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RUBINSTEIN
Published by Universitas Buddhi Dharma
ISSN : 29854520     EISSN : 29854512     DOI : -
Core Subject : Economy, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other
RUBINSTEIN juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora adalah jurnal yang menerbitkan artikel penelitian yang meliputi bidang ilmu multidisiplin, yang mencangkup Bisnis, Sains & Teknologi dan Sosial & Humaniora. Jurnal RUBINSTEIN menerima manuskrip atau naskah artikel dalam bidang riset, mencakup: Ekonomi publik Ekonomi industri Ekonomi intemasional Manajemen bisnis Manajemen keuangan Manajemen pemasaran Manajemen sumber daya manusia Administrasi niaga Akuntansi keuangan Perpajakan Pemeriksaan akuntansi Akuntansi manajemen Hukum Sosial Datamining Cloud computing Robotics and sensor Expert system IoT Decision support system Bussiness intelegent Linguistics Literature Culture studies with english requirements
Arjuna Subject : Umum - Umum
Articles 57 Documents
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Comparative Analysis of Support Vector Machine, Decision Tree, and Naive Bayes in Evaluating Machine Learning Effectiveness Hariyanto, Susanto; Indah Fenriana; Desiyanna Lasut; Febrian
RUBINSTEIN Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i1.4041

Abstract

This study aims to analyze and compare the performance of three widely used machine learning algorithms for data classification: Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. These algorithms employ distinct approaches in handling data, making it essential to evaluate their effectiveness and efficiency in classification tasks. In the digital era characterized by massive data growth, the selection of an appropriate classification algorithm is a critical determinant for accurate and efficient data-driven decision-making. The main contribution of this research is to provide a comprehensive understanding of the relative strengths and limitations of each algorithm under varying data conditions. This study not only highlights comparative performance outcomes but also emphasizes practical implications for researchers and data science practitioners in selecting algorithms suited to specific needs. In doing so, it addresses a research gap concerning integrated evaluations of data characteristics and algorithmic performance. The methodology adopts a quantitative approach through computational experiments using standardized datasets (Titanic, Spam Email, and Wine). The datasets were divided into training and testing sets and analyzed using Python with the scikit-learn library. Performance evaluation was conducted based on accuracy, precision, recall, and F1-score, validated through cross-validation techniques to ensure reliability of results. The findings indicate that SVM outperforms in terms of accuracy and recall on complex datasets, Naïve Bayes is more efficient in computational time particularly for text data, while Decision Tree stands out for model interpretability despite slightly lower accuracy. These results are expected to serve as a practical reference for selecting suitable algorithms according to data characteristics, thereby supporting more targeted and intelligent modeling strategies in the era of digital transformation.
PERAN KELURAHAN DALAM VERIFIKASI DAN VALIDASI DATA KEMISKINAN EKSTREM DI KELURAHAN MARGASARI KOTA TANGERANG Foharato Gulo; Udin, Wawan
RUBINSTEIN Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i1.4059

Abstract

Accurate data on extremely poor households determines the effectiveness of extreme poverty alleviation programs, so the process of verifying and validating (verval) extreme poverty data needs to be carried out.   This study aims to determine the role of the sub-district in verifying and validating extreme poverty data in Margasari Sub-district, Karawaci District, Tangerang City, and to identify the obstacles encountered in the verification and validation process. The background of this research focuses on the importance of accurate poverty data for more targeted policy planning, but there are still challenges in the process of verifying and validating data in the field. The method used in this study is a qualitative approach, conducting interviews and observations in Margasari Sub-district to explore information related to the implementation of verification and validation as well as the obstacles that arise. The results of this study show that the role of the sub-district in the implementation of extreme poverty data verification and validation includes coordination with RT/RW, community leaders, and related parties, conducting field verification, validation through sub-district deliberations, and reporting the results of verification and validation to higher authorities. This shows that the sub-district does not only act as an administrative implementer, but also as a facilitator and liaison between government policies and the community. The obstacles encountered in the implementation of verification and validation include limited human resources, inaccuracy of initial data from the central government, resistance from some members of the community to the data collection process, and limited information technology facilities.
Analisis Manajemen Kelas guru geografi di SMA Kampar Timur Ardiyansah; Addina Zaharah; Ibnu Thayibur Rizqi; Nila Armita; Ade Irma
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4273

Abstract

This study aims to analyze in depth the implementation of classroom management in Geography learning at public senior high schools in East Kampar to support effective and conducive learning processes. This research employed a descriptive qualitative approach, with data collected through field observations, in-depth interviews, and documentation. The findings indicate that Geography teachers implemented classroom management through structured lesson planning, flexible classroom arrangement, and the creation of a learning climate that encouraged students’ active participation. Positive interpersonal communication between teachers and students also played an important role in maintaining discipline and minimizing disruptions during instruction. Other findings show that classroom management directly improved learning motivation, discussion engagement, and students’ ability to understand Geography concepts contextually, although challenges remained, including differences in student characteristics and limited learning facilities. The theoretical and practical implications of this study strengthen the view that classroom management is an integral component of pedagogical strategy in Geography education. Practically, the findings may serve as a reference for teachers and schools in designing adaptive, effective, learner-centered management models. The study concludes that consistent classroom management improves Geography learning quality and outcomes.
Ethical Dimensions of Artificial Intelligence in the Digital Entrepreneurship Ecosystem: A Systematic Literature Review Salman Alfarisi; Adith Aulia Rahman; Zaqi Kurniawan; Aditiya Hermawan; Yan Everhard Riwurohi
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4580

Abstract

The rapid adoption of artificial intelligence (AI) in digital entrepreneurship has created new opportunities for innovation, efficiency, and data-driven decision-making, while simultaneously raising ethical concerns related to fairness, transparency, privacy, accountability, and explainability. This study presents a systematic literature review to examine the ethical dimensions of AI within the digital entrepreneurship ecosystem. Guided by the PRISMA 2020 protocol and the PICO framework, searches were conducted across Web of Science, Scopus, and IEEE Xplore for studies published between 2020 and 2026. From 512 initially identified articles, 24 studies met the inclusion criteria and quality assessment requirements. The selected studies were analyzed using thematic coding, narrative synthesis, and quality-based evidence mapping to identify recurring ethical dimensions, operational mechanisms, and governance gaps. The findings reveal four dominant ethical problem clusters: algorithmic fairness in entrepreneurial decision-making, transparency deficits in black-box AI systems, data privacy and cybersecurity vulnerabilities, and weak accountability mechanisms in AI governance. The review further shows that responsible AI frameworks, explainability techniques, bias audits, data governance protocols, and risk-based regulatory approaches are central mechanisms for translating ethical principles into practice. The findings contribute to responsible AI scholarship, digital entrepreneurship governance, and policy-oriented debates by offering practical guidance for entrepreneurs, regulators, and researchers concerned with ethical AI adoption in resource-constrained business environments. This study provides an evidence-based roadmap for strengthening ethical, accountable, and socially responsible AI implementation in digital entrepreneurship ecosystems.
Inventory Turnover as a Conduit: Sales Growth, Capital Structure, and Profitability Siti Mira Mutiah; Mudawanah, Siti; Pindonta Nalsal Purba
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4587

Abstract

This study aims to examine the impact of sales growth and capital structure on profitability, with inventory turnover positioned as a mediating variable, in food and beverage companies listed on the Indonesia Stock Exchange during the 2020–2024 period. Using a quantitative explanatory design, this study applies panel data regression with path analysis to evaluate both direct and indirect relationships among the variables. Model selection was conducted through the Chow test, Hausman test, and Lagrange Multiplier test, while the mediation effect was assessed using the Sobel test. The results show that sales growth has a positive and significant effect on inventory turnover and profitability. In contrast, capital structure has no significant effect on inventory turnover but negatively affects profitability. Inventory turnover is found to negatively affect profitability and does not mediate the relationships between sales growth and profitability or between capital structure and profitability. Theoretically, these findings challenge the conventional assumption that faster inventory turnover necessarily strengthens profitability or serves as an effective transmission mechanism between financial decisions and firm performance. Instead, profitability is more directly influenced by sales expansion and the debt structure. Practically, this study provides insights for financial managers to control leverage, protect profit margins, and align inventory policies with pricing and demand strategies. The findings also assist investors in evaluating firm performance by considering sales growth, capital structure, and operational efficiency simultaneously.
Effects of IoT–PLC-Based Smart-Industry Learning Model on Problem-Solving and Critical Thinking Skills of Electrical Engineering Students Chusna, Chufadatul; Purnomo; Sutadji, Eddy
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4588

Abstract

This study examines the effect of an IoT–PLC-based Smart-Industry learning model on students’ problem-solving and critical thinking skills in electrical engineering education. However, empirical studies investigating how IoT–PLC-based instructional models influence higher-order cognitive skills remain limited, particularly in vocational and electrical engineering education contexts. Despite the increasing integration of Industry 4.0 technologies in education, previous studies have primarily focused on technical skill development and often lack empirically validated pedagogical frameworks. The study employed a quantitative quasi-experimental design with a pretest–posttest control group, involving an experimental group taught through the IoT–PLC-based Smart-Industry learning model and a control group taught through conventional instruction. A total of 60 undergraduate students were selected through cluster sampling and assigned to experimental and control groups. Data were collected using validated instruments, with reliability confirmed by Cronbach’s alpha coefficients above 0.70. The results indicate that the experimental group achieved significantly higher improvements than the control group (p < 0.05), with large effect sizes observed for both problem-solving (d = 1.45) and critical thinking (d = 1.39). These findings suggest that an IoT–PLC-based Smart-Industry learning model may enhance higher-order cognitive skills, particularly problem-solving and critical thinking, within the specific context of electrical engineering education.
Dampak Sertifikasi terhadap Kinerja Usaha Mikro dan Kecil Indonesia : Analisis Propensity Score Matching Putra Irwandi; Novita, Ninda
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4589

Abstract

Certification is a strategic tool for improving business performance, competitiveness, and expanding market access. Although numerous studies on certification have been conducted, these studies are limited to specific sectors and regions. Furthermore, the types of certification examined typically focus on a single specific certification. No previous study has focused on the impact of certification on business performance using nationwide data and a quasi-experimental approach via PSM while covering all types of certification. This study aims to analyze the impact of certification on the performance of Micro and Small Enterprises in Indonesia using Propensity Score Matching. The research method employs a quantitative causal approach, with data sourced from the 2019 Micro and Small Enterprise Survey conducted by the Central Statistics Agency, covering a total of 52,551 enterprises. Data analysis was conducted using Propensity Score Matching with the assistance of STATA 17 software. The business performance variables measured were Turnover and Profit. The results indicate that certification has a positive and significant impact on business performance, with a T-statistic significance value of 2.55 for the turnover variable and 1.81 for the profit variable. The ATT values obtained for the revenue variable are 34.19 and for the profit variable are 7.89, which means that certified businesses saw an increase in revenue of Rp 34.19 million and an increase in profit of Rp 7.89 million. The theoretical implication is the need to expand and provide guidance on obtaining certification, as well as to facilitate access so that certification is distributed more evenly and is accessible to all business actors. The conclusion of this study is that the implementation of certification is important for Micro and Small Enterprises and improves business performance, specifically in terms of turnover and profit.
Analisis Konsentrasi Pasar di Industri Maskapai Penerbangan Komersial Domestik Indonesia (2019-2024): Bukti Tingkat Perusahaan vs Tingkat Grup Chandaka, Fernando Dhiraputra; Ariani, Mintarti
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4601

Abstract

This study examines competition in Indonesia’s domestic commercial airline industry during the 2019–2024 period. While prior studies have measured market concentration at the firm level, few have examined how ownership-group structures may reshape our understanding of effective competition. Using a quantitative descriptive approach, the study addresses this gap by applying the Four-Firm Concentration Ratio (CR4) and the Herfindahl-Hirschman Index (HHI) at two distinct levels of analysis: firm level and group level. The findings reveal a tight oligopoly, with CR4 consistently remaining above 70% and HHI values declining to the moderately concentrated category in 2023–2024. However, group-level HHI remained highly concentrated, exceeding 3,900. Parallel conduct, such as synchronized baggage policy changes, market segmentation, similar service offerings, and firm consolidation, was observed alongside the effects of the market structure. This condition suggests limited competition, which may influence pricing strategies and consumer welfare. These findings emphasize that firm-level analysis may underestimate the true degree of market concentration when group affiliations are ignored. Practically, group-level dominance provides a foundation for monitoring market concentration at the group level rather than solely at the individual-firm level. Effective competition in the industry remains highly concentrated within two dominant groups.
Presenteeism in Cigarette Workers in the Light of Job Insecurity and Job Demands Meilala, Putri Amanda; Trubus Raharjo
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4612

Abstract

This study aims to examine the relationship between job insecurity, job demands, and presenteeism among cigarette factory workers. Presenteeism refers to employees’ tendency to remain physically present at work despite experiencing physical or psychological health problems that may reduce work effectiveness. This study employed a quantitative correlational design involving cigarette factory workers selected through accidental sampling. Data were collected using three validated measurement scales, namely the presenteeism scale, job insecurity scale, and job demands scale. The data were analyzed using Pearson correlation and two-predictor regression analysis. The results showed a significant positive relationship between job insecurity, job demands, and presenteeism, with a correlation coefficient of 0.647 and a significance level of p < 0.01. Job insecurity and job demands jointly contributed 41.8% to presenteeism. Further analysis indicated that job insecurity had a positive relationship with presenteeism (r = 0.629; p < 0.01), while job demands were also positively associated with presenteeism (r = 0.546; p < 0.01). These findings indicate that workers who experience higher job insecurity and greater job demands are more likely to continue working despite being in poor physical or psychological condition. Practically, the results suggest that organizations need to reduce excessive job demands, improve perceived job security, and strengthen employee well-being policies to minimize presenteeism and maintain workplace productivity.
Green Human Resource Management and Sustainable Development: A Systematic Exploration Kusnawan, Agus; Setyani Dwi Lestari; Slamet Mudjijah
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4618

Abstract

This study examines the development of Green Human Resource Management (GHRM) research within sustainable development literature through bibliometric analysis. As a strategic human resource management approach, GHRM has received growing scholarly attention because of its contribution to promoting environmentally responsible behavior, strengthening organizational sustainability capabilities, and supporting long-term environmental performance. Nevertheless, systematic studies that map the evolution, thematic direction, and intellectual structure of this research field remain limited. Data were retrieved from the Scopus database, resulting in 76 open-access articles published between 2016 and 2024. The analysis was conducted using Biblioshiny to identify publication trends, dominant research themes, influential journals, and leading contributors. The findings reveal substantial publication growth after 2020, indicating increasing academic interest in GHRM and sustainability. Dominant themes include environmental performance, pro-environmental behavior, green HR practices, and sustainability-oriented organizational capabilities. Sustainability and the Journal of Cleaner Production emerged as the most influential journals, while Jabbour and Renwick were identified as key contributors. Overall, this study highlights GHRM’s shift toward broader sustainability and capability-based perspectives.

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