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All Journal Competitor : Jurnal Pendidikan Kepelatihan Olahraga Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Indonesian Journal of Cancer Chemoprevention Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Plans : Penelitian Ilmu Manajemen dan Bisnis REKAYASA ANIDA : Aktualisasi Nuansa Ilmu Dakwah Journal of Islamic Pharmacy ITEj (Information Technology Engineering Journals) Jurnal Pengabdian UntukMu NegeRI Alotrop Jurnal Pendidikan dan Ilmu Kimia Jurnal Ilmiah Wahana Akuntansi Justek : Jurnal Sains Dan Teknologi TIJAB (The International Journal of Applied Business) NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Riset Informatika JISIP: Jurnal Ilmu Sosial dan Pendidikan Jurnal Artefak Jurnal Teknologi Kedirgantaraan (JTK) Jurnal Informatika dan Rekayasa Elektronik ULIN: Jurnal Hutan Tropis POINT Jurnal Ilmiah Akuntansi Kesatuan Jurnal Akuntansi dan Keuangan Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Jurnal Kesehatan: Jurnal Ilmu- Ilmu Keperawatan, Kebidanan, Farmasi dan Analis Kesehatan Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Wahana Pendidikan Jurnal El Tarikh: Journal of History, Culture, and Islamic Civilization Jumat Pertanian: Jurnal Pengabdian Masyarakat Simpatik: Jurnal sistem Informasi dan Informatika Mejuajua Jurnal Iman dan Spiritualitas Al-Khidmah Jurnal Pengabdian Masyarakat Siwayang Journal: Publikasi Ilmiah Bidang Pariwisata, Kebudayaan, dan Antropologi Jurnal PkM MIFTEK Definisi: Journal of Religion and Social Humanities Jurnal Pengabdian Masyarakat Tjut Nyak Dhien Jurnal Pengabdian Kepada Masyarakat Mulawarman Jurnal Keuangan dan Akuntansi Terapan (KUAT) Jurnal Sanitasi Profesional Indonesia Jurnal Kajian Ilmu Manajamen Journal of Geoscience Engineering and Energy (JOGEE) Eastasouth Journal of Effective Community Services Praeparandi : Jurnal Farmasi dan Sains Madani: Multidisciplinary Scientific Journal Jurnal Pengabdian Masyarakat Bangsa Jurnal Farmasi Kryonaut Metta: Jurnal Penelitian Multidisiplin Ilmu Journal of Student Research Exploration Journal of Economics and Management Scienties Journal of Information System Exploration and Research Scientica: Jurnal Ilmiah Sains dan Teknologi Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Pengabdian Tri Bhakti Journal Information & Computer (JICOM) Journal Occupational Health Hygiene and Safety AB-JOIEC: Al-Bahjah Journal of Islamic Economics ARembeN Journal of Computer Science Contributions (Jucosco) Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Jurnal Siber Multi Disiplin Hakamain: Journal of Sharia and Law Studies Maharot : Journal of Islamic Education Jurnal Pemikiran dan Pengembangan Pembelajaran International Journal of Social Discussion JURNAL REKAYASA KIMIA & LINGKUNGAN Masyarakat: Jurnal Pengabdian Economica Didactica Mangabdi: Journal of Community Engagement in Religion, Social, and Humanities Alkasb: Journal of Islamic Economics Jurnal Ilmu Ekonomi dan Bisnis (JUKONI) Jurnal Pengabdian Masyarakat Wadah Publikasi Cendekia Welfare: Jurnal Pengabdian Masyarakat Research In Management and Accounting (RIMA) Jurnal Ilmiah Mahasiswa Pendidikan Geografi Journal Management and Hospitality
Ilham Maulana
Department of Chemistry, Faculty of Mathematics and Natural Science, Syiah Kuala University, Banda Aceh
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Journal : Jurnal Riset Informatika

 1 
IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET Maulana, Ilham; Ernawati, Siti; Indra, Muhammad
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.348

Abstract

The issue of data imbalance in image classification often hinders deep learning models from making accurate predictions, especially for minority classes. This study introduces AugOS-CNN (Augmentation and Over Sampling with CNN), a novel approach that combines oversampling and data augmentation techniques to address data imbalance. The The Simpsons Characters dataset is used in this study, featuring five main character classes: Bart, Homer, Agnes, Carl, and Apu. The number of samples in each class is balanced to 2,067 using an augmentation method based on Augmentor. The proposed model integrates oversampling and augmentation steps with a Convolutional Neural Network (CNN) architecture to improve classification accuracy. Evaluation results show that the AugOS-CNN model achieves the highest accuracy of 96%, outperforming the baseline CNN approach without data balancing techniques, which only reaches 91%. These findings demonstrate that the AugOS-CNN model effectively enhances image classification performance on datasets with imbalanced class distributions, contributing to the development of more robust deep learning methods for addressing data imbalance issues.
Enhancing Obesity Risk Classification: Tackling Data Imbalance with SMOTE and Deep Learning Syofian, Muhammad; Maulana, Ilham
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3011.529 KB) | DOI: 10.34288/jri.v6i4.349

Abstract

Data imbalance is a significant challenge in classification models, often leading to suboptimal performance, especially for minority classes. This study explores the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification model performance by balancing data distribution. The evaluation was conducted using a confusion matrix to measure prediction accuracy for each class. The results indicate that SMOTE successfully enhances minority class representation and improves prediction balance, although some misclassifications remain. Therefore, in addition to oversampling, additional approaches such as class weighting or ensemble learning are required to further improve model accuracy. This study provides deeper insights into the role of SMOTE in addressing data imbalance and its impact on classification model performance.
Prediction Of Flight Delays Using Feature Engineering, Catboost, And Bayesian Optimization To Improve Model Performance Maulana, Ilham; Ernawati, Siti; Wati, Risa
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.346

Abstract

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.
Explainable AI-Driven TabNet Model Enhanced with Bayesian Optimization for Lung Cancer Prediction and Interpretation Maulana, Ilham
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.354

Abstract

This study aims to develop an accurate and explainable lung cancer risk prediction model using a TabNet approach optimized with Bayesian Optimization and applying Explainable AI (XAI) methods through LIME (Local Interpretable Model-Agnostic Explanations). TabNet was selected for its efficiency in processing tabular data and its ability to produce high-accuracy predictions. In the initial stage, the TabNet model was tested using a dataset that was preprocessed through standardization and split into training and testing sets. The performance evaluation of the model without optimization showed an accuracy of 95.83%, precision of 95.87%, recall of 95.76%, and F1-Score of 95.81%. Subsequently, Bayesian Optimization was applied using the Optuna library to find the best hyperparameter combination for the TabNet model. The optimization results demonstrated a significant improvement, achieving an accuracy of 98.33%, precision of 98.48%, recall of 98.21%, and F1-Score of 98.32%. After optimizing the TabNet model, LIME was implemented to provide interpretability for the generated predictions. LIME was used to identify the most influential features contributing to the predictions, enhancing the model's transparency in the lung cancer risk prediction process. Through the combination of TabNet, Bayesian Optimization, and Explainable AI, this study successfully developed a lung cancer prediction model that is not only accurate but also highly interpretable. This model can assist medical professionals in identifying key risk factors and providing transparent explanations for each prediction made.
Co-Authors A. Sitompul, Sahril Abdul Aziz, Ridwan Abdul Kudus, Wahid Abdulloh, Syamsan Adha, Rahmi Aulia Adhitya, Muhammad Adifa, Safitri Fara Affandi Putra , Triana Afrizal " Afrizal, Stevany Agustin, Neli Agustin, Renita Agustin, Zeni Aristiya Ahmad Gumelar , Zami Ahmad Naufal, Ahmad Aksa, Danu Sakhi Aksa Al Fahmi, Ibrahim Nasrul Haq Al Hakim, Aldie Lukman Al Zahra, Reyhan Nandita Alfarizi, Kevin Gibran Algoni, Abdul Malik Alwi Ardiansyah Alwia, Nisa Amelia, Nova Ananda , Tasya Ananda, Lintang Tri Ananda, Resa Aulia ANDIKAYANA , I Made Dwi Andriyano, Ariyo Angganawani, Rahmadhani Tyas Angganawati, Rahmadhani Tyas Annisa, Syifa Annisa Apriyanti, Yeni Apriyanto, Tirto Ardiansyah , Ariel Ardiansyah, Rendi Ardillah, Destry Ariska, Risya Aritonang, MGS Arsyad, Muhammad Fikri Asep Ahmad Hidayat Asep Deddy Supriatna Asyah, Cha Cha Nisya Aulia, Dany Ayuchecaria, Noverda Azhar Aldaira , Muh Dava Aziza, Wardha Bahri, Abdul Halim Syaiful Bastami, Bastami Bayu, Yoni Setyo Nugroho Bianda , Azalia Budi Prasetiyo, Budi Budiman, Raihani Bunga , Ailsa Burhanudin, Asep Cahyadi, Pirgi Cahyowirawan, Angelica Maria Diahlaksmita Camila, Aysyah Fitria Chasanah, Luthfiyah Damayanti, Nabila Putri Daska Azis Denisnawa, Fahdy Ahmada Dewi Pratiwi Dewi, R. Retna Kinanti Dewi, Sintia Ayu Dewintha A, Mona Dhaifullah, Dafa Dina Maizana Dini, Dini Diva Djoko Poernomo Doni Setiawan Dunn, Shirly Sabrina Dwiantoro, Arko Edy Wahyudi Eka Safitri Elfrida Ratnawati Endang Sri Wahyuni Eri Satria Ernawati, Siti Evamarie Hey-Hawkins Fachrudin Fachrudin Fadzilah, Fahmi M Falasifa Bilqis, Luthfia Halum Fanaya, Siti Maharani Farhah, Aulia Rahma Fatonah, Fahmi Veny Fauzan , Dhafin Fauzi, Kasri Gunawan Febiolla, Novi Bunga Febriana Yakhuhindah , Putri Ferdiansyah, Deni Agung Fianri, Rahmat Yasin Fikri Zehan Fiqriansyah, Agung Firdaus Firdaus Firdaus, Adam Firdaus, Ardy Reza Firdaus, Nizar Alghifari Firgiawan , Wahyu Firmasyah, Azhari fitriansyah, Rizky Fried Sinlae Fuadah, Irma Wardatul Ginanjar, Agim Ginanjar, Ahmad Gosul, Nursandrawali Gotama, Dwi Gulistina, Ghania Raisa Gulo, Tris Ella Julita Gulo, Triss Ella Julita Gusprianto, Rian Habib Satria Habibah Habibah Haedarulloh, Ilhaq Hafid, Cep Abdul Haikel, Muhammad Husain Hakim, Annas Nurul Hakim, Muhammad Abror Hambaliana, Dandie Haq, Afdhal Dinil Harsoyo, Imam Tri Hartono, Ali Hati, Getar Herdiana, Dian Hermansyah Hidayat, Asep Ahmad Hidayat, Febrian Hidayat, Imam Nur Hikamah, Siti Roudlotul Husein, Muh Labib Ibnu Hajar Ibrahimi, Adha Ihwan Saputra, Arie Ilham, Muhamad Arifin Ilyasin, Yasa Tiyas Indawan, Isnanda Indri Dayana Indri Tri Julianto Indria Melanie , Nazma Insiyah, Cici Intan, Neng Wihelen Sri Iqbal, Alwi Muhamad Irawan, Olgi Irfan, Muhammad Nadirul Irsyad, Mhd. Irwan Rutlan, Rayhan Dewanta Isana, Widiati Ismail, Moch. ismiati, Silfia Iwan Setiawan Jamil, Rivansyah Syaiful Johari , Eric Joniwarta Jufri, Achmad Jumanto Jumanto, Jumanto Kartikasari, Yunia Koswara, Yoga KUNTARI, SEPTI Kurnia, Ahmad Hopan Kustiawan, Ripan Lailatul Fauziyah Laili, Khairunisa Latifah, Ummi Nadhifatul Lelifajri Lelifajri Lia Lia Lili Somantri Lise Asnur Lubis, Martinasari Mahesa, Restu Gusti Maisaroh, Santi Fatna Makhfud Efendy, Makhfud Marfuah, Efie Rohmatin Maulana, Adnan Fawwaz Melani, Nisa Mellisa, Mellisa Mirza Desfandi Misrani, Misrani Moh. Jafar Sodiq Maksum Mohammad Arif Moranain Mungkin Mubarok, Muhammad Farhan Muh Ihsan Muh. Darwis Muhamad Ihsan, Muhamad Muhammad Ali Misri, Muhammad Ali Muhammad Fadlan Siregar Muhammad Ihsan MUHAMMAD INDRA Mujibno, Mujibno Muliadi Ramli Munparik, Riyan Hakim Murrydan, Kuwatika Musaffa, Farhan N, Firza Much Asrizal N. Nazaruddin Nainggolan , Mulya Nawawi, Irpan Nina Yaya Bae, Sinha Ningsih, Ati’ Lia Ningsih, Silvi Rahayu Nirmalarani, Yayie Novi Triany Nugraha, Zamzam Surya Nur Faidah, Nur Nur hakim , Arif Nur'aeni, Shifa Nurandhini, Rosa Eliza Nurcahya, Yan Nurdin, Kaila Fashla nurfitriana nurfitriana Nurhalimah, Seli Nurhaliza, Siti Salsa Nurita Andriani Nurmalasari, Ary Nurrohmad, Abian Nurul Karimah, Naura Akhlakul Nuzula, Nike Ika Oksal, Efriyana Oktavia, Rindu Oktaviana, Rio Oktaviona, Nur dita Dwi Ote Fianury , Rinrin Palupi, Amelia Destiana Pamungkas, Tirta Rikal Patrisius Kusi Olla, Patrisius Kusi Peter Loennecke Pratiwi, Vivi Amalia Dwi Prio Pamungkas, R Wisnu Puspita Sari, Mega Putra, M. Zikril Oksa Putri Anantia , Sabilla Putri, Mentari Ghea Annisa QOMARIYAH, SITI NUR R Deni Muhammad Danial Rabeta, Bismil Rachman, Julyan Rachmat, Arif Fadhlillah Rachmawati , Yuliani Rafie, Muhammad Hafiz Ahza Rahayu, Norra Isnasia Rahmadanty, Shovy Dewi Rahmadhani Tyas Rahmadi Rahmadi Rahmah, Alya Siti Rahmajanida, Reza Rahmansah, Muhammad Hilmi Nabil Rahmawati, Deby Ramadhan, Dimas Ramdan, Doni Ramyana, Fitriyani Ratiani, Syifa Intan Reno Adi Pamungkas Retno Agnestisia Reza Ardiansyah Ridla Firdaus, Nabil Ridwana, Riki Rifai , Andi Anna Rinaldi Idroes Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Riski, Wesiur Rizkita, Aden Dhana Rodhiah, Widi Siti Rohmatullah Rosliyati Rozikin, Moch. Khoirur ruhyana, nanang Rusli, Zulfadhli Sa'adah, Lailatus Sa'adah, Putri Lailatus Safitri, Wulan Caesar Sagala, Edison Saiful Saiful Salsabila, Najwa Salsabillah, Zikra Saluky Samsuddin, M. Afdal Sanjaya, Hafiyan Rizqi Saputra, Hendi Saputra, Reza Pratama Sari, Novia Aspita Sari, Riska SASTRAWAN, I Komang Agus Adi Selay, Risma Eka Putri Septiana, Merida Nazwa Septiarosa, Putri Setiaji, Ruly Setiawan, Dzulkifli Putra Setiawan, Muhammad Arief Setiyansyah, Febri Setiyawanto, Roni Shapira, Shari Bella Sidik, Mohammad Dindin Hamam Sinaga, Enjelina C. Sindi Amelia, Rhika Siraj, Zaky Abdullah Sirojudin, Naufal Siti Aisyah Siti Annisa Solly Aryza Sopyan Saori Sugiarto, Deri Suhud, Khairi Sulalah Sulalah Sumiati . Supendi, Usman Supriatno Supriyatna, Samso Surya Gumilar, Surya Suryadi, Khaila Thsabita Suryati Budiwati, Dewi Syah, M. 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