Eggy Herlambang, Carola Basuki, Lorio Purnomo, Surjandy Surjandy

PENINGKATAN DONASI DENGAN STRATEGI PEMASARAN DIGITAL PEDULISEHAT.ID MENGGUNAKAN FRAMEWORK CRISP-DM

Introduction

Peningkatan donasi dengan strategi pemasaran digital pedulisehat.id menggunakan framework crisp-dm. Tingkatkan donasi Pedulisehat.id dengan strategi pemasaran digital dan CRISP-DM. Analisis sentimen & prediktif bantu kampanye efektif untuk pasien kronis.

Abstract

Pedulisehat.id is forum that offers services in the form of online, real-time, and transparent fundraising to collecting various donations from the public to help patients with chronic diseases and need funds for the treatment process. There are several problems that occur at Pedulisehat.id. Based on the data that has been collected, the researcher draws several problems that occur at Pedulisehat.id. First, the success of Advertising (a promo from a program) to increase public interest in donating to one of the existing programs at Pedulisehat.id. Second, Marketing Cost expenses in the Pedulisehat.id division are not and are not yet comparable to the donations that come in from the public. This research uses descriptive and sentiment analysis method to assist each campaigner in increasing respondents to the campaigns carried out by each user in terms of collecting and inviting the whole community to donate. The process of making this research design using 5 stages of CRISP-DM from 6 existing stages, there are: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation. The results of this study are expected to provide the best campaign suggestions to users running on Pedulisehat.id and predictive analytics techniques, which from the results of analytics based on existing data will display predictions that will be achieved by Pedulisehat.id.Keywords — Real time, Penggalangan dana, CRISP-DM, Media social, Advertising

Review

This paper, titled "PENINGKATAN DONASI DENGAN STRATEGI PEMASARAN DIGITAL PEDULISEHAT.ID MENGGUNAKAN FRAMEWORK CRISP-DM," addresses a pertinent and critical challenge faced by online fundraising platforms: optimizing digital marketing strategies to enhance donor engagement and increase contributions. Focusing on Pedulisehat.id, a platform dedicated to assisting patients with chronic diseases, the study directly tackles practical issues such as the limited success of advertising campaigns and the disproportionately high marketing costs relative to the donations received. The research's aim to improve fundraising efficiency is highly relevant, offering valuable insights for the sustainability and impact of charitable organizations operating in the digital landscape. The methodological approach combines descriptive and sentiment analysis within a structured 5-stage CRISP-DM framework (Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation). The adoption of CRISP-DM is a strength, providing a systematic and data-driven pathway for problem-solving. However, the abstract would benefit from greater specificity regarding the data sources intended for analysis; understanding what kind of data (e.g., social media interactions, past campaign performance, donor demographics, website analytics) will feed into these analyses is crucial for evaluating the study's scope and potential. Furthermore, while descriptive and sentiment analysis are excellent for understanding current trends and public perception, the abstract could more clearly delineate how these specific techniques will directly lead to the "predictive analytics techniques" mentioned as a key outcome in the modeling stage. The anticipated outcomes, including generating "best campaign suggestions to users running on Pedulisehat.id" and offering "predictive analytics techniques" to forecast future achievements, hold significant promise. These insights could empower individual campaigners and the Pedulisehat.id platform to refine their digital marketing efforts, ultimately leading to improved fundraising efficiency and greater support for patients in need. For a comprehensive review, it would be beneficial to see further detail in the full paper regarding the specific types of predictive models explored within the "Modeling" stage of CRISP-DM, and how these models integrate the findings from the descriptive and sentiment analyses to produce actionable recommendations and accurate predictions.

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