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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology INTECOMS: Journal of Information Technology and Computer Science Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika
Berliyanto Berliyanto
Institut Teknologi Budi Utomo
Computer Science & IT Other

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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
A System For Monitoring Indoor Air Quality Based On The Internet Of Things Aji Nurrohman; Berliyanto Berliyanto; Sigit Wibisono; Surya Darma; Wibisono Wibisono; Leni Devera Asrar; Triyono Budi Santoso
Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Vol. 7 No. 1 (2024)
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/inovatif.v7i1.925

Abstract

In this era of rapidly evolving information technology, the need to monitor and manage indoor air quality is becoming increasingly important. Poor air quality can have a negative impact on human health and productivity. Therefore, this research aims to design and implement an Internet of Things (IoT)-based Indoor Air Quality Monitoring System. The proposed system uses sensors connected to the IoT network to measure air quality parameters, such as temperature, humidity, particle content, and certain gases. The data collected by these sensors will be sent in real-time to a server via an internet connection. The server will process the data and present it in a form that can be accessed through a web interface. The implementation of IoT allows for more efficient and accurate monitoring, and provides easy access to data remotely. Users can monitor indoor air quality through their mobile devices or personal computers. The use of IoT technology in this monitoring system is expected to increase responsibility and awareness of indoor air quality. The results of this research are expected to contribute to the development of innovative solutions to improve the health and comfort of indoor environments. The conclusion of this research includes evaluation of system performance, analysis of the data generated, as well as potential development and improvement for further research.
Co-Authors Aji Nurrohman Faizal Riza Leni Devera Asrar Rachmat Setiabudi Sigit Wibisono Surya Darma Teguh Muryanto Triyono Budi Santoso Wibisono Wibisono