The growing demand for Location-based Augmented Reality (LAR) experiences has driven the integration of Visual Simultaneous Localization And Mapping (VSLAM) with Google Street View (GSV) to enhance the accuracy. However, the impact of the ambient light intensity on the accuracy and reliability is underexplored, posing significant challenges in outdoor LAR implementations. This paper investigates the impact of light conditions on the accuracy and reliability of the VSLAM/GSV integration approach in outdoor LAR implementations. This study fills a gap in the current literature and offers valuable insights into vision-based approach implementation under different light conditions. Extensive experiments were conducted at five Point of Interest (POI) locations under various light conditions with a total of 100 datasets. Descriptive statistic methods were employed to analyze the data and assess the performance variation. Additionally, the Analysis of Variance (ANOVA) analysis was utilized to assess the impact of different light conditions on the accuracy metric and horizontal tracking time, determining whether there are significant differences in performance across varying levels of light intensity. The experimental results revealed that a significant correlation (p < $0.05$) exists between the ambient light intensity and the accuracy of the VSLAM/GSV integration approach. Through the confidence interval estimation, the minimum illuminance $434 \mathrm{lx}$ is needed to provide a feasible and consistent accuracy. Variations in visual references, such as wet surfaces in the rainy season, also impact the horizontal tracking time and accuracy.

The increasing demand for data-driven decision making to maintain the innovations and competitiveness of organizations highlights the need for data science educations across academia and industry. At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. Integrating statistics learning into Python programming can convey an effective solution for this problem, where it has become essential in data science implementations, with extensive and versatile libraries. In this paper, we present a self-learning method for fundamental statistics through Python programming for data science studies. Unlike conventional approaches, our method integrates three types of interactive problems—element fill-in-blank problem (EFP), grammar-concept understanding problem (GUP), and value trace problem (VTP)—in the Programming Learning Assistant System (PLAS). This combination allows students to write code, understand concepts, and trace the output value while obtaining instant feedback so that they can improve retention, knowledge, and practical skills in learning statistics using Python programming. For evaluations, we generated 22 instances using source codes for fundamental statistics topics, and assigned them to 40 first-year undergraduate students at UPN Veteran Jawa Timur, Indonesia. Statistics analytical methods were utilized to analyze the student learning performances. The results show that a significant correlation ($\rho <0.05$) exists between the students who solved our proposal and those who did not. The results confirm that it can effectively assist students in learning fundamental statistics self-learning using Python programming for data science implementations.

Kasus COVID-19 menjadi pandemi dan hampir menghentikan aktifitas dan rutinitas seluruh warga negara di dunia, termasuk Indonesia. Pandemi ini telah berlangsung di Indonesia di mulai awal bulan Maret hingga saat ini. Pemerintah terus berupaya untuk mensosialisaiskan dan menginformasikan jumlah kasus terkonfirmasi positif, kasus pasien sembuh, pasien meninggal dan pasien dalam perawatan tiap harinya. Namun, dengan seiring bertambahnya kasus tiap hari, perlu upaya para akademisi dan praktisi untuk turut membantu pemerintah untuk menemukan solusi bersama. Exploratory Data Analysis, salah satu strategi yang tepat untuk melakukan analisis data dengan didukung teknologi Big Data, semakin mempermudah untuk melakukan eksplorasi secara mendalam. Berguna untuk menemukan pola-pola tersembunyi, mengungkap informasi yang belum diketahui sebelumnya, dan memberikan dampak untuk tindak lanjut ke depannya. Untuk merealisasikan hal tersebut, pertama, mengumpulkan data kasus COVID-19 dari Badan Nasional Penanggulangan Bencana, Republik Indonesia, lalu data di-load ke dalam Hadoop Environment dan diakses menggunakan HiveQL. Hasil eksperimen menunjukkan bahwa analisis statistik deskriptif menemukan total jumlah kasus COVID-19 tiap kategori paling sedikit terjadi pada bulan Maret sedangkan paling besar terjadi di bulan September 2020. Rata-rata harian kasus COVID-19 tiap bulannya selalu bertambah, dari 50 kasus per hari di bulan Maret hingga melonjak menjadi 3.740 per hari di bulan September. Analisis PDF-CDF menunjukkan laju pertumbuhan kasus terkonfirmasi positif COVID-19 semakin meningkat, belum ditemukan tanda-tanda melandai, dan analisis korelasi menunjukkan terdapat pengaruh kuat antara pertambahan jumlah kasus terkonfirmasi positif dengan jumlah kasus pasien sembuh dan kasus pasien meninggal sebesar 0.94 dan 0.9 masing-masing.

In recent years, Location-Based Augmented Reality (LAR) systems have been increasingly implemented in various applications for tourism, navigation, education, and entertainment. Unfortunately, the LAR content creation using conventional desktop-based authoring tools has become a bottleneck, as it requires time-consuming and skilled work. Previously, we proposed an in-situ mobile authoring tool as an efficient solution to this problem by offering direct authoring interactions in real-world environments using a smartphone. Currently, the evaluation through the comparison between the proposal and conventional ones is not sufficient to show superiority, particularly in terms of interaction, authoring performance, and cognitive workload, where our tool uses 6DoF device movement for spatial input, while desktop ones rely on mouse-pointing. In this paper, we present a comparative study of authoring performances between the tools across three authoring phases: (1) Point of Interest (POI) location acquisition, (2) AR object creation, and (3) AR object registration. For the conventional tool, we adopt Unity and ARCore SDK. As a real-world application, we target the LAR content creation for pedestrian landmark annotation across campus environments at Okayama University, Japan, and Brawijaya University, Indonesia, and identify task-level bottlenecks in both tools. In our experiments, we asked 20 participants aged 22 to 35 with different LAR development experiences to complete equivalent authoring tasks in an outdoor campus environment, creating various LAR contents. We measured task completion time, phase-wise contribution, and cognitive workload using NASA-TLX. The results show that our tool made faster creations with 60% lower cognitive loads, where the desktop tool required higher mental efforts with manual data input and object verifications.

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.

Tuberculosis (TB) is caused by bacteria (Mycobacterium tuberculosis) that most commonly attacks the lungs. TB is spread from person to person through the air. When people with pulmonary TB cough, sneeze, or spit, they propel TB germs into the air. By inhaling only a small number of these germs, a person can become infected. Tuberculosis is curable and preventable. Prevention that can be done is by providing education about TB and vaccines. While treatment can be done by treating infected individuals. This study examines the TB epidemic model with the application of control, by finding optimal control solutions using the Pontryagin Minimum Principle method. In this study, three control variables were applied, namely education, vaccination and treatment. Numerical calculations were carried out using the Forward Backward Sweep 4th order Runge Kutta method and and then simulated. The results of the numerical simulation of the TB epidemic model show that by implementing control in the form of education, vaccination, and treatment, the population of infected individuals can be reduced.