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Diana Eka Pratiwi
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SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Published by Universitas Negeri Makassar
ISSN : 20866755     EISSN : 25795686     DOI : https://doi.org/10.35580/sainsmat
Core Subject :
The objective of this journal is to publish original, fully peer-reviewed articles on a variety of topics and research methods in sciences, mathematics, statistics, education, and applied science. The journal welcomes articles that address common issues in mathematics, sciences, statistics, education, applied science, and cross-curricular dimensions more widely.
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Articles 282 Documents
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Geographically Weighted Negative Binomial Regression For Modeling Overdispersion And Spatial Heterogeneity In Malnutrition Among Children Under Five In East Java Wanda Yudi; Nurul Aulya Bakri; Siti Choirotun Aisyah Putri; Aswi Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ny32ty40

Abstract

Malnutrition among toddlers remains a major public health challenge in East Java Province and is shaped by diverse socio-economic and health factors that vary across regions. These disparities create spatial patterns that cannot be fully captured by global regression models. This study aims to analyse the spatial distribution of toddler malnutrition using Geographically Weighted Negative Binomial Regression (GWNBR), which accommodates local variations in the relationships between predictors and the outcome. The study uses secondary data from the East Java Provincial Health Office and the Central Statistics Agency for 2023, covering 38 districts/cities. Exploratory results indicate overdispersion, supporting the use of the Negative Binomial model, while the Breusch–Pagan test confirms spatial heterogeneity. The GWNBR findings show that the number of infants with low birth weight, exclusive breastfeeding, the availability of community health centres, deliveries in health facilities, complete basic immunisation, and the proportion of poor households significantly affect the number of malnourished toddlers, with varying directions and magnitudes across districts/cities. Spatial mapping identifies three significance groups, indicating differences in dominant contributing factors between regions. Overall, the study concludes that GWNBR provides more accurate and spatially adaptive results and can serve as a basis for more targeted malnutrition-control policies.
Mixed Geographically Weighted Regression Modeling Of Childhood Pneumonia Cases In West Java Province  Abdul M. Achmad; Aswi Aswi; Siti Choirotun Aisyah Putri; Nurul Aulya Bakri; Wanda Yudi; Rahmawati
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/j60rfb72

Abstract

Pneumonia is one of the leading causes of morbidity and mortality among children under five in Indonesia, including in West Java Province, which records the highest number of childhood pneumonia cases nationally. The spatial variation in pneumonia incidence indicates substantial heterogeneity across regions that cannot be adequately captured by global regression models. This study aims to analyze the factors influencing the number of pneumonia cases among children under five in West Java in 2024 using the Mixed Geographically Weighted Regression (MGWR) approach. Data were obtained from the West Java Provincial Health Office and Statistics Indonesia (BPS), comprising the number of pneumonia cases and predictor variables including exclusive breastfeeding coverage, poverty rate, percentage of low birth weight (LBW) infants, PHBS (clean and healthy living behavior), complete basic immunization coverage, and adequate housing. The global linear regression results show that not all variables have significant effects, and the model fails to adequately capture data variation, with indications of heteroskedasticity—thereby necessitating a spatial modeling approach. While the GWR model identifies local variation, model comparison demonstrates that the MGWR model performs best, yielding a lower AIC value (474.46) and a higher coefficient of determination (69.83%) than both GWR and the global model. MGWR identifies LBW and immunization coverage as global variables with consistent effects across all areas, whereas exclusive breastfeeding, PHBS, poverty, and adequate housing exhibit spatially varying influences. Cluster analysis using MGWR identifies five regional groups, each reflecting distinct determinants of childhood pneumonia in West Java. These findings highlight that public health interventions aimed at reducing childhood pneumonia must incorporate local spatial conditions, and MGWR provides a more appropriate analytical approach than global regression models.
THE EFFECT OF MAGICSCHOOL. AI (AI RESOURCE BOT) AS A LEARNING MEDIA IN THE DISCOVERY LEARNING MODEL ON STUDENTS’ MOTIVATION AND LEARNING OUTCOMES IN GRADE XI OF SMAN 5 GOWA (A Study on Acid–Base Topics) Muharram; Halimah Husain; Fandi Ahmad; Andi Rafidi Sumar
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/wt1x6v26

Abstract

This study aims to examine the effect of MagicSchool.AI (AI Resource Bot) as a learning medium within the Discovery Learning model on students’ motivation and learning outcomes in the acid–base topic among Grade XI students at SMAN 5 Gowa. The subjects consisted of Grade XI students enrolled in the 2024/2025 academic year. Employing a quasi-experimental design, the study investigated the relationship between variables involving both control and experimental groups through a Nonequivalent Control Group Design. Data were analyzed using descriptive and inferential statistical techniques. The analysis of students’ learning motivation in the experimental group revealed a significant increase after the implementation of MagicSchool.AI. Prior to the intervention, the average motivation score was 79.70 (high category), which increased to 93.50 (very high category) after the learning activities. Meanwhile, the control group’s motivation score rose from 78.15 (high category) to 89.98 (very high category). The Independent Samples T-Test for learning motivation yielded a p-value of 0.013 (< 0.05), indicating that H0 was rejected and Ha was accepted. The normality test (One-Sample Kolmogorov–Smirnov) showed an Asymp. Sig value of 0.193 (> 0.05), confirming normal data distribution. Additionally, the Independent-Samples T-Test for learning outcomes yielded a significance value of 0.011 (< 0.05). Overall, the findings indicate that the use of MagicSchool.AI significantly enhances both students’ motivation and learning outcomes.
Mathematical Model Analysis And Optimal Control The Spread Of Smoker By Considering Media Campaign Effect Ananda Noersena; Fatmawati; Cicik Alfiniyah
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/4m9qjq60

Abstract

Smoking remains a critical global health challenge, characterized by the persistent use of traditional tobacco and the rising prevalence of electronic cigarettes. This study introduces a novel mathematical model that captures smoking behavior dynamics by explicitly integrating two distinct smoker populations: traditional tobacco smokers and electronic cigarette users. The model incorporates optimal control strategies focused on prevention through public awareness campaigns and cessation via medical intervention. In the absence of control measures, the model identifies two basic reproduction numbers for tobacco and electronic cigarette smoking, denoted as and , respectively. The smoker-free equilibrium point locally asymptotically stable when . Based on the phase portrait, the characteristic of tobacco-free equilibrium point is unstable, while electronic-free and coexistence equilibrium point asymptotically stable. Numerical simulations demonstrate that the implementation of control strategies significantly reduces smoking prevalence, with the dual-strategy approach achieving the most substantial reduction in the smoking population.
Modelling Of Coronary Heart Disease Risk Using Penalized Spline Semiparametric Logistic Regression Based On Hypertension History And Fatty Food Consumption Naufal Ramadhan Al Akhwal Siregar; Nur Chamidah; Marisa Rifada
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ja9m9g46

Abstract

Coronary heart disease (CHD) remains a critical global health challenge, necessitating precise statistical modeling to unravel its complex risk factors. This study applies a binary semiparametric logistic regression model with a penalized spline estimator (SLR-PS) to investigate these determinants effectively. The model achieved a classification accuracy of 70.45% and a promising sensitivity of 77.8%. In clinical settings, this sensitivity is paramount as it ensures the accurate identification of true positive cases, minimizing the risk of undiagnosed severe conditions. The findings unveil a significant nonlinear effect of fatty food intake on CHD risk, emphasizing the critical role of dietary control. Parametrically, individuals with a history of hypertension are found to be 4.641 times more likely to experience CHD compared to their counterparts, while each incremental unit of fatty food intake is associated with a 1.8% increase in CHD odds. These results highlight the urgency of managing hypertension and reducing dietary fat to mitigate cardiovascular risks, directly contributing to the advancement of Sustainable Development Goal (SDG) 3: Good Health and Well-Being. Future research is recommended to expand this framework by incorporating physical activity, genetic predisposition, and stress to further enhance predictive accuracy and support evidence-based preventive strategies.
Impact Analysis Of Data Imbalance Handling Using Edited Nearest Neighbors In The Classification Of Abo₃ Perovskite Oxide Bandgap Materials
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xh3va614

Abstract

The classification of bandgap types in perovskite ABO₃ materials presents a significant challenge due to overlapping feature distributions and class imbalance between direct and indirect bandgap classes. This study investigates the impact of data cleaning using the Edited Nearest Neighbour (ENN) method on the performance of multiple machine learning classifiers, including Multilayer Perceptron (MLP), Gradient Boosting, CatBoost, and Extra Trees. Model evaluation focuses on accuracy and F1 macro to capture both overall performance and class wise balance. Under baseline conditions, the best performing model achieved a test accuracy of 0.8934 with an F1 macro of 0.7783, indicating strong predictive capability but limited sensitivity to the minority class. After applying ENN exclusively to the training data, the class distribution became more balanced, resulting in improved recall for the minority class across several models. However, this improvement was accompanied by a reduction in overall accuracy. The highest F1 macro under ENN was 0.7632, achieved by the Gradient Boosting model, demonstrating enhanced class balance despite a lower accuracy of 0.8646. Cross validation results confirm that ENN does not significantly increase performance stability but effectively refines decision boundaries by removing ambiguous majority class samples. These findings highlight the trade off between accuracy and class fairness and emphasize that model selection should be guided by the evaluation objective. For imbalanced bandgap classification tasks, F1 macro oriented evaluation combined with ensemble based models provides a more reliable and equitable predictive framework.
The Effect Of Papain And Bromelain Enzymes On The Quality Of Gelatine From The Scales Of The Tawes Fish (Barbonymus Gonionotus Mutiara Annisa; Hasri Hasri; Diana Eka Pratiwi; Satria Putra Jaya Negara
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/z4gs0809

Abstract

This study aims to determine the effect of papain and bromelain enzymes on the yield and quality conformity of gelatin from tawes fish scales (Barbonymus gonionotus) with the Indonesian National Standard (SNI). This research is an experimental study consisting of several stages, including gelatin production, yield analysis at various concentrations of papain and bromelain enzymes (1%; 1.5%; 2%), testing of moisture content, ash content, fat content, protein content, pH, melting point, viscosity, and functional group analysis of gelatin. The results showed that the highest yield was obtained at a concentration of 2% for papain and bromelain enzymes, with yields of 17.6% and 41.3%, respectively. The highest yield of gelatin extracted using papain enzyme had a moisture content of 7%; ash content of 2%; fat content of 0.08%; protein content of 46.76%; pH 5.97; melting point 30°C; and viscosity 3.83 cP. The highest yield of gelatin extracted using bromelain enzyme had a moisture content of 11.5%; ash content of 1%; fat content of 0.19%; protein content of 53.42%; pH 6.61; melting point 27°C; and viscosity 3.05 cP. The functional group analysis of gelatin using papain enzyme showed the presence of specific functional groups for gelatin, namely Amide A, Amide I, II, and III groups, while in gelatin extracted using bromelain enzyme, Amide I and Amide III groups were not found. It is concluded that the highest yield was obtained at a concentration of 2%, with the resulting gelatin quality meeting the Indonesian National Standard (SNI). However, the protein content did not meet the Indonesian National Standard (SNI).
THE EFFECT OF GIMKIT MEDIA IN DISCOVERY LEARNING MODEL ON THE CONCEPT UNDERSTANDING Alfian Mustafah; Muh. Yunus; Sumiati Side
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 01 (2025): Volume 14 Nomor 1 (Maret 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/bw5js046

Abstract

This study aims to analyze the effect of Gimkit learning media in discovery learningmodel on the conceptual understanding of class XI students of SMA Negeri 9Makassar on material of solubility and solubility product in 2023/2024 using quasiexperimental and posttest only control group design. Population includes all class XIstudents with simple random sampling. XI Kimia 1 (24 students) as experimentalgroup, while XI Kimia 3 (35 students) as control group. Independent variables useGimkit in discovery learning and discovery learning model without Gimkit, whiledependent variable is conceptual understanding as measured through cognitivelearning outcomes. Instrument uses a learning outcome test. The results of descriptiveanalysis that average learning outcomes of experimental group are 82.87 higher thancontrol group 78.7. Inferential analysis using Mann-Whitney Test shows Zcount> Ztable(2.00> 1.64), meaning that Gimkit in discovery learning affects students' conceptualunderstanding.
ANALYSIS OF PROBLEM SOLVING ABILITY OF HIGH SCHOOL STUDENTS IN SOPPENG REGENCY IN BIOLOGY MATERIAL Nurhayati B Nurhayati B; Dian Dwi Putri Ulan Sari Patongai; Abdul Hadis; Buraeda Nur
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 01 (2025): Volume 14 Nomor 1 (Maret 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/mbsk3b08

Abstract

This study aims to analyse and describe the problem-solving ability of senior-high-school students in Soppeng Regency. A quantitative descriptive design was employed. The population comprised all state senior high schools in Soppeng, and a cluster random sampling technique yielded 131 respondents. Data were collected using a problem-solving test whose validity and reliability had been previously confirmed. Descriptive statistics were used for data analysis. The mean problem-solving score was 74.41, placing the cohort in the “high” category; 84 % of students fell within this category. The highest-performing indicator was problem identification (M = 97.68, “very high”), whereas solution evaluation was the weakest (M = 50.66, “low”). These findings suggest that while students readily recognise biological problems, they require further support in reflective and evaluative aspects of problem solving.
SINTESIS KOMPOSIT KITIN-SILIKA (KI-SIL) DAN APLIKASINYA SEBAGAI ADSORBEN LOGAM Fe (III) DAN Cr (III) Hasri Hasri; Diana Eka Pratiwi; Muftihatu Rahma; Satria Putra Jaya Negara; Marlina Ummas Genisa; Haryanti Putri Rizal
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 01 (2025): Volume 14 Nomor 1 (Maret 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/45m24r41

Abstract

Rice husk waste can be utilized as a source of silica with a silica content of 86.90-97.30 %, which has potential as a silica-based material. Similarly, chitin sources are abundant in crustacean shells (crabs, shrimps, lobsters etc.), the combination of chitin-silica (Ki-Sil) as a composite is very interesting to study. So this research aims to synthesize Ki-Sil composite and its application as adsorbent of Fe(III) and Cr(III) metal ions. At first, the extraction of rice husk silica was carried out and then the synthesis of Ki-Sil composites. The obtained composite was optimized for pH with pH variation (2-6), adsorption capacity with concentration variation (50-150 ppm) and adsorption selectivity test of both metal ions with ratio (0.25:0.75; 0.75:0.25 and 1:1). Analysis of Ki-Sil functional groups using FTIR shows that the synthesis has been successful with wave numbers that match the standard and morphological analysis using SEM which shows the composite material is a porous material. The results obtained optimum pH 4 for adsorption of both target metals. The adsorption capacity of Fe(III) metal was 31.49 mg/g; 69.69mg/g; 89.83 mg/g and 138.57 mg/g, respectively. And the adsorption capacities of Cr(III) metal were 37.80 mg/g; 70.86 mg/g; 89.98 mg/g and 138.84 mg/g, respectively. The selectivity test results showed that Ki-Sil adsorbed Fe(III) metal by 0.9820 mmol and Cr(III) metal by 0.9949 mmol. It is concluded that the Ki-Sil composite is more selective in adsorbing Cr(III) metal.

Page 28 of 29 | Total Record : 282

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