Publication Search

This study investigated the logical function of clause complexes in two selected English nursery tales, Jack the Giant Killer and Jack and the Beanstalk, using Halliday’s Systemic Functional Linguistics (SFL) framework. The research focuses on the realization of clause interpendency through the taxis system (parataxis, hypotaxis, and taxis combination) and logico-semantic relations (elaboration, extension, enhancement, idea and locution). The method employed is qualitative content analysis based on Krippendorf involving processes such as unitizing, sampling, coding, reducing, interpreting, and narrating. From 1,048 clauses analyzed, 341 wereidentified as clause complexes. The findings show that enhancement is the most dominant logico-semantic relation (164 cases), followed by extension (123 cases), elaboration (100 cases), locution (57 cases), andidea (22 cases). The findings show that paratxis is the most dominant taxis in selected English nursery tales. While hypotaxis relations mostly realized in enhancement, while extension appeared dominantly in parataxis. These results reveal that although nursery tales are intended for children, they often employ complex grammatical structure, therefore, balancing narrative engagement and linguistic accessibility is crucial to support children’s language development and reading comprehension.

Plastic waste management in rural areas, particularly in tourism villages, remains an environmental challenge that requires sustainable solutions. Negeri Tongging Village, as a tourism area around Lake Toba, has experienced an increase in household plastic waste that has not been managed optimally, potentially affecting environmental quality and community well-being. This community service activity aimed to enhance community awareness and capacity in plastic waste management through the implementation of a circular economy approach based on a waste exchange program and ecobrick production. A participatory approach was employed by actively involving the community in all stages of the activity, including socialization, program implementation, and evaluation. The program was carried out during the 2025 Community Service Program of Universitas Islam Negeri Sumatera Utara, consisting of circular economy socialization, collection of plastic waste through an exchange scheme with basic necessities, training on ecobrick production, and the provision of supporting facilities for environmental cleanliness. The results indicate an increase in community participation and environmental awareness, a reduction in improperly disposed plastic waste, and an improvement in community skills in processing plastic waste into useful products. In addition, the activity strengthened mutual cooperation and environmental responsibility among community members. In conclusion, the application of a circular economy approach through ecobrick-based activities can serve as an effective alternative solution for plastic waste management while simultaneously supporting sustainable community empowerment in rural tourism areas.

This study aims to introduce the convenience of digital transactions through socialization of the use of the Quick Response Code Indonesian Standard (QRIS) to residents of Marihat Bukit Village. This activity was motivated by the low public understanding of the use of non-cash transactions, particularly QRIS, which is an innovative integrated digital payment system from Bank Indonesia. Through socialization and direct practice, residents were introduced to how to use QRIS in various daily transactions, such as shopping, paying for services, and other local economic activities. The results of the activity showed an increase in public understanding and interest in the use of digital transactions that are easier, faster, and safer. It is hoped that this activity will encourage digital financial inclusion in rural areas and support government programs to expand literacy and the application of financial technology in the community. Furthermore, active community participation in this activity shows great potential to reduce dependence on cash transactions and encourage digital transformation at the village level. This activity is also expected to accelerate the transition to a more inclusive and digital-based society.

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 selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

  This study examines the strategy of writing an expository narrative using a reflective approach in the production of the documentary film "2000 Untuk Parkir: Fenomena Juru Parkir Liar di Jatinangor" Using descriptive qualitative methods through participant observation, in-depth interviews, and data triangulation, this 15-minute film presents a three-act plot that integrates empirical facts (such as a daily income of Rp50,000 from a Rp2,000 per vehicle rate) with the filmmaker's critical reflections on the dynamics of the informal economy and urbanization in educational areas. Results of an effectiveness test on 20 student respondents showed an 85% comprehension rate and a 90% increase in critical awareness, demonstrating the superiority of this hybrid approach in building empathy without excessive subjectivity. The discussion confirms the strategy's contribution to the discourse of Indonesian documentary production, with implications for advocacy for inclusive parking policies.    

Password security is a critical component in protecting information systems, as passwords are often the primary target of various attacks, particularly brute force attacks. A brute force attack works by systematically attempting all possible character combinations until the correct password corresponding to a stored hash value is found. Therefore, the choice of an appropriate hash algorithm plays a significant role in determining a system’s resistance to such attacks. This study aims to analyze and compare the password cracking time of MD5, SHA-256, and SHA-512 hash algorithms under brute force attack scenarios. The research methodology involves generating hash values from a set of test passwords using each hash algorithm, followed by conducting brute force attacks to recover the original passwords based on the generated hash values. The collected data are analyzed by measuring the time required to crack passwords for each algorithm. The results indicate that MD5 has the fastest cracking time compared to SHA-256 and SHA-512, indicating a lower level of resistance to brute force attacks. SHA-256 demonstrates better security than MD5 but remains less resistant when compared to SHA-512. The SHA-512 algorithm requires the longest cracking time, reflecting the highest level of resistance to brute force attacks among the tested algorithms. In conclusion, hash algorithms with larger bit lengths provide stronger protection against brute force attacks and are more suitable for secure password storage in information systems.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

This research aims to implement a Multi-Attribute Decision Making (MADM) approach using the AHP–TOPSIS method to assist PT. XYZ in selecting the most suitable intern candidate for the Social Media Specialist position. The increasing number of applicants each year makes the selection process more complex, requiring a systematic and data-driven decision support system. AHP was used to determine the priority weights of five main criteria—Interview, Experience, Portfolio, Skill, and Achievement—along with their subcriteria. All pairwise comparison matrices met the consistency requirements, indicating valid weights. The TOPSIS method was then applied to calculate the preference scores of ten candidate alternatives based on the weighted normalized decision matrix, ideal solutions, and distance measures. The results show that candidate A3 achieved the highest preference score (0.9422), followed by A7 and A8, making them the top recommended candidates. This study demonstrates that integrating AHP and TOPSIS effectively supports companies in conducting objective, efficient, and accurate recruitment decision-making processes.

The community service program entitled “Implementation of Digital Technology through Mapping MSME Locations on Google Maps to Enhance Consumer Accessibility in the Bukit Lawang Tourism Area” aims to improve digital literacy and business visibility among local micro, small, and medium enterprises (MSMEs) in Bukit Lawang, Langkat Regency, North Sumatra. The program was carried out using a participatory approach through socialization, training, and technical assistance involving four local MSMEs, consisting of two wildlife-themed painting craftsmen, one culinary business, and one souvenir craftsman. The results show that all MSMEs were successfully registered and verified on Google Maps, leading to increased consumer accessibility and customer interaction through digital reviews. In addition to expanding market reach, this activity also fostered awareness and independence among business owners in managing their online business profiles. This program demonstrates that location mapping through Google Maps serves as a simple yet effective strategy to support MSME digital transformation and strengthen community-based creative economies in rural tourism areas.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

Cooking oil is a staple food widely used in daily cooking. However, repeated use of cooking oil, resulting in its conversion into used cooking oil, can have negative impacts on both health and the environment. Used cooking oil that has changed color and quality is carcinogenic and has the potential to increase the risk of cardiovascular disease if consumed again. Furthermore, improper disposal of used cooking oil can cause environmental pollution because it is difficult to decompose. This study aims to examine the wise management of household waste, especially used cooking oil, by utilizing it into products with utility value. One form of utilization is processing used cooking oil into environmentally friendly soap for laundry purposes. This activity was carried out as a form of community service through training in making soap from used cooking oil with the addition of essential oils for aromatherapy for residents of Pasar Rawa Village, Gebang District, Langkat Regency. The research method used was qualitative, with observation and direct participation in the training activities. The results showed that participants, particularly housewives, demonstrated high enthusiasm and were actively involved in both the presentation and the soap-making practices. This activity not only raised public awareness of the importance of managing used cooking oil waste but also provided new skills that have the potential to support family economic growth.

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapat membantu tim Marketing Officer (MO) PT. Alvarel Technology Innovation dalam menentukan status pelanggan secara objektif dan terstruktur. Sistem ini dirancang menggunakan kombinasi metode Analytical Hierarchy Process (AHP) dan Weighted Sum Model (WSM). Metode AHP digunakan untuk menentukan bobot kriteria yang meliputi Potensial Pasar, Urgensi, Finansial, serta Hubungan dan Reputasi, dengan memastikan konsistensi matriks perbandingan berpasangan. Hasil pembobotan kemudian digunakan dalam metode WSM untuk melakukan perhitungan skor total pelanggan dan menyusun pemeringkatan status berdasarkan nilai tertinggi hingga terendah. Data penelitian diperoleh dari catatan internal perusahaan dan wawancara dengan Marketing Officer, dengan jumlah sampel 30 pelanggan. Hasil pengujian menunjukkan bahwa sistem dapat menghasilkan peringkat status pelanggan dalam lima kategori, yaitu potensial, prospek, pending, pasif, dan skip. Temuan utama memperlihatkan bahwa kategori prospek memperoleh skor tertinggi dan menjadi prioritas tindak lanjut. Dengan demikian, sistem pendukung keputusan berbasis AHP–WSM ini mampu mengurangi subjektivitas, meningkatkan efisiensi, serta memberikan rekomendasi yang lebih akurat dan terukur untuk mendukung pengambilan keputusan strategis perusahaan dalam pengelolaan pelanggan.

The selection of a thesis topic that aligns with students’ interests and competencies often poses a challenge in academic environments. Inappropriate topic selection can lead to decreased motivation and delays in completing the final project. This study aims to develop a thesis topic recommendation system based on a genetic algorithm that considers students’ interests and academic abilities. The data used include grades from core courses, results of research interest questionnaires, and a list of thesis topics provided by academic supervisors. Each topic is represented as a chromosome, while the fitness function is calculated based on the level of compatibility between student attributes and topics. The selection process employs the roulette wheel method, with single-point crossover and random mutation to generate an optimal solution population. The test results show that the recommendation system based on the genetic algorithm achieves an accuracy rate of 86.7%, higher than the keyword-matching method, which only reaches 71.2%. Therefore, this approach is proven effective in assisting students to determine thesis topics that are suitable, objective, and efficient.

Epilepsy is one of the most common chronic neurological disorders in children. Long-term use of antiepileptic drugs carries the risk of Drug Related Problems (DRPs) such as drug interactions, inappropriate dosing, and untreated indications. This study aimed to identify the types and incidence of DRPs in pediatric epilepsy outpatients at Embung Fatimah General Hospital, Batam. This was a descriptive, non-experimental study with a retrospective design. Data were collected from pediatric medical records (<18 years) between January–December 2024, with a total of 45 patients. DRPs were identified using the American Society of Hospital Pharmacist (ASHP) classification. Of 45 patients, the majority were aged 1–5 years (38%) and female (53%). The most frequently used antiepileptic drug was sodium valproate (56.36%). Identification DRPs included drug interactions (63.16%), untreated indications (5.26%), and inappropriate drug selection (5.26%). No cases of overdose or failure to receive medication were found. The most dominant DRP in pediatric epilepsy patients was drug interactions, particularly between valproic acid and folic acid.

Thick leaf (Glochidion superbum) is a medicinal plant traditionally utilized by the community of East Panjang Island for the treatment of wounds, pain, and ulcers. This research was conducted to isolate the major compounds present in the ethyl acetate fraction of thick leaf and to assess its antioxidant activity using the DPPH method. The plant leaves were first cleaned, dried, and powdered, followed by maceration extraction using methanol, which resulted in an extract yield of 16.42%. The crude extract was fractionated by column chromatography using EtOAc and MeOH eluents with ratios of 9:1 (K1), 4:1 (K2), 1:4 (K3), and 1:9 (K4). UV–Vis spectrophotometric analysis showed an absorption peak at 288 nm, indicating the presence of phenolic compounds, identified as ferulic acid. Antioxidant testing using the DPPH method showed IC₅₀ values for fractions K1, K2, K3, and K4 of 12.981, 5.159, 9.658, and 10.971 µg/mL, respectively, with vitamin C as a positive control (3.563 µg/mL). Fraction K2 showed the strongest antioxidant activity. These results indicate that thick leaf contains ferulic acid with very strong antioxidant activity and has potential as a natural antioxidant source.

Inflammation is a common condition that affects everyone and can affect quality of life. Candlenut leaves (Aleurites moluccanus) are known to contain flavonoid compounds, such as swertisin and 2''-O-rhamnosylswertisin, which are thought to have anti-inflammatory activity. This study aims to test the anti-inflammatory activity of candlenut leaves using four treatment groups, namely positive control (sodium diclofenac), negative control (Na-CMC), n-hexane fraction (200 mg/KgBW), and ethyl acetate fraction (200 mg/KgBW). Leg edema volume measurements were carried out periodically for up to 6 hours using a digital caliper. The results showed that the percentage of edema inhibition in the negative control group reached 64.13%, positive control 38.91%, n-hexane fraction 39.99%, and ethyl acetate fraction 39.82%. Although the ethyl acetate fraction showed better anti-inflammatory activity than the n-hexane fraction, its inhibition percentage was still lower than that of the positive control. These findings suggest that candlenut leaves have potential as a source of anti-inflammatory compounds, particularly the ethyl acetate fraction. Further research is needed to evaluate its efficacy and mechanism of action.