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All Journal J@TI (TEKNIK INDUSTRI) el-Jizya: Jurnal Ekonomi Islam EKONOMIS : Journal of Economics and Business JURNAL PENDIDIKAN TAMBUSAI ILTIZAM Journal of Sharia Economic Research Jurnal Medika Malahayati JATI (Jurnal Mahasiswa Teknik Informatika) Bertuah : Jurnal Syariah dan Ekonomi Islam Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat (ABDIRA) Journal Of World Science Yudishtira Journal : Indonesian Journal of Finance and Strategy Inside Bureaucracy Journal : Indonesia Journal of Law and Social-Political Governance ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Jouska: Jurnal Ilmiah Psikologi Nanggroe: Journal Of Scholarly Service Jurnal Manajemen Keuangan Syariah Economics, Business, Management, & Accounting Journal Nanggroe: Journal of Scholarly Service Asian Journal of Islamic Studies and Da'wah JIMEB: Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Margin : Journal of Islamic Banking Exhibition and Seminar on Science and Creative Technology – Al Azhar Proceeding Jurnal Abdimas Kedokteran dan Kesehatan
Bambang Kurniawan
Program Studi S1 Teknologi Informasi Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Sumatera Utara
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Journal : Journal of Applied Data Sciences

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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.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Kurniawan, Bambang; Wahyuni, Refni; Yulanda, Yulanda; Irawan, Yuda; Habib Yuhandri, Muhammad
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.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Co-Authors Abkhoriah, Mir’atus Sa’adah Adillah, Ulfah Agustin Nengsih, Titin akbar, Akhmad Anam, M Khairul Andriani, Beid Fitrianova Angelina, Ranowo Usi Arsa Arsa Aryadi, Kian Asri, Ella Purlia Maya Asshidiqqi, Khatami Ayahrizal, Ahmad Brata kusuma, Royan Cindy Aulia Lorenza Dewi, Mia Clarissa Dharmawan, Fauzan Diandra, Nabila Diwantara, Victor Dwi Astharini Egiestine, Dea Putri Erwin Saputra Siregar Fahriani, Nur Daniah Faqih, Achmad Firman Syah Noor Firmansyah Firmansyah Fitri Harsanti, Eka Friscilla, Cerry Herina Fusfita, Nurlia Habib Yuhandri, Muhammad Habriyanto Habriyanto Hairul Anwar Dalimunthe Hamzah, Muhammad Maulana Hartomi, Zupri Henra Hasibuan, Khairani Afrida Hasibuan, Zefanya Herianto Herianto Hutabarat, Noel Rodo Hasiholan Isma, Asad Janah, Arfah Miftahul Khayyi, Arief Muya Mambaul Khusnul Istiqomah Lela Nurpulaela Lisa, Mirna Maulana Ihsan, Hafiz Mitrin, Abdullah Muanda, Tasya Muhammad Agus Muljanto Nikmatul Maula Nofriza, Eri Noviyanti, Fadhila Nurmaysarah, Siti Nurmusischa, Nadia Nurputri, Hasya Syifa Nursiha, Muhamad Oktapriant, Diah Oktaprianti, Diah Prasaja, Syukron Priyono, Aldo Falendra Putra, Dwi Putri, Claresta Vania Rahmah, Balqist Ar Rahman, Rudi Rahmatia, Suci Refni Wahyuni Ruslani, Alan Sadila, Meliana Safiyra, Cut Lailan Saifullah, Ricki Selviana, Nanda Rahayu Setya Nugraha Singgih Saptadi Siti Aisah Soleha Soleha Sucipto Sucipto Sugara, Asep Suherman, Andika Sulistiawan, Dexxi Susanto, Febi Susanto, Kevin Susilo, Ajeng Ishelina Suyono, Nono Wahyuda, Tri Waldan, Reta Amelia Warman, Aidil Dwi Wawanudin, Wawanudin Yayat Rahmat Hidayat, Yayat Rahmat Yogitriani, Rima Yuda Irawan Yulanda, Yulanda Zainal Fanani