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All Journal Dinar: Jurnal Ekonomi dan Keuangan Islam AGRIKAN Jurnal Ilmiah Agribisnis dan Perikanan INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Jurnal Penelitian Pendidikan IPA (JPPIPA) Georafflesia : Artikel Ilmiah Pendidikan Geografi Journal on Education Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Mudir : Jurnal Manajemen Pendidikan Jurnal Mantik Jurnal Pendidikan dan Konseling KOLOKIUM: Jurnal Pendidikan Luar Sekolah TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri JIIP (Jurnal Ilmiah Ilmu Pendidikan) Community Development Journal: Jurnal Pengabdian Masyarakat Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Quantitative Economics and Management Studies Jurnal Pengabdian Masyarakat (ABDIRA) Journal of Education Research Jurnal Minfo Polgan (JMP) Al-Hijr: Journal of Adulearn World Journal Of Human And Education (JAHE) Jurnal Pengabdian Teratai Journal of Innovation and Research in Primary Education International Journal of Community Service (IJCS) JISMA: Jurnal Ilmu Sosial, Manajemen, dan Akuntansi Indonesian Research Journal on Education The Eastasouth Management and Business Innovative: Journal Of Social Science Research Jurnal Pengabdian West Science West Science Journal Economic and Entrepreneurship West Science Interdisciplinary Studies Jurnal Geosains West Science West Science Interdisciplinary Studies West Science Social and Humanities Studies Jurnal Pendidikan Islam Journal Innovations Computer Science Journal of Selvicoltura Asean Journal of Loomingulisus ja Innovatsioon Cognitionis Civitatis et Politicae Research of Scientia Naturalis Edumaspul: Jurnal Pendidikan INTERNATIONAL JOURNAL OF FINANCIAL ECONOMICS (IJEFE) Scientechno: Journal of Science and Technology Journal of Social Science Utilizing Technology
Nofirman, Nofirman
Universitas Prof. Dr. Hazairin, SH
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Meta-analysis of the Effect of Jigsaw Model Based on Hybrid-Based Learning on Students' Critical Thinking Skills Wiwid Suryono; Linda Winiasri; Nofirman Nofirman; Mardiati Mardiati; Tomi Apra Santosa
Edumaspul: Jurnal Pendidikan Vol 7 No 2 (2023): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v7i2.6649

Abstract

The purpose of this study was to determine the effect of the jigsaw learning model based on hybrid learning on students' critical thinking skills. This type of research is quantitative research with a meta-analysis approach. Data sources come from analyzing national and international journals. Inclusion criteria are research must come from Scopus and SINTA-indexed journals or proceedings; Data source searches come from Google Scholar, ERIC, IEEE, and Wiley; Research must have experimental and control classes; Research must be published from 2017-2023; Research has a relationship with research variables and research has a value (t), (r) and (f). Data analysis is quantitative statistical analysis with the help of the JSAP application. The results of this study concluded that the average value of the summary effect size or mean effect size of the entire study (ES = 0.896; p < 0.001) with very high criteria. This finding explains that the jigsaw learning model based on hybridized learning has a great influence on students' critical thinking skills.
Pelatihan Pemanfaatan Artificial Intelligences Deepseek dalam Menurunkan Plagiasi Artikel Ilmiah bagi Mahasiswa Baru Annas, Annisa Nuraisyah; Nofirman, Nofirman; Haryanto, Abel; Machmud, Amir; Safarudin, Muhamad Sigid
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 1 (2026): Abdira, Januari
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i1.1602

Abstract

The Student Creativity Program (PKM) aims to train new students in utilizing DeepSeek Artificial Intelligence (AI) as a tool to reduce plagiarism in scientific article writing. The training focuses on improving their understanding of paraphrasing, ethical citation integration, and utilizing DeepSeek's advanced features as a responsible writing assistant. The PKM implementation method is training and mentoring, consisting of three stages: (1) Presentation of conceptual material on plagiarism and writing ethics; (2) Hands-on workshops on utilizing DeepSeek for paraphrasing, idea elaboration, and draft checking; and (3) ongoing mentoring through online discussion groups. Evaluation results indicate an increase in participants' ability to produce manuscripts with greater originality. Analysis using a plagiarism checker on drafts before and after the training showed a significant decrease in indications of plagiarism, as well as an increase in participants' understanding of integrative academic writing. This program effectively empowers new students with AI literacy for responsible academic productivity.
PARADIGM SHIFT: HOW THE DIGITAL ECONOMY IS REORGANISING THE WORLD'S ECONOMIC POWER Pemy Christiaan; Nofirman Nofirman
INTERNATIONAL JOURNAL OF FINANCIAL ECONOMICS Vol. 1 No. 4 (2024): INTERNATIONAL JOURNAL OF FINANCIAL ECONOMICS (IJEFE)
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The digital economy has led to a fundamental paradigm shift in the power structure of the global economy. Digital technologies, such as artificial intelligence, big data, and the Internet of Things (IoT), play an important role in improving countries' productivity, innovation, and competitiveness. Countries that are able to adopt and implement these technologies effectively tend to experience faster economic growth. In addition, the digital economy is also expanding economic inclusion by enabling greater access to markets and financial services for individuals and small businesses, especially in developing countries. However, this transformation also brings challenges, including digital inequality and the need for adequate regulation. Policies that support education, digital infrastructure and data protection, as well as increased international cooperation, are essential to maximise the benefits of the digital economy in an equitable and sustainable manner.
Transforming Traditional Farmers into Professionals: An Introduction to Human Resource Management in Rural Yohny Anwar; Venti Jatsiyah; M. Zahari; Arif Saefudin; Nofirman Nofirman
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.6543

Abstract

This research is focused on outlining experiences in rural human resource management through the introduction of a new professional farmer training process. The aim is to provide insights and references for developing countries in implementing farmer training and improving their human resource structure. The research methodology explores the literature combined with an inductive approach to describe and analyse experiences and lessons learned during training programmes to strengthen human resource capacity in the agricultural sector. The results of this study emphasise the importance of new professional farmer training in rural human resource management, with the Indonesian government prioritising the entire training process. The Indonesian government has taken strategic steps in farmer training, which include identifying appropriate training targets, selecting influential investment entities, developing practical training models, and designing supportive policies. These initiatives have significantly improved the quality and structure of rural human resources. The uniqueness of this research lies in its selection of topics that are less widely covered in the literature, especially regarding the training process and the new professional farmer model. This research is also important in enhancing the skills of rural individuals, increasing farmers' income, strengthening the role of agriculture, and supporting economic growth in rural areas. Furthermore, this research makes a significant contribution to the literature by enriching the theory of human capital investment in rural areas.
Perbandingan Kinerja Algoritma Machine Learning untuk Prediksi Cuaca di Wilayah Tropis Indonesia: Studi Komparatif Random Forest, SVM, LSTM, XGBoost, dan LightGBM Nofirman Nofirman; Munawir Munawir; Fegie Yoanti Wattimena
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.420

Abstract

Weather prediction in tropical Indonesia faces complex challenges due to high climate variability, persistent El Niño–Southern Oscillation (ENSO) influence, and uneven observational coverage. This study compared five machine learning algorithms — Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), XGBoost, and LightGBM — using 187,320 daily records from BMKG stations, ERA5 reanalysis, and TRMM satellite data (2000–2023). Preprocessing included MMDIF-RF imputation, Z-score normalization, and SMOTE for class imbalance correction. Models were evaluated on RMSE, MAE, R², Accuracy, Precision, Recall, F1-Score, and AUC-ROC. LSTM achieved the best performance (RMSE = 3.94 mm; R² = 0.891; F1-Score = 0.887; AUC-ROC = 0.941), reflecting its capacity to capture long-range temporal dependencies. XGBoost and LightGBM delivered competitive accuracy at 8–18 times lower training cost, while SVM recorded the lowest accuracy with the highest computational demand. Regional analysis showed station density and data completeness were more consequential than algorithm choice — LSTM RMSE ranged from 3.61 mm in West Java to 5.43 mm in East Nusa Tenggara. A tiered hybrid approach is recommended: LightGBM or XGBoost for routine forecasting and LSTM for extreme event detection, alongside expanded BMKG coverage in eastern Indonesia.  
MACHINE LEARNING ALGORITHMS FOR REAL-TIME DETECTION AND PREDICTION OF SEISMIC ACTIVITIES TO ENHANCE DISASTER RISK MITIGATION STRATEGIES Nofirman Nofirman; Daiki Nishida; Giovanni Rossi
Research of Scientia Naturalis Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i2.4170

Abstract

Earthquakes pose significant threats to human safety, critical infrastructure, and socioeconomic stability because their occurrence is highly complex and difficult to predict accurately in real time. Although conventional seismic monitoring systems have improved earthquake detection, they remain limited by computational constraints, delayed event recognition, and inadequate identification of nonlinear seismic patterns. This study evaluated the effectiveness of machine learning algorithms for real-time seismic detection and prediction and their contribution to disaster risk mitigation. A mixed-methods sequential explanatory design was employed using approximately 1.8 million seismic waveform segments representing 48,000 earthquake events collected from 320 monitoring stations across eight tectonically active regions. Quantitative analyses included comparative evaluation of supervised, ensemble, and deep learning algorithms using multivariate statistics, structural equation modeling, hierarchical regression, mediation, and moderation analyses, while qualitative evidence was examined through thematic analysis. Findings showed that deep learning and hybrid ensemble models consistently achieved higher prediction accuracy, computational efficiency, early warning reliability, and lower false alarm rates than conventional approaches. Improved prediction accuracy strengthened disaster response readiness, while dense sensor networks and institutional coordination enhanced operational effectiveness, supporting resilient earthquake risk mitigation and evidence-based emergency decision-making.
USING ARTIFICIAL INTELLIGENCE AND LIDAR DATA FOR HIGH-RESOLUTION FOREST INVENTORY AND ABOVE-GROUND BIOMASS ESTIMATION IN A SUMATRAN RAINFOREST Nofirman Nofirman; Ahmed Shah; Usman Tariq
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2483

Abstract

Accurate quantification of forest carbon stocks is critical for global climate change mitigation initiatives like REDD+. Traditional forest inventory methods are often labor-intensive, costly, and limited in scale, particularly in complex tropical ecosystems such as the Sumatran rainforest. The integration of advanced remote sensing technologies and artificial intelligence (AI) offers a transformative potential for overcoming these limitations. This study aimed to develop and validate a high-resolution model for individual tree detection and above-ground biomass (AGB) estimation in a Sumatran rainforest by synergizing airborne LiDAR data with machine learning algorithms. High-density LiDAR data was acquired over a 10,000-hectare study area. Concurrently, extensive field inventory data from 150 plots were collected to serve as ground truth. A deep learning model, specifically a Convolutional Neural Network (CNN), was trained to perform individual tree crown delineation (ITCD) from the LiDAR-derived canopy height model. Tree-level metrics were then used as predictors in a Random Forest algorithm to estimate AGB, which was calibrated against field-measured biomass. The CNN model successfully identified individual trees with an accuracy of 92.4%. The subsequent Random Forest model demonstrated high predictive power for AGB estimation, yielding a strong coefficient of determination ( = 0.89) and a low Root Mean Square Error (RMSE) of 25.8 Mg/ha. The approach generated a high-resolution (1-meter) AGB map, revealing detailed spatial variations in carbon stock across the landscape. The fusion of AI and LiDAR data provides a highly efficient methodology for forest inventory and AGB mapping in dense tropical rainforests. This approach significantly enhances our capacity to monitor carbon dynamics, forest conservation and climate policy.
Fostering Divergent Thinking in the Classroom: The Impact of Project-Based Learning on Student Creativity Harianta Sembiring; Nofirman Nofirman; Fini Widya Fransiska; Wahju Dyah Laksmi Wardhani; Rina Farah
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i5.2513

Abstract

The growing demand for 21st-century skills underscores the importance of nurturing students’ creativity through educational practices that promote divergent thinking. This study investigates the impact of Project-Based Learning (PBL) on fostering divergent thinking and creative performance among high school students. The research aims to analyze how the integration of PBL facilitates idea fluency, flexibility, originality, and elaboration in learners’ creative processes. A quasi-experimental design was employed, involving two groups of students: one receiving traditional instruction and the other exposed to PBL interventions across four project cycles. Data were collected using a validated creativity assessment rubric and analyzed through descriptive and inferential statistics (ANOVA). The findings reveal a significant improvement in divergent thinking indicators among students taught through PBL, particularly in their ability to generate multiple and original ideas. Qualitative observations also highlight that collaborative project environments enhanced motivation, self-expression, and problem-solving capacities. The study concludes that PBL serves as an effective pedagogical framework for cultivating creative and divergent thinking skills essential for innovation-driven learning. Implications emphasize the need for curriculum designers and educators to embed authentic, project-based tasks within classroom instruction.
Innovations in Higher Education Curriculum: A Case Study of Interdisciplinary Programs to Foster Creativity Hary Murcahyanto; Nofirman Nofirman; Firda Weri; Bernardus Agus Rukiyanto; Napat Chai
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i5.2517

Abstract

The evolving demands of the 21st century have compelled higher education institutions to redesign curricula that transcend disciplinary boundaries and cultivate creativity as a core graduate competency. Traditional mono-disciplinary programs often fail to equip students with the integrative thinking necessary for addressing complex, real-world problems. This study investigates curriculum innovations through interdisciplinary programs aimed at fostering creativity in higher education. The research seeks to identify how cross-disciplinary learning structures, collaborative projects, and flexible pedagogical designs contribute to the development of creative and adaptive learners. A qualitative case study approach was employed, involving document analysis, semi-structured interviews with faculty and students, and observation of interdisciplinary coursework across three universities. Findings reveal that interdisciplinary programs significantly enhance creative capacity by encouraging cognitive flexibility, collaborative problem-solving, and synthesis of diverse perspectives. Students reported heightened engagement and intrinsic motivation when exposed to integrated learning environments that connected theory and practice. The study concludes that curriculum innovation through interdisciplinary design is a powerful strategy for embedding creativity into higher education, though successful implementation requires institutional support, teacher training, and assessment reform aligned with creative learning outcomes.
THE GEOPOLITICS OF CRITICAL MINERALS: AN INTERNATIONAL RELATIONS PERSPECTIVE ON INDONESIA'S NICKEL DOWNSTREAM POLICY Nofirman Nofirman; Shamsul Anwar; Azimah Haji Ali; Bina Magar
Cognitionis Civitatis et Politicae Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/politicae.v2i2.2527

Abstract

The global race for critical minerals has transformed nickel into a strategic asset in contemporary geopolitics. Indonesia, possessing one of the world’s largest nickel reserves, has adopted a downstream policy to assert greater control over its mineral resources and strengthen national economic sovereignty. This study aims to analyze Indonesia’s nickel downstream policy from an international relations perspective, emphasizing its geopolitical, economic, and strategic implications. Using a qualitative method with a descriptive-analytical approach, the research draws on policy documents, trade data, and scholarly discourse to interpret Indonesia’s positioning within global power dynamics. The findings reveal that the policy reflects Indonesia’s attempt to transition from a resource supplier to a value-added industrial hub, balancing between China’s technological dominance and Western market access. Moreover, the policy redefines Indonesia’s bargaining power in international trade and its alignment in global supply chains for electric vehicles and renewable energy. The study concludes that Indonesia’s nickel strategy represents a form of resource nationalism adapted to 21st-century multipolar competition, offering insights into how developing nations can leverage critical minerals for geopolitical advantage.
Co-Authors Abd. Basir Abditama Srifitriani Abdul Latif Abdul Wahab Abdul Wahab Syakhrani Agus Nursalim, Agus Ahmad Suriansyah Ahmed Shah Akhmad Dahlan Akhmad Riandy Agusta Al Ghazali, Al Ghazali Ali Zaenal Abidin Amalia, Mekar Meilisa Amir Machmud, Amir Amjad Salong Andi Rahman ANDRI KURNIAWAN Andueriganta Fadhlihi Annas, Annisa Nuraisyah Antono Damayanto Apra Santosa, Tomi Ari Purwanti Arief Fahmi Lubis Arief Suardi Nur Chairat Arif Saefudin Arifin, Arifin Arifin, Syaadiah Arini, Rani Eka arjang Asbaruna, Latifah Wulandari Binti Astuti, Enny Diah Azhariah Rachman Azimah Haji Ali Azwar, Edy B.M.A.S. Anaconda Bangkara Bangun, Eka Uliyanti Putri Br Basir, Badirun Besse Arnawisuda Ningsi Bina Magar Budi Witjaksana, Budi cahyono, didik Cakranegara, Pandu Adi Cakranegara, Pandu Adi CNAWP, Rizal Perlambang Daiki Nishida Dara, Ravi Dedy Irfan Diana Yusuf Dodik Widarbowo Dulguun Amarsaikhan Edy Nurtamam, Mohammad Eka Melati Elisabeth Sitepu Ellina Rienovita Elvina Safitri Endang Fatmawati Endrawati, Titin Fadhlihi, Andueriganta Fatimah Malini Lubis Fegie Yoanti Wattimena Fini Widya Fransiska, Fini Widya Firda Weri Giovanni Rossi Gogor Christmass Setyawan Guntur Putrajaya Gusti Rusmayadi Haerawan, Haerawan Harianta Sembiring Harniati Hary Murcahyanto Haryanto, Abel Haryono Haryono Herlan Suherlan Ilham Arief Imam Jayanto Iriana Bakti Ita Soegiarto Jasiah Jasmid Edy Jauhari, Burhanuddin Jayadi Jayadi Jehosua S.V. Sinolungan Jekmal Malau Joni Wilson Sitopu Judijanto, Loso Juliana, Juliana Karundeng, Deby Rita Khasanah Khasanah Khobir, Khoirul Kurniawan Kurniawan Lendra Lendra Lestari, Nana Citrawati Linda Winiasri Loso Judijanto Luis Santos M. Zahari Machzumy, Machzumy Mardiati Mardiati Mardiati Mardiati Margaret Stevani Marjan, M Markus Asta Patma Nugraha Martiningsih, Evi Mas'ud Muhammadiah Massang, Berdinata Melinda Puspita Sari Jaya Meliza, Arini Michiel Martin Rumondor Miftahus Surur Momena Sultana Muhamad Ammar Muhtadi Muhammad Ade Kurnia Harahap Muhammad Subhan Iswahyudi Muhammad Yusuf Muhtadi, Muhamad Ammar Mukhtar Efendi Munawir Munawir Munkhzul Ganbat muriyanto, muriyanto Nanny Mayasari Napat Chai Nasril, Nasril Nizwardi Jalinus Novy Afanggi Pujianto Nur, Rezeki Nuridah, Siti Padilah Padilah Pemy Christiaan Purba, Ulfani Ikhwana R. Irma Rachmawati, R. Irma Rais, Rinovian Ramdhan Kurniawan Rani Eka Arini Raudya Setya Wismoko Putri Retnaningsih , Rahayu Rina Farah Rizki Ramadhani Romadhon, Ismi Amalia Rukhmana, Trisna Rukiyanto Rukiyanto, Rukiyanto Rully Novie Wurarah Rusdianto Rusdianto Safarudin, Muhamad Sigid Santos, Luis Santosa, Tomi Apra Saragi, Alexander Adrian Saryanto Saryanto Sefrizal, Levy Shamsul Anwar Siregar, Ade Perdana Sri Rahayu Sudadi Suharni Sultana, Momena Supriyanto Supriyanto Suryono, Wiwid Susanti, Maria Melani Ika Syamsulbahri Syamsulbahri Tamaulina Br Sembirin Tamaulina Taufik Abdillah Syukur Thamrin, Andi Thomasonan Lutfie Prananto Ti Aisyah Tomi Apra Santosa Tugsuu Jargalsaikhan Ulma Tiara, Inoki Usman Tariq Vann, Rithy Vendy Antono Venti Jatsiyah Wahju Dyah Laksmi Wardhani Wakit, Saipul Walenta, Abdi Sakti Winiasri, Linda Wirayasa, I Ketut Adi Wiwid Suryono Yasir Riady Yenny Anggreini Sarumaha Yohny Anwar Yolanda Efionita Yuniwati , Ika Yusda, Desi Derina Yusuf, Ramdan Yuyun Suprapto Zairin Zairin Zulkarnain, Ihwan Zunaidi, Arif Zuraida Zuraida