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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology INTECOMS: Journal of Information Technology and Computer Science TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains
Rachmat Setiabudi
Institut Teknologi Budi Utomo
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Perancangan Penerapan Quick Response (QR) Kode Dalam Sistem Pendaftaran Mochamad Sanwasih; Rachmat Setiabudi
Teknois : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 11, No 2 (2021): November
Publisher : Sekolah Tinggi Ilmu Komputer Binaniaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v11i2.118

Abstract

The rapid development of technology and information systems has now been able to attract the attention of many organizations to support their activities, one of which is the development of an online registration system. There are problems that occur due to the unavailability or lack of use of the technology. Systems that are still manual processing can cause problems in the future. One of them is from problems that occur in poor data management, ambiguous participant data, the use of time used to be wasted and other obstacles. The application of the Quick Response (QR) code in the registration system is expected to help problems that occur so that data management becomes better and creates a more efficient and structured use of time. The system development of this registration system design uses the SDLC method and the model used is the waterfall model, which provides an overview from the analysis to the maintenance of the information system of the designed application. The system design to facilitate research analysis of the system to be used, uses the Unified Modeling Language (UML) with the modeling used is the Waterfall model, which describes the flow of the existing system that will be developed
COMPARATIVE ANALYSIS OF AUTOMATION FUNCTIONAL TESTING TOOLS PERFORMANCE FOR PLAYSTORE APPS WITH DIA METHOD Faizal Riza; Berliyanto Berliyanto; Aji Nurrohman; Rachmat Setiabudi
Jurnal Techno Nusa Mandiri Vol 21 No 1 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i1.5363

Abstract

The complexity of smartphone applications presents challenges for developers, who must ensure flawless functionality despite limitations such as budget and time constraints. Manual testing is time-consuming, prompting a shift towards automated testing methods to ensure efficiency and reliability. In this context, researchers are evaluating the efficacy of three leading test automation frameworks—Robot Framework, Katalon Studio, and UI Path—against key performance parameters. Using the Distance to the Ideal Alternative (DIA) method on playstore apps. The main performance parameters used as a reference are automated testing progress and tools usability. Katalon Studio emerges as the top performer, securing the top rank with a remarkably close to the alternative ideal positive distance (Ri) value of 0.00001. UI Path occupies the second position with a Ri value of 0.00135, while Robot Framework trails behind with a Ri value of 0.00295. This research contributes to the understanding of the performance of different automation frameworks in the context of functional testing, providing valuable insights for developers and organizations seeking to optimize their testing processes. The findings underscore the significance of Katalon Studio's exceptional performance and highlight opportunities for improvement in UI Path and Robot Framework. Additionally, implementing a robust monitoring and evaluation framework is crucial for tracking the ongoing performance and optimizing the efficiency of these automation frameworks.
KOMPARASI DAN IMPLEMENTASI ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI KREDIT BERMASALAH PADA PT BPR NUSUMMA KLATEN Teguh Muryanto; Aji Nurrohman; Rachmat Setiabudi; Wibisono Wibisono; Berliyanto Berliyanto
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/r5rncz02

Abstract

Tingkat kredit bermasalah yang tinggi dapat mengganggu stabilitas keuangan lembaga perbankan, sehingga diperlukan sistem klasifikasi yang akurat untuk mendeteksi potensi gagal bayar sejak dini. Penelitian ini bertujuan untuk membangun dan membandingkan model klasifikasi risiko kredit menggunakan algoritma machine learning, yaitu Random Forest, XGBoost, dan Support Vector Machine (SVM). Permasalahan yang diangkat dalam penelitian ini meliputi ketidakakuratan dalam klasifikasi nasabah, kurangnya pemanfaatan data historis, serta belum diterapkannya metode analitik berbasis algoritma cerdas. Metode penelitian mengikuti pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM) yang mencakup pemahaman bisnis, eksplorasi data, praproses data, pemodelan, evaluasi model, hingga tahap implementasi. Dataset yang digunakan berasal dari laporan historis nasabah kredit di PT BPR Nusumma Klaten. Evaluasi dilakukan dengan mengukur akurasi, precision, recall, F1-score, dan AUC. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki kinerja terbaik dengan nilai evaluasi yang lebih stabil dibandingkan XGBoost dan SVM. Temuan ini diharapkan dapat membantu lembaga keuangan dalam meningkatkan efisiensi proses analisis risiko kredit dan pengambilan keputusan berbasis data. Kata Kunci: Kredit Bermasalah, Random Forest, XGBoost, SVM, Klasifikasi
Co-Authors Aji Nurrohman Berliyanto Berliyanto Faizal Riza Mochamad Sanwasih Teguh Muryanto Wibisono Wibisono