cover
Contact Name
Purwono
Contact Email
purwono@ptti.web.id
Phone
+6282113940427
Journal Mail Official
jahir@ptti.web.id
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Journal of Advanced Health Informatics Research
ISSN : -     EISSN : 29856124     DOI : https://doi.org/10.59247/jahir.v1i1
Journal of Advanced Health Informatics Research (JAHIR) is a scientific journal that focuses on the application of computer science to the health field. JAHIR is a peer-reviewed open-access journal that is published three times a year (April, August and December). The scientific journal is published by Peneliti Teknologi Teknik Indonesia (PTTI). The JAHIR aims to provide a national and international forum for academics, researchers, and professionals to share their ideas on all topics related to Informatics in Healthcare Research
Articles 6 Documents
Search results for , issue "Vol. 1 No. 2 (2023)" : 6 Documents clear
Managing Metabolic Acidosis in Chronic Renal Diseases Suandika, Made; Woung-Ru Tang; Dewi, Pramesti
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.29

Abstract

One of the most common side effects of chronic kidney disease is metabolic acidosis (CKD). It is associated with the development of CKD and various other functional disorders. Metabolic acidosis can be a common complication associated with progressive loss of kidney function. The form can be a metabolic acidosis with a non-anion gap or metabolic acidosis with a high or mixed anion gap. Reduced kidney ability to maintain acid-base homeostasis results in acid accumulation, causing various complications such as decreased nutritional status such as wasting muscle-hypoalbuminemia, inflammation, uremic bone disease and its association with increased mortality. In addition to the side effects associated with acid retention, metabolic acidosis can also cause kidney damage, possibly through stimulation of adaptive mechanisms aimed at maintaining acid-base homeostasis in the event of decreased renal function. chronic kidney disease (CKD), and therefore offers an effective, safe and affordable reno-protective strategy. This paper will discuss the physiology and pathophysiology of acid-base homeostasis in CKD, namely the mechanism of metabolic acidosis capable of impairing kidney function, and its relation to the benefits of alkaline therapy. based on clinical trials
Quality of Life, Social Support, and Physical Activity of Overweight Adolescents Suwarsi; Maria Elizabeth CoronelĀ Baua
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.32

Abstract

Adolescent health and well-being must be at the center of efforts to achieve the Sustainable Development Goals (SDGs) adopted by the World Health Organization in 2030. A solution is needed by first looking at the quality of life of overweight adolescents, their social support, and their physical activity behavior to suggest health interventions for overweight adolescents. This study contributed to test there is a significant difference in the level of involvement in participants' social support and physical activity with the quality of life. The method uses a quantitative, cross-sectional design. The sample is 80 overweight adolescents (13-19 years). The data was measured through a questionnaire tested for validity with a value of 0.9. The sample was selected using the purposive sampling method according to the inclusion criteria and data analysis using frequency and percentage. The result of this study is the majority of overweight adolescents have a low quality of life, good social support, and low physical activity
Correlation Between Knowledge of Health Information About Picky Eating, Supplementary Feeding, Management Ability of Appetite Herbs Muflih, Muflih; Widaryanti, Rahayu; Indrawati, Fika Lilik; Trisagita, Natasya Gabryella
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.34

Abstract

Picky eaters continue to pose challenges in providing additional food, hindering efforts to address child stunting. Thus, understanding the causes of picky eating in relation to maternal knowledge of health information about picky eaters, supplementary feeding management, and appetite herbs is crucial. This study aims to explore the correlation between knowledge of health information about picky eater, supplementary feeding, and the ability to manage food and herbal appetite in mothers of stunted children in Srimartani Village, Piyungan, Yogyakarta. Employing a cross-sectional survey design, the study involved 27 mothers (aged 23-42) of stunted children in October 2022. A research instrument comprising 21 questions was developed based on the concept of picky eating, adapted to the research context. Data analysis employed Kendall's tau_b statistical test to assess correlations. Stunting data were obtained from documented diagnoses at the local community health center, including malnourished toddlers and undernourished children. Findings revealed no correlation between knowledge of health information about picky eater and supplementary feeding, as well as between supplementary feeding and the ability to manage supplementary feeding and appetite herbs. However, a significant correlation was observed knowledge of health information about between picky eater and the ability to manage additional food and appetite herbs. In conclusion, higher picky eater knowledge was associated with better ability to manage additional food and appetite herbs, while knowledge of supplementary feeding did not show such a correlation. Further research with a more representative sample is recommended to provide a comprehensive understanding of the picky eater phenomenon
Effectiveness of Cream Preparations Combination of Bay Leaf Extract (Syzygium polyanthum Wight) and Papaya Leaf (Carica Papaya L) as Anti-Inflammation Samodra, Galih; Kusuma, Ikhwan Yudha; Melani, Reina
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.37

Abstract

Bay leaves (Sygyzium polyanthum Wight) and papaya leaves (Carica papaya L) are plants that have anti-inflammatory activity due to their flavonoid compounds. The purpose of this study was to determine the formulation of bay leaf and papaya leaf extracts into cream preparations. The treatment group consisted of 4 groups. The first group is the positive control (voltarene), the second group is the negative control (1% carrageenan). The third group was the group that was given the combination cream treatment of bay leaf and papaya leaf extract formula 2 and the fourth group was the group that was given the cream treatment combination of bay leaf and papaya leaf extract formula 3. The anti-inflammatory test was carried out based on observations for six hours by looking at edema volume and edema percentage. LSD test results showed that the cream formula 2 treatment (anionic) and the cream formula 3 treatment (nonionic) showed a significant difference with the negative control with a significance value of 0.000 (p <0.05). This shows that formula 2 cream treatment (anionic) and formula 3 cream treatment (nonionic) can potentially reduce edema volume and can inhibit edema on the soles of rats induced with carrageenin
Understanding User Sentiment: Analysis of SATUSEHAT Application Reviews on Google Play Store Ardianto, Rian; Marhoon , Hamzah M.
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.44

Abstract

The Covid-19 epidemic has caused substantial changes in Indonesia, causing the government to create SATUSEHAT Mobile, formerly known as PEDULILINDUNGI, which will be formally renamed on March 1, 2023. This software tracks the spread of Covid-19, offers vaccination information, and distinguishes distinct zones. Users can share their location data for travel purposes, allowing Covid-19 patients to be contacted. Sentiment analysis is used to assess SATUSEHAT users' perspectives based on Play Store reviews, with an emphasis on positive and negative comments. Textual data connected to certain items or entities is analyzed and modified using Data Mining techniques. The SATUSEHAT application was tested in this study. The program classified 43 positive and 1662 negative comments correctly, yielding 1705 successful classifications out of 1861 comments. The data from the confusion matrix allowed for the calculation of accuracy, precision, and recall, achieving 92% accuracy, 93% average precision, and 98% average recall. According to the research findings, the Naive Bayes algorithm with TF-IDF Vectorizer is the best at producing positive and negative labels with 92% accuracy, even for unbalanced data. In comparison to other algorithms, Naive Bayes with TF-IDF Vectorizer exhibited good accuracy, indicating a promising topic for further study.
Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice Purwono, Purwono; Agung Budi Prasetio; Burhanuddin bin Mohd Aboobaider
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.87

Abstract

Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended

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