Publication Search

This research critically investigates the circulation of disinformation concerning the instability of fuel prices on the digital platform X and its subsequent implications for the polarization of modern society. In an era where unverified economic news frequently dictates public reaction, fake news often acts as a potent catalyst for mass anxiety. By implementing a quantitative framework driven by lexicon-based computational sentiment analysis, this study effectively processed a dataset of 500 public opinion samples extracted via Google Colab spanning from April 2024 to April 2026. To ensure computational accuracy and eliminate textual noise, the data underwent a rigorous preprocessing phase encompassing case folding, alongside the systematic removal of URLs, account mentions, numbers, hashtags, and punctuation marks. The statistical outcomes revealed a highly disproportionate emotional landscape, overwhelmingly dominated by 451 negative reviews. In stark contrast, neutral observations and positive affirmations were nearly absent, recording only 40 and 9 instances, respectively. The data compellingly illustrates that the relentless influx of pessimistic narratives regarding economic instability directly induces financial panic, undermines rational discourse, and severely fragments cyberspace into deeply polarized factions.

The oil and gas industry requires efficient initial processing to separate reservoir fluids into oil, gas, and water phases. The Separator Unit is the main facility that plays a vital role in the surface facility production stage. This study aims to evaluate the type of separator used, identify control components, and understand the working principles and operational procedures of separators in the Main Production Facility (MPF) area. The methodology used is direct observation and literature studies during the implementation of practical work in July 2024 at PT. Citic Seram Energy Limited, Seram Non Bula Block, Maluku. The observation results show that the type of separator used is a Horizontal Three Phase Separator with tag codes 03-V-001A and 03-V-001B operating alternately. The separation process is carried out based on differences in fluid density utilizing gravity, supported by internal components such as deflector plates, mist extractors, weirs, and straightening vanes. Separator operation is maintained at an operating pressure of around 55 psig to ensure optimal separation efficiency and work safety. The conclusion of this study indicates that effective separator operation requires stable pressure and temperature control as well as routine maintenance to prevent sediment buildup and maintain product quality.

The exploitation of natural resources in coastal areas and small islands in Indonesia shows the strong influence of the ideology of extractive capitalism. This ideology often causes ecological conflicts. For example, in the case of the rejection of gold mining that occurred on Sangihe Island, North Sulawesi. This article examines the contemporary Indonesian novel Perempuan yang Tunggu di Lorong Menuju Laut by Dian Purnomo in representing this problem. This study is a qualitative study with an interdisciplinary approach. The main theoretical framework used is ecocriticism (Glotfelty & Buell), extractive capitalism (Gudynas, Harvey, and Bebbington), and the concept of slow violence (Nixon) with a close reading method of the text. The results of the study show that the novel represents: the ideology of anthropocentric-extractive capitalism that reduces Sangihe Island to an economic commodity; dismantles the mechanism of power that conquers law and politics through the practice of elite and ruler collusion; displays the ecocentric resistance of the Sangihe community based on ancestral wisdom and spirituality. In conclusion, this novel voices an ideological critique of the hegemony of anthropocentrism and extractive capitalism and calls for a just and sustainable ecological awareness. Given the novel's limited data, further studies could consider exploring other research subjects or developing a theory of this research subject.

Background: Nallaswamy Class III alveolar ridge refers to an edentulous alveolar bone that has undergone resorption, resulting in a moderate ridge height with a knife-edge ridge form. This condition is relatively common and has been reported to reach a prevalence of approximately 89% in edentulous mandibles. Objective: To describe the prosthodontic treatment implications in patients with Nallaswamy Class III alveolar ridge using an acrylic resin removable partial denture (RPD). Case Report: A 22-year-old female patient presented to RSGM Soelastri with complaints of discomfort while chewing due to the loss of several posterior teeth in the mandible. The teeth had been extracted three years earlier due to caries and retained roots. Intraoral examination revealed healthy dentition in the maxillary arch and edentulous areas at teeth 35, 36, 45, and 46 in the mandibular arch. The alveolar ridge in the edentulous area showed a knife-edge form with moderate height, consistent with Nallaswamy Class III. Discussion: The condition was classified as Applegate-Kennedy Class III modification 1P with bilateral bounded saddle areas, which may affect mastication, aesthetics, and speech. Conclusion: Partial edentulism with a knife-edge alveolar ridge can be effectively managed using an acrylic resin removable partial denture.

Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.

The moral crisis in contemporary education demands a reconstruction of moral values derived from the Qur'an as the epistemological foundation of Islamic education. This study aims to identify and analyze the moral educational values found in Surah Al-Imran, verse 159, particularly through the perspectives of Ibn Kathir’s tafsir and Quraish Shihab’s Tafsir Al-Misbah, and formulate its implications for contemporary Islamic education practices. Using a qualitative approach with a library research method, this study analyzes primary data from Ibn Kathir’s tafsir and Tafsir Al-Misbah, confronted with secondary literature on moral education theory. The analysis technique used was content analysis with a comparative-analytical approach to extract both explicit and implicit moral educational values. The findings revealed three fundamental values: first, gentleness (ar-rifq) as a humanistic pedagogical communication method; second, forgiveness (al-‘afw) as a mechanism for restoring educational relationships; third, consultation (asy-syura) as a democratic principle in educational decision-making. This study concludes that these values are not merely individual ethics but methodological principles that must be integrated into the culture of Islamic educational institutions. Practically, this requires a transformation from authoritarian-monological to dialogical-participatory educational approaches and strengthening educators' socio-emotional competencies.

This study aims to analyze promiscuous sexual behavior among adolescents as a manifestation of problematic behavior through a literature review approach. The method used was a literature review with a descriptive-analytical design, analyzing 20 national scientific articles published between 2007 and 2024. The analysis process was conducted systematically through the stages of identifying relevant sources, classifying themes, extracting important data, and synthesizing findings to build a comprehensive conceptual framework. The study results were then grouped into four main aspects: internal factors, external factors, impacts, and prevention strategies. The study results indicate that promiscuous sexual behavior among adolescents is influenced by internal factors such as knowledge, attitudes, and moral reasoning, as well as external factors such as family environment, peers, social media, and lack of parental supervision. The resulting impacts are multidimensional, encompassing health (sexually transmitted infections and unplanned pregnancies), psychological (anxiety, guilt), and social (stigma and educational disruption). Effective prevention efforts involve comprehensive sex education, active family involvement, and collaboration between schools and the community.

Accurate classification of rainfall intensity patterns is important for early warning systems, hydrometeorological risk assessment, and water resource management. Surface rain gauges have limited spatial coverage, so this study uses NOAA NEXRAD Level II radar data from the KTLX station in 2023. K-Means clustering was applied to identify rainfall intensity patterns from 30 randomly selected days, with scans stratified into four daily time intervals. Seven features were extracted from each radar sweep, including reflectivity statistics, convective and stratiform ratios, and rainfall coverage. The data were normalized and balanced before clustering. The optimal cluster count was determined through a combined evaluation of the Elbow Method, Silhouette Score, and Davies-Bouldin Index, yielding K=5 as the most representative configuration. Evaluation results demonstrated a Silhouette Score of 0.3871 and a Davies-Bouldin Index of 0.8599, indicating moderate cluster cohesion that reflects the inherent overlapping nature of rainfall intensity transitions in radar reflectivity data. The clusters represent rainfall regimes from non-precipitating conditions to intense convective events. These results support the use of K-Means for automated rainfall pattern recognition and flood forecasting applications. 

This study aimed to determine the effect of coffee husk waste application and shallot extract soaking on the growth of cat’s whiskers (Orthosiphon aristatus) cuttings. The research employed a factorial Randomized Block Design (RBD) consisting of two factors with 48 experimental plots. The first factor was the application of coffee husk waste, symbolized as “L,” with four treatment levels: L0 = 0 g/polybag (without coffee husk waste), L1 = 75 g/polybag, L2 = 150 g/polybag, and L3 = 225 g/polybag. The second factor was shallot extract soaking, symbolized as “B,” with four treatment levels: B0 = 0 ml/L of water, B1 = 5 ml/L of water, B2 = 10 ml/L of water, and B3 = 15 ml/L of water. Thus, there were 16 treatment combinations with three replications. The observed parameters included time to shoot emergence, number of shoots, plant height, number of leaves, leaf area, and survival percentage. The results showed that the application of coffee husk waste and shallot extract soaking had no significant effect on the growth of cat’s whiskers (Orthosiphon aristatus) cuttings across all observed parameters.

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.

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.