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Multi-satellite altimetry has been employed in tidal modelling as its accuracy has been proven acceptable in constructing good-quality models. However, the modelling process often fails to consider the varying temporal resolution and observation periods among satellites, which can affect the accuracy of the model due to aliasing and Rayleigh effects. In Indonesia, we investigate and propose a satellite selection procedure for tidal modelling utilizing the response method, validated with along-track data obtained from both older and newer generations of ERM (Exact Repeat Mission)-type altimetry satellites, including the TOPEX/Jason series (TOPEX, Jason-1, Jason-2, Jason-3, and SENTINEL-6A), ENVISAT, ERS-2, GFO, SENTINEL-3B, and SENTINEL3A. The most optimal satellite combination identified is the TOPEX/Jason series, GFO, and ENVISAT, as they contribute significantly less error compared to ERS-2, SENTINEL-3A, and SENTINEL-3B, with errors exceeding 50%, primarily due to aliasing and Rayleigh effects. This combination of three satellites with tide gauge stations achieves higher precision (11.31 cm) than the current global tidal model. This study provides empirical evidence that the accuracy of tidal models is not solely dependent on the number of altimetry satellites utilized, but implementing effective satellite selection procedures is also crucial to enhance the precision of a model. [ABSTRACT FROM AUTHOR]

The avian species virus that causes Newcastle disease is an extremely contagious illness. Avian paramyxovirus 1, or Newcastle disease virus (NDV), is a virus that brings harm to poultry's central nervous system and digestive tract. The NDV outbreak was initially documented in 1928 in Java, Indonesia. Newcastle disease does not currently require therapy. One popular and useful strategy for preventing and treating viral infections, such as Newcastle disease, is vaccination. With a variety of native medicinal plants and an abundance of biodiversity, Indonesia presents a promising area for biotechnology and pharmaceutical research. Laportea decumana, is a native plant in the Eastern part of Indonesia that contains alkaloids, glycosides, steroids, flavonoids, tannin, and saponin. Its cytotoxic, analgesic, antioxidant, and antibacterial properties have all been demonstrated. The antiviral properties of L. decumana have not been extensively researched. Thus, the purpose of this study is to examine L. decumana's antiviral activity, particularly against NDV, using a variety of techniques, including the plaque assay, cytotoxicity test, and gene expression experiment. L. decumana extracts at 100 μg/mL or less is a safe concentration to consider, as it still has 65% and above cell viability based on the results of the cytotoxic assay.

Interaction with many learning resources and motivated students are some of the indicators of ideal historical learning. The concept of Rajeboning (infographic project based outdoor learning) can be used to support historical learning. Furthermore, examining Liang Ulin 2 (which is still unexplored) and its artifacts can help to spur historical learning. This research involved the five steps of historical research (topic selection, heuristics, criticism, interpretation and historiography), as well as a qualitative research-based literature study. The archaeological and historical potential of Liang Ulin 2 can be reviewed from the findings of artifacts which include: stone artifacts (lithic), earthenware shards, jewelry, shells, bones, teeth, and ochre. The utilization of the Liang Ulin 2 Site as a historical source using the concept of Rajeboning consists of three stages: session 1 (orientation); session 2 (execution); and session 3 (appreciation). The three sessions are expected to motivate students and thus realize the real purpose of historical learning. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The Muslim Medical Students Questionnaire (MMSQ) is a self-assessment tool that was designed to measure student's Islamic competency in line with the implementation of the Islamic medical curriculum. For it to be applicable in Indonesia, this questionnaire needed to be translated and validated in the Indonesian language (Bahasa Indonesia). This study aimed to collect validity evidence of the Bahasa Indonesia version of MMSQ (BI-MMSQ). MATERIALS AND METHODS: The research process consisted of four stages: Forward and backward translation; Harmonization of the BI-MMSQ resulting from the initial stage; Pre-test of the BI-MMSQ among 30 clinical students, and finally construct validation among 225 clinical students. The completed questionnaires of 213 students were then analysed for their reliability and validity using exploratory factor analysis (EFA). RESULTS: The study showed that the scale had sufficient validity evidence; content, response process, and internal structure. The evidence of the internal structure of the tool is derived from the internal consistency (Cronbach's Alpha is 0.85) and also from the factor analysis. Most of the items had a factor loading value of > 0.3, but it was suggested that some of those with a value of < 0.3 should be rephrased to increase their quality. The Practice domain was the most stable sub-scale since most of the items contained high factor loadings. CONCLUSION: The BI-MMSQ had sufficient validity evidence and the internal structure may be improved by alteration of a few items. [ABSTRACT FROM AUTHOR]

Background: A growing number of recent literatures have attempted to document the factors related to drug use in different contexts, but limited studies analyzed the factors related to patient’s severity level. Objective: We aimed to examine the correlates of behavioral, environmental, socioeconomic, geographic factors, and severe drug addiction. Methods: We used a sample of 6790 people who use drugs (PWUD) from rehabilitation centers during 2019 to 2020 in Indonesia. Data were obtained from Drugs Rehabilitation Information System (SIRENA) by the Indonesian National Narcotics Agency (BNN) during 2019 to 2020. SIRENA used the adapted version of WHO’s Addiction Severity Index (ASI), which assesses patient’s addiction severity level and relevant factors. A composite of the ASI factors is classified as “poor” or “serious” if its score is 4 or higher. The data was analyzed using logistic regression model to estimate correlations of severe drug addiction. Results: We found a high prevalence (65.08%) of our sample reported having severe drug addiction, which needed intervention and support. Poor psychiatric condition (OR = 4.02, CI: 2.97-5.44), serious work-related issue (OR = 2.10, 1.75-2.51), poor medical condition (OR = 2.01, 1.32-3.06), and serious family and social problems (OR = 2.56, 2.15-3.05) were significantly associated with severe drug addiction. Male users had higher odds of severe drug addiction (OR = 1.81, 1.55-2.20), compared to female counterparts. Patients in urban areas had higher odds of severe drug addiction (OR = 1.19, 1.1-1.3) than those in rural. Those in less developed regions (eg, Sumatera, Kalimantan, and Sulawesi) had higher odds of severe drug addiction (OR = 2.33, 1.9-2.9), compared to those in the most developed region (eg, Java and Bali). Conclusion: This evidence can help policymakers in refining the harm reduction policies in Indonesia and other countries with similar settings.

National Economic Survey (Susenas) data is used by Statistics Indonesia to calculate the poverty rate in Bandung. However, the traditional data collection method of interviewing households one by one is time consuming, expensive, and may not capture a representative sample. Therefore, this research explores the use of Time Series models, specifically AutoArima, Croston, and Exponential Smoothing algorithms to predict the poverty rate in Bandung city. Based on this problem, a prediction is needed to determine the poverty rate in Bandung City. The poverty dataset used is sourced from the Bandung City Data Portal with test data from 2010 to 2018. This research will use 3 error parameters to evaluate the results of the poverty rate in Bandung City, namely MAE, MSE and MASE. Based on the tests conducted, the dataset produces the AutoArima model as the best method with MAE = 0.183, MSE = 0.053, MASE = 0.797, for the Croston model produces an error with MAE = 0.456, MSE = 0.374, MASE = 1.985. Meanwhile, the Exponential Smoothing model produces an error with MAE = 0.410, MSE = 0.215, MASE = 1.786. From the three tests, it was concluded that the AutoArima model successfully predicted the poverty rate in Bandung City with good results.

El Niño-Southern Oscillation (ENSO) affects climate patterns, leading to increased temperatures, high evaporation rates, and reduced precipitation in affected regions. This affects the amount of surface flow that keeps many rivers from moving until they dry up. This is also happened in Winongo, Code and Gajahwong rivers located in Yogyakarta Province. Stagnant water can affect water quality due to immobile water pollution. This study monitors river water quality by assessing Dissolved Oxygen (DO), Potential Hydrogen (pH), and Ferrum (Fe) levels. Water samples were collected from the upstream, midstream, and downstream sections of the Winongo, Code, and Gajahwong rivers to assess water quality. This approach helps identify pollution sources, understand spatial variability, and develop effective water management strategies.. Results indicate that the Winongo River had the highest Dissolved Oxygen (DO) level of 2.4 mg/L in the downstream section. The Code River had the highest pH of 8.1 in the upstream area, while the Gajahwong River showed a stable Iron (Fe) concentration of 2.5 mg/L. According to government regulations, the water quality in all three rivers is suitable only for irrigation purposes.

Population growth is a significant problem in the amount of land change that occurs. The Code River watershed in Yogyakarta has experienced urbanization due to the lack of land for housing, even though the Code River watershed has a vital role in community activities as the center of government, economy, tourism and history. In determining the width of the border, it adapts to morphological and demographic conditions. Satellite data processing utilizes deep learning using the Object Based Image Analysis (OBIA) method. The results obtained are the width of demographic boundaries varying from 3 m and 10 m. In the object evaluation carried out, the completeness rate is 83.5% and the quality is 69.1%. The number of buildings detected in the boundary area is 1178. The midstream area is an area with dense building conditions compared to the downstream and upstream. building use is dominated by residential buildings and MSME although there are other buildings such as hospitals, buildings, farms, schools, tourism area and factories.

The government through the Public Works Office launched Pamsimas (Community-Based Drinking Water and Sanitation Supply) program to realize 100% safe access to drinking water. However, this program is still unable to serve the community’s overall water needs. To prevent clean water crises, the community independently formed clean water management groups, one of which is the Tirto Lestari Clean Water Group located in Kaliapak Hamlet, Banjarsari, Samigaluh, Kulon Progo. The main problem that occurs in this group is the lack of water discharge so that the water supply system is not able to serve the needs of users optimally. The solution to this problem is to find potential sources of new groundwater and the construction of intake infrastructure. This research is focused on finding potential new water sources with geoelectric methods. Based on the results of this study, it shows that there is groundwater potential that can be used to meet clean water needs. The conclusion is that there is groundwater potential that can be used as a new source for the Pamsimas of Tirto Lestari group.

Oceanographic states are closely related to marine biota’s distribution and breeding patterns, especially in the intertidal zone. This study analyses the water quality variability and their relationship in the water of Prigi Bay. Water quality data was collected in situ using a Water Quality Checker. The statistical analyses were performed by applying PAST software version 4.11. The suitable waters with the best tendency value for the placement of lobster artificial reefs are stations 2 (Karang Gongso) and 4 (Damas). It is not only explained by salinity and turbidity but also by the ORP role, which has the solution potential to transfer electrons from oxidants to reductants, thus affecting the chemical processes. The high potential suitability at 2 locations is due to the concentration of a supportive environment with variability in ORP concentrations, having more potential for electron transfer, which significantly affects the attached benthic life as a pioneer for the reef. Salinity stability is safer from the impact of water mixing from the estuary. Parameter concentrations are more stable according to water temperature standards for coralline algae growth and are affected by turbidity and dissolved oxygen. In addition, other research is needed, such as social factors and fishermen’s activities.

Indonesia has already contacted the maritime nations due to its 5.8 million km2 of coastline. Consequently, fish products are among the most important commodities. Moreover, fish grading is a crucial step in the process of exporting fisheries products. Currently, in Indonesia, the process itself is manually inspected by an expert. In addition, this paper proposes to assist the industry by suggesting a method for grading fish. This method involves combining two essential fish parts with different resolutions: the high-level feature (the body) and the low-level feature (the eye) serve as defining characteristics. These two main parts are accurately localized using a deep learning-based object detection model, specifically YOLOv7, and extracted with an efficient and adaptive learned classification model, namely EfficientnetV2S. In the final stage, the two extracted features are combined and learned with Dense Layers to generate three distinct fish grades. Based on the experimental results, the proposed work achieved an accuracy, F1 Score, and recall of 96.88%, 97%, and 97%, respectively. The proposed model outperformed the baseline model, which relies solely on deep learning-based classification, by a significant margin.

Batik is one of the Indonesian cultures that has been recognized by UNESCO as an intellectual right of Indonesia. The popularity of batik internationally raises concerns about the Indonesian people’s understanding of batik if Indonesian people only refer to all types of batik just as ‘batik’. By utilizing K-Nearest Neighbour (KNN) algorithm which is a simple classification algorithm, then a system can be created that can classify batik types. The first step of KNN is training, which stores each training pattern. The second step is classification, whenever classifying a pattern, KNN examine all training patterns to determine the K closest patterns using certain calculations such as Euclidean Distance and Manhattan Distance. Before classification, a characteristic that represents a pattern is needed. Gray-Level Co-Occurrence Matrix (GLCM) is an algorithm that has proven to be very powerful as a feature descriptor in representing the texture characteristic of an image. This research experiments with the value of K in KNN = 1, 3, 5, and 7 with the distance calculation using Euclidean and Manhattan. The GLCM characteristic used are Entropy, Energy, Contrast, Homogeneity, Dissimilarity, Correlation, ASM, and the average of each characteristic. From the research that has been done, the system created obtained the highest accuracy of 75% with the combination of parameters; pixel distance = 7, K value = 1, 1st fold as test data and 2nd and 3rd fold as training data, and by using StandardScaler. But despite getting decent accuracy, there is still a need for further research to improve the accuracy of batik classification.

This research was written with a clear and specific focus, namely examining the tendency of tourist characteristics and attributes of tourism products where the attributes of tourism products are related to tourist satisfaction in visiting the attractions of Rammang-Rammang Limestone Forest Park in Maros Regency, Sulawesi Selatan (South Sulawesi). In this study we used descriptive statistics, in which the data analysis techniques are grouping data based on variables and types of respondents, tabulating data based on variables from all respondents, presenting data for each variable studied, doing calculations to answer the problem formulation. In this study there are two data analysis techniques conducted, namely quantitative descriptive statistics and Customer Satisfaction Index (CSI). Based on the results of the analysis, we obtained the response of tourists visiting the Rammang-Rammang Limestone Forest Park that based on psychographic segmentation from the seven demographic aspects of tourist characteristics, there are 5 (five) aspects that tend to be Midcentric and only 2 (two) are near allocentric, so it can be said that the majority of tourists visiting the Rammang-Rammang Limestone tourism object have a Mid-centric characteristic. Traveler ratings of the Tourism Product Attributes variables related to tourist satisfaction when viewed in the categories in the continuum line are in the high category. Sustainable tourism attractions such as tourism attraction Rammang-rammang Limestone Forest Park can provide satisfaction for visitors.

Indonesia is the fourth most populous country in the world, which is significant with energy consumption. Currently, Indonesia is heavily dependent on fossil fuels to its energy needs, but continued reliance on these fuels could lead to depletion. To overcome this problem, biogas is considered as an alternative energy source for cooking and electricity, especially from waste such as cow dung. This research provides an overview of biogas production from small cattle farms in Indonesia, with a focus on cow dung as a valuable resource. It covers factors that increase biogas production, multiple digesters, purification techniques, and integrates Internet of Things (IoT) technologies. Articles for this study were selected using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method from reputable journals indexed in Scopus and PubMed. Sustainable biogas from cattle farms offers energy generation using abundant cow dung. Optimizing production involves adjusting raw materials, temperature, pH, C/N ratio and HRT. Different types of digesters have unique advantages. Purification techniques such as water scrubbing, PSA, etc. increase methane production. Integrating IoT provides monitoring and optimization. Biogas production has enormous potential for renewable energy, requiring the use and application of efficient techniques, digester types, purification, and IoT integration for a greener future.

The increasing target of coal mining production every year needs to be supported by a reliable performance of the temporary stockpile reclaimer system, in order to ensure a smooth transfer of coal from the temporary stockpile to the main stockpile and achieve the production target. Based on the Overall Equipment Effectiveness calculation, it has been found that the performance of the reclaimer and conveyor system is still below the standard, at 31.04% and 40.26% respectively. Damage to one of the tools in the reclaimer system can cause production to stop and result in wastage and losses. By using the FMEA method and the Six Big Losses, two major potential losses have been identified as the causes of the decline in the reclaimer system performance, namely idling and minor stoppage losses, and equipment failure losses. Efforts to improve the system’s performance include making changes in the height and width of the temporary stockpile to minimize obstacles such as coal material overflow, and regular maintenance should be conducted to ensure optimal system performance.

Risk is very attached to every activity. Potential risks also occur in antenna installation projects at telecommunications company vendors, which create hazardous work. The aim of the research is to identify the causes of accidents at the Base Transceiver Station (BTS) tower and make recommendations for ways to reduce the accident rate for tower workers by using Failure Mode Effect and Analysis (FMEA) and Fault Tree Analysis (FTA) method. The results based on the FMEA method used indicate that: the highest work accident rate is obtained from being hit by heavy material (RPN = 128), with a percentage of 31%. Whereas in the FTA method, the most fundamental factor in the occurrence of material overlap work accidents is technical factors where the antenna installation is not perfect, the tools used are not based on standards, supported by the results of data verification by the internal control team which found 6% of engineers do not have proper helmets. Improvements were made by eliminating the existing failure modes, namely by equipping PPE for workers who do not yet have PPE and training certificates to work at tower heights.