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Formulation and Evaluation of Spirulina-Based Gel with Varying Carbopol Concentrations for Anti-Acne Activity against Staphylococcus epidermidis Emmellia Yunitha; Anita Nilawati; Mega Novita; Dian Marlina
Biology, Medicine, & Natural Product Chemistry Vol 14, No 2 (2025)
Publisher : Sunan Kalijaga State Islamic University & Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/biomedich.2025.142.699-705

Spirulina platensis is a blue-green microalga known for its antibacterial properties, offering potential as a natural alternative in acne treatment. Acne vulgaris, often caused by Staphylococcus epidermidis, requires effective topical solutions. Gels are favored for their non-greasy texture, ease of application, and good skin absorption. This study aimed to formulate and evaluate anti-acne gels containing 25% Spirulina extract with varying Carbopol concentrations (0.5%, 1%, 1.5%). Each formulation was assessed for physical properties, stability over 21 days, and antibacterial activity against S. epidermidis. All gel formulations met quality standards for pH, homogeneity, viscosity, spreadability, and adhesiveness. The gel with 0.5% Carbopol (FI) showed the best spreadability, ideal viscosity, and good adhesiveness, along with the highest antibacterial activity, exhibiting an inhibition zone of 16.5 mm—comparable to tetracycline. In conclusion, Spirulina-based gel with 0.5% Carbopol offers an effective, stable, and natural anti-acne option. These findings highlight the potential of Spirulina as a bioactive agent in topical formulations and encourage further research for clinical applications in acne management.

Analysis of water quality in watershed using heavy metal pollution index Rizky Muliani Dwi Ujianti; Mega Novita; Aan Burhanuddin; Iffah Muflihati; Lukman Anugrah Agung; Roies Nur Ingsan; Alfan Najihil Wafa; Cerly Nurlita Anggraeni; Tsaqif Muzakki
Depik Vol 13, No 2 (2024): AUGUST 2024
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/depik.13.2.35680

The quality of rivers and coastal is gradually deteriorating along with rapid population and socio-economic growth in the watershed to the estuary. Sampling was conducted in Semarang city rivers and Demak district rivers, Central Java Province, Indonesia, at four different stations according to geography and designation: river basins, estuaries, and rivers affected by industrial and domestic waste. Research time is August - October 2023 during the dry season. The research method uses descriptive analysis to determine the variables to be studied based on the research results in the field. River and coastal pollution levels are measured using the Heavy Metal Pollution Index method, with several water quality parameters measured, such as BOD, COD, Ammonia, TDS, TSS, and Total Coliform. In contrast, the heavy metal parameters measured are Cd, Ni, Zn, Cu, and Pb. The heavy metals and water quality parameters analyzed guided by Government Regulation of the Republic of Indonesia Number 22 of 2021 class 2. Water quality and heavy metal analysis use the Heavy Metals Pollution Index (HPI). HPI is an assessment method that shows the influence of individual heavy metal compounds on overall water quality. The results show that the status of non-metal water quality in terms of HPI analysis shows that Sampling Station (SS) 1 is 224.30 (unsuitable for drinking), SS 2 is 645.98 (unsuitable for drinking), SS 3 is 320.09 (unsuitable for drinking), SS 4 is 252.09 (unsuitable for drinking), and metal parameters in terms of HPI analysis show that SS1 is 26.43 (good), SS2 is 2345.84 (unsuitable for drinking), SS3 is 26.43 (good), and SS4 is 12.64 (excellent). The conclusions from these four research areas indicate that the status of water quality, according to the HPI is unsuitable for drinking, however, indications of heavy metals in 2 areas are still tolerable, namely good and excellent. The decline in water quality in the research area is caused by domestic and industrial waste polluting the waters. In conclusion, this river area requires further management from the collaboration of various stakeholders.Keywords:Water QualityHeavy MetalCoastalWatershed

Analysis of water quality in watershed using heavy metal pollution index Rizky Muliani Dwi Ujianti; Mega Novita; Aan Burhanuddin; Iffah Muflihati; Lukman Anugrah Agung; Roies Nur Ingsan; Alfan Najihil Wafa; Cerly Nurlita Anggraeni; Tsaqif Muzakki
Depik Vol 13, No 2 (2024): AUGUST 2024
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/depik.13.2.35680

The quality of rivers and coastal is gradually deteriorating along with rapid population and socio-economic growth in the watershed to the estuary. Sampling was conducted in Semarang city rivers and Demak district rivers, Central Java Province, Indonesia, at four different stations according to geography and designation: river basins, estuaries, and rivers affected by industrial and domestic waste. Research time is August - October 2023 during the dry season. The research method uses descriptive analysis to determine the variables to be studied based on the research results in the field. River and coastal pollution levels are measured using the Heavy Metal Pollution Index method, with several water quality parameters measured, such as BOD, COD, Ammonia, TDS, TSS, and Total Coliform. In contrast, the heavy metal parameters measured are Cd, Ni, Zn, Cu, and Pb. The heavy metals and water quality parameters analyzed guided by Government Regulation of the Republic of Indonesia Number 22 of 2021 class 2. Water quality and heavy metal analysis use the Heavy Metals Pollution Index (HPI). HPI is an assessment method that shows the influence of individual heavy metal compounds on overall water quality. The results show that the status of non-metal water quality in terms of HPI analysis shows that Sampling Station (SS) 1 is 224.30 (unsuitable for drinking), SS 2 is 645.98 (unsuitable for drinking), SS 3 is 320.09 (unsuitable for drinking), SS 4 is 252.09 (unsuitable for drinking), and metal parameters in terms of HPI analysis show that SS1 is 26.43 (good), SS2 is 2345.84 (unsuitable for drinking), SS3 is 26.43 (good), and SS4 is 12.64 (excellent). The conclusions from these four research areas indicate that the status of water quality, according to the HPI is unsuitable for drinking, however, indications of heavy metals in 2 areas are still tolerable, namely good and excellent. The decline in water quality in the research area is caused by domestic and industrial waste polluting the waters. In conclusion, this river area requires further management from the collaboration of various stakeholders.Keywords:Water QualityHeavy MetalCoastalWatershed

Web-Based Thermochemistry Media: Potential for ESD-Oriented Implementation Aries Setyo Wibowo; Siti Patonah; Mega Novita
Jurnal Pendidikan Kimia FKIP Universitas Halu Oleo Vol. 10 No. 2 (2025): August 2025
Publisher : Jurusan Pendidikan Kimia Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36709/jpkim.v10i2.183

The purpose of the research is to identify the potential for using educational media, such as websites that focus on Education for Sustainable Development (ESD), in thermochemistry materials. Data was gathered using a descriptive qualitative approach through observation, surveys, interviews, and an analysis of the teaching materials from 10 chemistry teachers and 72 11th-grade students at SMAN 1 Kedungwuni. The results show that 80% of teachers fall into the Proficient category, and all respondents (100%) expressed some or a great deal of agreement with the use of website-based media. About 40% of teachers have already integrated ESD, 40% understand it but have not yet implemented it, and 20% do not understand it. According to the analysis module, just 38% of the material fully explains ESD. According to 89% of respondents, media websites are effective in visualizing abstract concepts and have a high potential for implementation

Prediksi Risiko Depresi Berdasarkan Data Demografis dan Psikososial menggunakan Metode Ensemble Learning dengan Pendekatan Stacking Arwan Mangli; Noora Qotrun Nada; Mega Novita
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3102

Depression is a mental health problem with high prevalence that requires accurate and reliable computational-based prediction systems to support early detection. This study proposes a depression risk prediction architecture based on a stacking ensemble approach incorporating an out-of-fold (OOF) mechanism to prevent data leakage during meta-feature generation. The model combines Support Vector Machine and XGBoost as base learners, with Logistic Regression employed as the meta-learner. A public Depression Professional Dataset is processed using a stratified split strategy, class balancing on the training data through SMOTE, and feature standardization to enhance training stability. Experimental results demonstrate that the proposed approach achieves superior performance with an accuracy of 0.99, precision of 0.91, recall of 1.00, and an F1-score of 0.95, along with consistent detection capability for the minority class. These findings confirm that the systematic integration of OOF stacking and SMOTE improves model sensitivity while reducing false negative errors, making it suitable for the development of artificial intelligence–based mental health screening systems.

Implementation of Stacking Ensemble Learning on Decision Tree Regressor for Food Commodity Price Prediction in Indonesia Nukman Solikhudin; Mega Novita; Ramadhan Renaldy
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13092

Fluctuations in staple food prices across Indonesian regions exhibit complex, non-linear patterns vulnerable to market shocks. This study aims to construct an accurate, stable food price prediction model utilizing a Stacking Ensemble Learning approach. A raw dataset of 27,722 records from the National Food Agency was cleaned by removing invalid data and zero values, yielding 27,270 well-indexed observations. To address severe scale disparity between commodities and heteroscedasticity effects, a natural logarithm transformation was applied to the target variable. Time-series features, specifically Lag 1 and Moving Average 3, were locally constructed based on commodity-province groups to capture temporal dependencies. The proposed Stacking Ensemble model integrates four multi-architecture base learners Ridge Regression, AdaBoost, Gradient Boosting, and Extra Tree with a Decision Tree Regressor acting as the meta-learner. Model evaluation was conducted using a temporal split method with an 80:20 ratio to strictly prevent data leakage. Experimental results demonstrate that the proposed Stacking Ensemble model achieves superior performance on nominal test data compared to baseline models, securing an R^2of 0.895, RMSE of 2,531, and MAE of 1,461. Furthermore, the model proved highly robust in balancing bias and variance, yielding the smallest R^2Gap of 0.035. Model transparency analysis reveals a powerful temporal inertia, where historical features dominate the decision weight by up to 87.55%. However, per-commodity performance analysis highlights a performance limitation on subsidized commodities (Minyak Kita) due to data distortion caused by non-market Price Ceiling regulations. This study provides critical implications for food authorities to formulate data-driven, responsive market interventions.

Peningkatan Performa Prediksi Survival Pasien Gagal Jantung Menggunakan Stacking Ensemble Learning Faiza Rulla Salwa; Mega Novita; Ramadhan Renaldy
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2126

Prediksi kelangsungan hidup pasien gagal jantung merupakan aspek penting dalam mendukung pengambilan keputusan medis secara dini dan tepat. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi kelangsungan hidup pasien gagal jantung dengan menerapkan metode Stacking Ensemble Learning yang menggabungkan tiga base learners, yaitu Decision Tree, Naive Bayes, dan K-Nearest Neighbor, serta menggunakan Support Vector Machine sebagai meta-learner. Dataset yang digunakan adalah Heart Failure Clinical Records dari UCI Machine Learning Repository yang telah melalui proses pra-pemrosesan berupa standardisasi numerik dan pembagian data menggunakan stratified sampling dengan rasio 80:20. Eksperimen dilakukan menggunakan validasi silang (5-fold cross-validation) dan tuning hyperparameter pada meta-learner menggunakan GridSearchCV untuk menemukan kombinasi terbaik dari parameter C dan gamma. Hasil evaluasi menunjukkan bahwa model stacking mampu mencapai akurasi sebesar 98,7% dan F1-score 0,9791, mengungguli semua model tunggal. Keberhasilan ini menunjukkan bahwa strategi penggabungan beberapa model ringan mampu meningkatkan kinerja sistem prediktif secara signifikan, tanpa menambah kompleksitas yang berlebihan. Oleh karena itu, pendekatan ini sangat potensial untuk diterapkan pada sistem pendukung keputusan klinis berbasis data, khususnya dalam konteks prediksi penyakit kronis.

IMPLEMENTATION SIMPLE ADDITIVE WEIGHTING METHOD IN DETERMINING FEASIBILITY SACRIFICIAL ANIMALS Nugroho Dwi Saputro; Rahmat Robi Waliyansyah; Mega Novita
Jurnal Transformatika Vol. 20 No. 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.4542

Many things from the life sector have used the existence of technology. Where a technology is able to help various problems in various fields such as livestock and agriculture. Computers have been included in it as a tool to do a job or identify existing problems. However, sometimes as a practitioner in the field of animal husbandry, especially qurban animals, they come to the conclusion that it is often found that sacrificial animals in the market that want to be sacrificed do not meet the requirements both in syari ah (law) and health. With the application of determining the feasibility of sacrificial animals according to the Syariah using the web-based Simple Additive Weighting (SAW) method. This system is later expected to be able to determine whether or not a sacrificial animal will be sacrificed so that the community or people who sacrifice are not harmed and the reward for the sacrifice is perfect.

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