Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Progresif: Jurnal Ilmiah Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak
Dimas Eko Putro
Universitas Teknokrat Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Prediksi Keberhasilan Menindaklanjuti Pelanggan pada Dealer Mobil dengan Komparasi Algoritma Random Forest dan XGBoost Helma Nopijani Heidy; Dimas Eko Putro; Muhammad Fadli; Erliyan Redy Susanto
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2776

Abstract

The automotive industry is facing intense competition in boosting vehicle sales, where the follow-up process with prospective customers plays a crucial role in sales conversion. This study develops a predictive model for the success of follow-ups at car dealerships by comparing two machine learning algorithms: Random forest and XGBoost. A dataset of Honda car dealership customers from 2023 was processed through a preprocessing stage, including handling data imbalance and encoding categorical data. The models were evaluated using accuracy, precision, recall, and F1-score metrics. The results show that XGBoost outperforms with an accuracy of 91.67%, compared to Random forest's 88.89%. Both models demonstrate balanced performance across positive and negative classes, indicating a significant improvement over previous approaches. This study recommends expanding the dataset and developing a prediction-based decision support system to enhance the marketing effectiveness of car dealerships.Keywords: Machine learning; Random forest; XGBoost AbstrakIndustri otomotif menghadapi persaingan ketat dalam meningkatkan penjualan kendaraan, di mana proses tindak lanjut (Follow-up) kepada calon pelanggan menjadi faktor krusial dalam konversi penjualan. Penelitian ini mengembangkan model prediksi keberhasilan Follow-up pada dealer mobil dengan membandingkan dua algoritma machine learning, yaitu Random forest dan XGBoost. Dataset pelanggan dealer mobil Honda tahun 2023 diproses melalui tahap preprocessing, termasuk penanganan ketidakseimbangan data menggunakan encoding data kategorikal. Model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil menunjukkan XGBoost unggul dengan akurasi 91,67%, lebih baik dibanding Random forest dengan akurasi 88,89%. Kedua model menunjukkan performa yang seimbang pada kelas positif dan negatif, menandai peningkatan signifikan dari pendekatan sebelumnya. Penelitian merekomendasikan perluasan dataset dan pengembangan sistem pendukung keputusan berbasis prediksi untuk meningkatkan efektivitas pemasaran dealer mobil.Kata kunci: Machine learning; Random forest; XGBoost
Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks Bagastian Bagastian; Dimas Eko Putro; Muhammad Fahmi Fudholi; Ryan Randy Suryono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1437

Abstract

The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
Co-Authors Bagastian Bagastian Erliyan Redy Susanto Fadli, Muhammad Helma Nopijani Heidy Muhammad Fahmi Fudholi Ryan Randy Suryono