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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Ilmiah Mikrotek Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology Universa Medicina Journal of Midwifery Jurnal Linguistik Komputasional Jurnal Repositor Jurnal AbdiMas Nusa Mandiri NERO (Networking Engineering Research Operation)
Agustinus Bimo Gumelar
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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PEMBUATAN APLIKASI KOPERASI SIMPAN PINJAM DI DESA BAMBE, DRIYOREJO Purwanto, Devi Dwi; Winardi, Slamet; Subandoro, Philipus Suryo; Kartika Salim, Shierly; Gabeler, Robertus Geraldyn Alexandro; Tristianti, Berliana Az Zahra; Gumelar, Agustinus Bimo
Jurnal AbdiMas Nusa Mandiri Vol. 8 No. 1 (2026): Periode Januari 2026
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v8i1.7390

Abstract

This community service activity aims to support the Savings and Loan Cooperative in Bambe Village, Driyorejo District, in addressing problems of inefficient manual recording, potential errors, and suboptimal transparency of financial information for members. The proposed solution is the development of a web-based application that includes membership management, recording of savings and loan transactions, interest calculation, real-time financial reporting, transaction notifications, and automatic calculation of Operating Surplus (SHU). The implementation method consists of six stages: problem analysis, collaborative solution design with partners, system implementation, training and assistance, monitoring and evaluation, and report preparation and publication. The results show that the application is able to improve cooperative administrative efficiency, accelerate the reporting process, and increase the openness of information access for members. Based on questionnaire results from 30 respondents, the level of user satisfaction falls into the very good category with an average score of 4.5 out of 5. Further evaluation indicates the need to improve members’ understanding of application usage, leading to additional training and system improvements based on user feedback. Future development is directed toward the integration of digital payment systems.
Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition Adi, Derry Pramono; Junaedi, Lukman; Frismanda; Gumelar, Agustinus Bimo; Kristanto, Andreas Agung
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.571

Abstract

Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.
Co-Authors Achmad Teguh Wibowo Adi, Derry Pramono Ageng Salmanarrizqie Agung Widodo Andre Young Andreas Agung Kristanto, Andreas Agung Angelina, Zenia Ardianzah, Calvin Diaz Ari Andayani Ariesanti Tri Handayani, Ariesanti Tri Cahyo Darujati Devi Dwi Purwanto Farhandany Oktafarisky Frismanda Gabeler, Robertus Geraldyn Alexandro Hasan Dwi Cahyono Hendrawan, Kevin Henry Ricardo Handoyo Hermawan Arga Saputra Junaedi, Lukman Kartika Salim, Shierly Kevin Samsudin Ludia, Grace Margaretha Maulana Rizqi MY. Teguh Sulistyono Nugroho, Via Ardianto Nur Mira Permatasari Paul L Tahalele Salmanarrizqie, Ageng Samsudin, Kevin Slamet Winardi Subandoro, Philipus Suryo Suryo, Philipus Syamsul Anam Talahaturuson, Esther Yolanda Titiek, Ernawati Tristianti, Berliana Az Zahra Wahibullah, Wahibullah Yovita Vivi Megasari Zulkifli, Che Zalina