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All Journal Jurnal Manajemen Informatika Jayakarta Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Hotler Manurung
STMIK Kaputama
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Materials Science & Nanotechnology

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PENGELOMPOKAN DATA TUNGGAKAN PEMBAYARAN KREDIT MOBIL MENGGUNAKAN METODE CLUSTERING (STUDI KASUS: CV CITRA KENCANA MOBIL) Julia Br Sembiring; Hotler Manurung; Anton Sihombing
Jurnal Manajamen Informatika Jayakarta Vol 3 No 3 (2023): JMI Jayakarta (Juli 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v3i3.1186

Abstract

Arrears or installments are one of the problems for companies in dealing with customers who have delays in paying pre-approved car installment bills, while the cause of arrears in car payment installments is because of the necessities of life and problems that will occur in the future no one can predict. If at the beginning the payment was smooth, it is not certain that in the future there will be no customers who are late paying installments until they have to be withdrawn. (I Wayan Sudirman, 2000). CV Citra Kencana Mobil Medan is a company that sells used and new cars with credit and cash payment systems. Due to the many problems that occur in arrears of car installment payments made by customers in 2017-2022 which causes data to accumulate, and it is also difficult for companies to provide information and follow-up in dealing with problems to customers quickly, therefore it is necessary to have a method in processing these data by clustering customer data. Based on the research conducted, there were 3 groups of 20 data, namely group 1 with 7 data and 2 groups with 8 data and group 3 with 5 data, with the most results in cluster 2, namely the data group for arrears in car loan payments in the car brand group ( X) is the Honda Jazz RS, and for the sub-district group (Y) which is in arrears, namely Medan Amplas which is in arrears (Z) for 1-4 months.
Implementing the Procedural Generation Method for Placing Dynamic Objects in a Roblox-Based Adventure Game Muhammad Hiszat; Hotler Manurung; I Gusti Prahmana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2307

Abstract

Procedural generation is a content-creation technique that has become increasingly important in modern game development. However, on the Roblox platform, dynamic object placement still faces challenges such as overlapping, illogical positioning, and blocked navigation paths when relying solely on pure random methods. This research implements the Rule-Based Random Generation algorithm to manage the automatic placement of dynamic objects (enemies, treasure chests, and traps) in a Roblox-based adventure game. The proposed method combines randomization with constraint validation, including boundary check, overlap check using Euclidean distance, restricted zone check, and cross-type relational constraints. The system was developed in Roblox Studio with the Luau scripting language using a prototyping methodology and a modular architecture comprising ObjectSpawner, ConstraintValidator, SpatialGrid, and DungeonGenerator. Functional testing was conducted across 10 game sessions on a 1000 × 1000 studs map with a configuration of 340 enemies, 10 chests, and 50 traps. The results show that the system successfully placed all objects without any constraint violation, produced significant spatial variation between sessions (ranging from 86.31 to 2358.00 studs), and maintained level playability in every session. The average spawning execution time was 336.62 ms per session (0.84 ms per object), demonstrating the computational efficiency of the proposed method.
Co-Authors I Gusti Prahmana Julia Br Sembiring Muhammad Hiszat Sihombing, Anton