Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Computer Science Artificial Intelligence and Communications
Siti Sundari
Universitas Harapan
Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pemberdayaan GEN-Z melalui Pelatihan Desain Produk Berbasis Digital Printing untuk UMKM Lokal Siti Sundari; Supiyandi Supiyandi; Cindy Atika Rizki; Munjiat Setiani Asih; Nabila Khairuniza
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 4 No 2 (2025): Oktober 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v4i2.645

Abstract

Generasi Z memiliki potensi kreatif tinggi dalam bidang desain digital yang dapat dioptimalkan untuk mendukung perkembangan Usaha Mikro, Kecil, dan Menengah (UMKM). Namun, keterbatasan keterampilan teknis dan pemahaman teknologi digital printing sering menjadi hambatan dalam menghasilkan produk yang inovatif dan kompetitif. Program pengabdian ini bertujuan memberdayakan Gen-Z melalui pelatihan desain produk berbasis digital printing yang difokuskan pada kebutuhan UMKM lokal. Metode pelatihan dilakukan secara praktik langsung dengan pendekatan kolaboratif antara peserta dan pelaku UMKM, mencakup penguasaan perangkat lunak desain grafis, teknik produksi digital printing, hingga strategi pemasaran produk kreatif. Hasil kegiatan menunjukkan peningkatan keterampilan desain peserta, terciptanya produk-produk baru yang sesuai tren pasar, serta meningkatnya kolaborasi Gen-Z dengan UMKM dalam menciptakan nilai tambah ekonomi. Dengan demikian, pelatihan ini tidak hanya memperkuat kapasitas generasi muda, tetapi juga mendukung keberlanjutan dan daya saing UMKM di era digital.
Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions Fera Damayanti; Arief Budiman; Siti Sundari; Theodora MV Nainggolan
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.20

Abstract

Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.
Co-Authors Arief Budiman Asih, Munjiat Setiani Cindy Atika Rizki Fera Damayanti Nabila Khairuniza Supiyandi Supiyandi Theodora MV Nainggolan