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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JSI: Jurnal Sistem Informasi (E-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Bina Insani ICT Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Infortech Jurnal Inovasi Informatika RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Informatics and Computer Engineering Journal SATIN - Sains dan Teknologi Informasi Jurnal Komputer Antartika Information Technology and Systems Jurnal Infortech
Yopi Handrianto, Yopi
Universitas Bina Sarana Informatika
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science Social Sciences Other

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The Influence of MILI Digital Payment Service Quality on Partner Satisfaction using the Servqual Method Ricky Rivaldo; Yopi Handrianto
Information Technology and Systems Vol. 3 No. 1 (2025): November 2025
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/its.v3i1.528

Abstract

This study investigates the influence of service quality dimensions on partner satisfaction with MILI Digital Payment, a fintech application developed by PT Jendela Prima Indonesia in 2019. MILI provides digital transaction services such as prepaid credit, internet packages, bill payments, and ticket reservations, primarily targeting micro, small, and medium enterprises (MSMEs). Despite its benefits, user complaints regarding system disruptions, limited customer service, and ineffective feature socialization highlight potential service quality issues. This research adopts a causal-associative quantitative approach using the Servqual framework, which measures service quality across five dimensions: tangible, reliability, responsiveness, assurance, and empathy. Data were collected from 100 active MILI partners in Bekasi through purposive sampling and analyzed using multiple linear regression in SPSS, following validity, reliability, and classical assumption tests. Results demonstrate that, simultaneously, all five dimensions significantly affect partner satisfaction. However, responsiveness emerges as the only dimension with a significant partial effect, suggesting that timely responses and problem-solving play a critical role in shaping satisfaction. The findings contribute both theoretically and practically by confirming the applicability of Servqual in fintech contexts and providing strategic insights for improving digital service performance. Limitations include the study’s geographic focus on Bekasi and its restricted variables. Future research is recommended to expand the scope and explore additional factors such as trust and usability.
Klasifikasi Kanker Paru pada Citra CT Scan menggunakan Extreme Learning Machine dan Histogram Equalization Omar Pahlevi; Yopi Handrianto; Dewi Ayu Nur Wulandari; Henny Leidiyana; Luci Kanti Rahayu
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lung cancer is one of the deadliest types of cancer worldwide, making early detection crucial to improving patient survival rates. One of the primary methods for detecting lung cancer is through Computed Tomography (CT) scan images. However, automated analysis of these images faces challenges due to image quality being affected by noise and low contrast. This study aims to develop a lung cancer classification model from CT scan images using the Extreme Learning Machine (ELM) algorithm and Gray Level Co-occurrence Matrix (GLCM) feature extraction, supported by Histogram Equalization techniques to enhance image quality. Histogram Equalization is employed to improve image contrast, facilitating the extraction of texture features from GLCM, such as contrast, homogeneity, energy, and entropy. ELM was chosen for its speed and accuracy in handling complex medical image classification tasks. The study results demonstrate that the proposed model successfully enhances classification performance with an accuracy of 91.06%. The combination of ELM and Histogram Equalization techniques produces an efficient and accurate classification system for detecting lung cancer from CT scan images.
Co-Authors Achmad Nurhadi Ade Christian Adjat Sudradjat Ahmad Sinnun Alamul Huda, Alamul Aldo Priamnistiko Ali Haidir Alif Septiandi AlWafi, Muadz Zidan Amrin Amrin Anastasiaa Siwi Fatma Utami Andi Nugroho Budi Sanjaya Budi Waluyo Dewi Ayu Nur Wulandari Eka Wahyu Styani Fajar Arrizky Syuhada Ghofar Taufiq Hadi Sukmana, Sulaeman Hariyanto Harna Govin Pratama Hendra Supendar Hendra Supendar Henny Destiana Henny Leidiyana Ilham Thomas Saputra Karlena Indriani Latifa, Nurul Robiatul Luci Kanti Rahayu Maisyaroh Mateus Hantoro Winarsa Muadz Zidan AlWafi Muflih, Dzaky Fallah Muhamad Raka Fadilah Muhammad Farhan Muhammad Fatkhul Munir Muhammad Hamdan Sukri Noval, Qibran Omar Pahlevi Pamungkas, Jarseno Panjaitan, Leo Fernando Phindika, Phindika Pratama, Lutfi Fadillah Ricky Rivaldo Rusdiansyah Rusdiansyah Santiyana Yulia Santoso Setiawan Santoso Setiawan Sri Radhitya Murti Mahening Suhar Janti Sutisna, Alifan Widad Syamsiar, Syamsiar Tanzil Zirohim Tuslaela Wahyu Styani, Eka