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All Journal Riau Journal of Computer Science 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 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
Yuda Irawan
Universitas Hang Tuah Pekanbaru
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PEMODELAN DAN ANALISA EMPIRIS SUDUT BELOK RUDDER PADA SISTEM AUTOPILOT KAPAL BERBASIS STM32 Irawan, Yuda; Arfianto, Afif Zuhri; Riananda, Dimas Pristovani
Jurnal 7 Samudra Vol. 9 No. 1 (2024): Jurnal 7 Samudra
Publisher : PPPM - POLITEKNIK PELAYARAN SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54992/7samudra.v9i1.179

Abstract

Dalam beberapa dekade terakhir, teknologi pengendalian kapal telah mengalami perkembangan signifikan yang bertujuan untuk meningkatkan keselamatan dan efisiensi operasional. Sistem autopilot, khususnya, memiliki peran penting dalam menjaga stabilitas dan arah kapal secara otomatis. Penelitian ini bertujuan untuk mengembangkan dan menguji sistem kontrol rudder berbasis STM32 yang mampu mengoptimalkan kinerja rudder dalam mencapai sudut yang diinginkan dengan tingkat error minimal. Pendekatan yang digunakan dalam penelitian ini melibatkan pengembangan model perhitungan yang divalidasi dengan data empiris, serta pengukuran sudut menggunakan busur derajat sebagai alat bantu validasi. Sistem ini dilengkapi dengan proses kalibrasi awal untuk menentukan batas kiri dan kanan rudder, serta menetapkan posisi tengah sebagai titik referensi. Penelitian dilakukan dengan mengonversi masukan sudut ke dalam nilai potensio menggunakan algoritma yang telah dirancang, dan memerintahkan motor stepper untuk menggerakkan rudder sesuai dengan masukan tersebut. Hasil penelitian menunjukkan bahwa sistem kontrol rudder yang dikembangkan mampu mencapai tingkat kesalahan sudut output dibandingkan sudut input sebesar 1,9%, dan kesalahan sudut output dibandingkan dengan sudut pada busur derajat sebesar 3,3%. Kesalahan ini terutama disebabkan oleh ketidakakuratan mekanisme gearbox pada sistem autopilot. Meskipun demikian, sistem yang dikembangkan telah menunjukkan peningkatan presisi dan keandalan dalam pengendalian rudder.
Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator Febriani, Anita; Wahyuni, Refni; Mardeni, Mardeni; Irawan, Yuda; Melyanti, Rika
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.304

Abstract

This research develops a Smart Egg Incubator that integrates IoT technology, fuzzy logic, and the YOLOv9-S Deep Learning model to enhance the efficiency and accuracy of hatching chicken eggs. The system automatically regulates temperature and humidity, maintaining temperature between 34.3°C and 39.5°C and humidity between 57% and 68% with a fuzzy logic success rate of 90%. The YOLOv9-S model enables realtime chick detection and classification with mAP50 of 93.7% and mAP50:95 of 71.3%. Efficiency improvements are measured through the success rate of fuzzy logic and improved detection and classification accuracy. This research also uses CNN for high-accuracy object classification, with model optimization performed using SGD to accelerate convergence and improve accuracy. The results indicate significant potential in improving the egg hatching process. The high accuracy and robustness of the YOLOv9-S model enhance real-time monitoring and decision-making in hatcheries, leading to higher hatching success rates, reduced chick mortality, and increased operational efficiency. Future designs can leverage these technologies to create more intelligent, automated systems requiring minimal human intervention, enhancing productivity and scalability. Additionally, IoT and deep learning integration can extend to other poultry farming areas, such as broiler production and disease monitoring, providing a comprehensive approach to farm management. Future research could focus on integrating the YOLOv10 model for even higher accuracy and efficiency, exploring diverse data augmentation techniques, optimizing fuzzy logic algorithms, and integrating additional sensors like CO2 and advanced humidity sensors to improve environmental regulation. These advancements would benefit not only smart incubator applications but also broader poultry farming areas.
Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction Herianto, Herianto; Kurniawan, Bambang; Hartomi, Zupri Henra; Irawan, Yuda; Anam, M Khairul
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.316

Abstract

Graduating on time is crucial for academic success, impacting time, costs, and education quality. Hang Tuah University Pekanbaru (UHTP) is currently struggling to meet its goal of achieving a 75% on-time graduation rate. This study introduces an innovative approach using machine learning techniques, particularly ensemble learning with Stacking Machine Learning Optuna SMOTE (SMLOS), to address this issue. Our primary objective is to enhance data classification accuracy to predict student graduation timelines effectively. We employ algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (C4.5), Random Forest (RF), and Naive Bayes (NB). These were combined with meta-models, including Logistic Regression (LR), Adaboost, XGBoost, LR+Adaboost, and LR+XGBoost, to create a robust prediction model. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and utilized Optuna for hyperparameter tuning. The findings reveal that SMLOS with the Adaboost meta-model achieved the highest accuracy of 95.50%, surpassing previous models' performances, which averaged around 85%. This contribution demonstrates the effectiveness of using SMOTE for class imbalance and Optuna for hyperparameter optimization. Integrating this model into UHTP's academic information system facilitates real-time monitoring and analysis of student data, offering a novel solution for promoting a Smart Campus through more accurate student performance predictions. This technique is not only beneficial for predicting student graduation but can also be applied to various machine learning tasks to improve data classification accuracy and stability.
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Co-Authors -, Herianto A.A. Ketut Agung Cahyawan W Abdurrahman Hamid 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 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 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 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 Noratama Putri, Ramalia Nurhadi Nurhazimah Rafiah Octaria, Haryani 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 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 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