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Primary Journal of Multidisciplinary Research
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 30900972     DOI : https://doi.org/10.70716/pjmr
Core Subject : Religion, Economy, Science, Education, Art, Social,
Religion Arts Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice
Primary Journal of Multidisciplinary Research (PJMR) is a multidisciplinary journal published every bimonthly with online version of ISSN 3090-0972 by Lembaga Publikasi Ilmiah Nusantara and can be accessed openly. This journal is a peer reviewed, open access, scientific and scholarly journal which publishes research papers, review papers, case reports, case studies, books review, thesis, dissertation works, etc. PJMR journal provides a means for ongoing discussion of relevant issues that fall within the focus and scope of the journal that can be examined empirically. This journal publishes research articles covering multidisciplinary sciences, including humanities and social sciences, education, religious sciences, philosophy, economics, engineering sciences, and health sciences. PJMR journal provides open access to anyone so that the information and findings in these articles are useful for everyone. This journal article contents can be accessed and downloaded for free, free of charge, following the creative commons license used.
Arjuna Subject : Umum - Umum
Articles 59 Documents
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Pelatihan Mitigasi dan Tanggap Darurat untuk Masyarakat Desa Irfan Zayyadu
Primary Journal of Multidisciplinary Research Vol. 2 No. 1 (2026): PRIMARY: Journal of Multidisciplinary Research, February 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i1.348

Abstract

Flood Mitigation and Emergency Response Training for Rural Communities in Facing Flood Disasters is a strategic initiative aimed at enhancing community capacity to manage the increasing risk of floods caused by climate change, environmental degradation, and geographical vulnerability. Recurrent flooding not only threatens lives but also disrupts the socio-economic activities of rural communities, thereby necessitating participatory, community-based capacity-building efforts. This training program is designed to equip community members with knowledge about the causes of flooding, water flow patterns, identification of flood-prone areas, and mitigation techniques that can be implemented individually and collectively, such as maintaining drainage systems, reforesting critical areas, and developing risk-based spatial planning. In addition to mitigation aspects, the training emphasizes emergency preparedness through the development of evacuation procedures, the establishment of village volunteer teams, the management of assembly points and evacuation routes, and the use of basic safety equipment. The training methods are implemented in an integrated manner through lectures, group discussions, participatory risk mapping, field simulations, and learning evaluations. The results indicate a significant improvement in the community’s ability to recognize early warning signs of flooding, respond appropriately to emergency situations, and organize collective actions to reduce disaster impacts. Overall, this initiative contributes to the formation of more resilient, adaptive, and self-reliant rural communities capable of integrating local knowledge with disaster risk management principles. With strengthened capacity, communities are expected to reduce vulnerability and enhance village resilience against current and future flood threats.
Pengaruh Bimbingan Karir Berbasis Mind Map Dalam Meningkatkan Perencanaan Karir Siswa Di SMA Negeri 12 Banjarmasin Lisa Yuliana; Farial Farial; Sri Ayatina Hayati
Primary Journal of Multidisciplinary Research Vol. 2 No. 1 (2026): PRIMARY: Journal of Multidisciplinary Research, February 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i1.379

Abstract

Career guidance is indispensable because it is the first step in career decision-making. This study aims to determine the influence of mind map- based career guidance in improving the career planning of SMA Negeri 12 Banjarmasin students. The method used is pre-experimental with One Group Pretest-Posttest Design. The research sample amounted to 29 students who took the pretest and 15 students who were used as treatment subjects. The data collection technique used a career planning questionnaire. The data analysis technique used the Kolmogorov-Smirnov normality test and the t-test (paired sample t-test). The results showed an increase in the average score from 97.50 in the pretest to 110.70 in the posttest. The paired test of the t-Test sample showed a significance value of 0.000 < 0.05, which means that mind map-based guidance has an effect on improving students' career planning.
Evaluasi Program Sertifikasi Guru TK dalam Peningkatan Mutu Pendidikan Anak Usia Dini Nurlaili Hidayati; Zohra Fatimah
Primary Journal of Multidisciplinary Research Vol. 2 No. 1 (2026): PRIMARY: Journal of Multidisciplinary Research, February 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i1.404

Abstract

The Kindergarten (TK) teacher certification program is one of the government’s strategic policies aimed at enhancing teacher professionalism and improving the quality of early childhood education services. Certification not only serves as formal recognition of professional competence, but is also expected to promote improvements in the quality of the learning process, foster teachers’ creativity, and strengthen their role as facilitators of children’s holistic development. This study aims to evaluate the effectiveness of the kindergarten teacher certification program in improving teachers’ pedagogical, professional, social, and personal competencies, as well as its impact on the quality of classroom learning. This research employs a qualitative approach with a descriptive evaluative design. Data were collected through in-depth interviews with certified kindergarten teachers, school principals, and early childhood education supervisors, complemented by observations of classroom learning processes and documentation studies of teaching materials. Data were analyzed thematically to identify changes in teacher competencies before and after certification, as well as challenges encountered in its implementation. The findings indicate that certification contributes positively to improving curriculum understanding, more systematic lesson planning, the use of creative learning media, and increased teacher confidence and work motivation. In addition, there are indications of improved teacher–child interactions, which have a positive impact on students’ cognitive and socio-emotional development. However, this study also identifies several challenges, including limited post-certification continuous professional development programs, suboptimal academic supervision, and a tendency among some teachers to perceive certification primarily as an administrative requirement rather than as a process of ongoing competency improvement. Therefore, strengthening professional mentoring policies, providing advanced training relevant to the needs of kindergarten teachers, and implementing a sustainable evaluation system are necessary to ensure that the certification program truly generates a significant and lasting impact on the comprehensive and sustainable quality of early childhood education.
Green Economy sebagai Paradigma Baru Pembangunan Ekonomi Nasional Hermanto Hermanto; Ahyar Rosidi
Primary Journal of Multidisciplinary Research Vol. 2 No. 1 (2026): PRIMARY: Journal of Multidisciplinary Research, February 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i1.405

Abstract

The concept of the green economy has developed as a response to the various problems arising from conventional economic development, which has primarily focused on economic growth without adequately considering environmental carrying capacity and the sustainability of natural resources. Traditional development models have been proven to generate negative impacts such as environmental degradation, climate change, excessive resource exploitation, and widening social inequality. Therefore, the green economy emerges as an alternative approach that emphasizes the integration of economic growth, social equity, and environmental preservation within a sustainable development framework. This study aims to analyze how the green economy can serve as a new paradigm in national economic development and to identify the opportunities and challenges of its implementation across various strategic sectors. The research method employed is a descriptive qualitative approach through literature review and analysis of national and international policy documents related to sustainable development, renewable energy, green industry, and natural resource management. The findings indicate that the application of green economy principles has the potential to enhance energy and resource efficiency, promote environmentally friendly technological innovation, expand green jobs, and strengthen national economic competitiveness in the global market. Moreover, the green economy can serve as an instrument for poverty reduction through the development of environmentally based local economies and community empowerment. However, the implementation of the green economy still faces several challenges, including limited comprehensive regulations, low levels of environmental literacy among the public, high initial investment requirements, and resistance from conventional industrial sectors. Therefore, synergy among the government, private sector, academia, and society is required in the form of integrated policies, economic incentives, public education, and strengthened technological innovation to ensure that the transition toward a green economy proceeds effectively and sustainably. In this way, the green economy becomes not merely a discourse, but a concrete strategy for inclusive, equitable, and long-term oriented national economic development.
Pengaruh Transformasi Digital terhadap Kinerja Usaha Mikro, Kecil, dan Menengah di Indonesia: Peran Mediasi Inovasi Model Bisnis Agus Budianto; Fajar Nugraha; Rania Maheswari
Primary Journal of Multidisciplinary Research Vol. 2 No. 4 (2026): PRIMARY: Journal of Multidisciplinary Research, August 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i4.791

Abstract

Digital transformation has become a strategic imperative for micro, small, and medium enterprises in emerging economies, yet empirical evidence on how it converts into measurable business performance in Indonesia remains fragmented. This study examined the effect of digital transformation on the performance of micro, small, and medium enterprises in Indonesia and tested business model innovation as a mediating mechanism as well as digital literacy as a moderating condition. A quantitative explanatory design was applied to 342 enterprise owners and managers across five provinces, selected through purposive sampling. Data were collected using a five-point Likert questionnaire and analysed with partial least squares structural equation modelling. The measurement model satisfied the criteria of convergent validity, discriminant validity, and composite reliability. The results showed that digital transformation had a positive and significant effect on business performance and on business model innovation, and that business model innovation had a positive and significant effect on business performance. Business model innovation partially mediated the relationship between digital transformation and business performance, while digital literacy significantly strengthened the direct effect of digital transformation on performance. The model explained 61.4 percent of the variance in business performance. These findings indicate that technology adoption alone was insufficient; performance gains emerged when digital initiatives were translated into reconfigured value propositions, revenue mechanisms, and customer interfaces. The study offers practical implications for enterprise assistance programmes that prioritise capability building over the mere provision of digital devices and platforms.
Hubungan Faktor Genetik, Lingkungan, dan Gaya Hidup terhadap Risiko Penyakit Kardiometabolik pada Populasi Indonesia Kiki Fatmala Suaidi; Raqil Yusron
Primary Journal of Multidisciplinary Research Vol. 2 No. 4 (2026): PRIMARY: Journal of Multidisciplinary Research, August 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i4.793

Abstract

Penyakit kardiometabolik, seperti penyakit jantung koroner, diabetes melitus tipe 2, hipertensi, dan sindrom metabolik, merupakan penyebab utama morbiditas dan mortalitas di Indonesia. Risiko terjadinya penyakit tersebut dipengaruhi oleh interaksi kompleks antara faktor genetik, lingkungan, dan gaya hidup. Meskipun faktor genetik berkontribusi terhadap predisposisi individu, faktor lingkungan dan perilaku hidup merupakan determinan yang dapat dimodifikasi sehingga memiliki peran penting dalam upaya pencegahan. Artikel ini bertujuan menganalisis hubungan faktor genetik, lingkungan, dan gaya hidup terhadap risiko penyakit kardiometabolik pada populasi Indonesia berdasarkan bukti ilmiah terkini. Penelitian ini menggunakan pendekatan dengan menganalisis berbagai artikel penelitian nasional dan internasional yang membahas determinan genetik, lingkungan, serta gaya hidup terhadap kejadian penyakit kardiometabolik, khususnya pada populasi Indonesia. Berbagai penelitian menunjukkan bahwa riwayat keluarga dan predisposisi genetik meningkatkan risiko penyakit kardiometabolik, namun pengaruh tersebut diperkuat atau dilemahkan oleh faktor lingkungan dan gaya hidup. Pola makan tinggi lemak dan gula, kebiasaan merokok, obesitas, kurang aktivitas fisik, kualitas tidur yang buruk, serta paparan lingkungan yang tidak sehat terbukti meningkatkan risiko kejadian penyakit kardiometabolik. Sebaliknya, penerapan gaya hidup sehat mampu menurunkan risiko bahkan pada individu dengan predisposisi genetik tinggi. Risiko penyakit kardiometabolik pada populasi Indonesia merupakan hasil interaksi multifaktorial antara faktor genetik, lingkungan, dan gaya hidup. Strategi pencegahan yang mengintegrasikan identifikasi risiko genetik dengan intervensi gaya hidup dan pengendalian faktor lingkungan perlu menjadi prioritas dalam kebijakan kesehatan masyarakat untuk menekan beban penyakit kardiometabolik di Indonesia.
Integrasi Artificial Intelligence dan Internet of Medical Things (IoMT) dalam Meningkatkan Akurasi Diagnosis Penyakit Kronis Adib Ahmad; Faris Haydar
Primary Journal of Multidisciplinary Research Vol. 2 No. 4 (2026): PRIMARY: Journal of Multidisciplinary Research, August 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i4.794

Abstract

The increasing prevalence of chronic diseases such as diabetes mellitus, cardiovascular disease, chronic kidney disease, and respiratory disorders has become a major challenge for healthcare systems worldwide. Delayed diagnosis, limitations in continuous patient monitoring, and the high workload of healthcare professionals have encouraged the adoption of digital technologies in medical services. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) offers a novel approach that enables real-time health data collection, machine learning-based predictive analysis, and faster and more accurate clinical decision-making. This article aims to analyze the developments, benefits, challenges, and prospects of integrating AI and IoMT to improve the accuracy of chronic disease diagnosis through a narrative literature review approach. The literature was obtained from reputable international scientific publications published between 2022 and 2026 and was analyzed using a descriptive-critical approach. The review findings indicate that AI-IoMT integration can improve diagnostic sensitivity and specificity, accelerate the identification of risk factors, support continuous patient monitoring, and facilitate the implementation of precision medicine. Various algorithms, including deep learning, convolutional neural networks, recurrent neural networks, transformers, and generative AI, have demonstrated improved capabilities in analyzing clinical data obtained from IoMT sensors, electronic health records, and wearable devices. Nevertheless, the implementation of these technologies still faces challenges related to system interoperability, cybersecurity, patient data privacy, ethical considerations, and digital infrastructure readiness. Therefore, the development of interoperability standards, data protection regulations, and multidisciplinary collaboration is essential to ensure that AI and IoMT integration can be optimally implemented within modern healthcare systems. AI-IoMT technology is projected to become a key foundation for transforming chronic disease diagnosis toward more precise, proactive, and patient-centered healthcare.
Sistem Deteksi Gangguan Jaringan Listrik Berbasis Artificial Intelligence Eva Ruswandi; Zaenon Arsyadi
Primary Journal of Multidisciplinary Research Vol. 2 No. 4 (2026): PRIMARY: Journal of Multidisciplinary Research, August 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i4.795

Abstract

The management of electric power grids is undergoing a significant transformation from conventional reactive approaches toward predictive and intelligent systems through the integration of Artificial Intelligence (AI). This transformation enables real-time data analysis, anomaly detection, automated fault diagnosis, and more accurate decision-making. This article aims to analyze the development, effectiveness, and implementation trends of AI-based electric power grid fault detection systems based on findings from previously published studies. A descriptive-comparative qualitative approach was employed through a systematic literature review of reputable scientific articles addressing the application of AI in fault detection, grid condition monitoring, equipment failure prediction, and decision-making in electric power systems. The analysis involved identifying major research themes, comparing AI methods, and synthesizing the advantages and limitations of each approach. The findings indicate that machine learning and deep learning techniques, particularly when integrated with Supervisory Control and Data Acquisition (SCADA) systems and the Internet of Things (IoT), can significantly improve fault detection accuracy, accelerate fault localization, reduce outage duration, and enhance the reliability and resilience of electric power systems. AI integration also facilitates predictive maintenance, real-time operational decision-making, and the development of self-healing smart grids. However, challenges remain regarding data quality, cybersecurity, system interoperability, computational requirements, and model interpretability. Future research should therefore focus on developing adaptive, transparent, and robust AI models capable of operating effectively in complex and dynamic power grid environments.
Application of Deep Learning in Detecting Infrastructure Damage Using Digital Images Rauf Alfan; Sam Rasyid
Primary Journal of Multidisciplinary Research Vol. 2 No. 4 (2026): PRIMARY: Journal of Multidisciplinary Research, August 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/pjmr.v2i4.796

Abstract

Infrastructure damage detection is a fundamental component of structural maintenance and public safety. Conventional inspection methods generally rely on manual visual assessment, which is time-consuming, labor-intensive, and susceptible to human error. Recent advances in deep learning and computer vision have introduced image-based automated inspection systems capable of detecting, classifying, and assessing structural damage with high accuracy. This study aims to review and synthesize the application of deep learning techniques in digital image-based infrastructure damage detection. The study adopts a literature review approach by analyzing recent publications related to convolutional neural networks (CNN), transfer learning, semantic segmentation, transformer architectures, unmanned aerial vehicles (UAVs), and digital image correlation for structural health monitoring. The findings indicate that deep learning significantly improves detection performance for various infrastructure defects, including cracks, spalling, corrosion, and road surface deterioration. Advanced models integrating semantic segmentation and transformer-based architectures demonstrate superior accuracy in identifying damage under complex environmental conditions. Furthermore, UAV-assisted image acquisition enhances inspection efficiency while reducing operational costs and safety risks. Despite these advantages, several challenges remain, including limited annotated datasets, varying illumination conditions, model generalization, and computational requirements. Future research should emphasize multimodal data integration, explainable artificial intelligence, and real-time edge computing to improve practical implementation in infrastructure management. The adoption of deep learning-based inspection systems is expected to enhance preventive maintenance strategies and support sustainable infrastructure development.

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