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All Journal Journal of Applied Computer Science and Technology (JACOST) Journal of Artificial Intelligence and Digital Business
Ricky Mardianto
Universitas Widya Dharma Pontianak
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Klasifikasi Pemohon Pinjaman dengan Hyperparameter Tuning dan Teknik Penyeimbangan Data Donata Yulvida; Stefanie Quinevera; Ricky Mardianto; Steven Joses
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/krjtrh05

Abstract

Loan classification is a critical component of credit risk management, as it categorizes loans based on risk levels and supports the financial stability of banks, where loan-related income represents a substantial share of assets. Effective classification aims to ensure secure asset allocation, minimize credit risk, and prevent potential repayment issues. This study enhances loan classification performance through two strategies: hyperparameter optimization of Decision Tree and Random Forest algorithms, and data balancing techniques to address class imbalance. Experimental results show that the Decision Tree achieves 89.21% accuracy with an F1-Score of 70.17%, while the Random Forest demonstrates higher performance, reaching 94.04% accuracy and an F1-Score of 79.75%. Random Oversampling reduces bias toward majority classes by improving model sensitivity, while hyperparameter tuning with GridSearchCV identifies optimal parameter settings, thereby strengthening predictive performance. The findings highlight that combining data balancing with hyperparameter optimization effectively improves accuracy and F1-Scores. These approaches are not limited to the algorithms tested but can also be applied to other classification methods, offering broader potential for enhancing credit risk prediction in banking.
Aplikasi Absensi Asisten Laboratorium Menggunakan Validasi Geofencing dan QR Code Berbasis Flutter Ricky Mardianto; Thommy Willay; Amok Darmianto
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.10244

Abstract

Laboratorium komputer merupakan elemen krusial dalam menunjang kegiatan akademik di perguruan tinggi yang membutuhkan pengelolaan asisten laboratorium secara akurat, disiplin, dan transparan. Namun, sistem absensi konvensional yang berjalan saat ini sering kali terkendala oleh tingginya celah manipulasi lokasi risiko tindakan kecurangan seperti penitipan presensi, serta kerumitan birokrasi dalam proses rekapitulasi data secara manual. Penelitian ini bertujuan untuk mengembangkan aplikasi absensi asisten laboratorium menggunakan metode validasi berlapis berbasis lokasi (Geofencing) dan pemindaian QR Code dinamis. Sistem dibangun menggunakan framework Flutter dan bahasa pemrograman Dart pada sisi klien, serta Cloud Firestore sebagai backend database real-time NoSQL yang terdenormalisasi. Pengujian sistem dilakukan melalui metode Black-Box Testing untuk mengevaluasi fungsionalitas antarmuka, keandalan sensor, serta keamanan hak akses pengguna berbasis peran. Hasil penelitian menunjukkan bahwa integrasi package geolocator mampu mengunci otorisasi presensi asisten hanya dalam radius koordinat laboratorium yang valid, sehingga mengeliminasi tindakan kecurangan penitipan absen. Selain itu, fitur schedule swapping terbukti adaptif dalam mengelola dinamika pertukaran jadwal asisten tanpa risiko race condition berkat penerapan local tracking mutex. Aplikasi ini berhasil memotong birokrasi rekapitulasi manual dengan menyediakan kalkulasi otomatis beban kerja SKS yang dikompilasi langsung ke dalam format laporan dokumen PDF. Temuan ini menegaskan bahwa aplikasi ini layak diimplementasikan untuk mendukung tata kelola administrasi laboratorium yang efisien, transparan, dan akuntabel.
Co-Authors Amok Darmianto Donata Yulvida Stefanie Quinevera Steven Joses Willay, Thommy