Publication Search

Sweet orange peel (Citrus x aurantium L.) is an agricultural by-product rich in bioactive compounds including flavonoids, phenolics, terpenoids, and vitamin C with antioxidant and moisturizing potential. This study aimed to formulate and evaluate a body lotion using 15% ethanol extract of sweet orange peel obtained by maceration with 96% ethanol. Evaluations included organoleptic, homogeneity, pH, adhesion, spreadability, viscosity, irritation, cycling test, cream type, and DPPH antioxidant activity assessments. The preparation was semisolid, yellow, with a characteristic herbal aroma, homogeneous, pH 8, adhesion time of 4.10 seconds, spreadability of 9.9–11.1 cm, and acceptable viscosity. The preparation caused no skin irritation, remained stable through six cycling test cycles, and formed an oil-in-water (O/W) emulsion. Antioxidant activity showed an IC₅₀ of 284.6 ppm (weak category) compared to vitamin C as positive control (IC₅₀ 4.2 ppm). It was concluded that ethanol extract of sweet orange peel can be formulated into a stable and safe body lotion, though further optimization is needed to enhance its antioxidant activity.

Strawberries are horticultural commodities that are highly susceptible to postharvest deterioration due to their high respiration rate, microbial activity, and oxidative degradation, resulting in a relatively short shelf life. This study aimed to evaluate the effectiveness of edible coatings in extending strawberry shelf life through a systematic literature review (SLR) approach. Literature was collected from several scientific databases using keywords related to edible coating, shelf life, and strawberry, covering publications from 2019–2026. From an initial 109 articles, a selection process based on inclusion and exclusion criteria resulted in 35 articles specifically discussing the application of edible coatings on strawberries. The synthesis results showed that all studies reported an extension of shelf life after edible coating application, although the effectiveness was influenced by the type of material, formulation, and storage conditions. Chitosan was the most widely used coating material due to its natural antimicrobial activity and excellent film-forming ability. The incorporation of bioactive compounds such as essential oils, plant extracts, and phenolic compounds was proven to enhance antifungal and antioxidant activities. In addition, nanotechnology-based systems demonstrated better preservation performance compared to conventional systems. However, methodological standardization and industrial-scale validation are still required to support commercial implementation.

Consumption of ethylene glycol–containing drugs can cause liver damage. This study aimed to evaluate the hepatoprotective potential of Moringa leaf extract (Moringa oleifera L.) on AST and ALT levels, liver morphology, hepatosomatic index, and liver histology in ethylene glycol–induced white rats (Rattus norvegicus L.). A Completely Randomized Design (CRD) was used with 20 male rats divided into five groups: normal control, ethylene glycol control, and three treatment groups (150, 300, and 450 mg/kg BW). Ethylene glycol was administered for 30 days, while the extract was given for 20 days. Blood samples were collected on day 31. Data were analyzed using One Way ANOVA and Duncan’s test. The results showed significant hepatoprotective effects (P = 0.000). AST and ALT levels in the treatment groups differed significantly from the normal control. Liver morphological changes were observed in both control and treatment groups. The hepatosomatic index, number of normal hepatocytes, and central vein diameter also showed significant differences. In conclusion, Moringa leaf extract demonstrates hepatoprotective potential by reducing AST and ALT levels, improving liver morphology, increasing normal hepatocytes, and decreasing central vein diameter, with the optimal dose at 450 mg/kg BW

This study aims to comprehensively evaluate the potential pharmacological activities and safety profiles of seven secondary metabolite compounds (Caffeic Acid, Syringic Acid, Quercetin, Luteolin, p-Coumaric Acid, Ferulic Acid, and Epicatechin) identified in the Bajakah plant (Spatholobus littoralis Hassk.). The research approach integrates in silico analysis using the PubChem database, biological activity prediction via PASS Online, oral toxicity assessment through ProTox-II, and pharmacokinetic evaluation using pkCSM, which were subsequently validated through an empirical literature review. The results indicate that these compounds exhibit significant activity probabilities, particularly as antimutagenic, antiseptic, and antioxidant agents. Luteolin demonstrated the highest antimutagenic potential, while Quercetin showed dominant antioxidant activity. Toxicity profiling revealed that Luteolin and Caffeic Acid possess the highest safety levels (Class 5), whereas Quercetin requires special attention (Class 3). These computational findings strongly correlate with empirical evidence demonstrating that Bajakah extract exhibits broad-spectrum antibacterial activity against Staphylococcus aureus, antifungal activity against Candida albicans, as well as high antioxidant and anti-inflammatory capacities. This study provides a strong molecular foundation for the development of Bajakah as a safe and effective phytopharmaceutical candidate.

This study aims to determine the effect of palm oil fiber (PFFE) (Borassus flabellifer Linn.) levels in egg yolk citrate diluent (C-EY) on the quality of frozen semen from landrace crossbred boar. The material used was fresh semen from 3 landrace crossbred pigs aged 2-3 years. The experimental method was a Completely Randomized Design consisting of five treatments and five replications. T0 = S-KT, T1= C-EY + PFFE 0.75%, T2 = C-EY + PFFE 1.5%, T3 = C-EY + 2.25% PFFE, and T4 = C-EY + PFFE 3%, and the addition of 6% glycerol in each treatment. The parameters observed included motility, viability, abnormalities, and recovery rate of spermatozoa. The data obtained were analyzed using analysis of variance (ANOVA) and analysis using SPSS version 27. The results revealed that the addition of PFFE had a significant effect (P <0.05) on post-thawing semen motility. With a value of T2: 24.00±2.23%, followed by T3: 15.00±5.00%, T1: 14.00±6.51%, T4: 13.00±4.47% and T0: 12.00±7.58%. Post-thawing viability also revealed that the addition of palmyra fruit fiber extract had a significant effect (P<0.05) with a T2 value of 46.65±3.65% followed by treatment T3: 25.70±6.75, T1: 24.69±8.70, T4:24.24±7.81 and T0: 22.36±8.67. While semen abnormalities did not have a significant difference between treatments. It can be concluded that the use of 1.5% SSBL and S-KT resulted in the highest post-thaw semen motility in treatment P2, with a value of 24.00 ± 2.23% in crossbred Landrace boar semen.

Wolowiro Village, Paga District, Sikka Regency possesses abundant coconut resources; however, their utilization remains limited and has not yet generated optimal economic value for the local community. This community service program aimed to improve community knowledge and skills in processing coconuts into Virgin Coconut Oil (VCO) as a value-added product with the potential to increase household income. The program employed socialization activities, presentations, educational video screenings, and hands-on training in VCO production using the fermentation method. Participants included village officials, housewives, and local residents who were actively involved in all stages of the training, including raw material selection, coconut grating, coconut milk extraction, fermentation, oil separation, and final product filtration. The results demonstrated a significant improvement in participants’ understanding and practical skills related to VCO production. Community members were able to independently produce VCO and showed strong enthusiasm for developing coconut-based micro-enterprises. Furthermore, the program increased public awareness of the importance of utilizing local resources as economically valuable products. Therefore, VCO production training can serve as an effective community empowerment strategy to support economic development and improve community welfare through the utilization of local agricultural resources.

Background: Diabetes Mellitus (DM) is a disease that cannot be completely cured or cannot even be completely cured. The vile shard plant is empirically used by the community to treat diabetes (DM). This study aims to conduct phytochemical screening and test the activity of 96% ethanol extract of kejibeling leaves (Strobilanthes crispus (L.) Bl.) as a herbal antidiabetic in male white mice (Mus musculus) with alloxan induction. Method: This research is an experimental laboratory research with a true experimental posttest control design using a completely randomized design (CRD) with 5 treatments and 5 replications. Treatment P1 (without treatment) as normal control (N), P2 as positive control (+), P3 as negative control (-), P4 kejibeling leaf extract 250 mg/kg BW, P5 kejibeling leaf extract 500 mg/kg BW. Result:The results of phytochemical screening showed the presence of alkaloids, flavonoids, tannins, saponins, terpenoids and steroids. SPSS results show that the data is normally distributed (p>0.05) and homogeneous (p>0.05). The results of the ANOVA on the treatment of giving keji beling leaf extract 250 mg/Kg BW showed a sig. 0.393 (p>0.05) and treatment of 500 mg/Kg BW obtained a sig value. 0.517 (p>0.05). Conclusion:The conclusion from the research results shows that administering doses of 250 mg/kg BW and 500 mg/kg BW of keji beling leaf extract can reduce blood sugar levels in mice. It is hoped that the results of this research will be useful for the community as an antidiabetic therapy using kejibeling leaves (Strobilanthes crispus (L.) Bl.).

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

Wounds can be defined as the loss and damage of anatomical cells or skin function. Wound healing consists of coagulation, inflammation, proliferation, and remodeling stages. This study aims to determine the effectiveness of the leaf fraction of the thick (Glochidion superbum) on wound healing in male white mice (Mus musculus). This study is experimental. This study used 24 male mice that were given a 10 mm long cut wound. Fractionation was carried out using the liquid-liquid extraction method. Fractionation used 3 different types of solvents, namely methanol, ethyl acetate, and n-hexane. The results showed that the ethyl acetate fraction had a faster wound healing effectiveness than the other groups. The ethyl acetate fraction contains a phenolic compound, namely methyl gallate. Methyl gallate has an important role in wound healing. Methyl gallate has the potential to be an antibacterial, antioxidant, and anti-inflammatory. The results of the Bonferroni post-hoc statistical analysis confirmed the effectiveness of the ethyl acetate fraction in faster wound healing. The thick leaf fraction was effective in healing incisions in male white mice. The ethyl acetate fraction was more effective in accelerating incision healing.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

Tooth extraction is a procedure to remove a tooth from its alveolar bone socket. The causes for tooth extraction include caries, periodontitis, fractures, impacted teeth, the need for orthodontic treatment, and persistent primary teeth. Post-extraction bleeding is the most common complication that occurs. Hemostasis is a mechanism to stop bleeding from blood vessels to prevent excessive blood loss when an injury occurs, ensuring that blood continues to flow smoothly. In stopping bleeding, there are three processes involved: vasoconstriction (the narrowing of blood vessels), platelet activity, and the activity of blood clotting factors. Bleeding time is the time interval from when blood exits the blood vessel until the bleeding stops. The normal range for bleeding time is 1 to 3 minutes. Balakacida leaves contain active compounds including alkaloids, tannins, flavonoids, saponins, and phenolics. To determine the effect of Balakacida leaf extract (Chromolaena odorata) as a hemostatic agent following tooth extraction in Wistar rats (Rattus norvegicus). This study uses an experimental method with a Post-Test Only Control Group Design. The samples used in this research are male Wistar rats (Rattus norvegicus), aged 2–3 months, weighing between 200–250 grams. The research data were processed and analyzed using the One-Way ANOVA test. The results showed that treatments at concentrations of 10%, 20%, and 30% were able to significantly accelerate bleeding time compared to the control group. The administration of Balakacida leaf extract is effective as a hemostatic agent following tooth extraction in Wistar rats.  

This study presents ReflectAI, a web-based system designed to automate the creation of teaching materials tailored to students' learning styles using behavior data from a Learning Management System (LMS). Student digital activity data—such as logins, material access, forum participation, assignment submission, and quiz results—are extracted and processed using a Hierarchical Clustering algorithm to categorize students into three learning styles: visual, auditory, and kinesthetic. Based on the clustering results, the system automatically generates personalized learning modules using generative AI (ChatGPT API), aligned with each student's learning preferences. Employing a data-driven system development approach, the system was tested with data from 230 students in a mathematics course. The results show diverse learning style distributions and relevant, tailored content generation. ReflectAI is designed to reduce teachers’ administrative workload and enhance personalized and adaptive learning. This system contributes to educational transformation through deep, data-driven technology integration.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

This study investigates the effect of adding onion peel extract as a corrosion inhibitor on the corrosion rate and hardness of radiator pipes. The research employed an experimental method with inhibitor concentrations of 0 ppm, 100 ppm, 200 ppm, and 300 ppm. Corrosion rate testing was conducted using electrochemical methods, while hardness was measured using the Vickers method. The findings reveal that the addition of onion peel extract at a concentration of 300 ppm significantly reduced the corrosion rate to 0.081 mmpy, achieving an inhibition efficiency of 56.45%. Furthermore, the same concentration enhanced the surface hardness of radiator pipes to 255.403 Kgf/mm². These results demonstrate that onion peel extract has strong potential as an eco-friendly organic corrosion inhibitor. Its dual function in reducing corrosion and improving mechanical properties highlights its applicability in radiator pipe protection and sustainable engineering practices. The study contributes to the development of natural inhibitors as alternatives to synthetic chemicals, aligning with environmental preservation efforts and advancing green technology in material protection.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.

Brotowali stem (Tinospora crispa L.) is a plant that grows abundantly in Central Kalimantan and has been empirically used for generations as an antidiabetic remedy by the Dayak Ngaju community. Brotowali stem contains secondary metabolite compounds, including alkaloids, which possess various pharmacological activities, one of which is antidiabetic activity. This study aimed to determine the alkaloid content of Tinospora crispa stem extract in aquadest, ethyl acetate, and n-hexane fractions. The research employed an observational descriptive method by analyzing qualitative data through color reaction tests and quantitative data using UV-Vis spectrophotometry to determine alkaloid levels. The qualitative analysis results showed positive color reactions indicating the presence of alkaloid compounds. Quantitative analysis using UV-Vis spectrophotometry revealed that the total alkaloid content in the aquadest fraction was 20.19 mg or 20.19%, in the n-hexane fraction was 20.54 mg or 20.54%, and in the ethyl acetate fraction was 31.07 mg or 31.07%. The highest total alkaloid content was found in the ethyl acetate fraction. In conclusion, the extract of Tinospora crispa stem positively contains alkaloids, with the highest alkaloid content obtained in the ethyl acetate fraction at 31.07%.