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Pendampingan Kelompok UMKM di Garut Dalam Penggunaan Dompet Digital Untuk Mendukung Ekonomi Digital Rina Kurniawati; Leni Fitriani; Muhammad Rikza Nashrulloh
Journal of Community Development Vol. 5 No. 3 (2025): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i3.1292

Abstract

The community service program aimed to enhance the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in Garut Regency through the implementation of digital wallet technology. Addressing the challenges of market access and financial management faced by MSMEs, the program developed an application called MitraREID. This application was designed to assist MSMEs in managing transactions, recording expenses, generating financial reports, and optimizing product management. The implementation process included socialization, intensive training, application deployment, and technical assistance involving the UMKM community, Mikromega, in Garut. The results indicated a significant improvement in the MSMEs' ability to utilize digital technology for daily operations. The MitraREID application facilitated business management, enhanced transaction efficiency, and allowed MSMEs to structure their financial management more effectively. The program's impact was quantitatively measured by pre- and post-test scores, which showed an increase from an average of 79/100 before training to 99/100 after training. This significant improvement demonstrated the enhanced digital skills and understanding of participants in utilizing digital wallet technology to support their business operations, enabling them to compete more effectively in local and national markets.
Integrasi Payment Gateway Dalam Sistem Keuangan Sekolah Rinda Cahyana; Muhammad Rikza Nashrulloh; Hary Sholahudin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.1664

Abstract

Madrasah Aliyah Ma'arif Cilageni Kadungora is a formal educational institution established specifically for senior high school education. Madrasah Aliyah certainly has supervision, a financial payment system for tuition fees, and other payment systems. One of the problems in managing financial data for tuition payments is that it is still done manually, through written records and conventional number management applications. The focus of the problem may be on data recording or recapitulation, which is time-consuming and has a high level of human error. The methodology used in creating this system is the Rational Unified Process (RUP) with four stages: Inception, Elaboration, Construction, and Transition. System modeling was performed using the Unified Modeling Language (UML), and the programming language used was PHP with the Laravel framework. The main objectives of this system are to facilitate schools in managing finances, reduce delays in tuition payments, and provide tuition payment information to parents or guardians of students via WhatsApp notifications. The results of the study show that students can make payments via Virtual Account, Bank Transfer, QRIS, or other methods. With the existence of Payment Gateway technology, it is proposed as a solution so that the payment process is automatically recorded in the madrasah's financial system, enabling schools to provide better services.
Analisis Kinerja Perhitungan Jarak Hamming pada Model Klasifikasi Penyakit Paru-Paru Menggunakan Algoritma K-Nearest Neighbor (KNN) Fitri Nuraeni; Siti Luthfiah Khoirotunnisa; Ridwan Setiawan; Muhammad Rikza Nashrulloh
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2578

Abstract

Lung diseases are among the leading causes of death worldwide and require early, accurate diagnosis to minimize the risk of complications. In the digital era, developing artificial intelligence–based classification models has become a potential solution to support the diagnostic process, particularly for categorical data that represent symptoms such as coughing, shortness of breath, and smoking history. This study proposes a lung disease classification model using the K-Nearest Neighbor (K-NN) algorithm with a simple categorical distance approach, namely the Hamming distance. The dataset used is imbalanced; therefore, data balancing was performed using the random oversampling method. Model evaluation was carried out using two schemes—data splitting and 10-fold cross-validation—by testing multiple values of parameter k. The best results were obtained at k = 7 with an accuracy of 94.58%, precision of 95.25%, recall of 94.39%, and an F1-score of 94.53%. These findings demonstrate that the combination of the K-NN algorithm, Hamming distance, and oversampling can produce high and stable classification performance for categorical datasets in lung disease prediction.