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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering eProceedings of Engineering Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Journal of Production, Enterprise, and Industrial Applications
Yulianti, Femi
Telkom University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Prediksi Preferensi Waktu Pengiriman Pelanggan Untuk Optimalisasi Layanan Logistik Menggunakan Decision Tree Yulianti, Femi; Zahira, Aulia Dihas
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 7, No 2 (2026): Jurnal SENOPATI Vol 7, No 2 (in progress)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2026.v7i2.8113

Abstract

In the era of customer-centric logistics, the ability to predict delivery time preferences is critical to enhancing service quality and operational efficiecy. This study proposes a predictive analytics framework to classify customer-preferred delivery times – morning, afternoon, evening, or night – using historical logistics data and customer perception analysis. Utilizing the Amazon delivery dataset, categorical time intervals were derived through timestamp transformation, enabling classification modeling with three machine learning algorithms Naive bayes, Logistic regression, and Decision tree. Among these, the Decision tree algorithm yielded the best performance, achieving an accuracy of 77% and a macro F1-score of 0.77. Further analysis revealed that traffic conditions, vehicle types, and product categories were the most influential features in predicting delivery time preference. Survei results corroborated the model’s findings, with customer responses highlighting traffic and delivery timing as top priorities for service satisfaction. This research demonstrates the integration of data-driven modeling with customer insights to support decision-making in last-mile logistics. The findings can guide logistics providers in designing adaptive, preference-based delivery schedules that improve service realiability and user experience.Keywords: predictive analytics, delivery time preference, decision tree, last-mile logistics, customer satisfaction, machine learning.
Co-Authors Andrawin, Luciana Anya Shafira Pramesti Ari Yanuar Ridwan Ayu Masturo, Ami Ayudita Oktafiani Bayugiri Taufik Adiputra Detha Aulia Alfazah Dharma Air Putra Dhea Trullyani Savitri Erlangga Bayu Setyawan Fauziyah, Anisa Gisti Ayu Pratiwi Golden Pratama Hakim, Linda Arini Hardian Kokoh Pambudi Hariyono, Rizky Alif Oktaviani Herfi, Jeehan Hafizha Ilham Muhammad Faisal Iphov Kumala Sriwana Kevin Ahessa Julian Khoirunnisa, Alifa Luciana Andrawina Mauritz Edo Soumutul Muhamad Arvien Nugraha Muhammad Nashir Ardiansyah Murni Dwi Astuti Ni Wayan Savitri Arsa Putri Prafajar Suksessanno Muttaqin Putra, Kresna Nurfajri Setya Putu Giri Artha Kusuma Rafi S , Muhammad Rahma Putri, Annisa Rendra Bagus Kusuma Jati Ridho Abdhillah Permana Rizkiansyah, Muhammad Nabil Rofifah , Atikah Rosyid Kriswanto, Muhammad Salsabila, Tiara Rizky Simatupang, Michael Samuel Sri Rejeki, Afifa Rismila Sukma, Tania Ayu Zahira, Aulia Dihas Zahra, Arifa Fadhillah