Rillya Arundaa

SISTEM PENDUKUNG KEPUTUSAN UNTUK SELEKSI CALON PETUGAS SENSUS DI BADAN PUSAT STATISTIK KOTAMOBAGU MENGGUNAKAN TOPSIS

Introduction

Sistem pendukung keputusan untuk seleksi calon petugas sensus di badan pusat statistik kotamobagu menggunakan topsis. Optimalkan seleksi petugas sensus BPS Kotamobagu dengan Sistem Pendukung Keputusan (SPK) menggunakan metode TOPSIS. Identifikasi kandidat terbaik untuk kinerja optimal.

Abstract

Census officers are one of the critical factors in census activities organized by the Central Bureau of Statistics. Qualified officers will produce a good performance in the agency and support the achievement of goals. Seeing the importance of the quality of officers, the candidate selection process is an important part and must be carried out immediately in the agency. The decision support system for selecting candidate census officers is expected to produce officers who meet the institution's criteria. The method used in this study is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This method is one of the methods used to solve practical decision-making and can handle multi-dimensional problems in the selection of census officers. This research produces an application that provides user or users with recommendations. The recommendations given by the system are based on the assessment criteria and weighting criteria for each candidate census officer and then processed using TOPSIS to produce the best offers for the best candidate for the best census officer according to the ranking with the assessment parameters that have been determined according to the wishes/needs of the institution.

Review

The paper, titled "SISTEM PENDUKUNG KEPUTUSAN UNTUK SELEKSI CALON PETUGAS SENSUS DI BADAN PUSAT STATISTIK KOTAMOBAGU MENGGUNAKAN TOPSIS," addresses a highly relevant and critical operational challenge: the objective and efficient selection of qualified census officers. The abstract effectively highlights the pivotal role these officers play in the success of census activities and, consequently, in achieving the Central Bureau of Statistics' (BPS) organizational goals. The recognition that the quality of personnel directly impacts performance underscores the necessity of a robust selection process. Developing a Decision Support System (DSS) to streamline and enhance this process, particularly within the specific context of BPS Kotamobagu, presents a practical and timely solution to a common administrative bottleneck. The chosen methodology, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is well-suited for multi-criteria decision-making problems like candidate selection. As indicated, TOPSIS is adept at handling multi-dimensional problems and offers a systematic approach to ranking alternatives based on their proximity to an ideal solution and distance from a negative-ideal solution. The abstract clarifies that the system will process assessment criteria and their respective weightings for each candidate to generate a ranked list of the best potential census officers. This promises an objective output, moving beyond subjective evaluations and leveraging a quantitative framework to aid decision-makers. The stated outcome—an application providing recommendations to users—suggests a tangible and deployable solution. The primary strength of this research lies in its practical application to a significant real-world problem, offering a potentially substantial improvement over traditional, perhaps more subjective, selection methods. The use of TOPSIS ensures a structured and justifiable decision-making process. However, for a complete understanding, the full paper would benefit from elaborating on several key aspects. Specifically, details on the precise assessment criteria used, the method for determining their weights (e.g., expert judgment, AHP), and the validation process for the developed application would be crucial. Furthermore, discussing the system's user interface, scalability, and potentially comparing its results with current selection methods or other MCDM techniques would significantly strengthen the research's contribution and demonstrate its efficacy more comprehensively.

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