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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Economic, Bussines and Accounting (COSTING) MOVE: Journal of Community Service and Engagement Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Community Service (IJCS) Bulletin of Social Informatics Theory and Application Jurnal Teknologi dan Bisnis Cerdas
Dhimas Tribuana
Universitas Handayani Makassar
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Penerapan Algoritma XGBoost Untuk Prediksi Kepuasan Pelanggan Pada Layanan E-Commerce: Studi Pada Dataset Transaksi Nyata Tribuana, Dhimas; Baharuddin, Baharuddin; Muhammad Resky, Andi
Jurnal Teknologi dan Bisnis Cerdas Vol 1 No 1 (2025): Volume 1 Nomor 1 (Juni 2025)
Publisher : Plexi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64476/jtbc.v1i1.5

Abstract

Pertumbuhan e-commerce di Indonesia yang pesat memunculkan tantangan baru bagi penyedia layanan untuk menjaga kepuasan pelanggan di tengah kompetisi yang semakin ketat. Penelitian ini bertujuan untuk mengembangkan model prediktif berbasis Extreme Gradient Boosting (XGBoost) dalam memprediksi kepuasan pelanggan e-commerce dengan memanfaatkan dataset nyata berskala besar. Dataset yang digunakan berasal dari Kaggle (E-Commerce Customer Satisfaction) yang mencakup lebih dari 100.000 transaksi dengan atribut seperti harga, biaya pengiriman, waktu pengiriman, serta ulasan pelanggan. Data diproses melalui tahapan pembersihan, encoding, normalisasi, dan feature engineering. Model XGBoost dibandingkan dengan Random Forest dan Logistic Regression untuk mengevaluasi performa prediksi. Hasil eksperimen menunjukkan bahwa XGBoost mencapai akurasi 92,4%, F1-score 90,6%, dan ROC-AUC 0,941, mengungguli kedua model pembanding. Analisis feature importance dan SHAP mengidentifikasi bahwa review score, freight value, dan delivery delay merupakan faktor dominan yang mempengaruhi kepuasan pelanggan. Temuan ini memiliki implikasi praktis bagi pelaku e-commerce untuk mengoptimalkan strategi logistik dan layanan pasca-pembelian dalam meningkatkan pengalaman pelanggan. Penelitian ini juga menekankan pentingnya pemanfaatan machine learning dalam pemantauan kepuasan secara real-time dan memberikan kontribusi bagi literatur ilmu data di bidang e-commerce Indonesia.
Peran Strategis Informatika Manajemen dalam Mendorong Transformasi Digital: Sebuah Tinjauan Sistematis Literatur Tribuana, Dhimas; Puspita Ayu, Novalinda; Said Uddin, Abu; Firdania, Andi; Dewi Haryanti Agustan , Andi; Rusli, Muhammad
Jurnal Teknologi dan Bisnis Cerdas Vol 1 No 2 (2025): Volume 1 Nomor 2 (September 2025)
Publisher : Plexi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64476/jtbc.v1i2.11

Abstract

Digital transformation (DT) has become one of the most critical strategic issues in modern organizational management across both public and private sectors. This study adopts a Systematic Literature Review (SLR) approach guided by the PRISMA 2020 framework to examine 45 scholarly articles published between 2006 and 2025. The analysis aims to identify overarching patterns, key contributions, research gaps, and future research directions in the context of DT. The synthesis reveals five main clusters: (1) Governance & Alignment as the digital governance foundation ensuring strategic coherence, (2) Digital Capabilities & Dynamic Capabilities as performance and innovation enablers, (3) Artificial Intelligence & Generative AI as drivers of innovation as well as ethical challenges, (4) Public Sector & Smart Governance focusing on public values, transparency, and policy legitimacy, and (5) SMEs & Sustainability emphasizing contextual adaptation, resource constraints, and long-term resilience. The resulting conceptual model highlights that DT success is not solely determined by technology adoption, but by the interaction between governance, capabilities, value orientation, and socio-economic context. This study contributes to the literature by providing an integrative cross-cluster framework and offering implications for management practice and public policy. The findings are expected to serve as a reference for scholars, practitioners, and policymakers in developing inclusive, adaptive, and sustainable DT strategies.
Membangun Taxonomy Riset Big Data Analytics dan Business Intelligence: Systematic Literature Review dalam Konteks Manajemen Informatika Tribuana, Dhimas; Dewi Haryanti Agustan, Andi; Hidayat; Halimah, Endang; Dianah, Koas; Isiswanty
Jurnal Teknologi dan Bisnis Cerdas Vol 1 No 2 (2025): Volume 1 Nomor 2 (September 2025)
Publisher : Plexi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64476/jtbc.v1i2.12

Abstract

Digital transformation has propelled the role of Business Intelligence (BI) from a mere reporting system to a strategic data-driven platform. This study aims to map the state of the art of BI through a Systematic Literature Review (SLR) guided by the PRISMA 2020 framework. A total of 50 scholarly articles published between 2010 and 2025 were systematically analyzed, sourced from both open-access databases and standard repositories (Scopus, Web of Science, Google Scholar, Semantic Scholar, and DOAJ). The analysis produced a taxonomy dividing the literature into five main domains: BI Foundations, Big Data Analytics, Data Governance & Quality, Real-Time & Stream Processing, and BI-AI Integration. The findings indicate that BI research evolves progressively, beginning with conceptual foundations, expanding toward advanced analytic capabilities, reinforcing data governance, accelerating real-time processing, and culminating in integration with Artificial Intelligence (AI) and Generative AI (GenAI). The study offers theoretical implications by providing a comprehensive conceptual framework for BI research, practical implications by guiding organizations in adopting BI-AI technologies effectively, and policy implications by emphasizing the need for adaptive regulation in data governance and AI ethics. Limitations include the restricted publication period and reliance on academic literature. Future research is recommended to incorporate grey literature and empirical case studies to enhance practical relevance.
Co-Authors Abdul Latief Arda Ahmad, Andani Andani Achmad Baharuddin Baharuddin Budi Hermawan Dayanti Dayanti, Dayanti Dedi Triyanto Dewi Haryanti Agustan , Andi Dewi Haryanti Agustan, Andi Dianah, Koas Eman Suherman Fakbua, Nidanuch Firdania, Andi Halimah, Endang Hazriani Hazriani, Hazriani Hidayat Isiswanty Konate, Siaka Muhammad Resky, Andi Muhammad Rusli Puri Swastika Gusti Krisna Dewi Puspita Ayu, Novalinda Rael Astillero, Marlon Rahma Wahdiniwaty Ramdan Satra riswanto riswanto Said Uddin, Abu Syafei, M. Yani Umiyati, Hesti Usman Usman Whardani, Kristina Whardani Yuyun, Yuyun