Publication Search

School snacks are one of the food sources commonly consumed by elementary school children; however, they are at risk of microbiological contamination if not properly managed. One of the pathogenic bacteria that can contaminate food is Salmonella, which can cause diarrheal disease. This study aimed to analyze hygiene and sanitation factors of food handlers on Salmonella bacterial contamination in pentol snacks at elementary schools in Palang District, Tuban Regency. This study used an observational analytic design with a cross-sectional approach. The sample consisted of 20 pentol vendors and 20 pentol snack samples. Data were collected through observation using a hygiene and sanitation checklist and laboratory examination to detect the presence of Salmonella bacteria. Data analysis was initially planned using the Chi-square test; however, due to homogeneous results, the analysis was conducted descriptively. The results showed that most vendors had good hygiene and sanitation (85%) and sufficient (15%). Laboratory examination results showed that all pentol snack samples (100%) were negative for Salmonella bacteria. In conclusion, pentol snacks at elementary schools in Palang District were microbiologically safe from Salmonella contamination.

The prevalence of non-communicable degenerative diseases in Indonesia continues to rise, particularly affecting adolescents and productive age populations due to unhealthy lifestyle patterns and free radical exposure. Purple sweet potato leaves (Ipomoea batatas L.), a locally abundant resource often regarded merely as agricultural by-product, contain significant bioactive compounds including polyphenols, flavonoids, anthocyanins, and terpenoids demonstrating potent antioxidant properties with IC50 values of 47.99 ppm. This study employs a qualitative descriptive literature review approach, analyzing 28 scientific publications spanning 2018-2025 sourced from Google Scholar and PubMed databases to examine the potential of purple sweet potato leaves as effervescent tablet raw material for degenerative disease prevention. Findings reveal that bioactive constituents effectively neutralize free radicals, prevent lipid peroxidation, and protect cellular structures from oxidative damage linked to diabetes, cancer, and cardiovascular disorders. The effervescent tablet formulation provides strategic advantages including enhanced solubility, accelerated absorption rates due to elimination of disintegration processes, pleasant carbonation sensation masking unpleasant active ingredient taste, and superior gastrointestinal tolerance. This innovation offers promising prospects for degenerative disease prevention through local food resource utilization, though experimental validation regarding optimal formulation, effective dosage determination, and clinical efficacy assessment remains necessary for practical implementation.

Fruit rot disease is one of the main obstacles in the cultivation of guava (Psidium guajava) because it directly reduces the quality and selling value of the harvest. This research aims to identify and characterize the fungus that causes fruit rot disease in guava. The research was carried out by observing disease symptoms in the field, isolating the pathogen from infected fruit, as well as macroscopic and microscopic characterization of the fungus using Potato Dextrose Agar (PDA) media. The results of the research show that the initial symptoms are blackish brown spots on the surface of the fruit which develop into sunken lesions and spread to cause the fruit to dry out. The fungal isolate has white colonies with a flower-like pattern and forms black aservuli. Microscopic observation shows that the conidia are fusiform, insulated, with darker pigmented middle cells and have a transparent appendix. Based on these morphological characters, the fungus that causes fruit rot disease in guava is thought to belong to the genus Pestalotiopsis sp. Further research is needed through molecular approaches, such as PCR or DNA sequencing, as well as pathogenicity tests to strengthen and confirm the role of this fungus as the main cause of fruit rot disease.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

Indonesia, as a country with a high level of disaster vulnerability, faces an increased risk of communicable diseases during the emergency response phase due to disrupted sanitation, limited access to clean water, overcrowded evacuation shelters, and weakened health services. This study aims to analyze the strategic role of health workers in health education and the prevention of communicable diseases during disaster emergency response. The method used is a literature review of relevant national and international scientific articles. The findings indicate that health workers have a multidimensional role, not only as providers of curative services but also as educators, change agents, advocates, and collaborators in promotive and preventive efforts. Effective health education, participatory risk communication, strengthened epidemiological surveillance, and the implementation of Infection Prevention and Control (IPC) are key strategies to reduce the risk of communicable disease outbreaks. However, implementation in the field still faces challenges, including limited resources, cross-sectoral coordination constraints, heavy workloads, and suboptimal disaster-related policy systems and standard operating procedures (SOPs). Therefore, strengthening the capacity of health workers through training, policy support, and community-based approaches is necessary to enhance health system resilience in responding to disaster crises.

The development of bioinformatics has led to the availability of large amounts of genetic data through public databases such as NCBI Gene, OMIM, and Ensembl. However, the complexity of data presentation and the dominance of English language hinder students, novice researchers, and the general public in understanding the relationship between genes and disease. This research aims to develop a simple web-based information system to identify disease-causing genes with concise, Indonesian-language, and user-friendly information presentation. The method used is Research and Development (R&D), which includes literature study, needs analysis, system design, implementation, testing, and evaluation. The system was developed using a MySQL relational database with a web interface that displays basic gene information, chromosome location, biological function, and gene-disease relationships, complete with simple visualizations. Black Box testing results indicate that all main functions run according to user requirements. This system is expected to improve bioinformatics literacy and become an effective learning medium.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Chronic kidney disease (CKD) is a growing global health concern that frequently remains undiagnosed until advanced stages. Early detection through simple laboratory screening is essential to prevent disease progression and associated cardiometabolic complications. This community service program aimed to assess kidney function using serum creatinine and estimated glomerular filtration rate (eGFR), while increasing public awareness regarding CKD prevention. A total of 59 participants were included, with a mean age of 39.15 ± 15.39 years (range 16–75 years), predominantly female (74.58%). The mean serum creatinine level was 1.0 ± 0.19 mg/dL, and the mean eGFR was 91.08 ± 20.53 mL/min/1.73 m². Most participants demonstrated normal kidney function (28.8%) or mild decline (21.6%). A progressive reduction in eGFR with increasing age was observed, reflecting the physiological decline in nephron mass and renal perfusion. The program also provided education on kidney-protective practices, including optimal blood pressure control, diabetes management, adequate hydration, and avoidance of nephrotoxic agents. This intervention improved participants’ understanding of CKD risk factors and the importance of regular screening. In conclusion, serum creatinine and eGFR evaluation offer simple, accurate, and practical tools for early CKD detection, supporting promotive–preventive strategies to slow disease progression and enhance quality of life in at-risk populations.

Stunting remains one of the major chronic nutritional problems in Indonesia, including in Oesapa Village, Kelapa Lima District, Kupang City, East Nusa Tenggara Province. This condition is not only caused by inadequate nutritional intake but is also closely related to chronic infections resulting from poor hygiene and sanitation, particularly intestinal parasitic diseases. To reduce the risk of parasitic infections and improve children’s nutritional status, preventive efforts through education and health promotion are essential.This community service activity aimed to increase public awareness and knowledge, especially among parents and caregivers of stunted children, regarding the importance of maintaining hygiene and sanitation to prevent intestinal parasitic diseases. The methods used included participatory approaches such as interactive lectures, group discussions, and practical demonstrations of clean and healthy living behavior (PHBS), food hygiene, clean water management, and household waste disposal. Evaluation was conducted using pre-test and post-test instruments to measure changes in participants’ knowledge.The activities, carried out from May to September 2025 in Oesapa Village, showed a significant improvement in community understanding of hygiene and sanitation practices. Participants demonstrated greater commitment to maintaining environmental cleanliness and adopting healthier daily behaviors. Continuous educational efforts and multisectoral support are needed to help reduce the prevalence of stunting in the region

Non-communicable diseases (NCDs) are the leading causes of morbidity and mortality, particularly among older adults. Integrated Development Posts for Non-Communicable Diseases (Pos Pembinaan Terpadu Penyakit Tidak Menular / Posbindu PTM) play a crucial role in the early detection of NCD risk factors at the community level. This activity aimed to conduct NCD risk factor screening through the measurement of blood pressure, random blood glucose, total cholesterol, and uric acid levels, as well as to provide health consultations for participants of Posbindu Rosella, South Kembangan. The activity was conducted in November 2025 using a descriptive design. A total of 30 Posbindu participants were involved. Health examinations included blood pressure, random blood glucose, total cholesterol, and uric acid measurements. Data were analyzed descriptively and classified into normal and abnormal categories based on clinical guidelines. The majority of participants were female (80%), with a mean age of 60.5 ± 9.7 years. The mean systolic blood pressure was within the hypertensive range. Approximately 50% of participants had impaired glucose regulation, 60% experienced hyperuricemia, and 40% had total cholesterol levels classified as borderline or higher.  Health screening activities at Posbindu revealed a high prevalence of NCD risk factors among participants. Posbindu plays an essential role in early detection and community-based prevention of non-communicable diseases.

Gastric disease is a common health problem that can disrupt daily activities if not properly treated. To aid the initial diagnosis process, this study developed a web-based expert system capable of diagnosing various types of gastric disease based on the symptoms experienced by the user. This system uses the Dempster-Shafer method to address uncertainty in decision-making by combining a number of pieces of evidence in the form of symptoms to determine the level of confidence in a disease. The system was developed using the PHP programming language and a MySQL database and designed for easy browser access. Testing demonstrated that the system was able to provide fairly accurate diagnostic results that closely approximated the results of consultations with medical professionals. This system is expected to become an initial solution for the public in quickly and independently recognizing symptoms of gastric disease.

Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia which can lead to serious complications if not properly managed. This community service activity aimed to increase public knowledge regarding diabetes mellitus and its prevention among residents of Rejomulyo Village, Jati Agung District, South Lampung. The method used was health education through lectures supported by leaflets and blood glucose screening, followed by evaluation using pre-test and post-test. A total of 25 community members participated in this activity. The results showed a significant improvement in participants’ knowledge after the educational intervention. Pre-test results indicated limited understanding of blood glucose control and dietary management, while post-test results showed increased knowledge levels in both aspects. This activity demonstrates that health education is effective in improving community awareness and knowledge regarding diabetes mellitus prevention and management. This educational program is effective in increasing the knowledge of the people of Rejomulyo Village, Jati Agung District, South Lampung Regency regarding diabetes mellitus.

Depression is one of the mental health problems that often arise in patients with chronic diseases, such as diabetes mellitus, hypertension, and chronic kidney failure. This condition not only impacts the patient's quality of life, but also affects the treatment process and the burden on the family. However, mental health literacy in the community is still low, so targeted preventive and curative efforts are needed. The Community Service Program (PKM) aims to improve people's mental health through educational interventions with scientific booklet media that are easy to understand and apply. The activity method is carried out in a participatory manner, including the preparation stage, socialization, educational implementation, and evaluation. Education is provided to the community with a focus on preventing depression through a healthy lifestyle, stress management skills, and early intervention efforts by seeking professional support. Evaluation is carried out through pre-post tests, observations, and group discussions. The results of the activity showed an increase in public knowledge about depression, a change in attitudes to be more open in expressing feelings, as well as practical skills in relaxation, maintaining sleep patterns, and building social support. Participants also better understand the importance of seeking professional help when depressive symptoms are getting worse. The conclusion of this PKM is that the scientific booklet has proven to be effective as a preventive and curative educational intervention medium, and can be an innovative strategy in improving the mental health of people affected by chronic diseases.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

Currently, patients experiencing early symptoms of skin disease caused by the exanthema virus are unable to immediately consult a dermatologist due to the high cost and limited time available for specialists in hospitals. Therefore, the author needs to develop an expert system application that can address this issue. Through this application, users can consult with the system, much like an expert, to diagnose their symptoms and find solutions to their problems. This expert system is designed to provide answers based on whether the symptoms are correct or not, or to provide several recommended answer choices based on the symptoms. To diagnose skin disease caused by the exanthema virus, the author used the Case-Based Reasoning method. The CBR method is a weighting technique that compares new cases with previous cases. The diagnosis is based on data provided by the patient and expert, which is then analyzed using case-based reasoning and stored as a knowledge database in the expert system. Therefore, this expert system can help identify solutions for problems experienced by patients suffering from skin disease caused by the Exanthema Virus.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

The purpose of this activity is to socialize the implementation of biosecurity as an effort to prevent disease in cattle in Luhulely Village, Pulau Letti District, Southwest Maluku Regency. This activity was carried out using a socialization method involving beef cattle farmers and all Luhulely Village staff. The implementation of the activity was divided into 2 sessions, namely a material presentation session and an interactive discussion session. Some general steps in implementing biosecurity include the following: access management, animal movement control, cleanliness and sanitation, waste management, animal health monitoring, and vaccination. This socialization activity was attended by approximately 20 participants consisting of beef cattle farmers and several Luhulely Village staff. Several important things that can be concluded from the implementation of this activity are as follows: the majority of farmers showed an increased understanding of the importance of biosecurity after participating in the socialization, the farmers expressed their readiness to implement various biosecurity measures, such as quarantine of new livestock, waste management, and provision of adequate sanitation facilities in the livestock area, and several farmers requested further assistance regarding the implementation of more detailed biosecurity, including correct livestock vaccination techniques. The conclusion of this activity is that the socialization of biosecurity implementation on cattle farms is expected to increase farmers' awareness and understanding of maintaining livestock health and preventing disease transmission. Through proper biosecurity implementation, it is hoped that healthy, more productive, and sustainable livestock conditions will be created.

The livestock sector in Gowa Regency, South Sulawesi, has great potential but is often threatened by outbreaks of Foot and Mouth Disease (FMD), which cause significant economic losses. This study aims to evaluate the FMD control policies implemented by the Gowa Regency Government based on William Dunn's six policy evaluation criteria: effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The study used a qualitative approach with primary data collected through in-depth interviews with key informants from the Livestock and Animal Health Division of the Gowa Regency Livestock and Plantation Service and supporting data from various literature. The findings show that the FMD control policy is effective in achieving zero cases, but efficiency is hampered by limited medical personnel (only two veterinarians) and operational budget dependence entirely on the Central Government. Vaccine adequacy is a pull sistem (demand-driven) and compensation distribution is carried out fairly. Community responsiveness increased after severe losses, and policy accuracy was considered most optimal in the implementation of biosecurity and sanitation while promoting vaccination. As a recommendation, the Gowa Government needs to recruit contract workers in the field of animal health, allocate funds for the procurement of operational vehicles and equipment to support laboratory facilities, and issue a Regent Regulation that strictly regulates the implementation of biosecurity and livestock housing patterns in high-risk areas.