Jurnal Mahkota Bisnis (Makbis)

TRANSFORMASI MANAJEMEN SDM ERA 5.0: SINTESIS KOMPETENSI HOLISTIK, PENGEMBANGAN BERKELANJUTAN, DAN PENILAIAN KINERJA BERBASIS DATA

Edi Faisal Harahap — 2026-06-30

Background: The transition from the Industry 4.0 era to Society 5.0 demands a fundamental reconceptualization of Human Resource Management (HRM). While Industry 4.0 focused heavily on automation and digital technology integration, Society 5.0 repositions humans at the center of organizational sustainability (human-centricity), augmented by Artificial Intelligence (AI) and big data analytics. Existing literature still addresses digital competencies, HR development strategies, and performance appraisal systems in a fragmented manner, without an integrative framework linking the three. Purpose: This study aims to conceptually integrate three primary dimensions of HRM 5.0 transformation: a holistic competency taxonomy, strategic Human Resource Development (HRD), and the evolution of performance appraisal paradigms. Methods: This study employs a Systematic Literature Review (SLR) guided by the PRISMA 2020 protocol, searching Scopus, ScienceDirect, SpringerLink, SINTA, and Google Scholar for reputable literature published between 2010 and 2026, yielding 32 articles that met the inclusion criteria for synthesis. Results: The synthesis establishes an integrated HRM 5.0 framework consisting of: (1) a holistic competency taxonomy synergizing digital competencies with soft competencies categorized into Managing People, Managing Task, and Managing Self; (2) strategic HRD approaches driven by continuous upskilling/reskilling, HR Analytics integration, and cultural-structural barrier mitigation; and (3) the evolution of performance appraisal systems from retrospective annual reviews to Continuous Performance Management measuring Employee Digital Performance (EDP) embedded within ethical and human-centric values. Conclusion: These three pillars are mutually reinforcing and form an integrative, human-centric, adaptive, and sustainable HRM 5.0 conceptual framework that offers a strategic roadmap for academics and HRM practitioners navigating digital transformation.

KLASIFIKASI KEBERHASILAN USAHA MIKRO, KECIL, DAN MENENGAH (UMKM) DI INDONESIA MENGGUNAKAN ALGORITMA C4.5 DENGAN PENDEKATAN DATA MINING

Bunga nurul Lestari — 2026-06-30

This study aims to analyze and classify the success of Micro, Small, and Medium Enterprises (MSMEs) using a data mining approach based on the entropy-driven Decision Tree (C4.5) algorithm, with the Random Forest algorithm employed as a comparative model. The primary objective of this research is to identify the factors influencing MSME success accurately while providing an interpretable classification model to support business decision-making. The dataset consists of MSME data that underwent a preprocessing stage using one-hot encoding to transform categorical variables into numerical representations. The research methodology includes data exploration, data preprocessing, train-test data partitioning, model development using the C4.5 algorithm, and model evaluation based on accuracy, confusion matrix, classification report, Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), and k-fold cross-validation. In addition, feature importance analysis was conducted to identify the most influential factors affecting MSME success. The findings indicate that the C4.5 algorithm achieved competitive classification performance with stable predictive accuracy while offering a more interpretable model than the Random Forest algorithm. It can therefore be concluded that the C4.5 algorithm is an effective approach for classifying MSME success, as it provides an appropriate balance between predictive accuracy and model interpretability, thereby supporting data-driven decision-making in the MSME sector.

KLASIFIKASI REVIEW RATING PRODUK MENGGUNAKAN ALGORITMA DECISION TREE BERDASARKAN DATA TRANSAKSI PENJUALAN

Naufal Nur Hidayah — 2026-06-30

Increasing business competition requires companies to understand their product sales patterns in order to make appropriate decisions regarding inventory management and marketing strategies. One approach is to classify products based on their sales performance into best-selling and non-best-selling categories. This study aims to apply the C4.5 algorithm to classify products based on sales transaction data. The dataset consists of sales transaction records with attributes such as product category, price, quantity sold, stock, discount, and total sales. The research stages include data collection, data preprocessing, decision tree construction using the C4.5 algorithm, and model evaluation using a confusion matrix. The results indicate that the C4.5 algorithm is capable of generating easy-to-understand classification rules and achieving a good level of accuracy in determining whether products are best-selling or non-best-selling. The resulting model is expected to assist companies in making better decisions regarding inventory procurement, stock management, and the development of more effective promotional strategies.

ANALISIS KINERJA SUPPLY CHAIN MANAGEMENT BERDASARKAN STUDI LITERATUR DENGAN PENDEKATAN SCOR

Shine Highest — 2026-06-30

Supply chain management plays an important role in improving operational performance and company competitiveness. This study aims to analyze supply chain performance based on previous research. The method used is a literature review of several national journals discussing supply chain performance measurement using the Supply Chain Operations Reference (SCOR) approach. The results show that most companies have moderate supply chain performance, with main problems in planning, sourcing, and distribution. The SCOR model provides a structured and comprehensive overview of supply chain performance. Keywords: supply chain performance, supply chain management, SCOR

MEKANISME PERHITUNGAN DAN PEMUNGUTAN PAJAK PENGHASILAN PASAL 21 PADA KUALITAS PELAPORAN HUMAN CAPITAL MANAGEMENT PT PERKEBUNAN NUSANTARA IV REGIONAL II MEDAN TAHUN 2024

Diega Arazi Syubhan — 2026-06-30

This study was conducted at PT Perkebunan Nusantara IV Regional II Medan with the aim of examining and analyzing the calculation and withholding of Article 21 Income Tax (PPh 21) on employees' salaries, as well as evaluating whether the implementation of Article 21 Income Tax at the company complies with the applicable tax regulations. This research employed a descriptive approach using interview results and supporting data related to Article 21 Income Tax obtained from PT Perkebunan Nusantara IV Regional II Medan. The data analysis technique involved conducting field surveys to collect relevant data, analyzing the findings, and comparing the observed practices with established theories and applicable regulations to draw valid conclusions. The results indicate that the calculation and withholding of individual income tax (Article 21 Income Tax/PPh 21) on employees' salaries at PT Perkebunan Nusantara IV Regional II Medan have been implemented in accordance with the applicable tax provisions.

TEKNOLOGI BLOCKCHAIN PADA PEMASARAN DIGITAL

Ali Syah Putra — 2026-06-30

The modern era is characterized by the rapid advancement of innovative technologies and their widespread implementation across various economic activities. Digitalization involves the utilization of numerous specialized devices connected to the Internet that perform specific functions. Within an integrated system, server technologies play a crucial role by enabling the storage and processing of large volumes of information, including structured, semi-structured, and unstructured data, through high computational capabilities. The availability of powerful equipment capable of executing complex mathematical models using extensive datasets has accelerated the development and application of various machine learning algorithms. By employing different machine learning approaches, hidden patterns and relationships can be identified from the available information, allowing the creation of effective management solutions to optimize processes at the micro, macro, and meso levels. The growing volume of information has also increased the need to ensure security for both individual users and large confidential databases. At the current stage of technological development, blockchain technology is widely utilized to maintain information security and prevent data falsification within databases.

KLASIFIKASI PERFORMA PENJUALAN AKSESORIS MOTOR MENGGUNAKAN ALGORITMA C4.5 DENGAN PENDEKATAN DATA MINING

Difi Basyasyah Chan — 2026-06-30

This study aims to develop and evaluate a sales performance classification model for motorcycle accessories using the entropy-based Decision Tree (C4.5) algorithm, with Random Forest employed as a comparative model. The main objective is to identify the transaction-related factors that most significantly influence sales performance (High or Low) in an accurate and interpretable manner, thereby supporting inventory management decisions in motorcycle accessory businesses. The dataset consists of 832 motorcycle accessory sales transactions recorded throughout 2021, including attributes such as unit price, quantity sold, product category, and transaction month. The research methodology comprises data collection, preprocessing using one-hot encoding, stratified training and testing data partitioning (80:20), model development using the C4.5 and Random Forest algorithms, and performance evaluation based on accuracy, confusion matrix, classification report, ROC-AUC, and 5-fold cross-validation. The experimental results show that the C4.5 model achieved a test accuracy of 79.04% with an average cross-validation accuracy of 81.25%, while the Random Forest model achieved an accuracy of 76.05%. Feature importance analysis indicates that Quantity Tier and Unit Price Tier are the two most influential factors in determining sales performance. In conclusion, the C4.5 algorithm is effective for classifying motorcycle accessory sales performance, as it provides competitive predictive accuracy while maintaining high model interpretability, making it suitable for supporting inventory planning and marketing strategy development.

ANALISIS KLASIFIKASI HARGA PENJUALAN MOTOR MENGGUNAKAN METODE DECISION TREE BERBASIS ENTROPY DENGAN PENDEKATAN C4.5 DAN GAIN RATIO

Neza Namira — 2026-06-30

The rapid growth of the automotive industry has intensified competition in motorcycle sales. Determining the appropriate selling price is one of the key factors in improving a company's competitiveness and profitability. Various factors influence motorcycle selling prices, including brand, model, year of manufacture, engine capacity, vehicle condition, and mileage, resulting in complex data that are difficult to analyze manually. Therefore, a data mining method is needed to process historical motorcycle sales data into valuable information that supports decision-making. This study aims to analyze and classify motorcycle selling prices using the Decision Tree C4.5 algorithm based on entropy and gain ratio. The C4.5 algorithm was selected because it can generate decision rules that are easy to interpret while providing high classification accuracy for both categorical and numerical data. The research process includes data collection, data preprocessing, entropy, gain, and gain ratio calculations, decision tree construction, and classification model evaluation. The results indicate that the C4.5 algorithm is capable of identifying the most influential attributes affecting motorcycle price classification. The resulting decision tree can be used as a basis for effectively predicting motorcycle price categories. Therefore, the Decision Tree C4.5 method can assist motorcycle dealers and automotive businesses in determining appropriate sales strategies based on the characteristics of the vehicles they offer.

KLASIFIKASI HARGA MOTOR BEKAS MENGGUNAKAN ALGORITMA C4.5 UNTUK MENDUKUNG PENGAMBILAN KEPUTUSAN

Senhora Simanjuntak — 2026-06-30

The increasing volume of used motorcycle transactions has led to the rapid growth of vehicle-related data, providing valuable opportunities to support more objective vehicle price classification. However, in practice, the pricing of used motorcycles is often influenced by subjective assessments, resulting in prices that may not accurately reflect the actual condition of the vehicles. This study aims to apply the C4.5 algorithm to classify used motorcycle prices based on vehicle characteristics contained in the motor_second.csv dataset. The research methodology consists of data collection, data cleaning, data transformation, dataset partitioning into training and testing sets, Decision Tree model construction, and model evaluation using a confusion matrix, classification accuracy, and feature importance analysis. The results demonstrate that the C4.5 algorithm successfully generates an interpretable decision tree capable of explaining the relationship between vehicle attributes and price categories. In addition to producing a classification model, the proposed approach also generates decision rules that can serve as practical guidelines for estimating used motorcycle price categories. Therefore, the C4.5 algorithm can be effectively utilized as a decision support method for the classification of used vehicle prices.

PENERAPAN KLASIFIKASI ALGORITMA C4.5 BERBASIS KRITERIA PENJUALAN DALAM PENENTUAN STRATEGI PEMASARAN PRODUK HANDPHONE

Chris Ignatius Yoni — 2026-06-30

The rapid growth of the mobile device industry has intensified competition among smartphone brands in the retail market, making ineffective inventory management and inappropriate sales strategies potential sources of financial loss. This study aims to classify the sales performance of smartphone models based on brand attributes using the C4.5 decision tree algorithm. The training dataset consists of 41 smartphone models from five major brands (Infinix, Oppo, POCO, Vivo, and Xiaomi), with sales performance categorized into three classes: Low-selling, Moderately Selling, and Best-selling. Mathematical calculations indicate that the system's total entropy is 1.504, while the evaluation of the brand attribute produces an Information Gain value of 0.111. The resulting decision tree reveals that the Infinix, POCO, and Xiaomi brands exhibit complete class purity (Entropy = 0.000), consistently corresponding to the Best-selling category. The implementation of this decision tree enables retail management to predict product sales performance at an early stage, thereby optimizing inventory allocation and improving procurement efficiency.

DINAMIKA PERKEMBANGAN MODAL VENTURA DI INDONESIA: SEBUAH TINJAUAN LITERATUR

Arsyaf Tampubolon — 2026-06-30

Venture capital has become a strategic financing instrument for the growth of startups and MSMEs in Indonesia, particularly in driving the digital economic transformation. However, its development faces various complex challenges, including the gap between regulation and practice as well as the phenomenon of philosophical shifts that threaten its fundamental characteristics. This research aims to systematically analyze the dynamics of venture capital development in Indonesia through a comprehensive literature review approach. The method used is descriptive qualitative with library research, analyzing selected scholarly articles that discuss regulations, investment practices, comparison of conventional and sharia models, as well as industrial development challenges. The findings reveal three main results. First, there is a paradoxical development pattern where the number of Venture Capital Companies tends to decrease while total assets increase significantly, indicating industrial consolidation. Second, a significant gap exists between regulatory ideals and practices, reflected by the dominance of debt financing compared to equity participation, as well as the "loss of soul" phenomenon marked by the imposition of collateral requirements that shift from risk-sharing to risk-shifting mechanisms. Third, sharia venture capital still lags behind conventional in terms of asset scale due to low literacy rates and limited human resources. This study concludes that policy reformulation is needed to simplify administrative regulations, provide fiscal incentives, and establish a risk guarantee institution to restore the philosophy of venture capital as an inclusive risk-sharing instrument. The research implications provide strategic recommendations for regulators, investors, and business actors in strengthening the role of venture capital as an engine of innovation in Indonesia's digital economy.