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All Journal Jurnal Manajemen Dayasaing Cetta: Jurnal Ilmu Pendidikan JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi) JRB-Jurnal Riset Bisnis MABIS: Manajemen dan Bisnis Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Patria : Jurnal Pengabdian Kepada Masyarakat Community Empowerment Jurnal Manajemen Maranatha Journal of Computer Science and Technology Application
Ariesya Aprillia
Maranatha Christian University, Bandung
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Public Health Social Sciences Other

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PENGUKURAN KUALITAS LAYANAN DALAM BIDANG PENDIDIKAN TINGGI Ariesya Aprillia; Nonie Magdalena
Jurnal Manajemen Maranatha Vol 18 No 1 (2018)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jmm.v18i1.1092

Abstract

The service industry plays an important role in a nation's economy including higher education services. The performance of the quality of higher education services is determined by the quality of service delivery provided to the students. Quality of service in education is determined by the extent to which student needs and expectations can be met. How well students will later perceive the quality of their educational services will greatly contribute to the continuity of universities in the dynamics of long-term competition. The purpose of this study was to examine and analyze how the quality of service of Maranatha Christian University is viewed from the gap between the service experienced and the expected service. The service quality measurement instrument uses seven factors: contact personnel (faculty/ lecturer), reputation, physical evidence, contact personnel (administration), curriculum, responsiveness, and access to facilities. The technique used is simple random sampling with 418 respondents. Measurement instruments are tested using validity test, reliability test, and Importance and Performance Analysis (IPA). Based on the results of data processing using IPA five things that have not been matched as expected by the student when the five things are considered important by students. These are classroom arrangement, classroom comfort, immediate announcement notice, availability of parking space, and availability of access to nonclassroom learning. Keywords: IPA; Service Quality
Komitmen organisasional melalui evaluasi inti diri, dukungan sosial, dan motivasi Rony Setiawan; Ariesya Aprillia
Jurnal Manajemen Maranatha Vol 22 No 1 (2022): Jurnal Manajemen Maranatha
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jmm.v22i1.5223

Abstract

Humans are main actors in achieving organizational success. Every institution seems to be forced to adapt to changes due to the pandemic. The current situation requires that every aspect of employee life in almost every industry is carried out online, including education corporations. Campus Z faces extra challenges as a well-known private university to consistently demonstrate excellent transformational service performance. In achieving an excellent organization, various collaborations of qualified resources are needed, one of which is the ownership of administrative staffs who are highly dedicated to the organization. The positive attitude of employees is determined by the quality of their perspective on themselves and their assessment of their work and organization enviroment. By collecting data through a context review and Google Forms, the research team wanted to analyze the relationship between core self-evaluation, social support, extrinsic motivation, and intrinsic motivation as determinants of organizational commitment. Through multiple linear regression analysis, it has been proven that organizational commitment is positively influenced by extrinsic motivation and negatively influenced by intrinsic motivation. The research team hopes that the results of this study can provide constructive feedback on the leadership and management development of Campus Z in the long term.
Peran Elemen Lingkungan Toko Pada Perilaku Pembelian Impulsif Di Kalangan Pembeli Wanita Christine Aliwinoto; Ariesya Aprillia
Jurnal Riset Bisnis Vol. 7 No. 1 (2023): Oktober
Publisher : Fakultas Ekonomi dan Bisnis Universitas Pancasila Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jrb.v7i1.4954

Abstract

Tujuan penelitian ini adalah menguji dan menganalisis peran elemen lingkungan toko berupa tata letak toko, suasana toko, dan karyawan toko terhadap perilaku pembelian impulsif di kalangan pembeli wanita di toko pakaian H&M Kota Bandung. Pengumpulan data menggunakan kuesioner dengan purposive sampling sebagai teknik pengambilan sampelnya. Konsumen wanita toko pakaian H&M di Kota Bandung yang bertransaksi dalam 24 bulan terakhir menjadi sampel dalam penelitian ini. Untuk menganalisis data digunakan regresi linear berganda dilengkapi uji validitas, uji reliabilitas, uji asumsi klasik berupa uji normalitas, uji multikolinearitas, dan uji heteroskedastisitas, uji t, uji F, dan koefisien determinasi. Hasil penelitian ini menemukan bahwa tata letak toko, suasana toko, dan karyawan toko secara simultan berpengaruh terhadap pembelian impulsif dan secara parsial hanya suasana toko yang berpengaruh terhadap pembelian impulsif.
Deep Learning Driven Big Data Architecture for Scalable Intelligent Network Threat Detection Sigit Anggoro; Palma Juanta; Ariesya Aprillia; Adele Valerry
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vc0kmq21

Abstract

This study proposes a deep learning driven big data architecture designed to enable scalable and intelligent network threat detection in high volume traffic environments. Increasing network traffic volume and heterogeneity generated by enterprise systems, cloud services, and Internet of Things devices require more adaptive and intelligent security mechanisms beyond traditional signature-based approaches. This study aims to develop an intelligent threat-detection framework that leverages deep-learning models and big data analytics to enhance detection accuracy, scalability, and real-time response capabilities in large-scale network environments. A distributed big data architecture is integrated with advanced deep neural networks to process high-dimensional network traffic features, perform automated feature learning, and classify malicious activities using optimized training and validation strategies. The proposed framework is evaluated using benchmark intrusion detection datasets and simulated real-world network traffic scenarios to ensure robustness and generalizability. Experimental findings demonstrate that the proposed approach achieves superior detection accuracy, lower False-Positive Rates, and improved processing efficiency compared with conventional machine learning-based intrusion-detection systems. The integration of deep learning and big data analytics provides a scalable and adaptive solution for intelligent threat detection in computer networks, contributing to the development of next-generation cybersecurity systems capable of addressing evolving and sophisticated cyber attacks.
Co-Authors A. Rinny Maharsi Adele Valerry Agatha Evania Alanarima Agus Aribowo Allen Kristiawan Anny Nurbasari Arie Danu Subagja Asni Harianti Boedi Hartadi Kuslina Cen Lu Chandra Kuswoyo Christine Aliwinoto Hadi Nasbey Henky L. Suwarno Ida Ida Ika Gunawan Jason Marchel Christanto Jessica Agrippina Fedora Kartika Imasari Tjiptodjojo Kezia Kurniawati Maya Malinda Michelle Thiono Nonie Magdalena Nonie Magdalena Nonie Magdalena Nur Nur Palma Juanta Rafdhi Wira Oktavius Pelawi Robby Tan Rony Setiawan Rony Setiawan Rony Setiawan Rony Setiawan Rully Arlan Tjahyadi Sigit Anggoro Surya Setyawan Vinsensius Vinsensius Yolla Margaretha