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PEMANFAATAN DESAIN GRAFIS UNTUK PENGEMBANGAN BISNIS Lili Indah Sari; Wishnu Aribowo Probonegoro; Parlia Romadiana; AdeSeptryanti
BESIRU : Jurnal Pengabdian Masyarakat Vol. 1 No. 12 (2024): BESIRU : Jurnal Pengabdian Masyarakat, Desember 2024
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/764ec708

Abstract

Desain grafis saat ini banyak digunakan untuk membantu para pedagang yang memiliki usaha (UMKM). Perkembangan dan pemanfaatan desain grafis untuk pengembangan bisnis tidak terlepas dengan kemajuan teknologi dan digitalisasi saat ini. Ibu ibu yang berada di daerah air kepala tujuh pangkalpinang yang memiliki usaha, namun belum memiliki logo usahanya sebagai cirikhas dari usahanya perlu dibimbing dan dilakukan pelatihan desain grafis. Karean permasalaha itu penulis melakukan kegiatan PKM desain grafis agar bisa membantu ibu ibu bisa menggunakan aplikasi yang digunakan dari hp masing masing. Aplikasi yang sering mudah, sering digunakan oleh masyarakat untuk membuat desain grafis sepeti membuat logo, kemasan, banner dan laiinya dan bisa di download melalui HP yaitu aplikasi canva. Tujuan dari PKM ini memberikan pengaetahuan, ketrampilan dalam membuat desain usaha bagi ibu ibu agar dapat membuat desain sendiri seperti logo, kemasan, sticker dan lainnya sesuai dengan keinginann dan kreatifitasnya
Evaluasi Tata Kelola Teknologi Informasi Aplikasi Layanan Di PT.PQR Dengan Cobit 4.1 Wishnu Aribowo Probonegoro; Lili Indah Sari; Parlia Romadiana
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7544

Abstract

PT PQR is a company engaged in the application of shipping services. It is important for PT PQR to manage and utilize information technology well, but there has never been an assessment of the management system and its application. The author conducted research to assess the management of information technology using the COBIT 4.1 standard, which has 34 processes. This research was conducted through observations, interviews, and measurement of maturity levels, with the aim that PT PQR knows the information technology governance that is carried out, and knows the gaps that exist so that they can be resolved immediately to improve services and compete with other companies. The results showed that the current maturity level at PT PQR is close to level 3, with 14 Information Technology processes in two domains, namely Delivery Support and Monitoring Evaluate. PT PQR has clear and written standard procedures regarding the procedures and management of information technology, which are socialized among management and employees. Overall, the maturity level evaluation shows that PT PQR has fairly good IT governance, with an average achievement close to the expected target. The DS domain has an almost optimal performance with an achievement of 99%, while the ME domain requires more attention to increase the achievement from 90% to 100%. Improvement efforts can be focused on areas that require improvement based on the findings of this analysis.
Komparasi Algoritma Klasifikasi Performa Akademik Mahasiswa Bisnis Digital: SVM, Random Forest, XGBoost, dan LightGBM dengan Penanganan Class Imbalance Menggunakan SMOTE Lili Indah Sari; Burham Isnanto; Wishnu Aribowo Probonegoro
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10535

Abstract

This study aims to compare the performance of classification algorithms, namely Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM, in predicting the academic performance of Digital Business students at ISB Atma Luhur by handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The dataset consisted of 326 student records with 55 questionnaire-based Likert-scale features, GPA, and semester data classified into two academic performance classes. The research stages included data preprocessing, normalization, SMOTE implementation, feature selection using feature importance, model training, and evaluation using accuracy, precision, recall, F1-score, F1 Macro, AUC-ROC, and training time metrics. The results showed that the XGBoost algorithm achieved the best performance with an accuracy of 0.8621, an F1 Macro score of 0.85, and an AUC value of 0.91. LightGBM produced performance close to XGBoost while providing faster training time. The implementation of SMOTE successfully improved minority class classification performance across all algorithms, particularly in terms of F1-score. The findings indicate that the combination of boosting algorithms and class imbalance handling techniques is effective for machine learning-based academic performance prediction systems.