi perhari. Penggunaan Metode Algoritma Genetika ke dalam suatu sistem pendukung keputusan adalah solusi yang ditawarkan untuk mengatasi permasalahan tersebut. Dengan bantuan sistem pendukung keputusan, proses pembuatan saran makanan lebih cepat dan akurat sesuai dengan perhitungan nutrisi yang di butuhkan oleh pasien. Data aktifitas kebugaran juga dapat di jadikan parameter dalam menentukan saran makanan tersebut.
KEPUASAN KONSUMEN TERHADAP PENYELENGGARAAN EVENT OLAHRAGA SEPAK BOLA Firdaus Firdaus; Yuliani Yuliani; Rudy Noor Muktamar; Sulastri Sulastri
Altius: Jurnal Ilmu Olahraga dan Kesehatan Vol 9, No 2 (2020): Altius: Jurnal Ilmu Olahraga dan Kesehatan
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/altius.v9i2.12305

Abstract

Penelitian ini bertujuan untuk mengetahui kepuasan konsumen kepuasan konsumen dalam penyelenggaraan event olahraga sepak bola di stadion Gelora Sriwijaya Jakabaring Sport City, khususnya dalam hal sarana prasarana. Penelitian ini merupakan studi deskriptif dengan metode survey. Sampel pada penelitian ini merupakan 40 orang penonton  yang pernah menyaksikan pertandingan secara langsung event olahraga sepak bola di Gelora Sriwijaya Jakabaring Sport City, yang dipilih menggunakan teknik sampel nonprobality sampling (purposive sampling). Data dikumpulkan dengan teknik angket sebagai data utama dan wawancara sebagai data pendukung, kemudian angket dianalisis dengan metode penelitian deskriptif survey. Hasil penelitian ini secara deskriptif yaitu  kepuasan konsumen terhadap penyelenggaraan event olahraga sepak bola di stadion Gelora Sriwijaya Jakabaring Sport City sangat tidak puas pada aspek responsiveness (daya tanggap), tidak puas pada aspek tangibles (kualitas berwujud), emphaty (kemudahan), reliability (keandalan), dan netral pada aspek assurances (jaminan). Selanjutnya dapat disimpulkan bahwa kepuasan konsumen dalam penyelenggaraan event olahraga sepak bola di Stadion Glora Sriwijaya JSC tidak puas. Saran penelitian ini agar dapat menjadi rekomendasi pada PT JSC agar ditingkatkan secara optimal guna memenuhi kepuasan konsumsen.
Delineation of electrocardiogram morphologies by using discrete wavelet transforms Annisa Darmawahyuni; Siti Nurmaini; Hanif Habibie Supriansyah; Muhammad Irham Rizki Fauzi; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Bambang Tutuko
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp159-167

Abstract

The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted or biphasic T-waves and others.
Opinion Mining of Light Rail Transit Development in Indonesia Sarifah Putri Raflesia; Dinda Lestarini; Firdaus Firdaus; Desty Rodiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp791-796

Abstract

Light rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus Firdaus; Muhammad Fachrurrozi; Siti Nurmaini; Bambang Tutuko; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Anggun Islami; Masayu Nadila Maharani; Bayu Wijaya Putra
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.457

Abstract

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients.
LoTQA: Local Benchmarking of Large Language Models for Table Question Answering Muhammad Arya All Fajri; Muhammad Ikhsan Rizki Pratama; Firdaus Firdaus; Abdiansah Abdiansah
Computer Engineering and Applications Journal Vol. 15 No. 2 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i2.1350

Abstract

TableQA memainkan peran penting dalam mendukung pengambilan keputusan berbasis data dan meningkatkan efisiensi pencarian informasi. Penggunaan Large Language Models (LLM) melalui layanan cloud atau API eksternal memungkinkan sistem untuk secara otomatis memahami struktur tabel dan konteks pertanyaan, melakukan generalisasi, penalaran kontekstual, dan memahami hubungan semantik antar entitas dalam tabel untuk menghasilkan jawaban yang lebih relevan dan akurat. Pendekatan ini mengakibatkan peningkatan signifikan dalam biaya komputasi, potensi risiko keamanan data, dan keterbatasan dalam pengembangan, kustomisasi, dan pengujian model. Penelitian ini mengusulkan LoTQA untuk tugas TableQA. LoTQA adalah pendekatan yang memanfaatkan eksekusi lokal untuk mengevaluasi dan membandingkan metode LLM dalam menghasilkan jawaban dari data tabel terstruktur. Evaluasi kinerja pada LoTQA (Qwen3:4b, LoRA Fine-tuned) memperoleh nilai SacreBLUE sebesar 8,613, BLEU-1 sebesar 35,623, BLEU-2 sebesar 26,592, BLEU-3 sebesar 22,723, ROUGE-1 sebesar 0,364, ROUGE-2 sebesar 0,177, ROUGE-L sebesar 0,311, dan METEOR sebesar 0,317. Hasil ini menunjukkan bahwa metode LoTQA cukup baik dalam menyediakan kalimat yang bermakna secara semantik untuk prediksi, bahkan dengan sumber daya yang rendah. Hasil evaluasi kinerja untuk setiap model LLM yang digunakan menunjukkan bahwa model Qwen3:4b mencapai skor tertinggi untuk SacreBLEU, ROUGE-1, ROUGE-2, ROUGE-L, dan METEOR. Studi ini menunjukkan bahwa LoTQA berkinerja cukup baik pada tugas TableQA, meskipun dengan sumber daya yang rendah.
Fetal organ detection using feature enhancement with attention and residual block Nuswil Bernolian; Siti Nurmaini; Ade Iriani Sapitri; Annisa Darmawahyuni; Muhammad Naufal Rachmatullah; Bambang Tutuko; Firdaus Firdaus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1593-1604

Abstract

The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Delineating 12-lead ECG for automated ST-elevation and ST depression detection using deep learning Bambang Tutuko; Annisa Darmawahyuni; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Ade Iriani Sapitri; Anggun Islami; Sukemi Sukemi; Muhammad Fachrurrozi; Siti Nurmaini; Rendy Isdwanta; Jordan Marcelino
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10531

Abstract

ST-elevation or ST-depression are markers of an abnormal heart condition detected through an electrocardiogram (ECG) where the tracing in the ST-segment is unusually elevated above the TP-segment (baseline). Identifying the localization of the ST-segment on an ECG is difficult because even a minor change in the ST-segment can be obscured by filtering processes. The 12-lead ECG signal is a non-invasive tool in the early detection of ST-elevation based on ST- and TP-segment, with quick and accurate interpretation. This study proposes a standard 12-lead ECG delineation model using deep learning (DL). The ECG signal has been segmented to Pstart–Pend, Pend–QRSstart, QRSstart–Rpeak, Rpeak–QRSend, QRSend-Tstart, Tstart–Tend, and Tend–Pstart. The study interpreted ST-elevation or -depression using an ECG delineation approach guided by medical rules. The findings revealed that the DL model achieved an average accuracy of 99.18%, sensitivity of 92.55%,specificity of 99.55%, precision of 92.61%, and F1-score of 92.52% in limb leads. Similarly, in chest leads, the DL model attained an accuracy of 99.16%, sensitivity of 93.10%, specificity of 99.53%, precision of 93.32%, and F1-score of 93.11%. This study also validated the DL-predicted results by a cardiologist from Mohammad Hoesin Hospital, Indonesia.
Co-Authors A. Darmawahyuni A. I. Sapitri Abdiansah, Abdiansah Ade Iriani Sapitri Ade Iriani Sapitri Ahmad Zarkasi Alexander Edo Tondas Andre Herviant Juliano Anggun Islami Anggun Islami Annisa Darmawahyuni B. Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Burman Bagaskara Caesar Rizky Kurniawan Darmawahyuni, Annisa Deris Stiawan Desty Rodiah Dinda Lestarini Eni Triningsih Hanif Habibie Supriansyah Huda Ubaya Jordan Marcelino Julian Supardi M. Fachrurrozi . M. N. Rachmatullah Masayu Nadila Maharani Muhammad Arya All Fajri Muhammad Fachrurrozi Muhammad Ikhsan Rizki Pratama Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmatullah Nurracmah Hakim Puar Nuswil Bernolian Rendy Isdwanta Reza Firsandaya Malik Rina Yuniarti Rudy Noor Muktamar Saparudin Saparudin Sarifah Putri Raflesia Siti Nurmaini Sukemi Sukemi Sulastri Tio Artha Nugraha Varindo Ockta Keneddi Putra Yuliani .