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Exacta: Journal of Pure and Fundamental Research
Published by CV SCRIPTA INTELEKTUAL MANDIRI
ISSN : -     EISSN : 31251439     DOI : 10.65310
Core Subject :
Exacta: Journal of Pure and Fundamental Research is a peer-reviewed academic journal dedicated to publishing high-quality scholarly works in the fields of pure sciences and fundamental research. The journal provides an international platform for scientists, researchers, and academics to disseminate original research articles, theoretical analyses, experimental findings, and advanced scientific discussions that contribute to the advancement of foundational scientific knowledge. Exacta welcomes manuscripts in areas such as mathematics, physics, chemistry, biology, statistics, theoretical and computational sciences, and other branches of fundamental scientific inquiry. The journal emphasizes research that advances conceptual understanding, develops new theoretical frameworks, or provides rigorous experimental validation within the natural and formal sciences. All submissions undergo a rigorous double-blind peer-review process to ensure originality, scientific validity, methodological precision, and meaningful contributions to fundamental science. Published quarterly in March, June, September, and December, Exacta aims to foster global scientific dialogue, promote excellence in foundational research, and strengthen the advancement of pure science as the basis for long-term technological and intellectual progress at local, national, and international levels.
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Articles 8 Documents
 1 
Epigenetic Mechanisms in Environmental Adaptation: A Conceptual Review of Molecular Regulatory Systems Diana Widhi Rachmawati; Dian Pariska; Nisah Ayu Siregar
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 1 (2026): March: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/qkhzyp22

Abstract

Environmental change imposes escalating selective pressures that demand rapid yet reversible adaptive responses across biological systems. This study presents a non-empirical conceptual review that synthesizes epigenetic mechanisms as a hierarchical and cross-kingdom molecular regulatory architecture linking environmental stimuli to phenotypic plasticity and ecological memory. Drawing upon comparative systems mapping and structured narrative synthesis, the analysis integrates DNA methylation dynamics, histone modification codes, chromatin remodeling complexes, non-coding RNA networks, and metabolic–immune feedback circuits into a unified explanatory framework. The findings demonstrate that adaptive regulation operates through recursive feedback loops that recalibrate transcriptional states under fluctuating environmental conditions, enabling graded transitions from short-term plasticity to stabilized ecological memory. Cross-domain triangulation across plants, animals, fungi, and insects reveals conserved structural motifs underlying stress adaptation despite taxonomic divergence. The proposed model refines evolutionary interpretations of adaptation by embedding energetic constraints, immune modulation, and developmental plasticity within epigenetic regulatory systems. This integrative framework offers conceptual clarity, cross-kingdom generalizability, and predictive coherence, providing a structured platform for future empirical validation and computational modeling of adaptive resilience in the context of global environmental change.  
Microbial Resistance Evolution: Theoretical Perspectives on Genetic Mutation and Environmental Pressure Deford Cristy Birahy; Mia Audina; Anjela Karunia Amalia
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 1 (2026): March: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/smcw0074

Abstract

Microbial resistance has traditionally been interpreted through reductionist frameworks emphasizing isolated genetic mutations or clinical antibiotic exposure. This study advances a non-empirical theoretical investigation grounded in integrative conceptual modeling to reconceptualize resistance evolution as a cross-scale, eco-evolutionary phenomenon. A multi-scale analytical framework was constructed to synthesize stochastic mutation supply, horizontal gene transfer, metabolic rewiring, proteostasis regulation, environmental heterogeneity, and anthropogenic chemical pressures into a unified systems architecture. Structured theoretical synthesis and boundary-condition analysis were employed to test internal coherence and cross-context stability. The model demonstrates that resistance emerges as a metastable adaptive state maintained by feedback loops linking molecular mechanisms, community-level interactions, and physicochemical gradients across environmental compartments. Disinfectant exposure, nutrient enrichment, wastewater co-selection, and climate variability were incorporated as dynamic modulators of selective intensity. The resulting framework exhibits explanatory integration, cross-scale generalizability, and predictive plausibility under fluctuating selective regimes. By situating microbial resistance within interconnected ecological and evolutionary systems, this study provides a robust theoretical platform capable of guiding future simulation-based modeling and empirical corroboration within the One Health continuum.
Green Chemistry Principles in Sustainable Synthesis: A Fundamental Review of Environmentally Benign Reactions Eko Sutrisno; Verawaty Simarmata
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 1 (2026): March: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/f1mbas82

Abstract

This study develops a theory-driven, non-empirical framework that reconceptualizes green chemistry principles as an integrated architecture for sustainable synthesis. Through systematic literature triangulation and comparative principle-matrix construction, environmentally benign reactions are analyzed across three hierarchical domains: molecular efficiency, process integrity, and systemic impact. The findings indicate that atom economy, catalytic selectivity, solvent engineering, and alternative energy modalities function as mechanistic determinants that reduce intrinsic waste formation. At the operational level, solvent substitution, catalyst recyclability, and energy modulation mediate the conversion of reaction-level improvements into measurable environmental benefits. Systemic evaluation further demonstrates that lifecycle assessment, renewable feedstock integration, and material durability extend sustainability beyond laboratory metrics toward macro-scale ecological and socio-economic relevance. The review advances a multi-level conceptual model that links mechanistic chemistry with sustainability science, transforming normative green principles into analytically operational constructs. By bridging molecular design and lifecycle governance, the study provides a theoretically robust foundation for guiding future innovation in environmentally benign synthetic chemistry.
Nanostructured Materials and Catalytic Reactivity: A Theoretical Examination of Surface Chemistry Mechanisms Maryam Maryam
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 1 (2026): March: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/1q3exj81

Abstract

Nanostructured materials have transformed heterogeneous catalysis by introducing electronically and structurally heterogeneous surfaces whose reactivity cannot be explained through classical bulk descriptors. This study presents a non-empirical theoretical examination of surface chemistry mechanisms, constructing an integrative framework that correlates lattice distortion, facet-dependent coordination environments, vacancy formation energetics, and interfacial charge polarization with catalytic reaction pathways. Descriptor-based abstraction is employed to formalize structural perturbations into analytically tractable variables, enabling systematic interpretation of adsorption–activation–desorption sequences across material classes. The analysis demonstrates that catalytic performance arises from electronically mediated coupling between structural asymmetry and reaction coordinate modulation, while mechanistic competition is governed by interfacial charge dynamics and cross-site electronic communication. Stability–reactivity trade-offs are incorporated through thermodynamic constraint modeling, revealing that structural resilience and electronic activation are intrinsically interconnected. By synthesizing defect chemistry, surface thermodynamics, and electronic structure principles into a coherent explanatory scaffold, this work advances a predictive conceptual foundation for rational catalyst design. The framework offers transferable insight applicable to single-atom systems, core–shell architectures, and defect-engineered nanocomposites within sustainable catalytic technologies
Quantum Entanglement and the Foundations of Modern Physics: A Conceptual Analysis of Non-Locality Theories Wiwik Hidayati
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 1 (2026): March: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/1rpgnc96

Abstract

Quantum entanglement and non-locality remain central to the foundations of modern physics, yet their conceptual status continues to generate theoretical and philosophical debate. This study presents a systematic conceptual analysis of non-locality theories by integrating geometric state-space formulations, ontological reconstructions, and Bell-type logical diagnostics. Geometric approaches demonstrate that entanglement arises from the complex projective structure of quantum state space and is formally equivalent to established separability and CHSH criteria in both multiparticle and continuous-variable systems. Ontological models reinterpret these results through diverse commitments, including holistic realism, global-state dependence, informational emergence of time, and conceptual relationalism, each proposing distinct mechanisms for non-local coordination. Logical and sheaf-theoretic frameworks recast Bell violations as structural obstructions to global value assignments rather than as superluminal influence. Experimental photonic systems, high-energy reconstructions, and quantum communication protocols confirm the operational robustness of Bell-type correlations under realistic noise conditions. The analysis demonstrates that non-locality functions simultaneously as geometric invariant, logical constraint, empirical signature, and technological resource. By comparatively reconstructing these dimensions, the study clarifies the conceptual architecture underlying quantum non-locality and refines its role within contemporary foundational physics.
Sentiment Analysis of TikTok Comments on the Koperasi Merah Putih Program Using the Naive Bayes Algorithm Mitra Persadanta Tarigan; Zevania Pransisca Tarigan; Yosua Sinaga; Grace Oktorandalina Ginting; Elisama Simamora; Anatasya Matondang; Nova Pasaribu
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 2 (2026): : June: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/ztv1pp98

Abstract

The Koperasi Merah Putih program is a government policy aimed at strengthening grassroots economic cooperatives at the village level, and its launch has triggered diverse public reactions on social media, particularly TikTok. This study aims to analyze public sentiment toward the program using a machine learning approach and to identify patterns in the distribution of public opinion based on collected comments. This research adopts a quantitative approach using a Naive Bayes-based sentiment analysis method. Data were collected through scraping of 2,299 TikTok comments discussing Koperasi Merah Putih, automatically labeled using a positive and negative keyword dictionary, followed by text preprocessing (cleaning, normalization, and removal of empty entries). Non-neutral labeled data were split into training (70%) and testing (30%) sets using stratified sampling, then transformed using CountVectorizer with unigram and bigram features, and classified using Multinomial Naive Bayes. The test results show that the model achieved an accuracy of 85.92%, with a precision of 0.84 and recall of 1.00 for the negative class, while the positive class achieved a precision of 1.00 but a recall of only 0.50, resulting in a weighted F1-Score of 0.84. The overall sentiment distribution indicates a dominance of neutral comments (89.3%), followed by negative (7.7%) and positive (3.0%) comments, suggesting that most public interactions are informational rather than emotionally charged, while strongly opinionated comments tend to be critical. The study concludes that a dictionary-based Naive Bayes approach is effective for accurately detecting negative sentiment but has limitations in capturing the linguistic diversity of positive expressions, indicating the need for lexicon expansion and larger training data in future research.
Sentiment Analysis of Positive and Negative Comments on the Rise of the Dollar: A Case Study from TikTok Using the Naive Bayes Method Sindi Klarisa Malau; Laura Viona Br Kacaribu; Nadya Loviga Br Sitepu; Monika Putri Ronggun Br Kacaribu; Deo Gratyas Hulu; Ahmad Dani Hulu; Bazlan Al Hakim
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 2 (2026): : June: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/bwxvwc45

Abstract

This study investigates public sentiment toward the appreciation of the United States dollar against the Indonesian rupiah through comments collected from TikTok. The research applies an empirical machine learning framework that integrates social media based economic discourse analysis with the Multinomial Naive Bayes classification algorithm. Data were processed through a structured preprocessing pipeline consisting of text normalization, removal of irrelevant characters, and feature extraction using the Bag of Words approach. Sentiment categories were assigned through a lexicon guided labeling procedure and subsequently classified into positive and negative classes. Model validation was conducted using an independent testing dataset and evaluated through accuracy, precision, recall, F1 score, and confusion matrix analysis. The findings indicate that the classifier achieved a high level of predictive performance, demonstrating the computational efficiency of Naive Bayes for large scale textual data. At the same time, the evaluation reveals methodological challenges associated with class imbalance and limited sentiment coverage arising from rule based labeling. The study highlights the value of social media analytics for monitoring economic perceptions and contributes to the development of sentiment intelligence frameworks capable of supporting real time observation of macroeconomic discourse in digital environments.
Analysis of Public Sentiment Toward the Free Nutritious Meals Program Using the Naive Bayes Algorithm Afdal Perangin Angin; Indra Setiawan; Jeremia Revaldo Girsang; Aldo Jeremia Tumanggor; Andika Tioanta Ginting; Manuel Saputra Manalu; Desman Jaya Giawa
Exacta: Journal of Pure and Fundamental Research Vol. 1 No. 2 (2026): : June: Exacta: Journal of Pure and Fundamental Research
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/88fddw90

Abstract

The increasing use of digital platforms as spaces for expressing public opinion has created opportunities for applying natural language processing techniques to evaluate societal responses toward government policies. This study aims to analyze public sentiment toward the Program Makan Bergizi Gratis (MBG) using the Multinomial Naive Bayes algorithm. The research applies an empirical machine learning approach involving data preprocessing, lexicon-based sentiment labeling, feature extraction using Count Vectorizer with unigram and bigram representation, and model evaluation through accuracy, precision, recall, F1-score, and confusion matrix analysis. The dataset consists of 3,300 public comments processed through a structured computational pipeline and divided using stratified sampling into training and testing data. The findings indicate that public sentiment is dominated by negative responses, followed by neutral and positive categories, reflecting critical public evaluation toward program implementation aspects. The classification model demonstrates reliable performance in identifying sentiment patterns and provides an analytical framework for understanding digital public perception. This study contributes to computational social analysis by integrating machine learning techniques with policy-oriented sentiment monitoring.

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