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All Journal Riau Journal of Computer Science Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Komunikasi : Jurnal Komunikasi ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Sistem Informasi dan Aplikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informatika : Jurnal Informatika, Manajemen dan Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Technopreneurship and Information System (JTIS) Infotekmesin Jurnal Teknologi Informasi dan Multimedia Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Community Engagement and Emergence Journal (CEEJ) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal J-PEMAS Jurnal Ipteks Terapan : research of applied science and education pendidikan, science, teknologi, dan ekonomi Jurnal Rekam Medis (Medical Record Journal) Jurnal Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Telecommunication Control and Intelligent System Journal of Software Engineering and Information System (SEIS) SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Jurnal 7 Samudra Politeknik Pelayaran Surabaya Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Terapan Komunikasi : Jurnal Komunikasi

Yuda Irawan

Universitas Hang Tuah Pekanbaru

Author-ID : 1495941

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Analisa Prioritas Bandwidth Menggunakan Metode HTB (Hierarchical Token Bucket) Studi Kasus : SMK Taruna Mandiri Pekanbaru Yuda Irawan; Herianto; Siti Aisyah; Refni Wahyuni
SATIN - Sains dan Teknologi Informasi Vol 8 No 1 (2022): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v8i1.814

Banyaknya kebutuhan dunia pendidikan yang mengharuskan pihak pengembang aplikasi dalam mengembangkan berbagai terobosan teknologi untuk mendukung stabilitas dalam berinteraksi. Harga Bandwitdh yang cukup tinggi menyebabkan pihak sekolah melakukan pembatasan jumlah Bandwitdh yang diberikan oleh operator. Semakin meningkatnya kebutuhan akan internet hal ini menjadi permasaalahan bagi pengguna. Permasalahannya adalah semakin banyak yang membuka situs di internet tentu akan mengurangi kuota atau paket data. Untuk menyelesaikan permasalahan ini maka dilakukan proses tahapan analisa prioritas bandwidth menggunakan metode HTB (Hierarchical Token Bucket). Metode ini mempunyai kelebihan dalam pembatasan trafik pada tiap level maupun klasifikasi, sehingga bandwidth yang dipakai level yang tinggi dapat digunakan atau dipinjam oleh level yang lebih rendah. Berdasarkan hasil analisa dan pengujian yang telah dilakukan Penulis, maka dapat disimpulkan bahwa Metode antrian Hierarchical Token Bucket dinilai lebih efektif membagi bandwidth secara adil dan merata kepada masing-masing client yang membutuhkan bandwidth, terlihat dari grafik perhitungan nilai QoS yang telah dilakukan. Dari hasil perhitungan dalam pengujian metode HTB melalui download berkas, nilai rata-rata yang diperoleh berdasarkan standar kategori TIPHON untuk indeks parameter. Throughtput indeks parameter delay bernilai 4 dengan indeks parameter jitter indeks parameter packet loss.

A Comprehensive Stacking Ensemble Approach for Stress Level Classification in Higher Education Fonda, Hendry; Irawan, Yuda; Melyanti, Rika; Wahyuni, Refni; Muhaimin, Abdi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.388

This research focuses on developing a comprehensive ensemble stacking model for the classification of student stress levels in higher education environments, specifically at Hang Tuah University Pekanbaru. Using a physiological dataset that includes parameters such as SPO2, heart rate, body temperature, systolic, and diastolic pressure, this research categorizes the condition of college students into four main categories: anxious, calm, tense, and relaxed. The data taken from public health centers in the period 2021 to 2024 was processed using the SMOTE technique to overcome data imbalance and K-Fold Cross Validation for model validation. In model development, a combination of basic algorithms such as SVM, Logistic Regression, Multilayer Perceptron, and Random Forest is used which is enhanced by boosting techniques through ADABoost, and XGBoost as a meta model. The test results show that the proposed stacking model is able to achieve 95% accuracy, with an AUC of 0.95, which indicates excellent performance in classification. The model not only excels in detecting more extreme stress conditions such as anxiety, but also shows reliable ability in classifying more difficult to distinguish conditions such as tense and relaxed. The conclusion of this study shows that the applied stacking ensemble approach significantly improves prediction accuracy and stability compared to traditional models. For future research, it is recommended to explore the use of deep learning-based meta-models such as LSTM and BiLSTM as well as rotation techniques in stacking to improve model performance and flexibility. The findings are expected to contribute significantly to the development of more sophisticated and effective stress detection models.

Leveraging K-Nearest Neighbors with SMOTE and Boosting Techniques for Data Imbalance and Accuracy Improvement Lubis, Adyanata; Irawan, Yuda; Junadhi, Junadhi; Defit, Sarjon
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.343

This research addresses the issue of low accuracy in sentiment analysis on Israeli products on social media, initially achieving only 64% using the K-NN algorithm. Given the ongoing Israeli-Palestinian conflict, which has garnered widespread international attention and strong opinions, understanding public sentiment towards Israeli products is crucial. To improve accuracy, the study employs SMOTE to handle data imbalance and combines K-NN with boosting algorithms like AdaBoost and XGBoost, which were selected for their effectiveness in improving model performance on imbalanced and complex datasets. AdaBoost was chosen for its ability to enhance model accuracy by focusing on misclassified instances, while XGBoost was selected for its efficiency and robustness in handling large datasets with multiple features. The research process includes data pre-processing (cleaning, normalization, tokenization, stopwords removal, and stemming), labeling using a Lexicon-Based approach, and feature extraction with CountVectorizer and TF-IDF. SMOTE was applied to oversample the minority class to match the number of instances in the majority class, ensuring balanced representation before model training. A total of 1,145 datasets were divided into training and testing data with a ratio of 70:30. Results demonstrate that SMOTE increased K-NN accuracy to 77%. Interestingly, combining K-NN with AdaBoost after SMOTE achieved 72% accuracy, which, although lower than the 77% achieved with SMOTE alone, was higher than the 68% accuracy without SMOTE. This discrepancy can be attributed to the added complexity introduced by AdaBoost, which may not synergize as effectively with SMOTE as XGBoost does, particularly in this dataset's context. In contrast, K-NN with XGBoost after SMOTE reached the highest accuracy of 88%, demonstrating a more effective combination. Boosting without SMOTE resulted in lower accuracies: 68% for KNN+AdaBoost and 64% for KNN+XGBoost. The combination of K-NN with SMOTE and XGBoost significantly improves model accuracy and reliability for sentiment analysis on social media.

Enhancing Real Time Crowd Counting Using YOLOv8 Integrated with Microservices Architecture for Dynamic Object Detection in High Density Environments Prihandoko, P; Zufari, Faisal; Yuhandri, Y; Irawan, Yuda
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.575

This study presents the implementation of the YOLOv8 algorithm to enhance real-time crowd counting on the ngedatedotid application, which aims to provide accurate crowd density information at various locations. The proposed model leverages the advanced capabilities of YOLOv8 in detecting and localizing head-people objects within crowded environments, even in complex visual conditions. The model achieved a mAP of 85%, outperforming previous models such as YOLO V8'S (78.3%) and YOLO V7 (81.9%), demonstrating significant improvements in detection accuracy and localization capabilities. The custom-trained model further exhibited a detection accuracy of up to 95% in specific scenarios, ensuring reliable and real-time feedback to users regarding crowd conditions at various locations. By implementing a microservices architecture integrated with RESTful API communication, the system facilitates efficient data processing and supports a modular approach in system development, enabling seamless updates and scalability. This architecture allows for independent deployment of services, thereby minimizing system downtime and optimizing performance. The integration of YOLOv8 and the custom-trained model has proven to be effective in enhancing real-time monitoring and detection of crowd density, making it a suitable solution for diverse applications that require dynamic and accurate crowd information. The results indicate that the proposed model and system architecture can provide a robust framework for real-time crowd management, which is crucial for business owners, event organizers, and public safety monitoring. Future research should consider exploring newer versions of YOLO, such as YOLO V9-S, and expanding the dataset to address challenges related to varying lighting conditions, occlusions, and object orientations. Optimizing these factors will further improve the model’s accuracy and reliability, setting a new standard for crowd detection systems in public spaces and enhancing the overall user experience.

Improved Deep Learning Model for Prediction of Dermatitis in Infants Setiawan, Debi; Noratama Putri, Ramalia; Fitri, Imelda; Nizar Hidayanto, Achmad; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.542

Indonesia's equatorial climate, characterized by summer and rainy seasons, presents environmental conditions that contribute to a high incidence of dermatitis in infants. Dermatitis, an inflammatory skin condition, can lead to significant discomfort in infants, affecting their sleep, growth, and development. Early diagnosis is crucial for effective treatment; however, conventional diagnostic methods in clinics and hospitals—such as physical observation and parental interviews—are often time-consuming, subjective, and may lack precision, creating a need for more efficient diagnostic tools. This study explores the application of deep learning models to enhance the accuracy and speed of dermatitis diagnosis in infants. Four convolutional neural network (CNN) models were evaluated: MobileNet, VGG16, ResNet, and a Custom CNN model specifically designed for this study. Using a dataset of 1,088 skin images collected from three regions in Riau Province, Indonesia, we conducted training and testing to assess each model’s performance in distinguishing between dermatitis-affected and healthy skin. Results show that MobileNet and the Custom CNN outperformed other models, achieving accuracy rates of 97% and 85%, respectively. MobileNet’s high accuracy and efficiency make it a viable option for mobile applications, enabling rapid, on-site diagnosis in resource-limited settings. The Custom CNN model, tailored to the unique features of infant skin, also showed promising results. These findings demonstrate the potential of automated, image-based diagnostic tools for assisting medical professionals in early dermatitis detection, improving patient outcomes. This study contributes a valuable diagnostic solution that leverages deep learning to support healthcare providers, particularly in areas with limited access to specialized medical resources.

Application of Fuzzy Logic Mamdani in IoT-Based Air Quality Monitoring Systems Richi Andrianto; Nopi Purnomo; Yuda Irawan
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4291

This study aims to develop and implement an air quality monitoring system using Internet of Things (IoT) technology and Mamdani fuzzy logic. The system integrates sensors to detect PM2.5, PM10, and CO concentrations. Real-time data is processed using fuzzy logic to generate an easily understandable Indeks Standar Pencemar Udara (ISPU). Testing showed 95% accuracy in ISPU measurement, 2-second response time, and 99.5% uptime over 30 days. The Mamdani fuzzy logic effectively handles uncertain data, providing accurate air quality interpretations. The system classifies air quality into different ISPU categories (Good, Medium, Unhealthy, Very Unhealthy, Dangerous) in real-time. The study concludes that integrating IoT and fuzzy logic yields a high-performing, reliable air quality monitoring tool, significantly aiding pollution mitigation and public health. Further research is recommended to enhance algorithms and integrate additional technologies for improved functionality and accuracy.

Analisis Perbandingan Algoritma Machine Learning dengan SMOTE dan Teknik Boosting dalam Peningkatan Akurasi Yuda Irawan; Refni Wahyuni; Rian Ordila; Herianto
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4368

This research explores and enhances accuracy in sentiment classification related to Indonesia's Capital City relocation by combining Naive Bayes (NB), Random Forest (RF), SMOTE, and XGBoost. The study addresses challenges of unbalanced data and complexity in social media sentiment analysis. The combination of RF with SMOTE achieved the highest accuracy at 91.25%, demonstrating SMOTE's effectiveness in balancing the dataset and improving minority class detection. While adding XGBoost slightly reduced accuracy (90.92%), it increased the NB model's accuracy from 77.45% to 85.97% when combined with SMOTE. RF alone reached 87.46% and improved to 88.78% with XGBoost. The study underscores the importance of selecting and combining techniques to maximize sentiment prediction accuracy. Future research could explore deep learning or transformer models for even better results, offering deeper insights into public sentiment and aiding effective policy strategy development.

Expert System For Diagnosing Diseases in Toddlers Using The Certainty Factor Method Bayu Saputra; Agnita Utami; Edriyansyah Edriyansyah; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Disease is an abnormal condition in the body that causes body misalignment. There are various types of diseases that threaten humans, both parents and children. This disease can be caused by germs, bacteria, viruses, toxins, organ failure to function, and also by inherited/hereditary diseases. The difficulty of parents to find out the disease suffered by their children is one of the problems of parents today. So, we need a system to help with this predicament. The purpose of making this application is to provide information quickly and accurately in solving problems to help consult about diseases in toddlers aged 0-5 years. In addition, to find out ways to make programs that are expert systems using programming languages for artificial intelligence applications, namely PHP and Mysql, the certainty factor method is applied in web form. Using the certainty factor method is a decision-making strategy that starts from the section premise to conclusion. The result of system implementation is that the user chooses from the symptoms that already exist in the system based on the existing symptoms then processed, from the process the system provides information on diseases in children suffered by toddlers. From the results of testing this expert system has been able to diagnose diseases in children. After the diagnosis, the types of diseases and solutions will appear. Diagnosing disease in children by using this certainty factor is expected to make it easier to diagnose disease in children

Sara Detection on Social Media Using Deep Learning Algorithm Development M. Khairul Anam; Lucky Lhaura Van FC; Hamdani Hamdani; Rahmaddeni Rahmaddeni; Junadhi Junadhi; Muhammad Bambang Firdaus; Irwanda Syahputra; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5390

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.

Regulasi dan Kreativitas dalam Penyiaran Lokal: Studi Kasus Kebijakan Komisi Penyiaran Indonesia Daerah (KPID) Riau Mitrin, Abdullah; Rahman, Rudi; Anisa, Lia; Irawan, Yuda
Jurnal Komunikasi Vol 16, No 1 (2025): Maret 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jkom.v16i1.25763

Penelitian ini bertujuan mengevaluasi dampak kebijakan KPID Riau terhadap kreativitas dalam produksi siaran lokal, serta mengkaji tantangan dan peluang dalam menyeimbangkan regulasi dan inovasi. Pendekatan yang digunakan adalah kualitatif dengan metode studi kasus, dengan pengumpulan data melalui wawancara mendalam, observasi langsung, serta analisis dokumen kebijakan dan laporan tahunan. Analisis dilakukan secara tematik dengan teknik triangulasi data untuk menjaga validitas. Hasil penelitian menunjukkan bahwa regulasi KPID Riau berhasil menjaga standar etika dan melindungi nilai-nilai budaya lokal. Namun, regulasi yang ketat juga dinilai membatasi ruang inovasi lembaga penyiaran, mengakibatkan berkurangnya variasi program. Beberapa stasiun penyiaran yang mendapat ruang lebih fleksibel terbukti mampu meningkatkan daya tarik konten dan apresiasi publik. Penelitian ini merekomendasikan penerapan kebijakan yang lebih adaptif dan kolaboratif, melibatkan pelaku media dan akademisi dalam proses perumusan kebijakan. Dengan kebijakan yang responsif terhadap perkembangan teknologi dan kebutuhan masyarakat, penyiaran lokal di Riau diharapkan mampu tetap inovatif tanpa meninggalkan nilai-nilai budaya sebagai fondasi utama siaran publik.

Co-Authors -, Herianto A.A. Ketut Agung Cahyawan W Abdullah Mitrin Achmad Deddy Kurniawan Achmad Nizar Hidayanto Adhitya, Ryan Yudha Aditya Rickyta Adyanata Lubis Afresi Yunita Agnita Utami Agus Alamsyah Ahmad Fauzan Azim Akbar, Amri Akhmad Zulkifli Aldiga Rienarti Abidin Anam, M Khairul Andre Wahyu Novrianto Anisa, Lia Anita Febriani Aprilia, Ulfa Areta Sonya Rahajeng Arfianto, Afif Zuhri Arnawilis Arnawilis Arnawilis Bakhrizal Bambang Kurniawan Bayu Saputra Budy Mustika Debi Setiawan, Debi Desi Rahmawati Devis, Yesica Dhea Arina Ramadhini Dhini Septhya Diandra, Roni Edriyansyah Eka Sabna Elisawati, Elisawati Fachry Abda El Rahman Fatmawati, Kiki Fitri, Imelda Fonda, Hendry Gilang Citra Lenardo Habib Yuhandri, Muhammad Hadi Asnal, Hadi Hafizh Sallam Hamdani Hamdani Hamid, Abdurrahman Hartomi, Zupri Henra Hasnor Khotimah Hayami, Regiolina Hendro Agus Widodo, Hendro Agus heri, Herianto Herianto Herianto Herianto Herianto - Herianto Herianto Herianto Herianto Hidayati Kurnia Fitri Hohashi, Naohiro Irawan, Rina Irwanda Syahputra Jamaris, Muhamad Jenli Susilo Jenni Oinike Br Sitorus Jepisah, Doni Jeri Trio Sentana Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Khairunisa Khairunisa Khairunisa, Khairunisa Kharisma Rahayu Kurniawan, Bambang Leonita, Emy Lia Anisa Lubis, Mustopa Husein Lucky Lhaura Van FC, Lucky Lhaura Mardainis Mardeni Mardeni Mardeni, Mardeni Matthijs B Punt Maulita Yulia Sari Mbunwe Muncho Josephine Mbunwe Muncho Josephine Melyanti, Rika Mitrin, Abdullah Mohd Rinaldi Amartha Muhaimin, Abdi Muhamadiah, Muhamadiah Muhammad Bambang Firdaus Muhardi Muhardi - Muhardi Muhardi Muhardi Muhardi Mulya Rispani Mutiara Sari, Ria Naima Belarbi Naima Belarbi Nella Sari Nico Chandra Nopriadi Noratama Putri, Ramalia Nurhadi Nurhazimah Rafiah Octaria, Haryani Oktavia Dewi Ordila, Rian Perkasa, Reza Prihandoko, P Purnomo, Nopi Purwanti, Siti Putra Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmalisa, Uci Rahman, Rudi Refni Wahyuni Renaldi, Reno renaldi, reno Reza Perkasa Rian Ordila Rian Ordila Riananda, Dimas Pristovani Richi Andrianto Rickyta, Aditya Rofiqoh, Ummi Rometdo Muzawi, Rometdo Roni Diandra Rudi Rahman Ruwahida, Dewi Rizani Ruwahida Sabna, Eka Sakroni Indra Gunawan Salsabila Rabbani Saputra, Haris Tri Sarjon Defit Sentana, Jeri Trio Siti Aisyah Siti Aisyah Siti Purwanti Sugiati Suherman Sohor Suherman Suherman Suriandi Suriandi Susanti, Susanti Susi Oustria Simamora Susilo, Jenli Syamsul Arifin Uci Rahmalisa Ulfa Aprilia Utami, Urfi Vindi Fitria Winda Herrianti Manullang Winda Sari Wulan Sari Yesica Devis Yuhandri, Y Yulanda Yulanda Yulanda Yulanda, Yulanda YULISMAN Yulisman, Yulisman Yunior Fernando Zufari, Faisal Zufi Pratama Noviardi Zupri Henra Hartomi

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