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All Journal Bulletin of Electrical Engineering and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal of Robotics and Control Systems Sewagati Jurnal Penyakit Dalam Indonesia Journal of Medicine and Health Technology Bioma : Jurnal Biologi dan Pembelajaran Biologi
Yuri Pamungkas
Institut Teknologi Sepuluh Nopember
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian Alfonsus Haryo Sangaji; Yuri Pamungkas; Supeno Mardi Susiki Nugroho; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1377

Abstract

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%.
Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI) Yuri Pamungkas; Adhi Dharma Wibawa; Meiliana Dwi Cahya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1535

Abstract

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.
Pemanfaatan Algoritma Machine Learning dan Long-Short Term Memory untuk Prediksi Dini Diabetes Yuri Pamungkas; Meiliana Dwi Cahya; Endah Indriastuti
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.630.491-506

Abstract

Diabetes, a chronic condition, affects numerous populations. Poor insulin production from the pancreas combined with high blood sugar levels can result in the onset of diabetes. Diabetes can be caused by numerous factors. Observe and prevent these factors to reduce the high prevalence of diabetes. This study concentrates on medical record data for determining diabetes risk factors via statistical correlation analysis. These factors will be utilized as machine learning and LSTM input parameters for diabetes prediction. The factors analyzed include blood glucose levels, HbA1c levels, age, BMI, hypertension, heart disease, smoking habits, and gender. Based on the research results, we found that glucose levels (>137 mg/dL) and HbA1c levels (>6.5%) are the main benchmarks in diagnosing diabetes. It is also supported by the correlation value, which is relatively high (0.42 and 0.40, respectively) compared to other factors. Increasing age and BMI also increase the risk of developing diabetes. Comorbidities (such as hypertension or heart disease) and smoking habits can worsen the condition of people with diabetes. Meanwhile (based on gender), women are more at risk of developing diabetes than men because their body mass index increases during the monthly cycle. Apart from that, there is a tendency for blood sugar levels in women to increase in the last two weeks before menstruation. Based on the prediction results, the highest levels of accuracy, sensitivity, and F1 score were obtained (96.97%, 99.97%, and 98.37%) using the LSTM method. This performance shows that LSTM is relatively good for the diabetes prediction process based on existing factors/parameters.
Effectiveness of CNN Architectures and SMOTE to Overcome Imbalanced X-Ray Data in Childhood Pneumonia Detection Pamungkas, Yuri; Ramadani, Muhammad Rifqi Nur; Njoto, Edwin Nugroho
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21494

Abstract

Pneumonia is a disease that causes high mortality worldwide in children and adults. Pneumonia is caused by swelling of the lungs, and to ensure that the lungs are swollen, a chest X-ray can be done. The doctor will then analyze the X-ray results. However, doctors sometimes have difficulty confirming pneumonia from the results of chest X-ray observations. Therefore, we propose the combination of SMOTE and several CNN architectures be implemented in a chest X-ray image-based pneumonia detection system to help the process of diagnosing pneumonia quickly and accurately. The chest X-ray data used in this study were obtained from the Kermany dataset (5216 images). Several stages of pre-processing (grayscaling and normalization) and data augmentation (shifting, zooming, and adjusting the brightness) are carried out before deep learning is carried out. It ensures that the input data for deep learning is not mixed with noise and is according to needs. Then, the output data from the augmentation results are used as input for several CNN deep learning architectures. The augmented data will also utilize SMOTE to overcome data class disparities before entering the CNN algorithm. Based on the test results, the VGG16 architecture shows the best level of performance compared to other architectures. In system testing using SMOTE+CNN Architectures (VGG16, VGG19, Xception, Inception-ResNet v2, and DenseNet 201), the optimum accuracy level reached 93.75%, 89.10%, 91.67%, 86.54% and 91.99% respectively. SMOTE provides a performance increase of up to 4% for all CNN architectures used in predicting pneumonia.
Pelatihan Pengembangan Media Ajar Berbasis Tools Artificial Intelligence untuk Guru di SMAN 1 Probolinggo Pamungkas, Yuri; Sain, Anabela Aulia; Putri, Ziyan Nadia; Larasati, Alya Puti; Iqbal, Muhammad; Risald, Randi Achtiar; Kendenan, Valentino; Rachmadiana, Josephine Larissa; Ginting, Tsamarah Amelia Putri; Nur, Rossa Alfi; Balqis, Dayana Satira
Sewagati Vol 8 No 3 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i3.1022

Abstract

Pemanfaatan kecerdasan buatan (AI) memiliki peran yang sangat signifikan dalam pengembangan media ajar modern. AI memungkinkan media ajar dapat disesuaikan dengan kebutuhan masing-masing siswa. Sistem AI juga dapat memberikan umpan balik secara real-time dan membantu guru memahami kemajuan belajar siswa. Oleh karena itu, dalam kegiatan pengabdian masyarakat ini, tim dosen departemen teknologi kedokteran bermaksud untuk mengadakan pelatihan pengembangan media ajar berbasis tools AI kepada guru di SMAN 1 Probolinggo. Dengan adanya pelatihan ini, para guru diharapkan mampu memanfaatkan media ajar berbasis AI untuk menunjang proses pembelajaran yang interaktif di dalam kelas. Dalam pengabdian masyarakat ini, terdapat beberapa rangkaian kegiatan yang dilakukan mulai dari perencanaan, survei lokasi, penyusunan modul, pelaksanaan pelatihan, evaluasi acara, dan pembuatan laporan. Dalam pelaksanaannya, terdapat dua sesi acara yaitu penyampaian materi/diskusi dan praktik langsung. Pada akhir pelatihan, peserta juga diminta untuk mengisi formulir survei kepuasan untuk menilai keberhasilan pelaksanaan pelatihan. Dari seluruh rangkaian acara pelatihan di SMAN 1 Probolinggo, dapat disimpulkan bahwa proses pelaksanaan pengabdian masyarakat telah berjalan dengan lancar dengan jumlah peserta pelatihan mencapai 30 guru. Antusiasme peserta pelatihan juga tergolong sangat baik. Hal ini terlihat saat proses pre dan post-test berlangsung, serta hasil dari pengukuran tingkat kepuasan peserta yang mencapai 90%.
Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method Pamungkas, Yuri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2117

Abstract

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.
Work Fatigue Detection of Search and Rescue Officers Based on Hjorth EEG Parameters Pamungkas, Yuri; Indriani, Ratri Dwi; Crisnapati, Padma Nyoman; Thwe, Yamin
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23511

Abstract

Work fatigue can cause a decrease in cognitive function, such as decreased thinking ability, concentration, and memory. A tired brain cannot work optimally, interfering with a person's ability to perform tasks that require complex thinking. In general, to evaluate work fatigue in a person, self-assessment activities using the Perceived Stress Scale (PSS) are the method most often used by researchers or practitioners. However, this method is prone to bias because sometimes people try to hide or exaggerate their tiredness at work. Therefore, we propose to evaluate people's work fatigue based on their EEG data in this study. A total of 25 participants from SAR officers recorded their EEG data in relaxed conditions (pre-SAR operations) and fatigue conditions (post-SAR operations). Recording was performed on the brain's left (fp1 t7) and right (fp2 t8) hemispheres. The EEG data is then processed by filtering, artifact removal using ICA method, signal decomposition into several frequency bands, and Hjorth feature extraction (activity, mobility, and complexity). The main advantage of Hjorth parameters compared to other EEG features is its ability to provide rich information about the complexity and mobility of the EEG signal in a relatively simple and fast way. Based on the results of activity feature extraction, feature values will tend to increase during the post-SAR operation conditions compared to the pre-operation SAR conditions. In addition, the results of the classification of pre-and post-operative SAR conditions using Bagged Tree algorithm (10-fold cross validation) show that the highest accuracy can be obtained is 94.8%.
Analisis Prediktif Mutasi EGFR pada Adenokarsinoma Paru Menggunakan Pendekatan Pembelajaran Mesin Njoto, Edwin Nugroho; Pamungkas, Yuri; Putri, Atina I.W.; Haykal, Muhammad. Najib; Eljatin, Dwinka Syafira; Djaputra, Edith Maria
Jurnal Penyakit Dalam Indonesia
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Introduction. Lung adenocarcinoma is a prevalent form of lung cancer, and mutations in the epidermal growth factor receptor (EGFR) gene are known to play a crucial role in its pathogenesis. This study aimed to develop a machine-learning model to predict EGFR mutations in lung adenocarcinoma patients using clinical and radiological features. Methods. A case-control study was conducted using a dataset comprising 160 patients with lung adenocarcinoma. Several machine learning algorithms, including decision tree, linear regression, Naive Bayes, support vector machine, K-nearest neighbor, and random forest, were employed to predict EGFR mutations based on variables such as smoking status, tumor diameter, tumor location, bubble-like appearance on CT-scan, air-bronchogram on CT-scan, and tumor distribution. Results. Most study subjects were over 50 years old (83.75%) and female (53.13%). The analysis results indicated that the random forest model demonstrated the best performance, achieving an accuracy of 83.33%, precision of 86.96%, recall of 80.00%, and an Area Under the Curve (AUC) of 90.0. The Naive Bayes model also performed well, with an accuracy of 85.42%, precision of 82.61%, recall of 86.36%, and an AUC of 91.0. Conclusions. The study highlights the potential of machine learning techniques, particularly random forest and Naive Bayes, in accurately predicting EGFR mutations in lung adenocarcinoma patients based on readily available clinical and radiological features. These findings could contribute to the development of non-invasive, cost-effective, and efficient tools for EGFR mutation detection, ultimately facilitating personalized treatment approaches for lung adenocarcinoma patients.
Hyperparameter Tuning of EfficientNet Method for Optimization of Malaria Detection System Based on Red Blood Cell Image Pamungkas, Yuri; Eljatin, Dwinka Syafira
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2257

Abstract

Nowadays, malaria has become an infectious disease with a high mortality rate. One way to detect malaria is through microscopic examination of blood preparations, which is done by experts and often takes a long time. With the development of deep learning technology, the observation of blood cell images infected with malaria can be more easily done. Therefore, this study proposes a red blood cell image-based malaria detection system using the EfficientNet method with hyperparameter tuning. There are three parameters which are learning rate, activation function, and optimiser. The learning rate used is 0.01 and 0.001, while the activation functions used are ReLU and Tanh. In addition, the optimisers used include Adam, SGD, and RMSProp. In the implementation, the cell image dataset from the NIH repository was pre-processed such as resizing, rotating, filtering, and data augmentation. Then the data is trained and tested on several EfficientNet models (B0, B1, B3, B5, and B7) and their performance values are compared. Based on the test results, EfficientNet-B5 and B7 models showed the best performance compared to other EfficientNet models. The most optimal system test results are when the EfficientNet B5 model is used with a learning rate of 0.001, ReLU activation function, and Adam optimiser, with values of 97.69% (accuracy), 98.36% (precision), and 97.03% (recall). This research has proven that proper model selection and hyperparameter tuning can maximise the performance of cell image-based malaria detection system. The development of this EfficientNet-based diagnostic method is more sensitive and specific in malaria detection using RBCs.
Work Fatigue Detection of Search and Rescue Officers Based on Hjorth EEG Parameters Pamungkas, Yuri; Indriani, Ratri Dwi; Crisnapati, Padma Nyoman; Thwe, Yamin
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23511

Abstract

Work fatigue can cause a decrease in cognitive function, such as decreased thinking ability, concentration, and memory. A tired brain cannot work optimally, interfering with a person's ability to perform tasks that require complex thinking. In general, to evaluate work fatigue in a person, self-assessment activities using the Perceived Stress Scale (PSS) are the method most often used by researchers or practitioners. However, this method is prone to bias because sometimes people try to hide or exaggerate their tiredness at work. Therefore, we propose to evaluate people's work fatigue based on their EEG data in this study. A total of 25 participants from SAR officers recorded their EEG data in relaxed conditions (pre-SAR operations) and fatigue conditions (post-SAR operations). Recording was performed on the brain's left (fp1 & t7) and right (fp2 & t8) hemispheres. The EEG data is then processed by filtering, artifact removal using ICA method, signal decomposition into several frequency bands, and Hjorth feature extraction (activity, mobility, and complexity). The main advantage of Hjorth parameters compared to other EEG features is its ability to provide rich information about the complexity and mobility of the EEG signal in a relatively simple and fast way. Based on the results of activity feature extraction, feature values will tend to increase during the post-SAR operation conditions compared to the pre-operation SAR conditions. In addition, the results of the classification of pre-and post-operative SAR conditions using Bagged Tree algorithm (10-fold cross validation) show that the highest accuracy can be obtained is 94.8%.
Co-Authors Abdul Karim Abdurahman Abdurrahman Achmad Syaifudin Adhi Dharma Wibawa Adhi Dharma Wibawa, Adhi Dharma Alfonsus Haryo Sangaji Aung, Myo Min Balqis, Dayana Satira Cahya, Meiliana Dwi Derek, Natan Dian Puspita Hapsari Djaputra, Edith Maria Dwinka Syafira Eljatin Elfrida Ratnawati Evi Triandini Fadli, Sonny Faiqoh, Elok Nur Fitriani, Fatimah Nur Forca, Adrian Jaleco Ginting, Tsamarah Amelia Putri Hashim, Uda Haykal, Muhammad Nazhif Haykal, Muhammad. Najib Hedianto, Tri Hidayah, Rizka Nurul Hisbiyah, Yuni Imam Susilo Indriani, Ratri Dwi Indriastuti, Endah Indriastuti, Endah Karimah, Rumman Kendenan, Valentino Kuswanto, Djoko Larasati, Alya Puti Made Krisnanda Meiliana Dwi Cahya Meiliana Dwi Cahya Muhammad Faizi, Muhammad Nakkliang, Kanittha Njoto, Edwin Nugroho Nugroho Njoto, Edwin Nur Rochmah, Nur Nur, Rossa Alfi Padma Nyoman Crisnapati Parlindungan, Putra Gelar Perwitasari, Rayi Kurnia Pratasik, Stralen Putra, Gumilar Fardhani Ami Putri Alief Siswanto Putri, Atina I.W. Putri, Atina Irani Wira Putri, Ziyan Nadia Putu Adi Guna Permana Rachmadiana, Josephine Larissa Radiansyah, Riva Satya Ramadani, Muhammad Rifqi Nur Rangkuti, Rahmah Yasinta Ridhoi, Ahmad Risald, Randi Achtiar Sain, Anabela Aulia Sakina Sangsawang, Thosporn Shofwan Hanief Supeno Mardi Susiki Nugroho, Supeno Mardi Syafira Eljatin, Dwinka Syulthoni, Zain Budi Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Yulan, Gao