Optimasi Rute Penagihan Untuk Minimasi Jarak Menggunakan Qaco: Qgis Mapping Dan Algoritma Ant Colony Optimization (ACO)

Sihabuddin, Nurul (2025) Optimasi Rute Penagihan Untuk Minimasi Jarak Menggunakan Qaco: Qgis Mapping Dan Algoritma Ant Colony Optimization (ACO). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Percepatan cash-in PLN sangat dipengaruhi oleh pola pembayaran pelanggan yang dilakukan lebih awal. Oleh karena itu, PLN memerlukan solusi untuk mengubah kebiasaan pelanggan yang masih sering menunggak menjadi membayar sebelum jatuh tempo. Salah satu penyebab keterlambatan pembayaran adalah terbatasnya loket pembayaran, serta kelalaian pelanggan dalam melakukan pembayaran rekening listrik. Penelitian ini menganalisis pola pembayaran pelanggan listrik selama enam bulan di PLN ULP X, yang merupakan bagian dari PLN UP3 XYZ. Hasil analisis menunjukkan bahwa sebanyak 7,87% pelanggan merupakan penunggak berulang yang tersebar di berbagai lokasi. Untuk mengatasi hal tersebut, diperlukan strategi berupa penagihan preventif guna mencegah keterlambatan pembayaran. Namun, pelaksanaan penagihan preventif menghadapi kendala waktu karena petugas billman memiliki keterbatasan dalam menjalankan tugas lainnya. Oleh karena itu, diperlukan alat bantu berupa perencanaan rute penagihan yang efisien untuk meminimalkan jarak tempuh. Dalam merancang rute penagihan, digunakan pemodelan Traveling Salesman Problem (TSP) dengan pendekatan pemetaan geografis menggunakan QGIS serta algoritma Ant Colony Optimization (ACO). QGIS digunakan untuk memetakan lokasi pelanggan menunggak berdasarkan kondisi geografis. Berdasarkan hasil penelitian, algoritma ACO mampu menghasilkan total jarak tempuh sebesar 1.022 km, jauh lebih efisien dibandingkan kondisi eksisting yang mencapai 4.300 km, sehingga terdapat penghematan jarak sebesar 3.278 km (76,23%).
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The acceleration of PLN’s cash-in performance is significantly influenced by customers’ early payment behavior. Therefore, PLN requires a solution to shift the payment pattern of customers who frequently delay payments into making timely payments before the due date. One of the main causes of payment delays is the limited availability of payment counters, as well as customers forgetting to pay their electricity bills. This study analyzes customer electricity payment patterns over a six-month period at PLN ULP X, which is part of PLN UP3 XYZ. The analysis revealed that 7.87% of customers are recurring late payers, dispersed across various locations. To address this issue, a preventive collection strategy is required to reduce the occurrence of late payments. However, the implementation of preventive collection faces time constraints, as billman officers have limited availability due to other responsibilities. Thus, a supporting tool in the form of route planning is needed to minimize travel distance. In designing the collection route, the Traveling Salesman Problem (TSP) model is applied using geographic mapping through QGIS and optimized with the Ant Colony Optimization (ACO) algorithm. QGIS is used to map the distribution of late-paying customers based on geographic conditions. The study results show that the ACO algorithm successfully reduced the total travel distance to 1,022 km, compared to the existing condition of 4,300 km, resulting in a distance saving of 3,278 km (76.23%).

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Ant Colony Optimization (ACO), Optimasi Rute Penagihan, Penagihan Preventif, QGIS, Traveling Salesman Problem (TSP)
Subjects:	Q Science > Q Science (General) > Q337.3 Swarm intelligence Q Science > QA Mathematics > QA278.55 Cluster analysis Q Science > QA Mathematics > QA402.6 Transportation problems (Programming) Q Science > QA Mathematics > QA9.58 Algorithms
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26101-(S2) Master Thesis
Depositing User:	Nurul Sihabuddin
Date Deposited:	28 May 2025 04:15
Last Modified:	28 May 2025 04:15
URI:	http://repository.its.ac.id/id/eprint/119102

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