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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika
Wibisono Wibisono
Institut Teknologi Budi Utomo
Computer Science & IT Other

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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 Berliyanto Berliyanto Leni Devera Asrar Rachmat Setiabudi Sigit Wibisono Surya Darma Teguh Muryanto Triyono Budi Santoso