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Diabetic Foot Infection (Infeksi Kaki Diabetik): Diagnosis dan Tatalaksana Hutagalung, Muhammad Bayu Zohari; Eljatin, Dwinka Syafira; -, Awalita; Sarie, Vivi Permana; Sianturi, Gaby Demitria Agustina; Santika, Galenisa Falinda
Cermin Dunia Kedokteran Vol 46, No 6 (2019): Diabetes Mellitus
Publisher : PT. Kalbe Farma Tbk.

Infeksi kaki diabetik merupakan komplikasi yang paling sering ditemukan pada penderita diabetes. Derajat infeksi kaki diabetik ditentukan berdasarkan evaluasi keadaan lokal kaki yang terinfeksi, luas struktur terinfeksi serta adanya manifestasi sistemik. Tatalaksana meliputi pembedahan, pemberian antibiotik, perawatan luka serta manajemen hiperglikemia.Â Diabetic foot infection is the most common complication in diabetic patients. The assessment was based on local condition, the spread and systemic manifestation of the infection. Management includes surgical treatment, antibiotic, wound care and hyperglycemia management.

Diabetic Foot Infection (Infeksi Kaki Diabetik): Diagnosis dan Tatalaksana Muhammad Bayu Zohari Hutagalung; Dwinka Syafira Eljatin; Awalita -; Vivi Permana Sarie; Gaby Demitria Agustina Sianturi; Galenisa Falinda Santika
Cermin Dunia Kedokteran Vol 46, No 6 (2019): Diabetes Mellitus
Publisher : PT. Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v46i6.463

Infeksi kaki diabetik merupakan komplikasi yang paling sering ditemukan pada penderita diabetes. Derajat infeksi kaki diabetik ditentukan berdasarkan evaluasi keadaan lokal kaki yang terinfeksi, luas struktur terinfeksi serta adanya manifestasi sistemik. Tatalaksana meliputi pembedahan, pemberian antibiotik, perawatan luka serta manajemen hiperglikemia. Diabetic foot infection is the most common complication in diabetic patients. The assessment was based on local condition, the spread and systemic manifestation of the infection. Management includes surgical treatment, antibiotic, wound care and hyperglycemia management.

Excessive Use of Nipah Leaf Membrane Cigarettes Increases the Severity of Spontaneous Pneumothorax: A Case study from Jambi, Indonesia Dwinka Syafira Eljatin; Muhammad Ridho Akbar Eljatin; I Made Dwi Mertha Adnyana; Muhammad Bayu Zohari Hutagalung; Muhamad Frendy Setyawan
Journal of Pharmaceutical and Health Research Vol 4 No 1 (2023): February 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jharma.v4i1.2820

Nipah (Nypa fruticans) is a type of palm that is widely used by the community. The local people of Jambi use Nipah leaves as a tobacco membrane for cigarettes. However, we found reports of cigarette use cases having implications for the severity of spontaneous pneumothorax disease. A 57-year-old man came to the emergency room suffering from shortness of breath, right-side chest discomfort, yellowish-green sputum cough, abdominal pain, nausea, weakness, decreased appetite, and inability to sleep. For two years, this man consumed ten Nipah membrane cigarettes he made independently daily, resulting in lung disease. The lungs were found to be asymmetrical with the weakened fremitus of the right lung stem; percussion revealed hyper resonance in the right lung and resonance in the left lung; auscultation revealed the presence of a decrease in vesicular breathing sounds in the right lung, and other breathing sounds crackling in both lungs. The patient's severe partially compensatory respiratory acidosis indicated levels of pH, pO2, pCO2, HCO3, total CO2, and BE. Sinus tachycardia, normoaxis and suitable atrial hypertrophy were found. The luscen region is visible, and the white line of the pleura on the right hemithorax shows the pneumothorax of the right lung. This case is relatively rare, and excess Nipah leaf membrane cigarettes increase the severity of spontaneous pneumothorax disease that causes COPD.

Diabetic Foot Infection (Infeksi Kaki Diabetik): Diagnosis dan Tatalaksana Muhammad Bayu Zohari Hutagalung; Dwinka Syafira Eljatin; Awalita; Vivi Permana Sarie; Gaby Demitria Agustina Sianturi; Galenisa Falinda Santika
Cermin Dunia Kedokteran Vol 46 No 6 (2019): Endokrinologi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v46i6.434

Infeksi kaki diabetik merupakan salah satu komplikasi yang sering ditemukan pada penderita diabetes. Derajat infeksi kaki diabetik ditentukan berdasarkan evaluasi keadaan lokal kaki yang terinfeksi, luas struktur terinfeksi, serta adanya manifestasi sistemik. Tatalaksana meliputi pembedahan, pemberian antibiotik, perawatan luka, serta manajemen hiperglikemia. Diabetic foot infection is one of the most common complications in diabetic patients. The degree of infection was based on local condition, the spread, and systemic manifestation of the infection. Management includes surgical treatment, antibiotic, wound care, and hyperglycemia management.

Evaluation of The Poedji Rochjati Score Card (PRSC) on Digital Platform @hamilku.id Based on The Delphi Method Fadli, Sonny; Wibawa, Adhi Dharma; Eljatin, Dwinka Syafira
Jurnal Eksplora Informatika Vol 14 No 1 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v14i1.1108

The number of cases and deaths of mothers and babies in Indonesia is increasing, which is mediated by low-risk detection in early pregnancy, and a lack of knowledge resulting in the dissemination of pregnancy-related information tends to be poorly understood. As a solution to this problem, the purpose of this study was to analyze the effectiveness and usability of the Poedji Rochjati Score Card (PRSC) feature on the @hamilku.id Digital Platform based on the Delphi method. Qualitative research methods with technical observations were carried out online by obstetricians and gynecologists. The main focus of this research was usability testing involving 46 pregnant women who used the application and 9 randomly selected respondents. The assessment and evaluation were guided by the Delphi method, which involved two rounds of testing by six obstetricians and gynecologists. The results were descriptively analyzed. The findings showed that pregnant female respondents aged between 17 and 34 years had a higher education level, were dominated by people without jobs/housewives, were domiciled in Sidoarjo, had undergone antenatal care (ANC) ≤ 6 times, and had undergone ≥ 5 pregnancies. According to the PRS, 52.2% of pregnant women were classified as having high-risk pregnancies (HRPs). Based on the evaluation of the application from the usability aspect, 83.3% of the participants stated that the information was comprehensive and that the medical terminology was easy to understand. However, only half of them considered visualization in the form of images or animations to be very helpful in illustrating pregnancy risks. Delphi testing with obstetricians and gynecologists revealed that the digital PRSC features generated positive ratings, indicating that the tool is accurate, informative, easy to understand, and effective at improving the quality of health services. The second round showed an improvement in the quality and relevance of the digital PRSC features, with more diverse feedback from the respondents providing a broader perspective for future research and feature development. As a result, the digital PRSC feature can help individuals precisely and accurately identify pregnancy risks.

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

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

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.

Pelatihan Psychological First Aid (PFA) dan Stress Management untuk Mahasiswa Kedokteran Tahun Pertama FKK ITS 2024 Syulthoni, Zain Budi; Haykal, Muhammad Nazhif; Eljatin, Dwinka Syafira; Haque, Sayidah Aulia Ul; Rangkuti, Rahmah Yasinta; Fadhlina, Afia Nuzila; Indriastuti, Endah; Radiansyah, Riva Satya; Putri, Atina Irani Wira; Sari, Desiana Widityaning; Indriani, Ratri Dwi; Siswanto, Putri Alief; Mahdi, Faizal
Sewagati Vol 9 No 1 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i1.2413

Mahasiswa kedokteran tahun pertama menghadapi banyak tantangan dalam menye-suaikan diri dengan lingkungan akademik baru, yang dapat memengaruhi kesehatan mental mereka. Tujuan dari penelitian ini adalah untuk meningkatkan kemampuan siswa dalam mengelola stres dan memberikan dukungan psikologis melalui pelatihan manajemen stres dan dukungan psikologis pertama / Psychological First Aid (PFA). 30 mahasiswa kedokteran semester 1 FKK ITS mengikuti pelatihan, yang berlangsung selama dua hari dan total 16 jam. Pelatihan juga dievaluasi melalui pretest dan posttest menggunakan kuesioner. Melalui simulasi kasus, peserta menunjukkan kemampuan yang baik dalam menggunakan strategi PFA dan manajemen stres. Hasil analisis menunjukkan peningkatan signifikan dalam pemahaman dan keterampilan peserta setelah mengikuti pelatihan (p=0.000). Pelatihan mencakup pemaparan teori dan praktik keterampilan PFA, seperti keterampilan mendengarkan yang aktif, keterampilan komunikasi, keterampilan empati, dan keterampilan bertahan hidup. Dua modul program ber-ISBN dan publikasi di media massa. Kesimpulan dari pengabdian masyarakat adalah bahwa terjadi peningkatan pemahaman dan kemampuan mahasiswa kedokteran tahun pertama terhadap Psychological First Aid dan Stress Management yang diharapkan lebih siap menghadapi tantangan akademik.

Diabetic Foot Infection (Infeksi Kaki Diabetik): Diagnosis dan Tatalaksana Muhammad Bayu Zohari Hutagalung; Dwinka Syafira Eljatin; Awalita; Vivi Permana Sarie; Gaby Demitria Agustina Sianturi; Galenisa Falinda Santika
Cermin Dunia Kedokteran Vol 46 No 6 (2019): Endokrinologi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v46i6.434

Infeksi kaki diabetik merupakan salah satu komplikasi yang sering ditemukan pada penderita diabetes. Derajat infeksi kaki diabetik ditentukan berdasarkan evaluasi keadaan lokal kaki yang terinfeksi, luas struktur terinfeksi, serta adanya manifestasi sistemik. Tatalaksana meliputi pembedahan, pemberian antibiotik, perawatan luka, serta manajemen hiperglikemia. Diabetic foot infection is one of the most common complications in diabetic patients. The degree of infection was based on local condition, the spread, and systemic manifestation of the infection. Management includes surgical treatment, antibiotic, wound care, and hyperglycemia management.

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

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.

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