Senny Handayani Suarsa1*, Angga Dewi Anggraeni2, Cahyo Prianto3, Darfial Guslan4, Dodi Permadi5,Ade Pipit Fatmawati6, Mohamad Abdul Aziz7
1,2,7D4 Manajemen Perusahaan, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
3D4 Teknik Informatika, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
4,5D4 Logistik Bisnis, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
6D4 Akuntansi Keuangan, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
*Corresponding author: sennyhandayani@ulbi.ac.id
Rekonstruksi Segmentasi Pasar Angkutan Antar Kota pada PERUM DAMRI
Senny Handayani Suarsa(1)*, Angga Dewi Anggraeni(2), Cahyo Prianto(3), Darfial Guslan(4), Dodi Permadi(5),Ade Pipit Fatmawati(6), Mohamad Abdul Aziz(7)
1,2,7D4 Manajemen Perusahaan, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
3D4 Teknik Informatika, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
4,5D4 Logistik Bisnis, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
6D4 Akuntansi Keuangan, Universitas Logistik dan Bisnis Internasional, Bandung, INDONESIA
*Corresponding author:sennyhandayani@ulbi.ac.id
INTISARI
Penelitian ini bertujuan merekonstruksi pendekatan segmentasi pasar angkutan antar kota pada PERUM DAMRI melalui Systematic Literature Review (SLR) terhadap studi-studi segmentasi pasar dan penumpang pada sektor jasa dan transportasi. Proses pencarian dilakukan pada jurnal nasional terindeks SINTA 1–2 dan jurnal internasional Scopus Q1–Q2, serta portal penerbit utama menggunakan kombinasi kata kunci terkait market/customer/passenger segmentation, public/intercity transport,
dan hybrid/digital segmentation. Mengikuti alur PRISMA, diperoleh 137 artikel awal; 23 duplikasi dihapus, menyisakan 114 artikel unik. Setelah screening judul–abstrak, 49 artikel dieliminasi sehingga 65 artikel masuk penilaian kelayakan. Pada penilaian full-text, 22 artikel dikeluarkan sehingga 43 artikel lolos, dan akhirnya 36 artikel utama diinklusikan dalam sintesis SLR. Sintesis menunjukkan pergeseran menuju model segmentasi hibrida dan data-driven yang mengintegrasikan variabel demografis, psikografis, perilaku perjalanan, dan spasial dengan metode analitik seperti klasterisasi dan machine learning.
Hasilnya dirumuskan sebagai model segmentasi hibrida dan implikasi strategis untuk reposisi layanan, diferensiasi rute/produk, dan pemanfaatan digitalisasi dalam targeting dan positioning DAMRI.
REFERENSI
Abenoza, R., Cats, O., & Susilo, Y. (2016). Travel Satisfaction with Public Transport: Determinants, User Classes, Regional Disparities and Their Evolution. Transportation Research Part A-Policy and Practice, 95, 64–84. https://doi.org/10.1016/j.tra.2016.11.011
Afzal, M., Rahman, S., Singh, D., & Imran, A. (2024). Cross-Sector Application of Machine Learning in Telecommunications: Enhancing Customer Retention Through Comparative Analysis of Ensemble Methods. IEEE Access, 12, 115256–115267, Vol 12, 115256 – 115267. https://doi.org/10.1109/access.2024.3445281
Artiarno, A. M., Setiaji, P., & Nugraha, F. (2025). K-Means Clustering untuk Segmentasi Pelanggan: Mengungkap Pola Pembelian Strategi Pemasaran pada Sektor Ritel. Edumatic: Jurnal Pendidikan Informatika. 9(2), 442-451. https://doi.org/10.29408/edumatic.v9i2.30336
Benita, F. (2021). Human Mobility Behavior in COVID-19: A Systematic Literature Review and Bibliometric Analysis. Sustainable Cities and Society, 70, 102916. https://doi.org/10.1016/j.scs.2021.102916
Bösehans, G., & Walker, I. (2020). Do Supra-Modal Traveller Types Exist? A Travel Behaviour Market Segmentation Using Goal Framing Theory. Transportation, 47, 243–273. https://doi.org/10.1007/s11116-018-9874-7
Cortez, R., Clarke, A. H., & Freytag, P. (2021). B2B Market Segmentation: A Systematic Review and Research Agenda. Journal of Business Research, 126, 415–428. https://doi.org/10.1016/j.jbusres.2020.12.070
Cozma, A. T., & Cosma, S. (2023). B2C Market Segmentation: A Systematic Literature Review. The USV Annals of Economics and Public Administration 23(1), 25-45. https://doi.org/10.4316/aepa.2023.23.1(37).25-45
Cuandra, F., Suandri, H., Putra, E. Y., & Pahlevi, R. (2025). Dampak Inovasi Teknologi dalam Logistik Internasional. Equilibrium : Jurnal Ilmiah Ekonomi, Manajemen Dan Akuntansi, 14(1), 164-184. https://doi.org/10.35906/equili.v14i1.2313
Dassanayake, D. (2023). Demographic Segmentation in The Travel Market: A CHAID Analysis of Traveler Profiles and Trip Attributes Among Indian Prospective Visitors. Journal of Management Matters, 10(2), 37-50. https://doi.org/10.4038/jmm.v10i2.54
Djun, S. F., Gunadi, I., & Sariyasa, S. (2024). Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode KMeans Clustering pada Model Data RFM. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 5(4), 354-365. https://doi.org/10.35746/jtim.v5i4.434
Farida, I., & Setiawan, D. (2022). Business Strategies and Competitive Advantage: The Role of Performance and Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 1-16. https://doi.org/10.3390/joitmc8030163
Febrianto, D., Yuliati, N., & Atasa, D. (2023). Pengaruh Segmentasi Pasar terhadap Keputusan Pembelian Konsumen pada Cafe Super Glu! Rungkut. AGROTEKSOS, 33(2), 561-571. https://doi.org/10.29303/agroteksos.v33i2.933
Gomes, M. A., & Meisen, T. (2023). A Review on Customer Segmentation Methods for Personalized Customer Targeting in eCommerce Use Cases. Information Systems and E-Business Management, 21, 527–570. https://doi.org/10.1007/s10257-02300640-4
Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-Based Predictive Models for Online Purchases in The Era of Marketing 5.0: a Systematic Review. Journal of Big Data, 11, 1–39. https://doi.org/10.1186/s40537-024-009470
Hamed, O., & Olayinka, O. H. (2021). Data Driven Customer Segmentation and Personalization Strategies in Modern Business Intelligence Frameworks. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2021.12.3.0658
Hansson, J., Pettersson, F., Svensson, H., & Wretstrand, A. (2019). Preferences in regional public transport: a literature review. European Transport Research Review, 11, 1–16. https://doi.org/10.1186/s12544-019-0374-4
Huan, N., Hess, S., Yamamoto, T., & Yao, E. (2024). Modelling Intermodal Traveller Behaviour in Mega-City Regions: Simultaneous vVrsus Sequential Estimation Frameworks. Transportation, 53, 35-70. https://doi.org/10.1007/s11116-02410489-2
Iffan, M. (2020). Marketing Approach on Competitive Advantage of Online-Based Public Transportation. 85–88. https://doi.org/10.2991/aebmr.k.200108.021
Kieu, L., Bhaskar, A., & Chung, E. (2015). Passenger Segmentation Using Smart Card Data. IEEE Transactions on Intelligent Transportation Systems, 16, 1537–1548. https://doi.org/10.1109/tits.2014.2368998
Mostaghel, R., Oghazi, P., Parida, V., & Sohrabpour, V. (2022). Digitalization Driven Retail Business Model Innovation: Evaluation of Past and Avenues for Future Research Trends. Journal of Business Research., 146, 134-145. https://doi.org/10.1016/j.jbusres.2022.03.072
Nashiroh, A. A. S., Shiddiqy, I. A., & Hidayat, M. N. (2024). Exploring the Depths of Digital Marketing: A Systematic Literature Review on Segmentation, Targeting, Differentiation, and Positioning Strategies. International Journal of Business, Law, and Education, 5(1), 1270-1283. https://doi.org/10.56442/ijble.v5i1.549
Neco, A., Saputra, F. A., Abdullah, N., Ramadhani, R., Hermansyah, T. T., & Sitio, S. L. M. (2025). Penerapan AlgoritmaKMeans Clustering untuk Analisis Pola Data Ekonomi Historis. JRIS: Jurnal Rekayasa Informasi Swadharma, 5(2), 95-107. https://doi.org/10.56486/jris.vol5no2.879
Nuwaifila, B., Prayoga, B. K., Ashfiya, C., Qolbi, M. H., & Ma’arif, R. (2025). Analisis Segmentasi Media di Suara Merdeka Pekalongan. Jurnal Media Komunikasi. 2(2), 88-108. https://doi.org/10.32493/mdkm.v2i2.44659
Rahman, A. T., Hardiyan, H., & Yunita, Y. (2025). Analisis Pengelompokan Data Penjualan Terhadap Transaksi Pelanggan pada PT. Penguasa Nusantara Indonesia Menggunakan Algoritma K-Means. Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI). https://doi.org/10.32672/jnkti.v8i2.8914
Ridwan, I. B. (2025). Transforming Customer Segmentation with Unsupervised Learning Models and Behavioral Data in Digital Commerce. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0525.1652
Risma, R., Prastya, S. E., Nurhaeni, N., & Ansari, R. (2024). Implementasi Machine Learning untuk Meningkatkan Penjualan di Pasar Digital Melalui Strategi Point of Purchase. Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI). https://doi.org/10.32672/jnkti.v7i5.8004
Salam, K. N., Aslim, S., Palinggi, P. P. H., Marsela, I., & Megawaty, M. (2025). Analysis of Effective Marketing Strategies in Facing Tight Competition in the Marketplace Global. Paradoks : Jurnal Ilmu Ekonomi. 8(2), 525-539. https://doi.org/10.57178/paradoks.v8i2.1154
Setianingrum, H. W., Widyastuti, T., & Fitra, S. (2025). Comprehensive Marketing Management for Competitive Advantage. Digital Business: Tren Bisnis Masa Depan. 16(1), 54-60. https://doi.org/10.59651/digital.v16i1.267
Sharyanto, S., & Lestari, D. (2022). Penerapan Data Mining untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce. JURIKOM (Jurnal Riset Komputer). 9(4). https://doi.org/10.30865/jurikom.v9i4.4525
Shavaki, F. H., & Ghahnavieh, A. E. (2022). Applications of Deep Learning into Supply Chain Management: a Systematic Literature Review and a Framework for Future Research. Artificial Intelligence Review, 56, 4447–4489. https://doi.org/10.1007/s10462-022-10289-z
Silva, V., Amaral, A., & Fontes, T. (2023). Sustainable Urban Last-Mile Logistics: A Systematic Literature Review. Sustainability. 15(3), 3-27. https://doi.org/10.3390/su15032285
Sulistyawati, A. A. D., & Sadikin, M. (2021). Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan. SISTEMASI. 10(3), 516-526. https://doi.org/10.32520/stmsi.v10i3.1332
Talaat, F., Aljadani, A., Alharthi, B., Farsi, M., Badawy, M., & Elhosseini, M. (2023). A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics. 11(8), 126. https://doi.org/10.3390/math11183930
Tampubolon, A. L. M., Butar, T. M. E. Y. B., & Rochimah, S. (2024). Segmentasi Pelanggan Majalah pada Situs Web ECommerce dengan K-Means++ dan Metode RFM. Jurnal Teknologi Informasi Dan Ilmu Komputer. 11(6), 1243-1251. https://doi.org/10.25126/jtiik.1168208
Ullah, A., Mohmand, M., Hussain, H., Johar, S., Khan, I., Ahmad, S., Mahmoud, H., & Huda, S. (2023). Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23063180
Umoren, O., Didi, P. U., Balogun, O., Abass, O. S., & Akinrinoye, O. V. (2025). A Predictive and Segmentation-Based Marketing Analytics Framework for Optimizing Customer Acquisition, Engagement, and Retention Strategies. Engineering and Technology Journal. 10(9), 6758-6775. https://doi.org/10.47191/etj/v10i09.06
Wang, G. (2025). Customer Segmentation inTthe Digital Marketing Using a Q-Learning Based Differential Evolution Algorithm Integrated with K-Means Clustering. PLOS ONE, 20. https://doi.org/10.1371/journal.pone.0318519
Wu, S., Yau, W., Ong, T., & Chong, S.-C. (2021). Integrated Churn Prediction and Customer Segmentation Framework for Telco Business. IEEE Access, 9, 62118–62136. https://doi.org/10.1109/access.2021.3073776
Zhang, Y., & Zhao, Z. (2024). Optimal Dynamic Pricing for Public Transportation Considering Consumer Social Learning. PLOS ONE, 19. https://doi.org/10.1371/journal.pone.0296263