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All Journal Jurnal Teknik Informatika (JUTIF) Journal Islamic Business and Entrepreneurship / JIBE Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Supriyadi, Andy
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Computer Science & IT Economics, Econometrics & Finance Environmental Science Public Health Social Sciences

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Pengaruh Kelengkapan Produk dan Harga terhadap Loyalitas Konsumen pada CV. Pramita Kediri Supriyadi, Andy; Nur Hidayati; Nur Ali Agus
Jurnal Bisnis Islam dan Kewirausahaan Vol 3 No 2 (2024): Journal of Islamic Business and Entrepreneurship (JIBE)
Publisher : Fakultas Ekonomi dan Bisnis, Universitas Islam Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/jibe.v3i2.5490

Abstract

This research aims to determine the influence of product completeness (X1) and price (X2) variables on consumer loyalty (Y) at CV. Pramita Kediri. This type of research is quantitative research which is used to test the relationship of a variable to other variables which emphasizes the analysis on numbers processed using statistical methods. The data collection techniques used are primary data and secondary data. Sample selection used a purposive sampling method, the number of samples in the study was 83 respondents from CV. Pramita Kediri. This research data was obtained through questionnaires, interviews and literature studies. The analysis techniques used are Validity Test, Reliability Test, Classical Assumption Test, Multiple Linear Regression Analysis, T Test, F Test and Coefficient of Determination Test. From the results of the research, it shows that the variables Product Completeness and Price influence Consumer Loyalty to CV. Pramita Kediri. The research results showed that the multiple linear regression equation Y = 5.569 + 0.574 Price has a partially significant effect on consumer loyalty with a sig result of 0.000 < 0.05. The F test results obtained Fcount results with a significance value of 0.000 < 0.05. From the results of the analysis it can be concluded that product completeness and price have a simultaneous and significant effect on consumer loyalty to CV. Pramita Kediri.
MENTORA: Inovasi Digital untuk Pemberdayaan Masyarakat Berbasis Data Fiddin Yusfida; Hartatik, Hartatik; Firdaus, Nurul; Kusuma Riasti, Berliana; Supriyadi, Andy
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 3 (2025): Agustus
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v4i3.225

Abstract

Kegiatan Pelatihan dan Serah Terima Aplikasi MENTORA dilaksanakan oleh Grup Riset Applied Data Science and AI (DSAI) Universitas Sebelas Maret (UNS) Surakarta melalui skema Pengabdian Kepada Masyarakat Hibah Grup Riset (PKM HGR-UNS) pada 10 Juli 2025 di D3 Teknik Informatika, Sekolah Vokasi UNS. Kegiatan ini bertujuan meningkatkan efektivitas pengelolaan data pendampingan komunitas dengan memanfaatkan teknologi informasi. MENTORA adalah aplikasi digital inovatif yang dirancang untuk mendukung pemberdayaan masyarakat berbasis wilayah dengan fitur unggulan seperti Admin Center, Fasilitator Hub, Group Management, Community Management, Activity Management, Activity Insights Dashboard, dan Data Exporter. Pelatihan diikuti oleh admin dan fasilitator yang akan mengoperasikan aplikasi di lapangan untuk memastikan implementasi optimal. Acara ini juga menjadi momentum inisiasi kerja sama tridharma perguruan tinggi antara UNS dan Majelis Pemberdayaan Masyarakat PP Muhammadiyah. Diharapkan dengan hadirnya MENTORA, pengelolaan data pendampingan masyarakat menjadi lebih terstruktur, transparan, dan mendukung transformasi digital di komunitas.
Optimizing Alternating Least Squares for Recommender Systems Using Particle Swarm Optimization Yusfida A'la, Fiddin; Firdaus, Nurul; Supriyadi, Andy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5154

Abstract

Recommender systems play a crucial role in various digital platforms by assisting users in discovering relevant items. The research problem addressed in this study is the limited predictive accuracy of ALS-based recommender systems due to suboptimal parameter selection. This study explores how Particle Swarm Optimization (PSO) can be leveraged for parameter optimization to address this limitation. The dataset used is MovieLens 1M, which contains over one million user ratings for thousands of movies. The research process includes data preprocessing, data splitting, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as the primary metrics. The evaluation results indicate a significant improvement in model performance after optimization, with RMSE decreasing from 0.895 to 0.860 and MAE from 0.704 to 0.680. These findings demonstrate that optimization algorithms can effectively improve the prediction accuracy of recommendation systems. This research contributes to the application of swarm-based optimization techniques in enhancing matrix factorization-based recommender systems.
Co-Authors Berliana Kusuma Riasti Fiddin Yusfida Firdaus, Nurul Hartatik Nur Ali Agus Nur Hidayati Yusfida A'la, Fiddin