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All Journal Journal of Electrical Engineering and Computer (JEECOM) International Journal of Health and Information System (IJHIS) Akiratech
Rosidania, Nilla Putri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Medicine & Pharmacology 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.
Sistem Pendukung Keputusan Pemilihan Jenis Makanan Penderita Stunting Menggunakan Metode Simple Additive Weighting Utomo, Denny Trias; Istiqomah; Rosidania, Nilla Putri
Akiratech Vol. 1 No. 2 (2024)
Publisher : CV. Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63935/akiratech.v1i2.38

Abstract

Stunting adalah masalah kesehatan yang serius di seluruh dunia, terutama di negara negara berkembang. Salah satu faktor yang dapat mempengaruhi stunting adalah pola makan yang tidak sehat dan tidak memadai. Oleh karena itu, diperlukan sistem pendukung keputusan (SPK) yang dapat membantu pemilihan jenis makanan yang tepat bagi penderita stunting guna meningkatkan asupan gizi mereka. Penelitian ini bertujuan untuk mengembangkan sebuah SPK yang menggunakan metode Simple Additive Weighting (SAW) dalam memilih jenis makanan bagi penderita stunting. Metode SAW digunakan untuk memberikan bobot pada setiap kriteria yang relevan dalam pemilihan jenis makanan, seperti kandungan nutrisi, ketersediaan, dan biaya. Pertama, data kriteria yang relevan dikumpulkan melalui studi literatur dan wawancara dengan ahli gizi. Kemudian, bobot relatif untuk setiap kriteria ditentukan melalui analisis pairwise comparison. Setelah itu, data tentang jenis makanan yang tersedia dan data penderita stunting dikumpulkan untuk digunakan dalam SPK. SPK akan memproses data yang ada dan memberikan rekomendasi jenis makanan yang paling sesuai bagi penderita stunting. Rekomendasi tersebut didasarkan pada perhitungan nilai preferensi menggunakan metode SAW. Jenis makanan dengan nilai preferensi tertinggi akan dianggap sebagai rekomendasi terbaik bagi penderita stunting. Diharapkan bahwa SPK ini dapat menjadi alat yang berguna bagi ahli gizi dalam menentukan jenis makanan yang sesuai. Dengan memperbaiki pola makan mereka, diharapkan penderita stunting dapat meningkatkan asupan gizi dan mengatasi masalah stunting secara efektif. Penelitian ini dapat menjadi dasar untuk pengembangan lebih lanjut tentang SPK dalam bidang pemilihan makanan untuk kondisi kesehatan tertentu.
Design and Construction of Maternal and Infant Mortality Rate Mapping Using the K-Means Clustering Method Based on Geographic Information Systems (Case Study in Jember Regency) Rosidania, Nilla Putri; Utomo, Denny Trias
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.13911

Abstract

Indonesia’s population continues to grow each year, including in Jember Regency, which reached 2,584,771 people in 2023. Population density contributes to various health issues, such as the high maternal mortality rate (MMR) and infant mortality rate (IMR), with 17 maternal deaths and 81 infant deaths recorded in 2023. The primary causes of MMR include pregnancy at too young or old an age, short birth spacing, and delays in referral, while IMR is mainly caused by asphyxia and low birth weight (LBW) due to premature birth. The government has implemented a midwife and traditional birth attendant partnership program to address this issue. However, information regarding high-risk areas remains inadequately conveyed. Therefore, this study develops a Geographic Information System (GIS)-based system using the K-Means Clustering method with a predefined number of clusters to classify high-risk maternal and infant mortality areas. The results show that the K-Means Clustering method with a fixed number of clusters (k = 5) successfully groups Jember Regency into five risk-level clusters, namely very high, high, medium, low, and very low. Visualization through GIS facilitates effective access to spatial information and supports the identification of priority areas for targeted health interventions, aiming to reduce maternal and infant mortality rates more effectively.
Co-Authors Denny Trias Utomo, Denny Trias Istiqomah Maria, Nita Olivia, Zora Utomo, Adi Heru