Publication Search

Stunting remains one of the major nutritional problems in Indonesia. According to the 2024 Indonesian Health Survey (SKI), the prevalence of stunting in Papua Province reached 16.8%. A child’s nutritional status is strongly influenced by dietary intake, particularly protein, which plays a crucial role in growth. One potential source of animal protein is Indian mackerel (Rastrelliger kanagurta), a type of fish commonly found in Papua. Indian mackerel is rich in protein and omega-3 fatty acids but is highly perishable, limiting its shelf life. To address this issue, the fish is processed into flour. Additionally, green spinach is used as a source of fiber and minerals, although it is also prone to spoilage. The spinach is processed by extracting its juice, which is then incorporated into noodle products. Dried noodles were chosen as a medium for fortification because they are widely consumed across age groups and often used as a substitute for rice.This study aimed to determine the effect of adding Indian mackerel flour and green spinach extract on the chemical properties and sensory acceptance of dried noodles. The research methods included nutritional content analysis and organoleptic testing. The sensory evaluation was conducted by 25 semi-trained panelists who were nutrition science students

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.

Electronic money laundering has become a critical issue as a form of cybercrime. Advances in technology demonstrate that digital forensic applications, particularly those utilizing smartphones, can be employed to uncover digital traces of money laundering transactions. This study aims to analyze the relevance of existing legal frameworks, the effectiveness of smartphone forensic techniques, and the challenges faced in their implementation in Indonesia. Based on a review of the literature and regulatory analysis, it was found that legal frameworks such as UU No. 8 of 2010 on the Prevention and Eradication of Money Laundering (TPPU) and UU No. 19 of 2016 on Information and Electronic Transactions (ITE) provide an adequate legal foundation. However, gaps in implementation, such as limited human and technological resources, remain significant obstacles. Forensic techniques, including metadata analysis and device extraction, have shown great potential in identifying the flow of illicit funds. Nonetheless, their application is hindered by data confidentiality and the lack of uniform technical standards. Given the increasing complexity of digital crimes, an integrated approach is required, combining regulatory strengthening with technological capacity building, to enhance the effectiveness of smartphone forensics in addressing electronic money laundering.

A Caesarean section, or C-section, is a surgical procedure where a baby is delivered through an incision made in the mother’s abdomen and uterus. To aid the healing of post-Caesarean wounds, mothers need not only antibiotics but also a diet that provides high-quality nutrition and adequate calories. The research question being investigated is: "Do Age, Parity, Education, and Occupation Affect the Healing Process of Post-Caesarean Wound Stitches?" This study uses a quasi-experimental design with a Post-test Only Control Group Design. The study population includes mothers who had a C-section at Pemalang Hospital. Participants were divided into two groups: 49 received catfish extract tablets (the experimental group), and 49 received standard wound care (the control group). Sampling was conducted using purposive sampling techniques. Data were analyzed through both univariate and bivariate methods. The results showed a p value of 0.000 for the experimental group and 0.046 for the control group (both p values < 0.05), indicating that catfish extract tablets significantly influence the healing process of post-Caesarean wound stitches..

The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.

Red roses (Rosa damascena Mill.) are known to contain active compounds such as flavonoids, tannins, and vitamin C, which have potential antioxidant and anti-inflammatory properties. However, the utilization of rose flower waste after extraction remains limited. This study aims to formulate and evaluate gel preparations based on red rose extract and post-extraction rose flower waste as an innovation in natural-based topical formulations. This research is an experimental laboratory study using a post-test only design. Three concentration variations were formulated for both the extract (F1, F2, F3) and rose flower waste (F4, F5, F6) using HPMC as the gel base. Physical evaluations included organoleptic testing, homogeneity, pH, adhesion, spreadability, and viscosity. Data were analyzed descriptively and statistically using the Kruskal–Wallis test to determine the effect of concentration variation on physical parameters. All formulations showed good organoleptic and homogeneity results. The pH values were within the safe range for skin (4.75–5.92). Adhesion times met the criteria of >1 second, with a tendency to increase alongside higher concentrations of active ingredients. Spreadability fell within the acceptable range (3–5 cm), decreasing as viscosity increased. The viscosity values ranged from 20,150 to 20,182 cps. There was a significant effect of concentration variation on pH (p < 0.05), adhesion in extract-based gels (p = 0.021), and spreadability in waste-based gels (p = 0.024). In conclusion, gel preparations made from red rose extract and its waste can be successfully formulated with good physical stability and meet standard criteria for topical formulations. Rose flower waste has potential as an alternative active ingredient in the development of natural-based gel products.

The increasing incidence of bacterial resistance to antibiotics has become one of the major challenges in global health, necessitating the exploration of natural antibacterial sources as alternative treatments, such as bioactive compounds derived from sea urchins. This literature review aims to evaluate the antibacterial potential of sea urchin extracts based on existing research findings. The literature search was conducted using the PubMed and Google Scholar databases using the Boolean operators (AND, OR). Inclusion criteria include articles published between 2014 and 2024, available in full text, written in either Indonesian or English, and discussing the antibacterial activity of sea urchin extracts from the species Diadema setosum and/or Echinometra mathaei. Based on the selection process, eight articles met the inclusion criteria and were included in the review. The results of the literature review indicate that sea urchin extracts exhibit antibacterial activity against gram-negative bacteria, including Escherichia coli, Salmonella typhi, Salmonella typhimurium, Shigella flexneri, Pseudomonas aeruginosa, Aeromonas hydrophila, Acinetobacter sp., Citrobacter freundii, and Klebsiella pneumoniae, as well as gram-positive bacteria, including Propionibacterium acnes, Staphylococcus aureus, Bacillus cereus, Bacillus subtilis, Staphylococcus epidermidis, Streptococcus mutans, and Streptococcus sobrinus. These antibacterial effects are attributed to the presence of secondary metabolites such as alkaloids, steroids, flavonoids, saponins, and phenolics.

The threat of Distributed Denial of Service (DDoS) is increasing develop along with increasing use of the Internet of Things (IoT) and Software-Defined Networking (SDN) architecture . Although SDN provides convenience in management network , properties its centralized control make it prone to to flooding attacks that can paralyze controller performance . Detection method conventional , such as approach statistics and machine learning, still own limitations in matter accuracy , high false positive rate , and dependence on extracted features manually . To overcome problem said , research This propose a hybrid deep learning based DDoS detection and mitigation model that combines Convolutional Neural Network (CNN) to extraction feature spatial from RGB and Gated Recurrent Unit (GRU) images for understand temporal correlation between traffic data network . System tested through network test-bed Mininet based with Ryu/Floodlight controller, using simulation DDoS attacks (Hping3, LOIC) and normal traffic (video streaming, HTTP server). Traffic data cross recorded in PCAP format, processed become RGB image measuring 200×200 pixels, and labeled based on type traffic . Evaluation results with metric accuracy , precision, recall, F1-score, and MCC show that the CNN–GRU model has performance more superior compared to baseline approaches such as CNN-only, GRU-only, as well as classical ML methods such as SVM and Random Forest. In addition , the system capable apply mitigation adaptive through automatic flow rule creation on edge switches. Findings This confirm that effective deep learning- based spatial -temporal hybrid approach in increase detection early and response DDoS attacks on SDN networks adaptive and real-time.  

Mental health issues such as depression, anxiety, and stress continue to increase globally and are recognized as critical factors that influence social functioning, productivity, and overall quality of life. Conventional mental health services are often limited by barriers including high cost, geographical distance, and persistent stigma that discourage individuals from seeking timely help. The digital era provides an alternative through the integration of technology into mental health counseling, offering greater accessibility, flexibility, and anonymity. Nevertheless, a key limitation of many digital counseling platforms lies in their inability to fully capture and respond to the emotional nuances of users during interactions. This study aims to address that gap by developing a speech-based emotion detection framework designed to be integrated into digital counseling environments. The proposed methodology includes the collection and preprocessing of speech samples, feature extraction using acoustic parameters, and training machine learning models to classify emotions in real time. Experimental results demonstrate that this approach significantly improves the accuracy of emotion detection, enabling digital counseling systems to provide more adaptive and personalized support. Beyond counseling, the research highlights the broader applicability of speech emotion recognition in education, telemedicine, and interactive digital assistants, all of which benefit from improved sensitivity to human emotions. These findings underscore the potential of artificial intelligence to strengthen digital mental health interventions, ensuring services that are not only more efficient and inclusive but also capable of fostering long-term emotional well-being in diverse populations.

Sacha inchi (Plukenetia volubilis Linneo) is a plant from the Euphorbiaceae family known for its green star-shaped fruit and brown seeds. This plant has high nutritional value and significant antioxidant activity (Nuha and Sriwidodo 2022). This study aims to identify secondary metabolite compounds and determine the total phenolic content of the methanol extract of Plukenetia volubilis Linneo leaves. The samples were extracted using the maceration method, then phytochemical tests and total phenolic content analysis were carried out using the Folin-Ciocalteu method (Sari et al. 2021). The results of the phytochemical test showed that the methanol extract of sacha inchi leaves contained secondary metabolite compounds such as alkaloids, flavonoids, saponins, and tannins. The total phenolic content obtained from the extract was 19.94%, which indicates the potential for high antioxidant activity. The results of this study indicate that sacha inchi leaves have the potential to be developed as a source of natural bioactive compounds that are beneficial to health.

Background: Polycystic ovary syndrome (PCOS) is a complex endocrine-metabolic disorder common in women of reproductive age, characterized by insulin resistance (IR), anovulation, and hyperandrogenism, often leading to infertility. Metformin is the first-line therapy used. However, inositol, especially myo-inositol (MI) and D-chiro-inositol (DCI), have shown promising therapeutic potential. Direct comparison and optimal efficacy of both still require a comprehensive review. Objective: This systematic review aims to evaluate the efficacy of inositol (as monotherapy or in combination) compared with metformin, placebo, or no intervention, in improving reproductive, hormonal, and metabolic outcomes in women with PCOS. Methods: Conducted according to PRISMA guidelines, a systematic literature search was conducted in Pubmed and Ebsco databases for randomized controlled trials (RCTs) published in the last 10 years. Studies evaluating inositol and/or metformin in women with PCOS and reporting reproductive, hormonal, or metabolic outcomes were included. Study selection and data extraction were performed independently by five reviewers. Results: Six randomized controlled trials (RCTs) were included in this review. Metformin consistently showed improvements in insulin resistance and androgen levels. Myo-inositol also demonstrated efficacy in improving metabolic and hormonal parameters, as well as reproductive outcomes, often with a better tolerability profile, especially as monotherapy. The combination of metformin and myo-inositol has shown potential improvements in some reproductive functions (such as live birth rate) compared to metformin alone in some studies. However, other studies have shown comparable or superior efficacy of myo-inositol monotherapy with fewer side effects. Adjuvant therapies such as dexamethasone or calcium/vitamin D have shown benefits in certain subpopulations. Conclusions: Both inositol (especially myo-inositol) and metformin have significant therapeutic roles in the management of PCOS. Metformin remains an important intervention for metabolic dysregulation and hyperandrogenism. Inositol appears to be an effective and often better tolerated alternative, with significant reproductive benefits. The choice of therapy should be individualized based on patient phenotype and treatment goals.    

Centella asiatica (L) urban is a plant that has the potential to be a source of immunomodulatory agents because it contains several groups of active compounds, asiaticoside, madecassoside, madecassic acid and asiatic acid, which can act as anti-allergic, anti-inflammatory, and immunomodulators. This study aims to review relevant literature to summarize the immunomodulatory effects produced by Centella asiatica (L.) urban. This study used a literature review design involving 4 experimental study articles published in the last 10 years in the electronic databases  PubMed and ScienceDirect. The results of this study showed that C. asiatica has been shown to provide immunomodulatory effects through various mechanisms, including as an anti-inflammatory by decreasing nitrite production, PGE2 and TNF-α, reducing NF-κβ activation, and inhibiting pro-inflammatory mediators induced by IFN-γ and TNF-α, including IL-6 and COX-2. C. asiatica can also act as an antioxidant that inhibits ROS production, stimulates GSH, and binds free radicals. In addition, C. asiatica can also play a role in the adaptive immune response.

Diabetes mellitus is gathering disease metabolic with hyperglycemia caused by abnormalities in insulin secretion , insulin action , or both of them . Loss central type 2 diabetes is caused by insulin resistance in muscles and liver as well failure pancreatic beta cells . Purpose This research is to find out How extract ethanol leaf stalk palm affect the lipid values of diabetic rats. Type This research is a experimental . This research is divided into 6 groups with different treatment​ namely normal, control negative CMC-Na (0.5%), control positive glibenclamide (0.45mL), 125mg/kgBW extract , 250mg/kgBW extract and 500mg/kgBW extract . The results of the lipid parameter data were analyzed with SPSS 22 with level 95% confidence level Results study show extract ethanol dose 250 mg/kgBW and 500 mg/kgBW or more Lots lower level total cholesterol , triglycerides , and LDL as well increase HDL levels

Image processing is a branch of computer science that is growing rapidly and is widely used in various fields, including in security systems. Face identification is one of the main applications of image processing that aims to recognize and distinguish individual faces in a system. The methods used in face identification involve various techniques, such as facial feature detection, characteristic extraction, and classification using machine learning algorithms. This article discusses the application of image processing in a security system based on face identification and the technology used to improve the accuracy and reliability of the system. The results of the study show that the combination of deep learning algorithms with image pre-processing techniques can increase the success rate of face identification in security systems.

This study aims to analyze the perspectives of mathematics students on mixing aloe vera extract with weak acid solution for hair health and softness. The research method used is descriptive quantitative through the distribution of questionnaires to 43 mathematics students at Medan State University and other universities in North Sumatra. The questionnaire consists of 19 statements covering aspects of knowledge (12 items), environmental awareness (4 items), and psychomotor (4 items) with a Likert scale of 1-5. The results showed that the majority of respondents had good knowledge about the benefits of aloe vera for hair care (mean = 4.02), but the understanding of weak acid solution and its mechanism of action was still moderate (mean = 3.12). The environmental awareness aspect showed a high value (mean = 3.76) which reflects the tendency of students to choose natural products. Meanwhile, the psychomotor aspect showed a moderate value (mean = 3.44), which indicates the limitations of respondents' skills in applying hair care products optimally. This study found a positive correlation between knowledge about natural ingredients and the tendency to use natural-based products for hair care. The results of this study are expected to be the basis for developing hair care product formulations based on natural ingredients that suit the needs and preferences of students.

This research analyzes user sentiment reviews of the KAI Access application from Google Play Store to improve customer service at PT Kereta Api Indonesia. The study uses a Natural Language Processing (NLP) approach with the Latent Dirichlet Allocation (LDA) algorithm to extract main topics from 10,000 reviews collected from April 2024 to April 2025. Analysis results show 40.7% positive sentiment reviews and 49.3% negative. After data preprocessing through case folding, normalization, tokenization, stopword removal, and stemming, seven optimum topics were found from negative sentiment with a coherence score of 0.508343 and two optimum topics from positive sentiment with a coherence score of 0.511673. Analysis based on five service quality dimensions (tangibles, reliability, responsiveness, assurance, and empathy) reveals that the reliability dimension becomes the main issue, including system instability, transaction failures, login difficulties, and data inaccuracy. The responsiveness dimension is the second priority, with users expecting fast and responsive service to complaints. The results of this study provide recommendations for PT KAI to prioritize improvements in system reliability and responsiveness aspects to enhance the overall user experience, which will ultimately impact customer satisfaction and loyalty.    

This study aimed to identify the secondary metabolite compounds present in the ethanol extract of Plukenetia volubilis L. (sacha inchi) shells through phytochemical screening and LC-MS analysis. The extraction was performed using the maceration method with 95% ethanol as solvent, resulting in a yield of 47.33%. Qualitative phytochemical tests revealed the presence of alkaloids, flavonoids, tannins, terpenoids, saponins, and glycosides in the extract. Further LC-MS analysis tentatively identified several phenolic and flavonoid compounds with known biological activities, including sinapinic acid, 1-o-sinapoylglucose, and azelaic acid from the dicarboxylic acid group. The presence of these compounds highlights the potential of sacha inchi shell extract as a natural antioxidant and antidiabetic agent. These findings support the valorization of sacha inchi agricultural waste as a promising raw material for pharmaceutical and nutraceutical applications.

Indonesia is rich in biodiversity, there are around 40,000 species of plants, and they have different uses,such as traditional medicine, can be made into handicrafts, used as decorations, used as natural dyes. Butterflypea flowers have the potential as a natural dye because the anthocyanins contained in their petals emit a bluecolor. The purpose of this study was to determine the anthocyanin content in butterfly pea flowers using themaceration extraction method which is commonly used to determine the best anthocyanin concentration. Thisstudy used a completely randomized factorial design (CRD), namely drying time, (fresh, 1, 2 days) andmaceration temperature (50, 60, 70oC), so that 9 combinations were obtained and each treatment was repeatedtwice. The results of this study indicate that the longer the drying time and the higher the macerationtemperature, the higher the levels of anthocyanin, total phenol, and the darker the color. The researchconducted showed that the best treatment combination was the drying time of 2 days with a macerationtemperature of 70°C to produce butterfly pea flower extract which had antioxidant activity of 54.66%, totalphenol 4.16 KTF (mgGAE/g), anthocyanin 123.48 mg/g, pH 6.03. Color sensory test analysis was 4.5 andwater content of butterfly pea flower was 10.79%. The optimal drying time and maceration temperature willproduce high total phenol and anthocyanin levels. Butterfly pea flowers have high levels of anthocyanins, sothey have the potential to be studied further. 

In Indonesia, dry noodles are one of the most widely consumed foods because they are easy to cook. The use of banana flour as a substitute for making noodles is an effort to diversify food in reducing imports of wheat flour. This study dried noodles were made from katuk leaf extract and from the substitution of banana kepok flour with wheat flour. The chemical and organoleptic properties of noodles added with banana kepok flour and katuk leaf extract as natural dyes are the focus of this study. A two-factor factorial Complete Randomized Design (RAL) was used in this study, specifically the ratio of wheat flour to cocoon banana flour (5:95, 10:90, 15:85 g) and the concentration of katuk leaf extract (10/100, 20/100, 30/100 ml). The results showed that katuk leaf dried noodles the ratio of cocoon banana flour and wheat flour 5:95 with katuk leaf extract 30g/100ml contain an ash content of 3.43%; protein content of 14.72%; crude fiber of 11.11%; antioxidant activity of 17.32%; reduced sugar content of 0.24%. The organoleptic test results of dry noodles of katuk leaves compared to banana kepok flour and wheat flour 5:95 with katuk leaf extract 30g/100ml resulted in a stable green color with a score of 2.95 (quite like), aroma 2.52 (strong enough), taste 2.10 (quite like), and overall liking 2.99 (quite like). The highest antioxidant activity parameter was obtained with a ratio of banana kepok flour and wheat flour 15:85 with katuk leaf extract 30g/100ml with a yield of 23.07%. The noodles that the panelists liked the most were the ratio of banana kepok flour and wheat flour 5:95 with katuk leaf extract 30g / 100ml with a result of 2.99 (quite like). Dried noodles made from katuk leaves and banana kepok have high enough antioxidant activity so that they can become nutritious foods that also have beneficial values for body health.