UMA - Open Access Journals (Universitas Medan Area)
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Decision Support System for Human Resource Program Prioritization Using AHP–SMART
Full text linkHuman Resource (HR) programs are a crucial aspect of improving the quality of life in rural communities; however, they are often constrained by limited resources and urgent needs. Therefore, this study implements the Analytical Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART) methods within a decision support system. The AHP method is employed to determine the criteria weights based on the relative importance of various factors influencing program implementation, including cost, benefits, community participation, needs, sustainability, ease of implementation, and risk of failure. Subsequently, the SMART method is applied to rank program alternatives based on the evaluated criteria. Accuracy testing shows that the system produces results fully consistent with manual calculations, achieving an accuracy rate of 100%. Functional testing using the black-box method indicates that all system features operate properly without errors. Meanwhile, the User Acceptance Test (UAT) results demonstrate that all respondents provided positive evaluations (scores ranging from 3 to 5), with no reported dissatisfaction, indicating that the system is feasible and well accepted by users. The results reveal that the integration of AHP and SMART provides accurate program priority recommendations, with the Community Health Program (0.7150) ranked as the top priority and Food Security Training (0.6550) as the second priority. This decision support system is expected to enhance the efficiency and accuracy of human resource decision-making in Blang Pulo Villag
The Performance Analysis Of Naïve Bayes Method In Classifying Diabetes Disease
No full textDiabetes is a chronic disease that affects millions of people worldwide and poses a major challenge to public health. In recent years, machine learning methods have been widely applied in the medical field to assist in disease classification and diagnosis. Among various classification algorithms, the Naïve Bayes method has gained attention due to its simplicity, efficiency, and effectiveness in handling probabilistic data. This study focuses on three variants of Naïve Bayes, Gaussian Naïve Bayes, Bernoulli Naïve Bayes, and Multinomial Naïve Bayes. The dataset used in this research consists of 768 rows and 9 columns. Data preprocessing includes checking for missing values, cleaning missing data, and normalizing data to standardize the scale of numerical features. The method used is Min-Max Scaling, which transforms feature values into a range between 0 and 1. In this study, three data-splitting scenarios were conducted with different proportions: 90:10, 80:20, and 70:30. In the first, second, and third experiments, Gaussian Naïve Bayes achieved accuracies of 80%, 82.28%, and 76.27%, respectively. Based on these results, it can be concluded that Gaussian Naïve Bayes is more optimal in classifying diabetes disease
Peran Pemerintah dalam Pengembangan dan Peningkatan Ekspor Rempah Nusantara: Analisis Hukum dan Kebijakan Publik di Indonesia
Full text linkSpices are a strategic commodity that has important historical, economic, and geopolitical value for Indonesia. Although Indonesia is known as one of the world's main spice producers, the spice sector's contribution to the national economy and export performance has not been optimal. This research aims to analyze the role of the government in the development of the archipelago's spice potential as well as the policies and regulations taken to increase the competitiveness and export of spices in the global market. The research method used is normative legal research with a statutory approach and a policy approach, supported by secondary data in the form of laws and regulations, policy documents, official government reports, and relevant scientific literature. The results of the study show that the government has established various strategic policies in the development of the spice sector, but their implementation still faces obstacles in the form of weak institutional coordination, limited supporting infrastructure, and suboptimal protection and support for farmers and spice business actors. Therefore, it is necessary to strengthen integrated policies, harmonize regulations across sectors, and increase regulatory and fiscal support to optimize the potential of the archipelago's spices in a sustainable manner. The conclusion of this study emphasizes that an active, consistent, and integrated government role is the main key in encouraging the increase of competitiveness and export of Indonesian spice
EXPLORATION OF INDIGENOUS ENDOPHYTIC ACTINOBACTERIA AS BIOCONTROL AGENTS AGAINST STEWART’S WILT (Pantoea stewartii subsp. stewartii) AND THEIR ROLE IN ENHANCING MAIZE GROWTH AND YIELD
Full text linkStewart’s wilt disease in maize is caused by the seed-borne bacterium Pantoea stewartii subsp. stewartii. The use of actinobacteria as biological control agents represents an environmentally friendly alternative for disease management, as these microorganisms can produce antagonistic secondary metabolites against plant pathogens. This study aimed to identify the best indigenous actinobacterial isolate for controlling Stewart’s wilt disease and improving the growth and yield of sweet corn. The research was conducted in two stages: (1) isolation and characterization of actinobacteria and P. stewartii subsp. stewartii, and (2) selection of indigenous actinobacteria for disease suppression and plant growth promotion. The experiment was arranged in a completely randomized design with 22 treatments and three replications. Observations included actinobacterial morphology, biosafety testing, disease development, and growth and yield parameters of sweet corn. A total of 25 actinobacterial isolates were obtained, of which five were pathogenic to maize based on biosafety tests. Isolate APPS1.4 showed the highest effectiveness by extending the incubation period to 8.98 days and reducing disease incidence and severity to 16.94% and 17.26%, respectively. Isolate APPS1.4 also increased plant height to 140,80 cm, leaf number to 10,70 leaves, and ear weight of sweet corn to 837,66 g
ANTIFUNGAL EFFECTIVENESS OF BAJAKAH ROOT EXTRACT (Uncaria acida) AGAINST THE ACTIVITY OF Malassezia furfur, THE CAUSATIVE AGENT OF SEBORRHEIC DERMATITIS IN MICE
Full text linkSeborrheic dermatitis is a chronic skin disorder associated with colonization by Malassezia furfur and is commonly treated with topical antifungal agents and corticosteroids, which may cause adverse effects when used long term. Therefore, safer and more sustainable alternative therapies derived from natural products are needed. One such candidate is bajakah root (Uncaria acida), which is known to contain flavonoids, alkaloids, tannins, terpenoids, and saponins with potential antifungal activity. This study employed a true experimental posttest-only design involving 24 male BALB/c mice induced with Malassezia furfur and divided into a negative control group (distilled water), a positive control group (ketoconazole), and treatment groups receiving Uncaria acida extract at concentrations of 25%, 50%, 75%, and 100%. The research stages included extr act preparation using ethanol maceration, ointment formulation, fungal infection induction, and clinical evaluation. Phytochemical analysis confirmed a total flavonoid content of 28.5 mg QE/g. The results demonstrated significant differences across all clinical parameters (p < 0.05), with the 75% extract concentration providing the most optimal clinical improvement. These findings indicate that Uncaria acida extract has strong potential for development as a topical herbal therapeutic candidate and provides a foundation for further studies focusing on formulation optimization and clinical trials
PRELIMINARY ASSESSMENT OF PLANT DIVERSITY IN FOUR HABITAT TYPES IN REBONJARO SURVEY AREA, SOUTH SUMATRA
Full text linkLand-use change in tropical landscapes poses increasing threats to plant diversity, yet baseline floristic data from transitional forest–agroecosystem areas in South Sumatra remain limited. This study aimed to document plant species diversity, floristic composition, and conservation status across four habitat types in the Rebonjaro forest landscape: secondary forest, shrubland, mixed rubber plantation, and oil palm plantation. Vegetation surveys were conducted in 2024 using purposive belt line transects adapted from rapid vegetation assessment methods. Species were recorded through direct field observation within ±10 m of transect lines, with transect lengths ranging from 100–300 m. Conservation status was assessed using the IUCN Red List (version 2024-2) and Indonesia’s Ministry of Environment and Forestry Regulation No. 106/2018. A total of 175 plant species representing 61 families were recorded, with Euphorbiaceae, Fabaceae, and Moraceae being the most species-rich families. Most species were categorized as Least Concern, while Aquilaria malaccensis was identified as Critically Endangered and Gluta renghas and Syzygium laxiflorum as Near Threatened. The results indicate that modified landscapes, particularly mixed rubber plantations, retain considerable plant diversity. This checklist provides essential baseline data to support long-term biodiversity monitoring and conservation planning in landscapes undergoing rapid land-use change
Reassessing the Role of Culture in Achieving the Sustainable Development Goals (SDGs): The Nigerian Narrative
Full text linkNigeria’s extensive cultural diversity, spanning over 250 ethnic groups, and its expanding creative economy hold significant potential for advancing sustainable development. However, the underutilization of cultural assets in national development strategies represents a persistent challenge. This study investigates how culture contributes to sustainability in Nigeria and evaluates the extent to which government policies, particularly the 1988 National Cultural Policy, align with the United Nations Sustainable Development Goals. Adopting a qualitative research design, the study analyses policy documents, government reports, and academic literature using thematic and content analyses, guided by the Cultural Heritage and Creativity Framework. Findings reveal that culture has been deployed in sustainability efforts through arts, music, media campaigns, and creative industries to promote civic education, peace-building, and environmental awareness. Despite this progress, weak policy implementation, funding limitations, and exclusion of cultural stakeholders constrain impact. The study concludes that systematic integration of cultural heritage and creativity into development planning, education, and SDG monitoring is crucial to strengthening Nigeria’s sustainability agenda and providing a replicable model for other African states
Anomaly Detection in Cloud Device-Based Information Technology Infrastructure Using Isolation Forest Algorithm
Full text linkCloud device-based information technology infrastructure generates large volumes of operational data that are dynamic and heterogeneous, increasing the complexity of monitoring and anomaly detection processes. Conventional rule-based approaches and supervised learning methods are often less effective due to limited labeled data and their inability to detect newly emerging anomaly patterns. Therefore, this study aims to apply and evaluate the Isolation Forest algorithm as an anomaly detection method for cloud device-based information technology infrastructure. The research data consist of system and network performance metrics, including CPU usage, memory utilization, disk activity, and network traffic collected from a cloud environment. The research stages include data preprocessing, normalization, and feature selection to improve data quality and model performance. The Isolation Forest algorithm is implemented using an unsupervised learning approach, where anomalies are identified based on the algorithm’s ability to isolate data points that exhibit characteristics deviating from the majority of normal data. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics, while parameter optimization is conducted using the grid search method to obtain the best configuration. The results indicate that the Isolation Forest algorithm is able to detect anomalies effectively, achieving high accuracy and a good balance between precision and recall. The model with optimal parameters demonstrates improved performance by reducing detection errors compared to the baseline configuration. Thus, the Isolation Forest algorithm can serve as a reliable and scalable solution to support monitoring activities and enhance the reliability of cloud infrastructure
Analisis Efektivitas Platform Digital Pariwisata dalam Meningkatkan Keterlibatan UMKM Lokal di Era Pemerintahan Digital di Kota Tangerang
No full textThis study analyzes the effectiveness of digital platforms in increasing the involvement of local MSMEs in the tourism sector in Tangerang City in the era of digital government. Digital transformation plays a crucial role in accelerating the adoption of technology, and the tourism sector is one of the most benefited. However, the involvement of MSMEs in digital platforms is still limited and unevenly distributed. This study adopts Technology Readiness Theory (TRT), Theory of Planned Behavior (TPB), and Digital Engagement Theory (DET) to identify factors that affect the involvement of MSMEs in digital platforms. The research method used is a quantitative approach with Structural Equation Modeling (SEM) techniques using SmartPLS software. The results show that both theories, TRT and TPB, have a positive effect on Digital Engagement, but TRT has a greater influence than TPB. The dimensions of optimism and innovativeness in TRT significantly encourage the adoption of digital technology (p < 0.05), while attitude, subjective norms, and perceived behavioral control in TPB influence the intentions and behaviors of technology adoption. In contrast, discomfort and insecurity show no significant effect. Unlike previous research that focused on general MSMEs, this study highlights tourism-oriented MSMEs operating in urban environments. This research provides important recommendations for policies that support the digitalization of MSMEs in the tourism sector, with a focus on digital literacy training, increasing trust in digital platforms, and simplifying the technology adoption process.Penelitian ini menganalisis efektivitas platform digital dalam meningkatkan keterlibatan UMKM lokal di sektor pariwisata di Kota Tangerang di era pemerintahan digital. Transformasi digital memainkan peran penting dalam mempercepat adopsi teknologi, dan sektor pariwisata adalah salah satu yang paling diuntungkan. Namun, keterlibatan UMKM dalam platform digital masih terbatas, dan tidak merata. Penelitian ini mengadopsi Technology Readiness Theory (TRT), Theory of Planned Behavior (TPB), dan Digital Engagement Theory (DET) untuk mengidentifikasi faktor-faktor yang mempengaruhi keterlibatan UMKM dalam platform digital. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan teknik Structural Equation Modeling (SEM) menggunakan software SmartPLS. Hasil penelitian menunjukkan bahwa kedua teori tersebut, TRT dan TPB, memiliki pengaruh positif terhadap Digital Engagement, namun TRT memiliki pengaruh yang lebih besar daripada TPB. Dimensi optimisme dan inovasi dalam TRT mendorong adopsi teknologi digital, sementara sikap, norma subjektif, dan kontrol perilaku yang dirasakan dalam TPB semakin mempengaruhi niat dan perilaku adopsi teknologi. Selain itu, hasil penelitian juga mengidentifikasi tantangan utama dalam adopsi teknologi, seperti ketidaknyamanan dan ketidakamanan yang menghambat keterlibatan UMKM. Penelitian ini memberikan rekomendasi penting untuk kebijakan yang mendukung digitalisasi UMKM di sektor pariwisata, dengan fokus pada pelatihan literasi digital, meningkatkan kepercayaan pada platform digital, dan menyederhanakan proses adopsi teknologi
Integration of Maja Labo Dahu in Case-Based Learning to Strengthen Political Awareness of Students in Anti-Corruption Education from a Gender Perspective
Full text linkThis study evaluates the effectiveness of integrating Maja Labo Dahu (MLD) honesty, self-control, and courage to report into Case-Based Learning (CBL) to strengthen political Awareness, anti-corruption intentions, and readiness to act among students, as well as to assess gender moderation and process mechanisms. An explanatory mixed-methods Design was employed through a quasi-experimental pre–post study with cluster randomization at the class level (CBL–MLD vs standard CBL). The BOPPPS–CBL syntax was enriched with SAO mapping, reason-giving, and dialogic role-play/World Café forums. T0–T1–T2 measurements were analyzed using ANCOVA and multilevel models, accompanied by process regression and qualitative triangulation. Results showed that the intervention outperformed the control group across all outcomes, with effects persisting at follow-up. Manipulation checks confirmed higher MLD internalization without differences in exposure time. Condition×gender interactions were detected: political awareness gains were relatively larger among males, while increases in anti-corruption intentions and readiness to act were more substantial among females. Process analysis identified value internalization as the primary driver of change, followed by equal participation and implementation fidelity. In conclusion, CBL–MLD is effective, replicable, and auditable, with curricular implications for standardizing value-based case drivers, reason-giving protocols, safe dialogic facilitation, and gender-sensitive strategies