UMA - Open Access Journals (Universitas Medan Area)
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VIRULENCE AND HORIZONTAL TRANSMISSION POTENTIAL OF ENTOMOPATHOGENIC FUNGI AGAINST SUBTERRANEAN TERMITES (Macrotermes gilvus)
Full text linkIndonesia’s tropical climate provides favorable conditions for subterranean termites, particularly Macrotermes gilvus, which cause significant economic losses as wood-destroying pests. Chemical control methods may lead to resistance and environmental problems; therefore, alternative control strategies are needed. This study aimed to evaluate the virulence of Metarhizium sp., Beauveria sp., Aspergillus sp., and Penicillium sp. against M. gilvus based on termite mortality, wood weight loss, and horizontal transmission. Isolates of Metarhizium sp. and Beauveria sp. were obtained from the BSIP Lembang laboratory collection, while Aspergillus sp. and Penicillium sp. were isolated from rhizosphere soil at the same site. The experiment used 13 treatments with three replications, consisting of three conidial concentrations (10⁶, 10⁷, and 10⁸ conidia/ml) and a control. Bioassays were conducted at 26–28 °C and 70–95% relative humidity using no-choice termiticide and horizontal transmission tests. Data were analyzed using the Kruskal–Wallis test. All fungal isolates caused termite mortality and wood weight loss. Beauveria sp. at 10⁸ conidia/ml showed the highest efficacy, achieving 100% mortality within 24 hours. Horizontal transmission was indicated by white hyphal growth on termite bodies. These results demonstrate that entomopathogenic fungi, particularly Beauveria sp., are promising environmentally friendly biological control agents against M. gilvus
Empat Wajah Kekuasaan: Kajian Spektrum Kepemimpinan Politik Indonesia Pascareformasi (1999–2024)
Full text linkThis study develops a Spectrum Model of Indonesian Political Leadership through an analysis of the political thought and exercise of power of four post-reform presidents: Abdurrahman Wahid, Susilo Bambang Yudhoyono, Joko Widodo, and Prabowo Subianto. The study aims to map variations in political leadership within Indonesia’s electoral democracy from 1999 to 2024 and to explain their implications for democratic quality. Using content analysis, the research examines 86 state speeches, 24 policy documents, and 212 national media articles. The analysis employs frequency coding of key thematic terms representing orientations toward power and democracy. The findings identify four ideal types of political leadership: Normative–Pluralist, Rational–Stable, Populist–Pragmatic, and Militaristic–Adaptive. These types exhibit fundamental differences in power orientation, state–society relations, and strategies of political consolidation. The spectrum model offers a new analytical framework for understanding leadership dynamics in electoral democracies that are vulnerable to oligarchic influence and power centralization. Policy implications underscore the importance of strengthening non-executiv
Representasi Ideologi Media Sosial dan Netizen Indonesia pada Film Budi Pekerti (2023)
Full text linkThe phenomenon of social media has given birth to a new culture in social life, where digital public opinion often plays the role of moral controller. This study analyzes the representation of Indonesian social media and netizens in the film Budi Pekerti (2023) by Wregas Bhanuteja using Teun A. van Dijk's Critical Discourse Analysis approach. This research aims to reveal how the ideology of social media and Indonesian netizens is represented in ethical films. The results of the study show that in the text dimension, films depict the distortion of meaning and public judgment in the digital space as a form of instant morality ideology. In the dimension of social cognition, it displays ideological awareness of netizens' culture that is reactive and loses empathy. Meanwhile, in the context dimension, it reflects the condition of Indonesian people who live in the hegemony of social media, where image is more important than humanity
The Role of Mosque Communities in Preventing Recidivism among Former Prisoners in Jakarta
Full text linkRecidivism remains a significant challenge in the social reintegration of former prisoners, particularly due to persistent social and economic pressures after release. This study aims to analyze the role of mosque-based communities in providing religious guidance as a mechanism for preventing recidivism among former prisoners in Jakarta. This research employs a qualitative case study approach, using unstructured interviews and participant observation involving three former prisoners and one mosque community mentor. Data analysis is conducted using General Strain Theory to examine the relationships among post-release pressures, emotional responses, and the tendency to reoffend. Findings indicate that former prisoners experience ongoing economic difficulties and social stigma, which increase vulnerability to recidivism. Mosque communities serve as alternative social spaces that offer religious mentorship, social support, and adaptive coping mechanisms to address these pressures. Religious guidance contributes to emotional regulation and the development of more constructive life orientations. This study concludes that mosque-based communities serve as effective informal social actors in supporting social reintegration and preventing recidivism among former prisoners
Transformasi Digital Melalui Aplikasi Signal dalam Meningkatkan Pelayanan Pajak Kendaraan di Kabupaten Bojonegoro
Full text linkThis study aims to analyze the digital transformation of motor vehicle tax services through the use of the SIGNAL (National Digital Samsat) application in Bojonegoro Regency. The study uses a qualitative approach with a case study method through interviews, observations, and documentation of eight informants consisting of Samsat officers, police, and application users. Data analysis was conducted using the Technology Acceptance Model (TAM) framework, taking into account social dimensions, public policy, and institutional readiness. The results show that the SIGNAL application provides benefits in the form of time efficiency, ease of access, and increased service transparency. However, its use still faces technical obstacles, low digital literacy, and limited user socialization and assistance. This study confirms that the success of digital transformation in public services is not only determined by the quality of technology, but also by institutional support and sustainable policy implementation. Theoretically, this study expands the application of TAM in the context of public services by incorporating social and institutional aspects
The Diffusion K-Means And Cluster Estimation With Iteration Analysis For Optimal Clustering
No full textThis study introduces the Diffusion K-Means method, which aims to improve the quality and efficiency of data clustering compared to the conventional K-Means algorithm. Diffusion K-Means integrates a diffusion process to enhance the selection of initial centroids, addressing one of the main weaknesses of K-Means. This diffusion process helps capture the intrinsic geometric structure of the data, thereby avoiding suboptimal local solutions. In addition, the proposed method incorporates an iterative analysis to estimate the optimal number of clusters, which is often a major challenge in data clustering. By dynamically adjusting the number of clusters during the clustering process based on performance evaluation at each iteration, the method reduces dependence on user-defined initial parameters. Experimental results on three datasets with varying levels of complexity demonstrate that Diffusion K-Means significantly reduces the number of iterations required to achieve convergence compared to conventional K-Means. Moreover, the method is able to determine the optimal number of clusters more accurately, as reflected by improved Silhouette Score evaluations. For larger and more complex datasets, Diffusion K-Means shows superior capability in capturing complex data structures, resulting in more compact and well-separated clusters. These findings indicate that Diffusion K-Means provides a more adaptive and effective solution for a wide range of data clustering applications, particularly in complex and non-linear data contexts
A Hybrid Approach For Multi-Class Fault Diagnosis in Imbalanced Data Using Minority Class Density and Layered Support Vector Machines
Full text linkData imbalance in multi-class fault diagnosis can lead to model bias toward majority classes, thereby reducing prediction accuracy for minority classes. This study proposes a hybrid approach that integrates minority class density measurement with Layered Support Vector Machines (LSVM) to improve accuracy for minority classes without sacrificing overall performance. Experimental results demonstrate that the proposed method effectively addresses data imbalance and improves average accuracy by up to 15% compared to conventional methods
Combined Barker-M-Sequence Coded LFM for High-Performance Subarray-MIMO Radar Applications
Full text linkSubarray-Multiple-Input Multiple-Output (SMIMO) radar is an advanced technology that integrates the advantages of phased-array and MIMO radars to enhance target detection resolution. A key challenge in SMIMO implementation lies in improving velocity resolution without compromising spectral efficiency, while maintaining accurate target detection capability under high sidelobe levels and inter-channel interference. This study proposes a novel approach—Combined Barker-M-Sequence Coded LFM—in which the LFM signal is phase-modulated using a hybrid code formed by concatenating a Barker sequence (length 11) and an M-sequence (length 7). Simulation results show that the proposed signal achieves a Peak Sidelobe Ratio (PSLR) of −20.83 dB, significantly outperforming LFM-Barker (−8.45 dB) and LFM-M-sequence (−16.3 dB). It also delivers a velocity resolution of 0.95 m/s and a range resolution of 225 m, representing a 38% improvement over standard LFM. Moreover, under SNR = −5 dB, the system achieves a SINR gain of 4.7 dB relative to LFM-M-sequence. This approach enables more efficient waveform utilization in modern radar applications—such as air surveillance, military defense, and autonomous vehicles—particularly in challenging environments characterized by low SNR, multipath propagation, and high clutter
IoT System for Monitoring Protection IED Switch Status
Full text linkThe reliability of protection systems in transmission networks depends on the readiness of devices, particularly the switch status that governs trip functions during a fault. In many substations, monitoring is still manual, conducted via panel checks or photos, which can lead to delays and misconfigurations, especially after maintenance. This risks protection unreadiness, causing operational delays or trip failures. This research develops an IoT-based system to monitor the status of protection switches, ensuring real-time readiness. The system reads IED coil registers via Modbus TCP, processes data using Node-RED, and displays results on a Node-RED UI dashboard. Results show the system reads switch status with 100% communication success. Status changes are detected in 0.2–0.3 seconds, matching the 0.5-second polling interval. Tests conducted on four IED test scenarios demonstrated full conformity between the IED status and the dashboard. All five protection channels per IED were read without discrepancies, and the dashboard handled parallel IED updates without data loss. This demonstrates that the system can replace slow and error-prone manual methods. This IoT system enhances protection reliability and supports efficient subsystem management. The implication is that this digital monitoring can be implemented without additional SCADA infrastructure
Performance Evaluation of Augmented Reality-Based Smart Farming for Rice and Corn Pest Detection
Full text linkAn Augmented Reality application for detecting pests and diseases in rice and corn has been developed to overcome the limitations of visual identification in the field, which still relies on subjective interpretation by users. This system utilises image processing and AR overlay based on smart farming to classify symptoms in real time, improving the precision of diagnosis and consistency in control decision-making. This study aims to design, implement, and evaluate the performance of an augmented reality (AR)-based smart farming system for the visual and interactive detection of pests and diseases in rice and corn crops. The research method uses an evaluative approach by assessing the performance of the Augmented Reality system in the field based on detection accuracy, operational reliability, and the suitability of the results to the predetermined performance indicators. Testing was conducted in Gampong Releut Barat, Dewantara District, North Aceh. The results showed that pest and disease detection accuracy increased from 42.4% to 66.7%, with a system response time of <2 seconds, accompanied by an 18% reduction in crop damage and a 24% increase in productivity, confirming the reliability of the system for field diagnosis. This achievement is significant because it meets the operational performance threshold for smart farming and demonstrates the system's readiness for adoption as an Augmented Reality-based decision support tool at the farmer level. The research conclusion indicates that Augmented Reality-based smart farming has the potential to improve detection accuracy, control efficiency, and crop productivity as a support for precision agriculture and sustainable village food security