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All Journal Progresif: Jurnal Ilmiah Komputer MANAJEMEN DEWANTARA Jurnal Ilmu Sosial Ekonomi dan Politik (JISOSEPOL)
Alfina Damayanti
Universitas Pelita Bangsa
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice

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PENGARUH DIGITAL MARKETING, PROMOSI DAN KUALITAS PELAYANAN TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN COFFE SHOP DI CIKARANG Alfina Damayanti; Elsa Nurcholisa; Siti Dewiningsih Alawiyah; Ahmad Gunawan
MANAJEMEN DEWANTARA Vol 7 No 1 (2023): MANAJEMEN DEWANTARA
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/md.v7i1.13776

Abstract

The research was conducted to determine the effect of digital marketing,promotion, and quality of service on consumer decisions to purchase at a coffee shop in Cikarang. In this research, the researcher uses the Lemeshow formula in determining the sample. The data used is primary data which is obtained by the author through a questionnaire distributed to 100 respondents. The significant effect of X1 and Y variables is 4.092 from the table value 1.66071 with a significant number 0.000 <0.05, the significant effect of the variable X2 and Y are worth 0.500 > 1.66071 with a significant number of 0.618 > 0.05 and the significant effect of X3 and Y variables is 8.084 from the table value of 1.66071 with a significant number of 0.000 < 0.005. The influence of the simultaneous variables X1, X2, X3 on Y is 72.748 > 2.7 which has a greater meaning than F table which means that there is an influence between digital marketing, promotion, and service quality on purchasing decisions at a coffee shop in Cikarang.
PENGARUH STRATEGI INFLUENCER MARKETING TERHADAP KEPUTUSAN PEMBELIAN PRODUK SKINCARE SKINTIFIC DI PLATFORM E-COMMERCE Alfina Damayanti; Fia Amalia; Pupung Purnamasari
JISOSEPOL: Jurnal Ilmu Sosial Ekonomi dan Politik Vol. 3 No. 1 (2025): JISOSEPOL : Jurnal Ilmu Sosial Ekonomi dan Politik, Edisi Januari-Juni 2025
Publisher : Samudra Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61787/0dhxh660

Abstract

This study aims to analyze the impact of Influencer marketing strategies on purchasing decisions for the skintific skincare brand on e-commerce platforms. Influencer marketing has become one of the most effective marketing tactics in the digital era, especially in the beauty industry. This research employs a quantitative approach by distributing questionnaires to 100 respondents who are active skintific consumers on e-commerce platforms. The variables analyzed include influencer credibility, content appeal, and the relevance of the promoted product. The results show that influencer credibility significantly influences purchasing decisions, followed by content appeal, which effectively enhances consumer interest in trying the product. The relevance between the influencer and the product also plays an essential role in building consumer trust. These findings illustrate that the success of Influencer marketing strategies is not solely determined by the influencer’s popularity but also by their alignment with the target audience and the quality of the content delivered. This study is expected to serve as a reference for business practitioners to optimize marketing strategies through influencers to increase product sales.
Analisis Klasifikasi Risiko Penyakit Jantung Menggunakan Metode Random Forest Alfina Damayanti; Donny Maulana; M. Zubair Abdurrohman
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3609

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection crucial to reduce the risk of complications and mortality. The advancement of machine learning technology enables fast and accurate analysis of medical data to support the diagnostic process. This study aims to develop a classification model for heart disease risk using the Random Forest algorithm. The dataset used is the Heart Disease Dataset from Kaggle, consisting of 1,025 patient records with 14 medical attributes, such as age, gender, blood pressure, cholesterol level, and maximum heart rate. The methodology applied is CRISP-DM, which includes Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Model Evaluation is conducted using a confusion matrix, cross-validation, and ROC-AUC. The results show that the Random Forest algorithm achieves a high Accuracy of 99.96% and a cross-validation score of 0.996. The variables chest pain, ca, and thalach are identified as the most influential factors in the prediction.Keywords: Heart Disease; Random Forest; Machine learning; Classification; CRISP-DM AbstrakPenyakit jantung merupakan salah satu penyebab utama kematian di dunia sehingga deteksi dini sangat penting untuk mengurangi risiko komplikasi dan kematian. Perkembangan teknologi machine learning memungkinkan analisis data medis secara cepat dan akurat dalam membantu proses diagnosis. Penelitian ini bertujuan membangun model klasifikasi risiko penyakit jantung menggunakan Algoritma Random Forest. Dataset yang digunakan adalah Heart Disease Dataset dari Kaggle yang terdiri dari 1025 data pasien dengan 14 atribut medis, seperti usia, jenis kelamin, tekanan darah, kadar kolesterol, dan detak jantung maksimum. Metode yang digunakan adalah CRISP-DM meliputi Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Evaluasi model dilakukan menggunakan confusion matrix, cross validation, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Random Forest menghasilkan akurasi tinggi dengan nilai 99,96% serta cross validation sebesar 0,996. Variabel chest pain, ca, dan thalach menjadi faktor paling berpengaruh dalam prediksi.Kata kunci: Penyakit jantung; Random Forest; Machine learning; Klasifikasi; CRISP-DM.
Co-Authors Ahmad Gunawan Donny Maulana Elsa Nurcholisa Fia Amalia M. Zubair Abdurrohman Pupung Purnamasari Siti Dewiningsih Alawiyah