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All Journal Dinamik ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Teknologi Pangan dan Hasil Pertanian Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Science Technology and Management Journal (STMJ) Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Putri Novianingrum, Milka
Universitas Nasional Karangturi Semarang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Library & Information Science

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Blue Swimming Crab’s Conservation Area Determination in The North of Java Sea Using Reproductive Indicator Putri Novianingrum, Milka; Hartati, Retno; Pribadi, Rudhi; Käll, Sofia; Redjeki, Sri
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 28, No 4 (2023): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.28.4.321-333

Abstract

Despite being operated on a small scale, Blue Swimming Crab (BSC), Portunus pelagicus fishery substantially contributes to Indonesia's fisheries as the country's third-largest export commodity after tuna and shrimp. The high of BSC’s demand led to pressure on its stock. Hence a conservation area is needed to be set up, in this study, was proposed using reproduction indicators. with the case study of BSC Stock in Keboromo Waters, Pati Regency, North Central Java. The samples were collected from 38 sampling points at a distance of 2-12 miles from the coastline during November-December 2022 using collapsible crab traps.  A reproduction observation on female crabs was carried out on their carapace width and gonad maturity stage. The data then were analyzed for the percentage of egg-berried females (EBF), size at first maturity (Lm) and first captured (Lc), and their spawning potential Ratio (SPR).  This study found that the mature crabs (GMS2) in November and December were higher than in other stages while the proportion of ovigerous females (EBF) in December was higher than in November. At several sampling points, the size at first captures (Lc) was higher than that at first maturity (Lm) indicating a decrease in resource stocks due to a delay in the recruitment process. SPR of 19% showed that reproductive potential should be maintained before recruitment is limited, therefore based on the existence of EBF in particular sampling points it is recommended three conservation areas as a temporary no-take zone in BSC fishing ground in Keboromo Watres, Pati Regency.
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.
OPTIMASI MODEL DETEKSI ALERGEN PADA PRODUK PANGAN DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN ADAPTIVE BOOSTING (ADABOOST) Siska Narulita; Sekarlangit; Milka Putri Novianingrum
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.21316

Abstract

One important aspect that needs to be considered in food production is food safety. The implementation of this food safety aspect includes food products that avoid contamination of chemical, physical, and biological substances that can be harmful to human health. In the implementation of the Makan Bergizi Gratis (MBG) program, problems were found related to allergies in the recipients of this assistance program. According to the World Health Organization (WHO), food allergies are ranked as the fourth most serious public health problem, and the only effective treatment for allergy sufferers is to avoid foods that contain allergens. Allergens themselves are compounds or food ingredients that cause allergies and/or intolerances. Laboratory tests of food products for allergen testing that are still carried out traditionally require a lot of time and money, making food producers reluctant to carry out product testing. A way to detect allergen content in food products that is easier, more practical, and more accurate is needed. The research conducted aims to build a prediction model that can be used to detect allergen content in food ingredients through the implementation of the Support Vector Machine (SVM) data mining algorithm optimized with the Adaptive Boosting ensemble learning boosting algorithm (AdaBoost). The research conducted obtained a model that produces the most optimal performance, namely SVM optimized with the AdaBoost algorithm with the split validation method.
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.
Deteksi Alergen pada Produk Pangan Menggunakan Algoritma Support Vector Machines (SVM) Siska Narulita; Sekarlangit Sekarlangit; Milka Putri Novianingrum
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 1 (2025): Februari: Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v3i1.393

Abstract

Food allergies are medical conditions caused by particular immunological reactions brought on by exposure to certain foods. All age groups can experience food allergies, albeit the prevalence varies between children and adults, with children experiencing this condition more frequently than adults. Find food ingredients or substances that can trigger allergies, often known as allergens. This project attempts to determine whether or not the food includes allergies by applying the SVM data mining method to a public dataset of food goods and allergens that was acquired via Kaggle. High accuracy, effective memory use, and the ability to handle non-normally distributed data are some of the benefits of the SVM method. Data collection is the first step in the research process. Data pre-processing, which includes data transformation, handling missing values, and copy objects, comes next. Validation comes next. Split validation with 90% training data and 10% testing data, 10-fold cross validation, and split validation with an 80%–20% ratio were all compared in this study. The SVM method is applied after the dataset has passed validation, and the confusion matrix is used for the last evaluation step. SVM has an accuracy rate of 97.24% when using 10-fold cross validation, according to the accuracy value produced by the validation process comparison. Split validation yields an accuracy value of 97.50% when the ratio of training data to testing data is 90% to 10%. In contrast, an accuracy rate of 98.75% was achieved by using split validation with a ratio of 80% and 20%.
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.
Analisis Kandungan Gula Minuman Kemasan Menggunakan Pendekatan Refraktometri Sebagai Dasar Evaluasi Mutu Pangan Hapsari, Martina Widhi; Anggraeni, Novia; Novianingrum , Milka Putri; Murti, Paulus Damar Bayu
Science Technology and Management Journal Vol. 5 No. 2 (2025): Agustus 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i2.487

Abstract

Konsumsi minuman kemasan berpemanis semakin meningkat dan berkontribusi signifikan terhadap asupan gula harian masyarakat. Evaluasi kandungan gula pada produk minuman kemasan umumnya dilakukan melalui analisis laboratorium yang relatif kompleks dan memerlukan biaya tinggi, sehingga diperlukan pendekatan alternatif yang lebih sederhana dan cepat. Penelitian ini bertujuan untuk menganalisis kandungan gula minuman kemasan menggunakan pendekatan pengukuran total padatan terlarut (°Brix) sebagai dasar evaluasi mutu pangan. Penelitian dilakukan dengan desain deskriptif-analitik terhadap beberapa kategori minuman kemasan, meliputi teh kemasan, minuman berperisa, minuman isotonik, minuman buah, dan minuman susu. Pengukuran nilai °Brix dilakukan menggunakan refraktometer genggam dengan tiga kali ulangan, sedangkan data kandungan gula diperoleh dari label informasi gizi produk dan dikonversi ke satuan gram per 100 mL. Hasil penelitian menunjukkan bahwa nilai °Brix minuman kemasan berada pada kisaran 6,2–14,8°Brix, dengan minuman berperisa memiliki nilai tertinggi dan minuman isotonik terendah. Analisis hubungan antara nilai °Brix dan kandungan gula pada label menunjukkan adanya kecenderungan hubungan positif, meskipun pada beberapa kategori minuman ditemukan deviasi akibat kontribusi komponen terlarut lain selain gula. Hasil ini menunjukkan bahwa pengukuran total padatan terlarut menggunakan refraktometer berpotensi digunakan sebagai metode awal yang cepat dan sederhana untuk mengevaluasi kandungan gula minuman kemasan. Penelitian ini menyediakan data dasar yang dapat dimanfaatkan sebagai landasan pengembangan metode uji cepat dan riset lanjutan di bidang mutu pangan.
Co-Authors Anggraeni, Novia Hapsari, Martina Widhi Käll, Sofia Lusiawati Dewi Parameswari, Genoveva Visi Paulus Damar Bayu Murti Retno Hartati Rudhi Pribadi Sekarlangit Sekarlangit Sekarlangit Sekarlangit, Sekarlangit Siska Narulita Sri Redjeki