Articles
<![CDATA[Segmentasi Pelanggan dengan Algoritma Clustering Berdasarkan Atribut Recency, Frequency dan Monetary (RFM): Customer Segmentation with Clustering Algorithm Based on Recency, Frequency, and Monetary (RFM) Attributes ]]>
<![CDATA[Fadhillah, Muhamad Fikri]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Puspitasari, Ira]]>
<![CDATA[ MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025 ]]>
Publisher : <![CDATA[Institut Riset dan Publikasi Indonesia ]]>
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DOI: 10.57152/malcom.v5i1.1491
<![CDATA[Perdagagan bebas yang disepakati dengan negara maju menimbulkan perubahan karakter konsumen di Indonesia [1] ditambah efek pandemi Covid-19 semua komoditi dalam negeri hampir mengalami penuruan penjualan dan laba usaha tiap tahunnya[2]. Perusahaan harus mengubah strategi lain dalam menarik pelanggan menggunakan data karakteristik pelanggan yang berbeda-beda salah satunya data transaksi penjualan [3]. Data tersebut nantinya akan disegmentasi dengan metode clustering. Clustering adalah teknik analisis data yang bertujuan mengelompokkan objek-objek ke dalam grup atau klaster berdasarkan kesamaan karakteristik atau fiturnya. Analisis data dengan clustering diperlukan untuk mengidentifikasi pola dan mengekstrak informasi dari kumpulan data dengan variasi dan jumlah besar, seperti pada identifkasi segmentasi pelanggan. Hasil clustering ini akan memudahkan dalam merumuskan startegi pemasaran berorientasi pelanggan. Penelitian ini menggunakan model Recency, Frequency, and Monetary (RFM) sebagai atribut utama dan teknik clustering k-means, agglomerative, dan DBSCAN. Evaluasi berdasarkan silhouette score, Davis-Bouldin index, dan Calinski-Harabasz index menunjukkan hasil terbaik pada pembentukan tiga cluster dengan algoritma k-means untuk segmentasi pelanggan dalam penelitian ini dengan indeks silhouette score sebesar 0.364, davis-bouldin sebesar 0.93 dan calinski-harabasz sebesar 1303.6 ini menghasilkan 3 klaster pelanggan loyal customer, adequate customer dan churn atau lost customer.]]>
<![CDATA[IMPLEMENTASI FAKTOR KEGAGALAN KINERJA PROYEK PADA PROYEK-PROYEK DI X PROYEK JAWA TIMUR ]]>
<![CDATA[Permana, Hieka Wahyu]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Agustina, Tri Siwi]]>
<![CDATA[ Journal of Economic, Bussines and Accounting (COSTING) Vol 7 No 5 (2024): Journal of Economic, Bussines and Accounting (COSTING) ]]>
Publisher : <![CDATA[Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE) ]]>
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DOI: 10.31539/costing.v7i6.12871
<![CDATA[The types of projects include, public school building procurement projects, procurement of main transmission materials, relocation and various other projects. This infrastructure will support services for additional students for the next ten years so that a comprehensive implementation of project performance failure management analysis is needed to be able to identify, analyze, evaluate, and respond to project failure risks so that project work runs well. The following is data related to the procurement of goods and services projects associated with monitoring results during the project.This study examines the effect of project external environment and construction material supply related problems on project performance failure through ineffective contract management on building construction projects. This study uses a quantitative method with SEM-PLS (Structural Equation Modeling) Partial Least Square analysis. The results of the study indicate that project external environment and construction material supply related problems affect project performance failure through ineffective contract management on building construction projects.]]>
<![CDATA[Effect of Using Artificial Intelligence on Service Frequency and Public Satisfaction ]]>
<![CDATA[Nurfitarini, Wiwik]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Ekowati, Dian]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 5 No. 1 (2025): July - January ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v5i1.794
<![CDATA[This study aims to assess how Indonesia's Government Office of Kediri District responds to integrating Artificial Intelligence (AI) in improving service frequency and public satisfaction. An explanatory quantitative approach was adopted to investigate the causal relationship between AI adoption and the two dependent variables—service frequency and public satisfaction—through multiple linear regression analysis, utilizing data from a sample of 15 districts between 2018 and 2023. The study reveals that AI adoption significantly enhances public satisfaction and service frequency. Implementing AI, facilitated through the Digital Service Living Lab (DSLL) platform, increases efficiency, responsiveness, and service quality. The study underscores that AI integration can boost public service effectiveness and efficiency, encouraging local governments to broaden AI usage and invest in staff training and capacity development. This research offers strategic recommendations for governments to expand AI adoption further, strengthen inter-district collaboration, and optimize the use of Electronic Government (E-Government) initiatives to support the goals of smart city and provincial development.]]>
<![CDATA[Implementation of Power BI for Dashboard Visualization on Brand Product Sales Dataset Adidas in the United States from Kaggle ]]>
<![CDATA[Sulistyawati, Sri]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Mardhiyah, Dien]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 5 No. 1 (2025): July - January ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v5i1.796
<![CDATA[This article describes the implementation of Power BI to create an interactive dashboard based on a dataset of Adidas product sales in the United States from Kaggle. Power BI processes sales data to provide deep insights into sales trends, product performance, market analysis, and consumer preferences. The result is a clear and intuitive dashboard that assists management in strategic decision-making. This dashboard shows Total Sales of 899.90 million USD, Units Sold of 2 million units, and Operating Profit of 332.13 million USD, with the highest sales in New York, California, Florida, and Texas. Profitability analysis shows strong profit margins in New York and Florida, while the Men's Street Footwear product category is the top seller. This information is essential for identifying popular products and developing effective business strategies.]]>
<![CDATA[The Impact of AI Adoption on Job Engagement and Employee Trust ]]>
<![CDATA[Adityaksa, Rezha]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>
<![CDATA[ Golden Ratio of Human Resource Management Vol. 5 No. 1 (2025): August - February ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grhrm.v5i1.701
<![CDATA[The adoption of Artificial Intelligence (AI) within organizations has introduced several phenomena impacting employee motivation. One significant area of interest is the perception of AI principles among employees Employees' views on the effectiveness of AI principles in their organization can vary greatly depending on their roles and responsibilities. For example, a junior data scientist might perceive the principles differently than a Chief Analytics Officer due to their varying levels of involvement and understanding of AI's strategic implementation. This research aims to examine the influence of AI Adoption in moderating job engagement on employee trust compared to the direct influence of job engagement on employee trust. Quantitative research method using non-probability sampling and purposive sampling technique method of 200 mass media employees in Surabaya. Data analysis techniques using Smart PLS software with hypothesis testing using PLS-based SEM. The research results show that AI Adoption is able to moderate the influence of job engagement on employee trust and there is a direct influence of job engagement on employee trust.]]>
<![CDATA[Literature Review of Transformational Leadership: A Study of Effective Leadership Strategy ]]>
<![CDATA[Kartikasari, Retno]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Agustina, Tri Siwi]]>
<![CDATA[ Golden Ratio of Human Resource Management Vol. 5 No. 1 (2025): August - February ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grhrm.v5i1.744
<![CDATA[In the Industrial Revolution 4.0 and Society 5.0 era, companies face the challenge of continuously adapting and improving human resource competencies as the key to success. High team member performance is crucial and is influenced by transformational leadership. An in-depth understanding of effective leadership strategies supporting positive organizational transformation is important to study. Although previous research has explored transformational leadership, comprehensive analysis of leadership strategy variables that support transformational leadership effectiveness is still limited. This study seeks to fill this gap by profoundly analyzing the leadership strategy variables that are key to the success of transformational leadership. This study aims to explore and analyze the variables of effective leadership strategies, especially in the context of transformational leadership. This research uses a qualitative approach by reviewing 10 related journals, consisting of 7 national and three international journals, published in the last 5 years (2018-2023). The data processing technique used content analysis to identify and examine leadership strategy variables that support the effectiveness of transformational leadership. The results revealed that effective communication, developing an inspiring vision, empowering employees, and establishing an organizational culture that supports change are key leadership strategy variables in supporting the success of transformational leadership. The findings of this study have important implications for management practitioners and policymakers in developing effective transformational leadership in organizations. Future research can explore the empirical impact of implementing leadership strategy variables on organizational performance.]]>
<![CDATA[Business Intelligence Capabilities and Firm Performance: Empirical Study on PT. Erlangga Mahameru Publishers ]]>
<![CDATA[Piqri, Mochammad]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 4 No. 2 (2024): February - June ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v4i2.793
<![CDATA[This research aims to analyze the influence of business intelligence on firm performance. Erlangga Mahameru Publishers. Using quantitative explanatory methodology, the study utilized Structural Equation Modeling with Partial Least Squares (SEM-PLS), which was used in this work via SmartPLS software. A structured survey was used to gather information from 156 workers in PT. Erlangga Mahameru Publishers. The findings reveal a positive relationship between business intelligence and firm performance. There is a positive relationship between sensing capability and transforming capability, with a path coefficient of 0.877. There is a positive relationship between transforming capability and driving capability, with a path coefficient of 0.904. A positive relationship exists between driving capability and firm performance, with a path coefficient of 0.828.]]>
<![CDATA[Effect of Using Artificial Intelligence on Service Frequency and Public Satisfaction ]]>
<![CDATA[Nurfitarini, Wiwik]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Ekowati, Dian]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 5 No. 1 (2025): July - January ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v5i1.794
<![CDATA[This study aims to assess how Indonesia's Government Office of Kediri District responds to integrating Artificial Intelligence (AI) in improving service frequency and public satisfaction. An explanatory quantitative approach was adopted to investigate the causal relationship between AI adoption and the two dependent variables—service frequency and public satisfaction—through multiple linear regression analysis, utilizing data from a sample of 15 districts between 2018 and 2023. The study reveals that AI adoption significantly enhances public satisfaction and service frequency. Implementing AI, facilitated through the Digital Service Living Lab (DSLL) platform, increases efficiency, responsiveness, and service quality. The study underscores that AI integration can boost public service effectiveness and efficiency, encouraging local governments to broaden AI usage and invest in staff training and capacity development. This research offers strategic recommendations for governments to expand AI adoption further, strengthen inter-district collaboration, and optimize the use of Electronic Government (E-Government) initiatives to support the goals of smart city and provincial development.]]>
<![CDATA[Project Success Factors Analysis Business Construction ]]>
<![CDATA[Suyoso, Aldo Lovely Arief]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 4 No. 2 (2024): February - June ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v4i2.795
<![CDATA[Project success factors relate to the risk of uncertainty in a job that experiences additions or deletions from the original scope of work according to the contract, thereby changing the entire contract value and even the completion time of the job. This research aims to analyze the influence of soundness of business and workforce, planning and control, and safety performance on the success of construction projects. Using a quantitative explanatory methodology, the research utilized Structural Equation Modeling with Partial Least Squares (SEM-PLS), used in this work via SmartPLS software. A structured survey was used to gather information from 100 contractors in Surabaya. The findings reveal a positive relationship between the soundness of the business and the workforce and planning, control, and safety performance to project success in construction projects.]]>
<![CDATA[Implementation of Power BI for Dashboard Visualization on Brand Product Sales Dataset Adidas in the United States from Kaggle ]]>
<![CDATA[Sulistyawati, Sri]]>;
<![CDATA[Suyoso, Aldo Lovely Arief]]>;
<![CDATA[Mardhiyah, Dien]]>
<![CDATA[ Golden Ratio of Marketing and Applied Psychology of Business Vol. 5 No. 1 (2025): July - January ]]>
Publisher : <![CDATA[Manunggal Halim Jaya ]]>
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DOI: 10.52970/grmapb.v5i1.796
<![CDATA[This article describes the implementation of Power BI to create an interactive dashboard based on a dataset of Adidas product sales in the United States from Kaggle. Power BI processes sales data to provide deep insights into sales trends, product performance, market analysis, and consumer preferences. The result is a clear and intuitive dashboard that assists management in strategic decision-making. This dashboard shows Total Sales of 899.90 million USD, Units Sold of 2 million units, and Operating Profit of 332.13 million USD, with the highest sales in New York, California, Florida, and Texas. Profitability analysis shows strong profit margins in New York and Florida, while the Men's Street Footwear product category is the top seller. This information is essential for identifying popular products and developing effective business strategies.]]>
