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Journal : JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI

Analisis biaya dan efektivitas WhatsApp Blast terhadap respons pelanggan dengan regresi berantai. ardiningrum, Talitha widyadhana; Alam, Syariful; Sanjaya, Leonard Putra; Masykur, Alia Azizah; Dewi, Selvy Kirana
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.9755

Abstract

This research analyzes the effectiveness of sending broadcast messages through the Cakraflash service at MH Thamrin University in the period from 8 July 2024 to 3 September 2024. This research was conducted because of the importance of understanding audience response in supporting digital communication strategies. The data used includes the number of messages sent, delivered, read, and replied. The chain regression analysis method was used to identify the relationship between these variables. The results showed that each stage of message delivery, from delivered to replied, significantly influenced each other. An increase in the number of messages received and read tends to increase the number of messages replied, which is an indicator of successful information delivery. The conclusions of this study provide important insights for universities to develop more effective, data-driven digital communication strategies based on audience response. Keywords— Chain Regression, Broadcast Messaging, Data Processing Complexity
Prediksi Keuntungan Harian dari Investasi Kripto Menggunakan Model Regresi Berganda Time Series Sanjaya, Leonard Putra; Alam, Syariful; Shahidzinda, Rezha; Nurzainah, Nia Siti; Hermawan, Muhammad Rizki
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.12870

Abstract

Crypto investment is gaining traction with students thanks to the ease of access through digital platforms like Mobee, especially the Flexi Earn feature that offers APR-based daily returns. Unfortunately, many investment decisions are still intuitive without quantitative analysis. This study aims to predict daily returns on crypto investments using linear time series multiple regression models on four assets: TRX, ENA, HBAR, and SUI. Data was collected for 30 days from the Mobee app, with the variables day n and coin price as predictors, and daily profit as the response. Results show that the regression model has high accuracy and meets the classical assumption test, although some assets violate normality within reasonable limits. The findings provide a simple yet effective analytical basis for students to make more targeted and data-driven investment decisions.