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All Journal Jurnal Ekonomika Dan Bisnis Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Maharani, Ananda putri
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Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Other

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Exploring The Impact Of Islamic Branding On @Safiindonesia's Instagram Followers' Beauty Product Purchasing Decisions Mustofa, Mufid Habib; Munandar, Asep Nur Imam; Maharani, Ananda Putri
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 4 No. 4 (2024): Juli-Agustus
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v4i4.1933

Abstract

The purpose of this study is to ascertain how followers of Instagram account @safiindonesia are influenced by Islamic branding on cosmetic items when making purchases. The Slovin formula was utilized by the researcher to pick a sample of one hundred individuals. The research data, which were analyzed using SPSS 27.0 for Windows, led to the conclusion that Islamic branding had a major impact on consumer decisions. With a significance level of 0.05, the t-test produced a regression model significance value of 0.000. Consequently, it may be concluded that the Islamic Branding variable (X) has a significant impact on the purchasing choice (Y) when 0.000 < 0.05. The Islamic Branding variable accounts for almost 52% of the variability in followers' purchase decisions, according to the R square value of 0.529.
Implementasi Teknik Data Mining untuk Prediksi Kanker Paru – Paru Menggunakan Algoritma Decision Tree C4.5 : Penelitian Rudhiyanto, Angela Putri; Damayanti, Najla; Anggita, Esthi Ayu; Maharani, Ananda Putri; Juanda, Amanda Chickita Aprilia
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4976

Abstract

This study aims to implement data mining techniques to predict lung cancer using the Decision Tree C4.5 algorithm. The dataset used in this research is the Lung Cancer Dataset, consisting of 309 records and 16 attributes covering demographic factors, lifestyle habits, and clinical symptoms of patients. The research process was conducted using RapidMiner software through several stages, including data collection, data preprocessing, model construction, and model evaluation. The C4.5 algorithm generates a decision tree based on the Gain Ratio criterion to identify the most influential attributes in lung cancer classification.The results show that the Decision Tree C4.5 algorithm achieved excellent classification performance with an accuracy of 96.76%, a classification error of 3.24%, a Kappa value of 0.844, a weighted mean recall of 89.86%, and a weighted mean precision of 94.93%. The generated decision tree indicates that the ALLERGY attribute is the most dominant factor in classifying lung cancer, followed by other attributes such as Yellow_Finger, Peer_Pressure, and Swallowing_Difficulty. These findings indicate that the Decision Tree C4.5 algorithm is effective and highly interpretable for lung cancer prediction and has strong potential as an early decision-support tool in medical diagnosis.
Co-Authors Anggita, Esthi Ayu Damayanti, Najla Juanda, Amanda Chickita Aprilia Munandar, Asep Nur Imam Mustofa, Mufid Habib Rudhiyanto, Angela Putri