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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Interdisciplinary Social Studies Anggapa
Alexander, Daniel
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Perencanaan dan Perancangan Taman Rekreasi Alam di Surabaya dengan Pendekatan Arsitektur Perilaku Alexander, Daniel; Mutfianti, Ririn Dina; Rosilawati, Hana
Jurnal Anggapa Vol 2 No 2 (2023): ANGGAPA Volume 2 No 2 November 2023
Publisher : Faculty of Engineering, Widya Kartika University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61293/anggapa.v2i2.618

Abstract

Perencanaan dan perancangan taman rekreasi alam di Surabaya dengan pendekatan arsitektur perilaku dilatarbelakangi oleh belum optimalnya wadah rekreasi alam di Kota Surabaya yang ditandai oleh terjadinya kasus kecelakaan dalam berekreasi di Kenjeran Park, Surabaya. Oleh sebab itu, tujuan utama dari perancangan ini adalah memberikan usulan perancangan taman rekreasi alam di Surabaya yang berwawasan arsitektur perilaku. Untuk menjawab tantangan ini, metode desain oleh Donna P. Duerk digunakan agar alur desain terarah dengan efektif. Pengumpulan data dilakukan secara kuantitatif, berupa pengukuran luas dan dimensi-dimensi, serta secara kualitatif berupa pembelajaran kondisi perilaku, sosial budaya dan kebutuhan ruang. Lokasi perencanaan dan perancangan terpilih di Jalan Lingkar Luar Timur, Kelurahan Kedung Cowek, Kecamatan Bulak, Kota Surabaya, Indonesia. Hasil analisis data memberikan sintesa berupa konsep makro dan mikro dengan pendekatan arsitektur perilaku. Hasil analisis tersebut memberikan rekomendasi penggunaan konsep makro sungai. Mikro konsep bentuk dihubungkan dengan persepsi lingkungan yang menghasilkan bentuk gua, akar pohon, dan pohon. Mikro konsep ruang dengan kognisi spasial yang menghasilkan tatanan ruang memusat untuk membantu pengunjung mengetahui posisinya dalam site. Mikro konsep tatanan dengan persepsi lingkungan dalam budaya berekreasi yang menghasilkan tatanan massa secara linear dengan peletakan pintu masuk dan pintu keluar yang berdekatan, yang mengacu pada bentukan arah arus aliran sungai yang linear.
Performance Analysis of Deep Learning Model Quantization on NPU for Real-Time Automatic License Plate Recognition Implementation Alexander, Daniel; Wildanil Ghozi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9700

Abstract

Neural Processing Units (NPUs) are dedicated accelerators designed to perform efficient deep learning inference on edge devices with limited computational and power resources. In real-time applications such as automated parking systems, accurate and low-latency license plate recognition is critical. This study evaluates the effectiveness of quantization techniques, specifically Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), in improving the performance of YOLOv8-based license plate detection models deployed on an Intel NPU integrated within the Core Ultra 7 155H processor. Three model configurations are compared: a full-precision float32 model, a PTQ model, and a QAT model. All models are converted to OpenVINO’s Intermediate Representation (IR) and benchmarked using the benchmark_app tool. Results show that PTQ and QAT significantly enhance inference efficiency. QAT achieves up to 39.9% improvement in throughput and 28.6% reduction in latency compared to the non-quantized model, while maintaining higher detection accuracy. Both quantized models also reduce model size by nearly 50 percent. Although PTQ is simpler to implement, QAT offers a better balance between accuracy and speed, making it more suitable for deployment in edge scenarios with real-time constraints. These findings highlight QAT as an optimal strategy for efficient and accurate license plate recognition on NPU-based edge platforms.
Comparative Analysis of Machine Learning Models For Predicting Default in Home Credit Companies Alexander, Daniel; Supriyanto, Raden
Interdisciplinary Social Studies Vol. 4 No. 4 (2025): Regular Issue: July-September 2025
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/iss.v4i4.930

Abstract

Credit scoring is an important process in the financial world to accurately identify the risk of prospective debtors. This study aims to build a credit scoring model by comparing the performance of several machine learning algorithms, namely XGBoost, random forest, and logistic regression. The dataset used is part of the training data and test data with several ratios, namely 70:30, 75:25, and 80:20. The preparation process is carried out through variable selection using the 5C principle, missing value imputation, categorical transformation of variables, and creation of derived features. Furthermore, modeling and optimization are carried out for each model to improve classification performance, especially in recognizing debtors who have the potential to default. The evaluation results show that the XGBoost model has the best performance with an accuracy of 84.8%, a precision of 84.9% and a recall of 84.7%, and an AUC of 92.3%. The main assessment of the character principle is the external credit score variable, the main assessment of the capacity principle is income, the main assessment of the capital principle is car ownership, the main assessment of the collateral principle is the credit financing ratio, and the main assessment of the condition principle is the regional rating.
Co-Authors Ririn Dina Mutfianti Rosilawati, Hana Supriyanto, Raden Wildanil Ghozi