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Varicella is a contagious skin infection caused by the varicella zoster virus (VZV). Varicella, scabies, and pityriasis versicolor are diseases with the same risk factors, namely in adolescents with high physical activity and living in crowded places such as dormitories. Case: A 13-year-old male adolescent complained of fluid-filled bumps on the body and face accompanied by itching that had been felt since yesterday. Low fever that fluctuates accompanied by dizziness, nausea since 2 days ago. Due to the itching, the patient scratched the area of ​​the bumps so that the bumps burst. The patient is currently undergoing scabies treatment. The patient also complained of white spots all over the body that had appeared since 2 months ago and worsened when the patient sweated. The patient is a student living in an Islamic boarding school. The patient said he often changed clothes, sarongs, and caps with his dorm mates. The patient also has high physical activity such as playing ball and does not immediately change clothes if he sweats. The diagnosis of varicella, scabies, and pityriasis versicolor is based on anamnesis and physical examination. Treatment options in this case are administration of drugs in the form of Acyclovir 400mg 4 x 2, Paracetamol 500mg 3 x 1, Permethrin 5% applied to the entire body 1x for 8 hours, Fucilex cream 2 dd ue, and Ketoconazole 2% shampoo. Conclusion: in this case, a 13-year-old patient with varicella, scabies, and pityriasis versicolor with risk factors of adolescence with high physical activity and living in crowded places such as dormitories was found.

Lung adenocarcinoma is one of the most common types of lung cancer, especially in non-smoking women. This disease can develop progressively to cause complications such as pleural effusion and hydropneumothorax. Risk factors involved include exposure to secondhand smoke, air pollution, radon, and a history of previous tuberculosis. A 61-year-old woman came with complaints of right chest pain radiating to the back, shortness of breath, dry cough, weakness, and weight loss. The medical history showed that the patient had undergone chemotherapy for lung cancer and had a history of pulmonary tuberculosis that had been resolved. Supporting examinations such as CT-scan, bronchoscopy, and cytology showed a mass in the right lung inferior lobe and massive pleural effusion leading to a diagnosis of stage IVA right lung adenocarcinoma (T3N1M1A) complicated by hydropneumothorax. The patient underwent management in the form of chemotherapy, supportive therapy, and symptomatic care.

Physical mobility impairment refers to limited physical movements from one or more extremities independently, this condition can lead to significant difficulties in movement and the inability to perform daily activities. This study aims to apply acupressure therapy to address physical mobility impairment in Mrs. N, a patient diagnosed with ischemic stroke and left-sided hemiparesis at Mardi Rahayu Hospital, Kudus. The method used in this study is a case study involving nursing care and the implementation of interventions, including assessment, diagnosis, planning, implementation, and evaluation, as well as the application of acupressure therapy. The result of the case study acupressure therapy intervention for 7 days at a frequency of once daily for 15 minutes, it was effective in addressing the problem of physical mobility impairment in the patient with ischemic stroke and sinistra hemiparesis, as indicated by increased muscle strenght. The conclusion from this acupressure therapy is that before the intervention, it improved to    and after the intervention . It is expected that this acupressure therapy can be applied and integrated into the provision of medical-surgical nursing care for patients with ischemic stroke and sinistra hemiparesis.  

Tingkat keberhasilan deteksi penyakit jantung sangat bergantung pada akurasi model klasifikasi yang digunakan. Penelitian ini bertujuan membandingkan kinerja dua algoritma klasifikasi, yaitu K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM), dalam mendeteksi penyakit jantung menggunakan dataset berjumlah 1025 sampel dengan dua kelas target, yakni sehat dan penyakit jantung. Proses pra-pemrosesan data meliputi pembersihan dan normalisasi fitur medis seperti usia, tekanan darah, serta kadar kolesterol. Evaluasi performa model dilakukan menggunakan metode Confusion Matrix, K-Fold Cross Validation, kurva Receiver Operating Characteristic (ROC), dan kurva Precision-Recall untuk mengukur akurasi, presisi, recall, serta keseimbangan antara presisi dan recall. Hasil pengujian menunjukkan bahwa algoritma KNN unggul dalam menghasilkan akurasi tinggi yaitu 99% dengan AUC ROC sempurna 1.00 dan presisi yang hampir konsisten sepanjang recall, sementara SVM menunjukkan performa stabil dengan akurasi 91%, AUC ROC 0.97, dan AP Precision-Recall sebesar 0.96. Penelitian ini menegaskan efektivitas KNN dalam menghasilkan prediksi penyakit jantung yang sangat akurat dengan potensi risiko overfitting pada parameter k kecil, sedangkan SVM memberikan kestabilan model dengan kemampuan generalisasi yang lebih baik. Temuan ini diharapkan dapat menjadi referensi dalam pemilihan algoritma klasifikasi yang sesuai untuk mendukung diagnosis penyakit jantung secara klinis.

Abstrak Penelitian ini mengembangkan sistem deteksi aritmia pada lansia menggunakan sinyal elektrokardiogram (EKG) 5-lead dan algoritma Support Vector Machine (SVM). Data EKG yang diperoleh melalui perangkat Smart Holter direkam secara kontinu dan diproses melalui tahapan praproses, meliputi koreksi baseline, filtering dengan metode Butterworth, ekstraksi fitur, normalisasi, serta pelabelan manual oleh dokter spesialis jantung untuk validitas klinis. Model SVM kemudian dilatih dan diuji dengan hasil akurasi sebesar 95,80% pada data pelatihan dan 94,57% pada data pengujian. Evaluasi performa model menggunakan confusion matrix, nilai presisi, recall, dan kurva ROC menunjukkan kemampuan klasifikasi empat kategori aritmia secara akurat dan seimbang dengan nilai AUC antara 0,98 hingga 1,00. Hasil ini menunjukkan potensi sistem sebagai alat bantu diagnosis dini aritmia khususnya pada pasien lansia. Untuk penelitian selanjutnya, disarankan peningkatan variasi data, perbandingan dengan metode lain seperti CNN atau LSTM, peningkatan kualitas sinyal dan fitur, serta pengujian di lingkungan klinis guna mengoptimalkan penerapan sistem dalam praktik medis.    Kata Kunci: Elektrokardiogram (EKG), Aritmia, Support Vector Machine (SVM), Lansia

Inguinal hernia is a condition where a bulge occurs in the groin area and is the most common type of hernia with 75% of all abdominal wall hernias. The diagnosis of inguinal hernia can be made easily through physical examination, where the lump can be seen or felt when pressed on the external inguinal ring. In this case study, a 51-year-old man complained of a lump on the right underarm to the scrotum area that had been painful since 1 day before admission to the hospital. The lump had been felt since 2 years ago, but the lump seemed to disappear. The patient was diagnosed with incarcerated lateral inguinal hernia from the results of anamnesis, physical examination, supporting examination, and performed hernioraphy with mash graft.

Low Back Pain (LBP) is one of the most common musculoskeletal problems experienced by people in various age groups and work backgrounds. This complaint can be acute, subacute, or chronic, with various causes ranging from mechanical, degenerative, to psychosocial factors. This study aims to examine the characteristics of LBP through a literature review of several previous studies that focus on prevalence, risk factors, and their impact on patients' daily lives. The method used is a narrative literature review, with data sources coming from national and international journals, research reports, and academic repositories. The results of the study show that the prevalence of LBP is very high, with a significant percentage of occurrence in the elderly, pregnant women, administrative workers, and individuals with a sedentary lifestyle and non-ergonomic work posture. In addition to age, female gender, non-ideal body mass index, and long work duration, it was also found that psychosocial characteristics such as low social support and high work pressure play a role in triggering or worsening LBP. A study at Ibnu Sina Hospital Makassar showed that most LBP patients were elderly women with acute pain and radiological results showed lumbar spondylosis as the most common diagnosis. The conclusion of this study emphasizes the importance of prevention through ergonomic education, work stress management, and early intervention based on a healthy lifestyle. These findings are expected to be the basis for designing promotive and preventive strategies for high-risk groups.

The utilization of Artificial Intelligence (AI) in healthcare services and medical diagnosis in Indonesia has grown rapidly alongside the digital transformation of the health sector. AI technology has been employed to improve service efficiency, accelerate diagnostic processes, and enhance disease detection accuracy, particularly through medical imaging and ECG data analysis. Algorithms such as K-Nearest Neighbor (KNN) and Chi-Square have shown effectiveness in heart disease classification. However, despite its benefits, AI implementation presents legal challenges. The absence of specific regulations regarding legal liability in cases of AI-based diagnostic errors creates uncertainty for both medical professionals and patients. Additionally, the lack of national standards, weak patient data protection, and digital literacy gaps present significant obstacles. Adaptive policies, the establishment of dedicated regulations, and collaboration between government, medical practitioners, technology developers, and academics are essential to develop a legal framework that accommodates AI advancements responsibly. With clear legal certainty, AI technology can be optimally utilized to support more inclusive and high-quality healthcare services.

A 52-year-old female patient presented to the non-communicable disease clinic at the Banda Baro Community Health Center with complaints of pain in the right toe joint for the past two weeks. The pain was described as severe, especially when touched or moved. The patient reported that the toe initially became red and swollen, but the redness had subsided by the time she arrived at the clinic. The patient also complained of feeling weak and occasional tingling in the tips of her toes. She admitted to frequently consuming chicken liver and other offal. She mentioned having experienced a similar condition previously, and previous examinations indicated elevated uric acid levels. She had previously taken allopurinol two years ago, obtained from the health center, to alleviate her symptoms. On physical examination, the patient's blood pressure was 120/70 mmHg, heart rate 87 beats per minute, respiratory rate 19 breaths per minute, and temperature 36.5°C. The patient was prescribed allopurinol 2x100 mg. Primary data was obtained through anamnesis and physical examination by conducting home visits, filling out family folders, and completing patient records. Assessment was based on the initial, ongoing, and final holistic diagnosis of the visit.  Interventions included education on the importance of clean and healthy living habits, avoiding risk factors, medication adherence, and preventing complications that may arise due to hyperuricemia.

Blepharitis or inflammation of the eyelids is one of the common eye diseases, characterized by inflammation of the edges of the eyelids that can cause discomfort, irritation, and even visual disturbances. This disease can be chronic with recurrent symptoms such as red eyes, itching, watering, and the appearance of crust on the eyelashes. Proper and prompt diagnosis is necessary so that medical treatment can be carried out effectively and further complications can be prevented. This study aims to design and build an expert system based on the Fuzzy Logic method in helping diagnose blepharitis. The fuzzy method was chosen because it is able to handle the uncertainty of symptom data that often arises in the medical diagnosis process. This system is developed through the identification of the common symptoms of blepharitis, then processed using the fuzzy membership function to determine the type of disease based on the degree of symptom onset. The output of the system is in the form of the results of the diagnosis of blepharitis along with initial treatment recommendations that can be used as a reference for users. The results of the system test show that the application of fuzzy logic is able to provide diagnosis results that are quite accurate, fast, and easy to understand both medical personnel and the general public. This system is expected to help increase public awareness about the importance of early detection of blepharitis, as well as being a tool in the initial medical decision-making process. However, the limitations of this study lie in the limited amount of data and coverage of the type of blepharitis, so further development is needed, both in expanding the knowledge base, increasing the variety of symptoms, and improving system interaction with users.

Acute Otitis Media (AOM) is a common middle ear infection in children, particularly in rural areas. This study aims to explore the clinical characteristics of AOM in children treated at rural health centers (puskesmas) and to examine the barriers to effective management. A descriptive qualitative method was employed through a literature review analyzing symptoms, stages, risk factors, and treatment approaches. The findings reveal that AOM in children often begins with an upper respiratory tract infection and presents with sudden ear pain, high fever, sleep disturbances, and ear discharge. Risk factors include young age, poor environmental conditions, and limited access to healthcare services. Management of AOM depends on its clinical stage and requires accurate diagnosis. However, in rural puskesmas, a lack of diagnostic tools and limited public awareness often leads to delayed treatment. Therefore, it is essential to provide health workers with basic ENT training, ensure the availability of diagnostic equipment, and educate parents to enable early detection and prevent complications that could affect a child’s development.

The importance of data mining in Indonesia is increasing along with the growth of big data in various strategic sectors. Data mining plays an important role in transforming complex data into useful information to support data-driven decision making, which is urgently needed in the face of competitive challenges and operational complexity. This research aims to examine the development of data mining techniques and applications in Indonesia over the last decade (2015-2024). Through a systematic literature review approach, data was collected from academic publications in SCOPUS indexed databases. From the initial 95 papers found, a further selection was made based on accessibility, title, and abstract until 64 papers were included in the article review. The results show that techniques such as K-Means, Naive Bayes, and Decision Tree are most commonly used. In the business sector, clustering through K-Means is widely applied for market segmentation and consumer pattern analysis. The healthcare sector mainly utilizes classification techniques, such as Naive Bayes and Decision Tree, for disease risk prediction and early diagnosis. Meanwhile, the education sector uses data mining to assess student performance and predict potential dropouts, assisting institutions in optimizing learning strategies.

Acute bronchitis, a common respiratory infection, is a significant medical problem, especially among adults, characterized by the sudden onset of a persistent cough, with or without sputum production. Symptoms are self-limiting, usually mild, and resolve spontaneously within 1 to 3 weeks. Current epidemiological data in Indonesia for bronchitis are not recorded with certainty. Still, the incidence of acute respiratory infections with symptoms similar to acute bronchitis is recorded as the highest in Papua at 10.5%, and in Jakarta itself only recorded at 2.7%. This case study discusses a 20-year-old man with complaints of cough with phlegm for 1 week before coming to the health center with thin, white sputum that worsens with activity and exposure to air pollution. Complaints are accompanied by fever and headache. The patient is a light smoker and lives with the patient's brother, who has similar complaints and is also an active smoker in a house with minimal ventilation in a polluted environment due to street dust. Physical examination of the thorax found coarse rhonchi in both lung fields, which led to the diagnosis of acute bronchitis. Based on this case, it was found that the importance of complete and comprehensive treatment of acute bronchitis in men is expected to be a guide to promote treatment and prevention in cases of acute bronchitis.

Hypertension is a common disease in society and can lead to serious complications if not properly managed. This study aims to design a web-based expert system to assist in diagnosing hypertension using the Certainty Factor and Dempster Shafer methods. The Certainty Factor method is used to measure the confidence level of reported symptoms, while the Dempster Shafer method combines information from various sources to generate more accurate decisions. The system is designed with a user-friendly interface to facilitate access for medical personnel and patients. The research results show that the Certainty Factor method achieved an accuracy 97,9%, while the Dempster Shafer method reache 96,4%. The accuracy difference of 1,4% indicates that the Certainty Factor method is more effective in handling the uncertainty of hypertension symptoms than the Dempster Shafer method.

Background: Research of glucose detection is important in diabetes management and biosensor development. Taking into account the large amount of water retention, the biocompatibility of hydrogels makes them ideal candidates for glucose detection. The PBA groups, when grafted to hydrogel matrices, improve the glucose-related response of the hydrogels significantly. Objective: To create a novel PBA (phenylboronic acid) holographic glucose sensor, MAAm-co-4VPBA, for uninterrupted monitoring of blood glucose levels, which demonstrates the first successful glucose measurement in whole blood using PBA sensors. Methods: Methylacrylamide (MAAm) was copolymerized with 4-vinyl phenyl boronic acid (4-VPBA) by means of free radical polymerization employing 1,6-hexanedioldiacrylate (HDODA) as a crosslinker and the photoinitiator 1-hydroxycyclohexyl phenyl ketone. The responsive glucose behavior of the resulting polymer was characterized in terms of swelling dynamics, ex vivo flow tests, and error grid analysis mechanisms. Results: The MAAm-co-4VPBA polymer exhibits reversible glucose binding via PBA-diol interactions that allow complexation at different pH and concentration levels. Modulation of the responsive elements of the microcapsules by hydrophobic PBA and hydrophilic MAAm units yields maximum swelling and shrinking dynamics at 37 trail degrees. The sensor successfully detected glucose in opaque biological fluids, blood plasma, without any interference from antibiotics or other therapeutics or endogenous compounds. Ex vivo tests showed real-time glucose monitoring without hysteresis. Most importantly, this work is the first to report the use of PBA-based sensors in whole blood for measuring glucose. Conclusion: The MAAm-co-4VPBA holographic sensor possesses outstanding features such as accurate records and strong resistance to chemicals and slow response to detection, confirming its effectiveness in continuous glucose monitoring. Moreover, the ability to operate in the real world enables the aid in clinical diagnosis of diabetes.

Colon In Loop examination is an important radiographic procedure in the evaluation of Hirschsprung cases in pediatric patients. At Brebes Regional Hospital, there are differences in procedures compared to existing literature, especially in the method of administering contrast media and the projections used. This study aims to review and examine the Colon In Loop examination procedure in pediatric patients with Hirschsprung cases at Brebes Regional Hospital. This study uses a qualitative descriptive approach with a case study method. Data were collected through observation, interviews, and documentation involving three radiographers and one radiologist as research subjects. Data analysis techniques were carried out through data reduction, data presentation, and drawing conclusions. Results Colon In Loop examination in pediatric patients at Brebes Regional Hospital was performed without any special preparation for the patient. Contrast media was used with a stepwise method, namely 50cc in the first stage and added 50cc in the second stage. The projections used included AP (Antero Posterior) and AP oblique to overcome visualization limitations. The Colon In Loop examination procedure at Brebes Regional Hospital differs from the literature regarding the method of contrast media insertion and additional projections. This provides better visualization for Hirschsprung's diagnosis in pediatric patients.

Phlegmon is a serious infection affecting the maxillofacial area. Cases of phlegmon cause more than 50% mortality before the development of antibiotics, and 90% are due to acute infection of molar teeth. Examination of cervical vertebrae can help the diagnosis by using AP Axial, Lateral, right posterior oblique (RPO)/left posterior oblique (LPO) projections of 15º-20º cephalad, and right anterior oblique (RAO)/left anterior oblique (LAO) 15º-20º caudad while in the Radiology Installation of Yogyakarta City Hospital, using AP Axial projection 15º cephalad, Lateral and oblique (RPO and LPO) with perpendicular beam direction without other modalities such as MRI and CT Scan. This study aims to further examine radiographic examination procedures and projections of RPO and LPO with perpendicular ray directions. This type of research is qualitative with a case study approach at the Radiology Installation of Yogyakarta City Hospital from September 2024 to May 2025. Data were collected by observation, documentation and interviews with 3 radiographers and 1 radiology specialist. Data analysis was done by data reduction, data presentation and conclusion drawing. The results showed 3-position photographs to evaluate the overall condition of the cervical vertebrae and surrounding tissues. RPO and LPO projections are not angled according to the applicable SOP and have no significant effect, because the main focus of phlegmon is soft tissue evaluation and is not related to the evaluation of nerve abnormalities in the intervertebral foramen. However, for phlegmon cases with serious severity, other modalities such as MRI, CT scan or ultrasound should be added in order to detect soft tissue and the cause of phlegmon for further action.

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).

An expert system is a computer program designed to imitate the decision-making ability of an expert in a particular field, combining knowledge and rules obtained from experts to diagnose problems, provide advice, or make complex decisions, especially in dental diseases, so this requires the design of an expert system application for website-based dental disease diagnosis with a UML model for users who support the diagnostic process using the certainty factor method, in terms of the explanation of the certainty factor which has a concept based on symptoms and diagnoses from the weight of an expert's value and the user's value, then calculated with the formula in CF, the final result is the creation of a website-based dental disease diagnosis expert system.

Pediculosis capitis is an infestation of head lice Pediculus humanus capitis that live on the scalp and suck blood as a source of nutrition. Transmission occurs through direct contact or sharing personal items, especially in environments with poor sanitation. Risk factors include age, population density, personal hygiene, and socioeconomic status. Symptoms include severe itching due to a reaction to the saliva of the lice, which can cause excoriation, irritation, and secondary infections such as pyoderma. In this report, a 17-year-old girl complained of scabs, pain, heat, and itching on the head and neck. Lice and lice eggs were found, as well as ecthyma lesions in the form of pustules, yellow crusts, and excoriations in the occipital area and back of the neck. The therapy given was a combination of drug and non-drug treatment. The evaluation results showed improvement in symptoms. This case emphasizes the importance of early diagnosis and comprehensive management to prevent reinfection.