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Malaria remains a major public health challenge in eastern Indonesia, particularly in the remote highland areas Papua, where access to health services is very limited. In early 2008, a suspected malaria outbreak with several deaths was reported in Agisiga District, a remote highland area in Papua. This study aimed to investigate the epidemiological characteristics, diagnostic results, and mortality patterns of the malaria outbreak as a basis for formulating future control strategies. The outbreak investigation was conducted using a cross-sectional design in Agisiga District from January to March 2008. Capillary blood samples were examined using Rapid Diagnostic Tests (RDTs) and thin blood smear microscopy. Malaria-related deaths were traced through verbal autopsy methods and interviews with families and community leaders. Descriptive analysis was performed to describe the demographic, clinical, and parasitological characteristics the cases. A total of 92 suspected malaria cases were identified, with the highest proportion coming from Tomosiga Village (52.2%), followed by Bigasiga Village (40.2%) and Agisiga Village (7.6%). Women accounted for 58.7% of all cases, and the most affected age group was 15–35 years (34.8%). Microscopic examination confirmed malaria infection in 55.0% of samples examined, with Plasmodium vivax predominating (45.0%), while the RDT positivity rate was much lower. The malaria outbreak in Agisiga District was characterized by high mortality, delayed diagnosis, and significant gaps in diagnostic capacity, particularly in remote villages. Strengthening the surveillance system, increasing diagnostic capacity, and improving access to timely treatment are crucial steps prevent similar outbreaks and reduce malaria mortality in the Papuan highlands.

Latar Belakang: Malaria menjadi target dunia untuk mencapai status eliminasi terutama bagi negara yang memiliki kasus tinggi. Pemerintah meluncurkan berbagai program dan peta jalan untuk mencapai target eliminasi malaria pada tahun 2030. Papua merupakan salah satu provinsi yang mempunyai endemis malaria tinggi dan menuju program eliminasi yang dilakukan oleh pemerintah. Agar eliminasi malaria tercapai harus dilakukan peningakatan pengetahuan pada Masyarakat melalui edukasi Kesehatan Tujuan Meningkatkan pengetahuan anak panti asuhan tentang penyakit malaria. Metode: Metode yang digunakan adalah Pendidikan Kesehatan tentang penyakit malaria yang disampaikan dengan teknik tanya jawab, diskusi serta memanfaatkan komik malaria Hasil: Hasil menunjukkan Tingkat pengetahuan peserta tentang malaria sebelum diberikan penyuluhan yaitu pengetahuan kurang berjumlah 20 orang (45,5%), pengetahuan cukup berjumlah 20 orang  (45,5%) dan pengetahuan baik berjumlah 4 orang (9%). Sedangkan pengetahuan peserta setelah diberikan intervensi edukasi malaria melalui komik malaria menjadi pengetahuan kurang berjumlah 5 orang (11,4%), pengetahuan cukup berjumlah 10 orang (22,7%) dan pengetahuan baik berjumlah 29 orang (65,9). Kesimpulan: Sebagian besar anak panti asuhan berpengetahuan baik setelah diberikan edukasi tentang penyakit malaria melalui media komik Malaria di Panti Asuhan Komba Sentani Jayapura.

Millions of people suffer from malaria, one of the most serious parasitic diseases that threatens human life and causes high rates of morbidity and mortality, particularly in tropical and subtropical regions. Traditional diagnostic methods, such as blood smear examination, which can be performed using a microscope, face many challenges due to the inaccuracy of manual analysis and the reliance on individual skills. Therefore, the use of machine learning or deep learning algorithms to automate malaria detection offers promising solutions to improve accuracy, reduce diagnosis time, and enhance scalability. In this paper, a multi-class convolutional neural network (CNN)-based model is designed to classify cells infected with Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) and uninfected cells from blood smears, as most severe cases and deaths are caused by P. falciparum and P. vivax. This is achieved by building and training a CNN from scratch, rather than using transfer learning from pre-trained models. The proposed network was trained and tested on the Kaggle dataset, which consists of 27,558 images of infected and uninfected individuals. These images were divided into 13,779 images of uninfected individuals, 6,890 images of individuals with P. falciparum malaria, and 6,889 images of individuals with P. vivax malaria. The images were preprocessed using several operations, including blurring, denoising, and morphological processing. The proposed model achieved the best evaluation accuracy when compared with other deep learning algorithms, with an accuracy rate of 96.5%, a sensitivity rate of 95%, a specificity rate of 97.6%, and an F1-score rate of 96.5%. These results demonstrate the effectiveness of the proposed model as a tool to assist clinicians in malaria diagnosis, reducing reliance on manual analysis.

Milkwood bark (Alstonia scholaris (L.) R. Br.) is one of the medicinal plants of the genus Alstonia. This plant is traditionally used by the community as a medicine for diarrhea, diabetes, malaria, hemorrhoids, and antihypertensives. This plant contains secondary metabolites that have the potential as antibacterial such as alkaloids, flavonoids, saponins and tannins. The purpose of this study was to determine the inhibition of antibacterial activity and the most effective concentration in inhibiting Staphylococcus aureus and Escherichia coli. The sample used in this study was milkwood bark, the sample was extracted by maceration method using 96% ethanol as solvent. The antibacterial testing method used in this study was disc diffusion. The study used a completely randomized design with 9 treatments, namely 1000 ppm, 750 ppm, 500 ppm, 250 ppm, 100 ppm, 50 ppm, 10 ppm, Ciprofloxacin (positive control), sterile distilled water (negative control). The results of the antibacterial activity test at a concentration of 500 ppm showed that milkwood bark extract could inhibit the growth of S. aureus by 8.22 mm and at a concentration of 250 ppm showed that milkwood bark extract could inhibit the growth of E. coli by 8.75 mm. From the test results, it can be concluded that milkwood bark (A. scholaris (L.) R. Br.) has antibacterial activity that has moderate potential in inhibiting S. aureus and E. coli.

Dengue fever, malaria, and other mosquito-borne diseases remain significant health problems and challenges in various regions, especially in rural areas. Dengue fever (DBD) is caused by the dengue virus types 1-4, which are transmitted through the bites of Aedes aegypti and Aedes albopictus mosquitoes. The majority of the residents of Karya Mukti Village work as rubber tappers. They usually start their activities early in the morning or even before dawn. However, mosquitoes are most active in the morning and late afternoon, making the local community highly vulnerable to mosquito bites. To address this issue, the people of Karya Mukti Village still widely use mosquito repellents available on the market, which contain harmful chemicals that pose risks to both health and the environment if used over the long term. Therefore, this community service initiative aims to conduct outreach and hands-on training on the use of citronella (Cymbopogon nardus) as a natural mosquito repellent alternative. This program is carried out using a socialization and product-making approach through participatory engagement, where Dasawisma (women's community groups) are encouraged to identify their needs and challenges, as well as appropriate solutions to address them. By involving Dasawisma members in the learning and production process of natural mosquito repellents, it is hoped that they will gain better knowledge and skills in making natural mosquito repellents.

Malaria remains a global health issue, particularly in tropical and subtropical regions. In Indonesia, Papua Province specifically Nabire Regency reports a high incidence of malaria, although a decline has been observed since 2010. Infants and pregnant women are the most vulnerable groups. Nutritional status, influenced by dietary patterns, environment, and access to healthcare, plays a crucial role in susceptibility to malaria. Malnourished children are more prone to severe malaria infections due to their underdeveloped immune systems. This research focuses on Kimi Health Center in Nabire Regency and aims to explore the relationship between the nutritional status of children under five and malaria incidence in 2024. The goal of this study is to determine the relationship between the nutritional status of children under five and malaria incidence at Kimi Health Center, Teluk Umar District, Nabire Regency, in 2024. The research design employs an analytical descriptive approach with a cross-sectional method to analyze the relationship between nutritional status and malaria incidence. Data was collected at one point in time and analyzed using univariate and bivariate analysis with the Kendall tau-b test through SPSS software. The results show that nutritional status significantly influences susceptibility to malaria. Children with poor nutrition are at a higher risk of experiencing severe malaria due to immune system dysfunction. Malnutrition, such as Protein-Energy Malnutrition (PEM), worsens parasitemia and anemia. Nutrient deficiencies, including iron, vitamin A, and zinc, also weaken the immune system. Therefore, improving nutritional status is a crucial effort in malaria control within endemic areas.

Health is a condition where the body system and organs in the body can carry out their respective functions. However, in some cases that occur in Indonesian society, health problems have begun to appear, such as diabetes, malaria, and so on. According to the analysis, the incidence of diabetes continues to increase every year. Furthermore, the plant extract that is used as a source of antioxidants is bamboo shoots. The incidence of malaria resistance has reached >25% so that several drugs such as chloroquine are no longer used as the main drug in cases of malaria. The purpose of writing is to analyze the results of a review of the pharmacological potential of plant extracts that are effective as antidiabetic, antioxidant, and antimalarial supplements. The writing method used in writing this journal is to use a qualitative type. The results of the study are that bamboo leaf extract provides effectiveness as an antioxidant. Furthermore, antidiabetic can be found in the extract of the butterfly pea flower. Then the extract of the moon flower leaf has effectiveness as an antimalarial. The conclusion drawn is that the use of natural plant extracts can be used as antidiabetic, antioxidant, and antimalariall.

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.

In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.

. Introduction: Malaria is a disease caused by parasites (protozoa) of the genus Plasmodium which are transmitted by the bite of a female Anopheles mosquito. People with malaria typically experience flu-like initial symptoms, high fever, chills, and headache. This disease can attack all age groups. Symptoms of malaria will appear after 10 days to 4 weeks in the form of fever, headache, vomiting and chills. Prevention by vector control and preventing mosquito bites to humans needs to be done to break the chain of transmission. Purpose: in this study to determine the relationship between the level of family knowledge and efforts to prevent malaria in the Maranatha Unit, Nolloth Village, East Saparua District. Method: Using a Cross Sectional design. and a total sample of 65 respondents using an instrument in the form of a questionnaire. The analysis used was the chi-squaer test. Results: there is a relationship between the level of family knowledge and efforts to prevent malaria in the Marantha Unit in Zeroth Village (p-value = 0.000). Conclusion: there is a relationship between the level of family knowledge and efforts to prevent malaria in the Marantha Unit in Zeroth Village (p-value = 0.000).

Malaria is still a public health problem in Indonesia because of its high morbidity and mortality rates, especially in areas outside Java and Bali. In transmigration areas where there is a mixture of people from endemic and non-endemic areas of malaria, cases or epidemics still frequently occur which cause many deaths. Data from the Tebo District Health Office for January-July 2014 were 519 people out of a population of 328,564 people (0.16%) with an AMI of 1.6. The highest incidence of malaria was in the Mengupeh Health Center, Tengah Ilir District, with 75 people out of a population of 11,820 people (0.63%) with an AMI of 6.34. The purpose of this study was to find out the description and relationship between malaria prevention efforts in the Working Area of ​​the Mengupeh Health Center, Tengah Ilir District, Tebo Regency in 2015. This research is a quantitative study with a cross sectional research design. In this study, the sample was the community in the Working Area of ​​the Mengupeh Health Center, Tengah Ilir District, Tebo Regency, with a sample size of 96 people. The results showed that of the 96 respondents, 57 (59.4%) respondents had poor motivation, 59 (61.5%) respondents had a bad environment, respondents had poor malaria prevention efforts. The results of the analysis showed that there was a relationship between motivation and a p value of 0.007 (p <0.05), environment with a p value of 0.009 (p <0.05) on malaria prevention efforts in the Working Area of ​​the Mengupeh Health Center, Tengah Ilir District, Tebo Regency. Advises the public to be active in seeking information about prevention of malaria in order to reduce the incidence of malaria. At the same time actively maintaining the cleanliness of the home environment and their respective settlements.