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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Muhammad Itqan Mazdadi
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Implementasi Principal Component Analysis (PCA) dan Gap Statistic untuk Clustering Kanker Payudara pada Algoritma K-Means Afifa, Ridha; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Indriani, Fatma; Muliadi, Muliadi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4015

Abstract

Breast cancer is one of the most common causes of death worldwide. Data mining can be utilized to detect breast cancer, where information is extracted from data to provide valuable insights. Clustering of breast cancer is conducted to assist medical professionals in grouping the characteristics of each cancer type. However, multicollinearity in breast cancer data can impact clustering results. To address this issue, dimensionality reduction through Principal Component Analysis (PCA) is employed. PCA can effectively handle multicollinearity issues and enhance computational efficiency. Additionally, the K-Means method has limitations in determining the optimal number of clusters. Therefore, the Gap Statistic method is employed to find the optimal K value suitable for breast cancer data. This study compares the evaluation results of the K-Means clustering model, the combined PCA-KMeans clustering model, and the combined PCA-GapStatistic-KMeans clustering model. The findings indicate that the evaluation results for the K-Means model with PCA dimensionality reduction and optimal Gap Statistic K are superior to the K-Means model without dimensionality reduction. The Gap Statistic suggests 2 clusters as the optimal number, with an evaluation result of 1.195513.
Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants Fitriani, Karlina Elreine; Faisal, Mohammad Reza; Mazdadi, Muhammad Itqan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Prastya, Septyan Eka
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/t158qq37

Abstract

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
Machine Learning Implementation for Sentiment Analysis on X/Twitter: Case Study of Class Of Champions Event in Indonesia Hafizah, Rini; Saragih, Triando Hamonangan; Muliadi, Muliadi; Indriani, Fatma; Mazdadi, Muhammad Itqan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.81

Abstract

Sentiment analysis on social media is becoming an important approach in understanding public opinion towards an event. Twitter, as a microblogging platform, generates a large amount of data that can be utilized for this analysis. This study aims to evaluate and compare the performance of three classification algorithms, namely Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), in sentiment analysis related to the Clash of Champions event in Indonesia. To represent the text data, two feature extraction techniques are used, namely Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). In addition, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle data imbalance, while model optimization is performed using GridSearchCV. The research dataset consists of 1,000 tweets collected through web scraping, then manually processed and labeled before model training and testing. The results showed that the TF-IDF technique provided superior results compared to BoW. The Random Forest model with TF-IDF achieved the highest accuracy of 91%, while XGBoost with TF-IDF had the highest Area Under the Curve (AUC) of 0.91. The findings confirm that the selection of appropriate feature extraction techniques and algorithms can improve accuracy in sentiment analysis. This study can be applied in public opinion monitoring and data-driven decision-making. Future research can explore word embedding techniques and transformer-based deep learning models to improve semantic understanding and accuracy of sentiment analysis.
Application of Adaboost Algorithm with SMOTE and Optuna Techniques in Sleep Disorder Classification Anshory, Muhammad Naufal; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Saputro, Setyo Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.99

Abstract

Data imbalance is a serious challenge in developing machine learning models for sleep disorder classification. When models are trained on an uneven distribution of classes, classification performance for minority classes such as insomnia and sleep apnea is often low. As a result, the overall accuracy may seem elevated, yet the sensitivity to important cases to be weak. Therefore, this research aims to design and develop a robust sleep disorder classification model with the AdaBoost algorithm, with improved performance through the integration of two main approaches, namely data balancing technique utilizing SMOTE and hyperparameter optimization using Optuna. This research contributes by showing that the combination of the two approaches can significantly improve model performance, not only in terms of global accuracy, but also accuracy on previously overlooked minority classes. The dataset utilized is the Sleep Health and Lifestyle Dataset which consists of 374 synthesized data and is divided into three categories: insomnia, sleep apnea, and none. This method stages include data preprocessing, data division using train-test split (80:20), application of SMOTE to balance the class distribution, hyperparameter tuning using Optuna, and model training with the AdaBoost algorithm. Evaluation was performed using classification metrics: accuracy, precision, recall, and F1-score. Results showed that mix of SMOTE and Optuna yielded the best results, accuracy 90.6%, F1-score 0.83871 for insomnia, and 0.81250 for sleep apnea. This performance was consistently superior to scenarios with no SMOTE or no tuning. This confirms the importance of using combination strategies to obtain fair and accurate classification on medical data. Future research is recommended to use real datasets as well as test the capabilities of this research on other models such as XGBoost or LightGBM.
Revitalisasi Pengemasan Produk UMKM “Woro Production” sebagai Upaya Peningkatan Daya Saing Melalui Penerapan Teknologi Inovatif Mazdadi, Muhammad Itqan; Sari, Anna Khumaira; Normaidah, Normaidah; Saputra, Adryan Maulana; Rahmah, Indah Noor; Ramadhani, Muhammad Irfan; Rahmawati, Nanda Hesti
Jurnal Pengabdian UNDIKMA Vol. 6 No. 4 (2025): November
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v6i4.17645

Abstract

This community service program aims to strengthen the capacity and technical skills of the “Woro Production” MSME by providing modern packaging equipment and training on its use to improve product efficiency and competitiveness. The implementation method involved training sessions and packaging simulations. Evaluation instruments included observation sheets and interviews to assess the partner’s skills in operating the packaging machine, and the resulting data were analyzed descriptively. The outcomes of this program indicate that participants were able to operate the equipment effectively, and the packaged products demonstrated improved hygiene, practicality, and visual appeal. This initiative is expected to enhance the competitiveness of Woro Production in local, national, and global markets.
Comparative Analysis of YOLO11 and Mask R-CNN for Automated Glaucoma Detection Fayyadh, Muhammad Naufaldi; Saragih, Triando Hamonangan; Farmadi, Andi; Mazdadi, Muhammad Itqan; Herteno, Rudy; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1266

Abstract

Glaucoma is a progressive optic neuropathy and a major cause of irreversible blindness. Early detection is crucial, yet current practice depends on manual estimation of the vertical Cup-to-Disc Ratio (vCDR), which is subjective and inefficient. Automated fundus image analysis provides scalable solutions but is challenged by low optic cup contrast, dataset variability, and the need for clinically interpretable outcomes. This study aimed to develop and evaluate an automated glaucoma screening pipeline based on optic disc (OD) and optic cup (OC) segmentation, comparing a single-stage model (YOLO11-Segmentation) with a two-stage model (Mask R-CNN with ResNet50-FPN), and validating it using vCDR at a threshold of 0.7. The contributions are fourfold: establishing a benchmark comparison of YOLO11 and Mask R-CNN across three datasets (REFUGE, ORIGA, G1020); linking segmentation accuracy to vCDR-based screening; analyzing precision–recall trade-offs between the models; and providing a reproducible baseline for future studies. The pipeline employed standardized preprocessing (optic nerve head cropping, resizing to 1024×1024, conservative augmentation). YOLO11 was trained for 200 epochs, and Mask R-CNN for 75 epochs. Evaluation metrics included Dice, Intersection over Union (IoU), mean absolute error (MAE), correlation, and classification performance. Results showed that Mask R-CNN achieved higher disc Dice (0.947 in G1020, 0.938 in REFUGE) and recall (0.880 in REFUGE), while YOLO11 attained stronger vCDR correlation (r = 0.900 in ORIGA) and perfect precision (1.000 in G1020). Overall accuracy exceeded 0.92 in REFUGE and G1020. In conclusion, YOLO11 favored conservative screening with fewer false positives, while Mask R-CNN improved sensitivity. These complementary strengths highlight the importance of model selection by screening context and suggest future research on hybrid frameworks and multimodal integration
Co-Authors AA Sudharmawan, AA Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Ade Agung Harnawan, Ade Agung Adela Putri Ariyanti Afifa, Ridha Ahdyani, Annisa Salsabila Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Shofi Khairian Ahmad Tajali Aidil Akbar Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Amalia, Raisa Andi - Farmadi Andi Farmadi Andi Farmadi Anna Khumaira Sari Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Antoh, Soterio Ardiansyah Sukma Wijaya Athavale, Vijay Anant Athavale, Vijay Annant budiman, irwan Buih, Putri Helena Junjung Deni Sutaji Dina Arifah Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Fayyadh, Muhammad Naufaldi Fitriani, Karlina Elreine Fitrinadi Friska Abadi Haekal, Muhammad Hafizah, Rini Helma Herlinda Herteno, Rudi Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman M. Apriannur M. Khairul Rezki Mafazy, Muhammad Meftah Muflih Ihza Rifatama Muhamad Fawwaz Akbar Muhamad Ihsanul Qamil Muhammad Khairin Nahwan Muhammad Mada Muhammad Mirza Hafiz Yudianto Muhammad Mursyidan Amini Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nabella, Putri Normaidah, Normaidah Nugraha, Muhammad Amir Nursyifa Azizah P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Putri Nabella Radityo Adi Nugroho Rahmah, Indah Noor Rahmat Hidayat Rahmat Ramadhani Rahmat Ramadhani Rahmawati, Nanda Hesti Ramadhani, Muhammad Irfan Ramadhani, Rahmat Ratnapuri, Prima Happy Riadi, Agus Teguh Rifki Izdihar Oktvian Abas Pullah Rifki Rinaldi Rozaq, Hasri Akbar Awal Rudy Herteno Saputra, Adryan Maulana Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satrio Yudho Prakoso Setyo Wahyu Saputro Shalehah Syahputra, Muhammad Reza Tajali, Ahmad Totok Wianto Wahyu Dwi Styadi Wijaya Kusuma, Arizha Yanche Kurniawan Mangalik YILDIZ, Oktay Yoga Pambudi Yudha Sulistiyo Wibowo Zaini Abdan