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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara dan Kebijakan Publik Malcom: Indonesian Journal of Machine Learning and Computer Science
Rofiq, Hanif Noer
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Pemanfaatan Artificial Intelligence untuk Optimalisasi PNBP: Studi Kasus Bea Lelang pada Lelang Indonesia Sujak, Galuh Mafela Mutiara; Rofiq, Hanif Noer
Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara dan Kebijakan Publik Vol 9 No 2 (2024): Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara dan Kebijakan
Publisher : Direktorat Jenderal Perbendaharaan, Kementerian Keuangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33105/itrev.v9i2.873

Abstract

Misclassification of auction objects can result in an inaccuracy of the Auction Fee that is imposed, resulting in under/overpayment of government revenue, a decline in public reputation, and differences in auction fee data in SIMPONI and Portal Lelang Indonesia. These errors can be anticipaed by adding verification step by the Auctioneer. Meanwhile, the increase in the frequency of auctions is disproportionate to the number of Auctioneer, thus a mechanism that can assist the Auctioneer to do verification without adding additional work is needed. The authors propose the use of a Convolutional Neural Network to carry out the automatic classification of auction objects in the form of Buildings, Demolition, Cars, and Motorcycles. The dataset was obtained from the Portal Lelang Indonesia. The results of training and validation accuracy were 96.13% and 96.50%. The model is then applied to a dashboard for manual testing, and 100% accuracy results are obtained from all the images tested.
Implementasi K-Means Clustering untuk Optimalisasi Anggaran Penyakit Tidak Menular: Implementation of K-Means Clustering for Optimizing Non-Communicable Disease Budgets Sujak, Galuh Mafela Mutiara; Rofiq, Hanif Noer; Tawakal, Farhan Iqbal
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1597

Abstract

Pandemi menunjukkan pentingnya penganggaran, baik untuk menjalankan pelayanan kesehatan yang telah ada maupun untuk menghadapi COVID-19. Pandemi juga menunjukkan bahaya penyakit komorbid seperti diabetes melitus, hipertensi, dan obesitas sebagai pemicu tingginya risiko kematian akibat COVID-19. Untuk membuat kebijakan terkait penganggaran yang tepat guna, diperlukan analisis terkait anggaran kesehatan pemerintah daerah. Dalam penelitian ini penulis melakukan analisis clustering k-means anggaran kesehatan pemerintah daerah Tahun 2021 untuk mengelompokkan anggaran terkait penyakit komorbid seperti diabetes melitus, hipertensi, dan obesitas, serta gangguan mental emosional. Tujuan dari penelitian ini adalah memberikan insight mengenai pola pendanaan pemerintah daerah terkait penyakit tersebut. Clustering menghasilkan empat cluster dengan silhouette score sebesar 0,6156. Selanjutnya berdasarkan perbandingan dengan prevalensi penyakit masing-masing terdapat indikasi potensi optimalisasi dana untuk sub kegiatan lain atau untuk digunakan sebagai dana darurat pandemi.
Comparison of Transfer Learning Architecture Performance for Indonesian Auction Object Classification Rofiq, Hanif Noer
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6082

Abstract

The Indonesian auction, one of the sources of Indonesia's income for Non-Tax State Revenue (PNBP), faces challenges in accurately classifying auction objects, limiting revenue optimisation. This research aims to compare the performance of several transfer learning architectures on the Indonesian Auction Object Dataset, which includes categories such as Buildings, Cars, Motorbikes, and Salvage Materials. Seven pre-trained transfer learning models—MobileNetV2, NASNetMobile, EfficientNetV2B0, DenseNet121, Xception, InceptionV3, and ResNet50V2—were evaluated against a baseline model, focusing on validation accuracy, model size, and computational efficiency. MobileNetV2, NASNetMobile, DenseNet121, Xception, InceptionV3, and ResNet50V2 all achieved 100% validation accuracy, outperforming the baseline model's 96.5% accuracy. MobileNetV2 stands out for its efficiency, reaching 100% accuracy in just eight epochs with a compact model size of 11.1 MB. In contrast, EfficientNetV2B0 performed poorly on this dataset, achieving only 25% validation accuracy. These findings confirm that transfer learning architectures can significantly improve auction object classification accuracy while reducing the model size and training time, highlighting the benefit of transfer learning for optimising Indonesian auction systems.
Co-Authors Afrisca, Cindy Clara Atmoko, Darmawan Dwi Sujak, Galuh Mafela Mutiara Tawakal, Farhan Iqbal