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All Journal ARTERI : Jurnal Ilmu Kesehatan International Journal of Health and Information System (IJHIS) Jurnal Teknokes
Olivia, Zora
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health

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Detecting Dehydration Based on Urine Color Using Fuzzy Logic Image Processing and Regulating Water Intake with an Automatic Water Pump According to Dehydration Level Using an IoT-Based Utomo, Denny Trias; Utomo, Adi Heru; Olivia, Zora; Maria, Nita; Rosidania, Nilla Putri
International Journal of Health and Information System Vol. 1 No. 3 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijhis.v1i3.32

Abstract

Dehydration is a condition where the body lacks the fluids it needs to carry out its functions optimally. Dehydration can cause various health problems, including decreased mental and physical performance, and can even cause death if not treated immediately. Therefore, it is important to be able to detect and treat dehydration early. One way to detect dehydration is through urine color analysis. Urine that is darker than normal can be a sign of dehydration. The classification of dehydration level according to urine color is as follows: 1-2: Hydrated, 3-4: Mildly dehydrated, 5-6: Dehydrated, 7-8: Very dehydrated. This research aims to develop an IoT-based dehydration detection system that can detect the level of dehydration in a person based on urine color and regulate water intake automatically using a water pump.  The novelty of this research is the method of integrating drinking water intake with dehydration detection based on real-time urine color based on IoT using the Fuzzy Logic method. The results of this research are used by the Jember State Polytechnic TeFa Nutrition Care Center (NCC) in serving patients. The methodology used in this research is Fuzzy Logic image processing to process urine color data and determine a person's level of dehydration. After carrying out this research, the following conclusions were obtained: Based on the literature study in this research, 8 levels of hydration status according to NSW Health were obtained, then from this literature a method was obtained to measure a person's hydration based on urine color using image processing using the Fuzzy Logic method.
Uji Keamanan Cemaran pada MP-ASI Berbahan Kedelai Kulit Buah Naga Olivia, Zora; Rosiana, Nita Maria; Suryana, Arinda Lironika; Widiyawati, Agatha
ARTERI : Jurnal Ilmu Kesehatan Vol 6 No 3 (1): Mei
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/arteri.v6i3.634

Abstract

Complementary feeding is nutritious food in addition to breast milk given to infants during the complementary feeding period to achieve nutritional adequacy. Complementary feeding products to be marketed should be guaranteed to be safe from metal and microbial contamination. SNI 01-7111.1-2005 explains the minimum limits for contamination of arsenic, lead, tin, mercury and free from Escherichia coli, Salmonella sp, Staphylococcus aureus microbes. This study aims to determine safety by comparing the metal contamination and microbial contamination figures between the P1, P2 and P3 complementary feeding formulas. P1 and P2 are complementary feeding made from soybean powder and dragon fruit skin. P1 consists of 25% soybean powder, 45% powdered milk, 10% dragon fruit skin powder, 20% sugar. P2 consists of 35% soybean powder, 35% powdered milk, 10% dragon fruit skin powder, 20% sugar. While P3 is a commercial complementary feeding. The methods used for metal contamination detection are ICP-MS, coliform and Escherichia coli microbial contamination with the Most Probable Number (Apm) technique and APL with the pour plate technique, detection of salmonella spp. according to ISO 6579-1: 2017, Positive Coagulation Staphylococci. The results include arsenic metal contamination in P2 and P3 while lead in P2 with levels below the maximum SNI limit. Microbial contamination ALT, Escherichia coli, Salmonella sp, Staphylococcus aureus. P1, P2, P3 according to SNI standards while coliform only in P2 meets SNI standards. So it can be concluded that the MPASI formula from soybean powder and dragon fruit skin P2 is the safest to consume because it has metal and microbial contamination results according to SNI standards.
NutriTalk: Nutrition Intervention by Experts to Reduce the Impact of Stunting Through Mobile Based Applications Using Agile Method Kurniasari, Arvita Agus; Olivia, Zora; Suryana, Arinda Lironika; Widiyawati, Agatha; Maria Rosiana, Nita
Jurnal Teknokes Vol. 16 No. 3 (2023): September
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The prevalence of childhood stunting, a pervasive global health concern primarily attributed to persistent malnutrition, underscores an urgent need for intervention. In Indonesia, where stunting rates are alarmingly high, with approximately 27.6% of children under five affected, innovative solutions are imperative. This study introduces "Nutri Talk," a mobile application developed using Agile Methodology to revolutionize nutritional consulting services. The application facilitates seamless communication with nutrition specialists, offering evidence-based information and personalized consultations to empower parents in making informed dietary decisions for their children. The application demonstrates robust functionality and user satisfaction through rigorous testing, including Boundary Value Analysis (BVA) and User Acceptance Testing (UAT). "Nutri Talk" stands poised to mitigate the long-term impacts of stunting, leveraging technology to enhance nutritional outcomes. This research advocates for a comprehensive approach to combat stunting, combining mobile technology advancements with targeted interventions, ultimately contributing to improved childhood nutrition and development.
Co-Authors Agatha Widiyawati, Agatha Denny Trias Utomo, Denny Trias Kurniasari, Arvita Agus Maria Rosiana, Nita Maria, Nita Rosiana, Nita Maria Rosidania, Nilla Putri Suryana, Arinda Lironika Utomo, Adi Heru