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All Journal JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan) Journal of English Education and Teaching (JEET) Meteor STIP Marunda SCAFFOLDING: Jurnal Pendidikan Islam dan Multikulturalisme Dinasti International Journal of Education Management and Social Science Darmabakti: Jurnal Inovasi Pengabdian dalam Penerbangan Journal of Social Work and Science Education Logistica : Journal of Logistic and Transportation
Masito, Fitri
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Religion Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Transportation Other

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Journal : Logistica : Journal of Logistic and Transportation

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Intrinsic Versus Extrinsic Motivation in Logistics: A Quantitative Analysis of Performance Outcomes Budiyanto, Albert; Masito, Fitri; Toja, Andi Batari
Logistica : Journal of Logistic and Transportation Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i1.733

Abstract

Employee motivation is a critical determinant of organizational performance, particularly in logistics warehousing where efficiency and resilience are paramount. This study investigates the influence of intrinsic and extrinsic motivation on employee performance at PT Aerojasa Cargo using Herzberg’s two-factor theory as a framework. A quantitative survey was conducted with 55 employees selected from 120 using Slovin’s formula. Motivation (knowledge, skills, rewards, behavioral direction, persistence) and performance (accuracy, timeliness, quality, quantity, neatness) were measured through a 5-point Likert-scale questionnaire, and data were analyzed using SPSS 26. Regression analysis revealed a strong correlation (R² = 0.833, p < 0.05), with intrinsic factors such as persistence and recognition emerging as the strongest predictors of performance, surpassing extrinsic motivators like salary. These findings provide robust empirical evidence of the relevance of Herzberg’s theory in Indonesia’s logistics sector and underscore the need for HR strategies that prioritize intrinsic motivators. Practical contributions include designing recognition systems, training, and career development programs to enhance employee persistence and behavioral alignment.
Bridging Gaps in Transport Demand Forecasting through Artificial Intelligence and Machine Learning Masito, Fitri; Toja, Andi Batari; Judijanto, Loso
Logistica : Journal of Logistic and Transportation Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i4.1065

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool for transportation demand forecasting, addressing the limitations of traditional statistical approaches. This study systematically reviews recent literature to evaluate AI methodologies, their applications, and the systemic factors that shape adoption. Peer-reviewed studies published between 2018 and 2025 were identified from Scopus, Web of Science, and Google Scholar. Findings reveal that AI techniques, particularly deep learning and ensemble models, consistently outperform conventional forecasting methods in predictive accuracy and adaptability. Integration of spatio-temporal and geospatial data further enhances model robustness, supporting more responsive strategies for sustainable urban mobility. Applications span passenger transport, freight logistics, public transit optimization, and electric vehicle charging demand. Nonetheless, challenges persist, including data scarcity, computational demands, interpretability concerns, and uneven adoption between developed and developing regions. The review underscores the need for supportive policies, collaborative data management, and fairness-aware models. Overall, leveraging AI in transport forecasting is essential to build efficient, adaptive, and inclusive mobility systems while aligning future research with long-term planning and sustainability goals.
Co-Authors Abdullah, Anton Ala, Asman Badeni Badeni, Badeni Budiyanto, Albert Eko Risdianto Hariri, Ahmad Hermala, Zusnita Iwan Setiawan Judijanto, Loso Maryansyah , Yupika Minulya Eska Nugraha Muis, Muhammad Abdul Nazilul Hamidi Oka, I.G.A. Ayu Mas Pesilette, Mohammad Sukri Pesilette, Mohammad Syukri Rusdi Efendi Selasdini, Vidya Sudarwan Danim Sukahir, Sukahir Sunardi Sunardi Sutiyo Sutiyo Thursina Andayani Toja, Andi Batari Wildan Nugraha Wulandari, Dinda Yeti Komalasari Yoniessa, Siska Yuniar, Dwi Candra