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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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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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (820.708 KB) | DOI: 10.37385/jaets.v4i1.916

Abstract

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

Abstract

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

Abstract

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.
Optimasi Algoritma C5.0 untuk Peningkatan Akurasi dalam Klasifikasi Ulasan Masyarakat Terhadap Layanan BPJS Kesehatan Mohd Rinaldi Amartha; Refni Wahyuni; Yuda Irawan
JEKIN - Jurnal Teknik Informatika Vol. 5 No. 1 (2025)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v5i1.995

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi sentimen ulasan masyarakat terhadap layanan BPJS Kesehatan dengan mengoptimalkan algoritma C5.0 menggunakan teknik SMOTE dan XGBoost. Pengujian dilakukan dengan beberapa kombinasi, termasuk C5.0, C5.0 dengan XGBoost, C5.0 dengan SMOTE, dan kombinasi ketiganya. Hasil menunjukkan bahwa algoritma C5.0 dasar mencapai akurasi sebesar 67.18%, kombinasi C5.0 dengan XGBoost mencapai 73.55%, C5.0 dengan SMOTE memiliki akurasi 67.00%, sementara kombinasi ketiganya (C5.0, SMOTE, dan XGBoost) memberikan akurasi tertinggi sebesar 80.87%, mengungguli metode lain. Analisis sentimen mengindikasikan bahwa mayoritas ulasan cenderung negatif, menyoroti ketidakpuasan konsumen terhadap layanan BPJS Kesehatan. Peningkatan akurasi yang signifikan dengan penerapan SMOTE dan XGBoost menunjukkan bahwa penanganan ketidakseimbangan kelas dan penguatan model melalui Boosting dapat memperbaiki kelemahan algoritma C5.0. Hal ini memperjelas pentingnya strategi ensemble dalam klasifikasi teks yang kompleks. Temuan ini menunjukkan bahwa penggunaan SMOTE dan XGBoost secara signifikan dapat meningkatkan performa model, membantu dalam memahami persepsi publik secara lebih akurat.
Model Prediksi Risiko Kebakaran Hutan Menggunakan Algoritma Random Forest dengan Seleksi Fitur Lasso Regression Refni Wahyuni; Muhardi; Yulanda; Yuda Irawan
JEKIN - Jurnal Teknik Informatika Vol. 5 No. 1 (2025)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v5i1.998

Abstract

Kebakaran hutan di Indonesia telah menyebabkan kerusakan lingkungan, polusi udara, serta dampak serius pada kesehatan dan ekonomi. Penelitian ini mengembangkan model prediksi risiko kebakaran hutan menggunakan algoritma Random Forest dengan seleksi fitur melalui Lasso Regression, berdasarkan data meteorologi dari BMKG (2011-2024). Variabel utama yang digunakan meliputi temperatur rata-rata, kelembapan, curah hujan, dan kecepatan angin. Hasil evaluasi model menunjukkan akurasi 100%, dengan precision, recall, dan F1-score masing-masing 1.00 untuk semua kelas risiko kebakaran. Confusion matrix dan kurva ROC mengonfirmasi kemampuan model dalam mengklasifikasikan setiap instance tanpa kesalahan. Analisis fitur menyoroti temperatur rata-rata, kelembapan, dan curah hujan sebagai faktor utama. Model ini berpotensi menjadi komponen penting dalam sistem peringatan dini kebakaran hutan di indonesia. Penelitian ini merekomendasikan integrasi data tambahan dan implementasi real-time untuk meningkatkan akurasi dan aplikabilitas model di masa mendatang.
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.
YOLOv12 Model Optimization for Monitoring Occupational Health and Safety in Hospital Archive Rooms Jepisah, Doni; Octaria, Haryani; Muhamadiah, Muhamadiah; Irawan, Yuda
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.936

Abstract

The application of artificial intelligence technology in occupational safety monitoring systems within healthcare facilities has become an urgent necessity, particularly to support compliance with Occupational Safety and Health (OSH) standards in hospitals. This study aims to develop an automated detection model based on YOLOv12 to identify visual OSH elements in hospital archive rooms, such as APAR, evacuation signs, windows, and Personal Protective Equipment (PPE) including masks, gloves, and shoes. The initial dataset consisted of 2,866 documented images, which were expanded through augmentation to 6,886 images to increase data diversity and prevent overfitting. The YOLOv12 model was trained over 100 epochs using SGD as the optimization technique. The dataset was divided into three subsets training, validation, and testing in a proportional manner. Model evaluation employed metrics such as precision, recall, mAP@0.5, and mAP@0.5–0.95, supported by visualizations including the confusion matrix, F1-confidence curve, and precision-recall curve. One of the key advantages of YOLOv12 lies in its architectural efficiency and enhanced generalization capability, enabled by the integration of R-ELAN, Area Attention Mechanism, and FlashAttention. These components allow for broader receptive field processing with reduced computational complexity. Furthermore, the removal of positional encoding and adjustment of the MLP ratio make the model lighter and faster without compromising accuracy. Compared to previous versions (YOLOv8–YOLOv11), YOLOv12 demonstrates more stable and accurate performance in detecting complex OSH objects in indoor environments. The system was also implemented in a real-time user interface using Streamlit, automatically displaying personnel PPE completeness and room safety compliance status. In conclusion, the optimized YOLOv12 model has proven effective for real-time visual detection in OSH contexts. Future studies are recommended to incorporate data balancing approaches, spatial segmentation, and IoT sensor integration to expand the system’s coverage and resilience across diverse workplace conditions.
Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas Devis, Yesica; Muhamadiah, Muhamadiah; Yulanda, Yulanda; Irawan, Yuda; Wahyuni, Refni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.898

Abstract

Dengue fever is an endemic disease that poses a serious threat to public health in tropical regions such as Indonesia. Efforts to control this disease require a data-based approach that is able to accurately predict the level of risk so that interventions can be targeted. This study aims to develop a predictive model of DHF risk using ensemble stacking method optimized with Optuna algorithm and integrated into an interactive dashboard based on Streamlit. The dataset used includes environmental, climate, and socio-demographic indicators from 2015 to 2024 with a total of 1,440 data entries. The preprocessing process includes normalization with Standard Scaler, feature selection using LASSO, and label data balancing with the SMOTE method. Model validation was performed using 10-Fold Cross Validation to ensure model generalization to new data. The stacking model is built with three basic algorithms, namely SVM, KNN, and Random Forest, which are combined using Logistic Regression as a meta-learner. The evaluation results show that the model is able to achieve an average accuracy of 97.57%, with high precision, recall, and f1-score values in all three prediction classes (low, medium, high). The ROC-AUC for each class also showed near-perfect performance. The implementation of the model in the Streamlit dashboard allows non-technical users such as health center or health office staff to perform regional risk prediction and obtain data-driven intervention recommendations automatically. This research not only contributes to the development of predictive technology, but also strengthens evidence-based health promotion practices in urban areas. Further research is recommended to integrate IoT-based real-time data and expand the scope of application areas.
IMPLEMENTASI SISTEM INFORMASI AKADEMIK DI PKBM AR ROYYAN UNTUK MENINGKATKAN EFISIENSI ADMINISTRASI DAN MONITORING Yuda Irawan; Refni Wahyuni; Abdi Muhaimin; M. Khairul Anam
Jurnal Masyarakat Berdikari dan Berkarya (Mardika) Vol 3 No 2 (2025): Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Publisher : Fakultas Teknik, Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/mardika.v3i2.12965

Abstract

PKBM Ar Royyan menghadapi tantangan dalam pengelolaan data akademik seiring dengan peningkatan jumlah siswa dan kompleksitas administrasi. Pengelolaan data yang masih dilakukan secara manual menyebabkan inefisiensi, risiko kehilangan data, serta keterbatasan sumber daya manusia. Oleh karena itu, adopsi sistem informasi akademik digital menjadi sangat penting untuk mengatasi masalah ini. Tujuan dari kegiatan pengabdian masyarakat ini adalah meningkatkan efisiensi pengelolaan data akademik dan administrasi di PKBM Ar Royyan melalui implementasi Sistem Informasi Akademik (SIAKAD). Selain itu, kegiatan ini bertujuan menjamin keamanan dan keberlanjutan data, serta meningkatkan kapasitas teknologi informasi di sekolah melalui pelatihan penggunaan sistem digital. Hasil kegiatan menunjukkan bahwa penerapan SIAKAD secara signifikan berhasil meningkatkan efisiensi pengelolaan data dan mempercepat proses administratif. Meskipun terdapat tantangan infrastruktur dan keterampilan teknologi, masalah tersebut berhasil diatasi melalui pelatihan dan dukungan teknis. Implementasi ini juga mendorong transparansi dan memberikan contoh bagi sekolah lain di wilayah tersebut dalam memanfaatkan teknologi untuk meningkatkan kualitas pendidikan. Secara keseluruhan, kegiatan ini membangun fondasi bagi pengembangan teknologi pendidikan yang berkelanjutan di masa depan.
Sistem Komunikasi Mikrokontroler dan PLC Berbasis Komunikasi Serial Host Link dan Protokol C-Command RS232 Ruwahida, Dewi Rizani Ruwahida; Rachman, Isa; Widodo, Hendro Agus; Adhitya, Ryan Yudha; Irawan, Yuda
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1924

Abstract

The communication between microcontrollers and PLC has become an important technology development that needs to be enhanced. One of the reasons for the communication between these two devices is to facilitate the input process of data or information from sensors, which are responsible for detecting or measuring specific parameters. Not all sensors can be directly read by PLC, as some sensors have their own communication protocols or specific libraries that are not directly supported by PLC. Microcontrollers are often used as intermediaries between sensors and PLC. Microcontrollers can read sensor data and then convert it into a format that can be understood by PLC. Microcontrollers can perform flat processing tasks such as conversion and status evaluation to provide more relevant data, and then send this data to the PLC through a compatible communication protocol. In this study, the communication protocol used is the Host Link serial communication using the C-mode Command method. The communication testing conducted using 10 different data samples resulted in accurate values with an error percentage of 0%.
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