Article Per Year (5 Year)
p-Index From 2021 - 2026
2.408
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Economic, Bussines and Accounting (COSTING) Aptisi Transactions on Technopreneurship (ATT) 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 IIJSE International Transactions on Artificial Intelligence (ITALIC) Jurnal Teknologi dan Bisnis Cerdas
Dhimas Tribuana
Universitas Handayani Makassar
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search

 1   2 
Deteksi Sampah Otomatis Pada Lingkungan Terbuka Menggunakan YOLOV8 Dan Dataset Roboflow Tribuana, Dhimas; Usman, Usman; Dayanti, Dayanti
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.v1i1.4

Abstract

Peningkatan volume sampah di ruang publik menuntut solusi cerdas untuk mendeteksi dan mengelola kebersihan secara efisien. Penelitian ini bertujuan untuk mengembangkan sistem deteksi sampah otomatis berbasis model deteksi objek YOLOv8 dengan fokus pada lima kategori sampah: plastik, kertas, logam, kaca, dan lainnya. Dataset diperoleh dari platform Roboflow, kemudian dianotasi secara manual dan digunakan untuk melatih dua varian model YOLOv8, yaitu YOLOv8s dan YOLOv8l. Hasil pelatihan menunjukkan bahwa YOLOv8l mencapai mAP@0.5 sebesar 93,1% dan F1-score 91,1%, sementara YOLOv8s memberikan kecepatan inferensi lebih tinggi dengan akurasi yang kompetitif. Evaluasi lapangan terbatas dilakukan menggunakan kamera laptop dan smartphone di lingkungan terbuka seperti taman dan trotoar. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi sampah secara real-time dengan tingkat akurasi visual yang baik, meskipun terdapat penurunan performa pada objek kecil atau tertutup sebagian. Studi ini menunjukkan potensi besar model YOLOv8 dalam mendukung pengembangan sistem monitoring lingkungan berbasis visi komputer. Ke depan, integrasi ke perangkat edge dan pelatihan ulang dengan data lokal direkomendasikan untuk meningkatkan ketahanan model dalam kondisi nyata.
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.
Digital Transformation and Psychological Welfare at MNC Bank Makassar Branch Tribuana, Dhimas; Narimawati, Umi; Syafei, M. Yani
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 7 No 2 (2024): Sharia Economics
Publisher : Sharia Economics Department Universitas KH. Abdul Chalim, Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v7i2.4874

Abstract

In the era of digital transformation, the banking industry, particularly in Indonesia, has experienced significant growth, with a 22.13% annual increase in digital transactions. This study investigates the influence of digital transformation on employee psychological well-being, focusing on the MNC Bank Makassar Branch. Using explanatory and quantitative methods, questionnaires were administered to all 37 employees. Results indicate a positive and significant relationship between digital transformation and psychological well-being, explaining 37% of the variance. Overall evaluation yielded excellent results (87%). The study underscores the importance of enhancing technology adoption, digital data exchange, and IT competence to promote employee well-being. Future research should explore additional variables for a comprehensive understanding.
A Multi-Group Structural Analysis of Digital Banking Adoption Determinants Across Generational Cohorts in Indonesia Tribuana, Dhimas; Narimawati, Umi; Syafei, M. Yani
Aptisi Transactions On Technopreneurship (ATT) Vol 8 No 1 (2026): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v8i1.590

Abstract

This study investigates factors affecting Digital Banking Adoption (DBA) across generations in Indonesia, focusing on performance expectancy, effort expectancy, social influence, facilitating conditions, and trust. Employing a cross-sectional design, the study collected data through a structured questionnaire administered to 360 respondents, selected through purposive and clustering sampling, from major cities including Jakarta, Bandung, Surabaya, and others. Structural Equation Modelling (SEM) and Multi-Group Analysis were applied to test hypotheses and assess generational differences in DBA. Findings reveal that performance expectancy, facilitating conditions, and trust significantly influence DBA, with notable differences across generations: Baby Boomers prioritize facilitating conditions, Generation X emphasizes performance expectancy, and Generation Y values both performance and effort expectancy. Generation Z, despite being tech-savvy, benefits from enhanced support structures for improved banking experiences. These results highlight the importance of tailored, generation-specific strategies in digital banking, providing valuable insights for service providers aiming to enhance user experience and adoption across demographic groups.
AIoT Driven Smart Solar System for Real Time Predictive Sustainable Energy Management Indrawan, Rizki; Very, Eka Dawn; Tribuana, Dhimas; Nabila, Efa Ayu
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.968

Abstract

The rapid expansion of solar photovoltaic (PV) technologies has increased the demand for intelligent, adaptive, and data-driven energy management systems. However, conventional and IoT only solar infrastructures still face limitations, including inefficient energy distribution, delayed fault detection, and an inability to respond dynamically to fluctuating environmental conditions. This study proposes an AIoT-based Smart Solar System that integrates IoT-enabled sensing modules with artificial intelligence for real-time monitoring, predictive analytics, and autonomous control. The system employs a distributed architecture consisting of edge devices, cloud analytics, and machine learning models particularly Long Short-Term Memory (LSTM) networks and regression-based predictors to enhance forecasting accuracy and operational responsiveness. The objective of this research is to improve power utilization, predictive reliability, and maintenance efficiency within solar energy systems. Experimental results demonstrate a 22.8% increase in power utilization, a 17% reduction in maintenance downtime, and a forecasting accuracy of 95.2% (R2 = 0.952). These findings indicate that AIoT integration significantly enhances energy intelligence, system reliability, and sustainability. Overall, the proposed architecture establishes a scalable foundation for next generation renewable energy systems capable of self learning, adaptive optimization, and real-time decision making.
Co-Authors Abdul Latief Arda Ahmad, Andani Andani Achmad Baharuddin Baharuddin Dayanti Dayanti, Dayanti Dedi Triyanto Dewi Haryanti Agustan , Andi Dewi Haryanti Agustan, Andi Dianah, Koas Eman Suherman Fakbua, Nidanuch Febriansyah, Angky Firdania, Andi Halimah, Endang Hazriani Hazriani, Hazriani Hidayat Isiswanty Konate, Siaka Muhammad Resky, Andi Muhammad Rusli Nabila, Efa Ayu Puri Swastika Gusti Krisna Dewi Puspita Ayu, Novalinda Rael Astillero, Marlon Rahma Wahdiniwaty Ramdan Satra riswanto riswanto Rizki Indrawan Said Uddin, Abu Syafei, M. Yani Syafei, M. Yani Syafei Umi Narimawati Umiyati, Hesti Usman Usman Very, Eka Dawn Whardani, Kristina Whardani Yuyun, Yuyun