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All Journal Dinamik Jurnal Teknologi Pangan dan Hasil Pertanian Science Technology and Management Journal (STMJ)
Novianingrum, Milka Putri
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering

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POTENTIAL IRON AND VITAMIN C FROM MORINGA LEAVES AS A FOOD PRODUCT TO OVERCOME ANEMIA: SYSTEMATIC REVIEW Hapsari, Martina Widhi; Parameswari, Genoveva Visi; Novianingrum, Milka Putri
Science Technology and Management Journal Vol. 5 No. 1 (2025): Januari 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i1.343

Abstract

Anemia is a significant global health problem, especially in developing countries, with a high prevalence among adolescents and pregnant women. This study explores the potential of Moringa (Moringa oleifera) leaves as a nutrient source rich in iron and vitamin C to address anemia. Moringa leaves contain iron up to 7 mg per 100 g and vitamin C as much as 1,89 mg/g, which plays an important role in increasing hemoglobin levels and absorption of non-heme iron. The method used was a literature study with information analysis from various relevant literature sources. The results show that consumption of moringa leaves in the form of food products, such as biscuits and flour, can significantly increase hemoglobin levels, especially in anemic adolescent girls and pregnant women. The interaction between iron and vitamin C in Moringa leaves supports hemoglobin formation and overall health. Therefore, the integration of moringa into the daily diet can be an effective strategy in preventing and overcoming anemia and improving public health.
Sifat Fisikokimia Produk Bakso Tempe dengan Substitusi Ikan Kembung (Rastrelliger sp.) untuk Strategi Pencegahan Stunting Novianingrum, Milka Putri; Dewi, Lusiawati
Jurnal Teknologi Pangan dan Hasil Pertanian Vol. 20 No. 2 (2025): September
Publisher : Faculty of Agricultural Technology, Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jtphp.v20i2.12768

Abstract

This study aims to analyze the physicochemical properties of tempe meatball products with the substitution of mackerel (Rastrelliger sp.) as a stunting prevention strategy. Tempe meatballs were substituted with four different levels of mackerel concentration (0 g, 17,2 g, 25,8 g, and 34,4 g) using a one-factor Complete Random Design (RAL) with three replicates. The analysis includes physical (hardness, cohesiveness, gumminess) and chemical (moisture content, ash, fat, protein, carbohydrates, dietary fiber, and free fatty acids). The results showed that the increase in mackerel substitution significantly increased protein content by 23.13% at the best treatment (34.4 g substitution) and improved the physical quality of meatballs. All relevant test parameters have complied with SNI 7266:2017 standards. Thus, mackerel substitution tempe meatballs have the potential to become a highly nutritious functional food to support stunting prevention programs while increasing the added value of local food products.
SuperBoost-AllerScan: Deteksi Alergen pada Produk Pangan - Pendekatan Data Mining dan Machine Learning Narulita, Siska; Sekarlangit, Sekarlangit; Novianingrum, Milka Putri
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10325

Abstract

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.
Co-Authors Hapsari, Martina Widhi Lusiawati Dewi Parameswari, Genoveva Visi Sekarlangit, Sekarlangit Siska Narulita