OJS UNPATTI Publication Center (Universitas Pattimura)
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The Development of Cultural Ecotourism-Based Tourism: The Role of Village Governments in Managing Sustainable Tourism in Badung Regency
Get PDFThis study aims to describe the role of village governments in the development of cultural ecotourism-based tourism by examining four key tourism components: attractions, supporting facilities, accessibility, and additional services. A qualitative approach was employed, using observation and in-depth interviews as data collection techniques, focusing on Abiansemal Village, Badung Regency. The findings reveal that Abiansemal Village offers main attractions such as jogging trails with natural rice field panoramas, historic traditional bathing facilities, and the Melasti Site for self-purification rituals. However, supporting facilities in the village still require development to enhance tourist comfort. Accessibility to destinations is relatively adequate, but additional services, such as the involvement of investors and community elements, need improvement to ensure tourism sustainability. This study highlights the importance of collaboration between village governments, local communities, and private sectors in managing cultural ecotourism-based tourism. As a novel contribution, this research provides insights into how village governments can play a strategic role in managing sustainable tourism at the local level. Recommendations include strengthening infrastructure, enhancing local community capacity, and implementing collaborative strategies to support the development of social sciences and humanities in the context of sustainable tourism
From Trauma to Resilience: A Comprehensive Review of Counseling and Intervention Methods for PTSD
Get PDFPost-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition stemming from exposure to traumatic events, characterized by symptoms such as flashbacks, anxiety, sleep disturbances, and emotional instability. This study explores the complexities of PTSD, its symptoms, and impacts across psychological, physiological, and social dimensions. Utilizing a Library Research methodology, the study examines existing literature to identify gaps in age-specific interventions, the application of therapeutic approaches in non-clinical settings, and the role of family involvement in recovery. Effective treatments, including Eye Movement Desensitization and Reprocessing (EMDR) and Cognitive Behavioral Therapy (CBT), are highlighted for their success in reducing PTSD symptoms and fostering resilience. Counseling emerges as a critical intervention, particularly when combined with holistic approaches that incorporate spiritual and community support. The findings emphasize the need for comprehensive, multidisciplinary strategies to address PTSD’s wide-ranging effects, advocating for targeted interventions that align with developmental, contextual, and familial dynamics to enhance recovery and overall well-being
The Role of Digitalization Development for Micro, Small and Medium Enterprises (MSMEs) in Ambon City
No full textThis study aims to explore the role of digitalization in the development of Micro, Small, and Medium Enterprises (MSMEs) in Ambon City, particularly MSMEs under the guidance of Bank Indonesia Maluku Province. Using a descriptive qualitative research method, data were collected through in-depth interviews and observations of three MSMEs: Ralsasam Tenun, Sumakul, and Kabeta Craft. The findings show that digitalization training and facilitation through exhibitions significantly enhanced MSMEs' capacity to utilize digital media, such as Instagram and marketplaces, for product promotion and marketing. Digitalization has proven effective in expanding market reach and increasing sales, with the three MSMEs reporting a 50% increase in revenue. The study implies that the implementation of digitalization in MSME marketing strategies can be a solution for improving competitiveness and business sustainability in the digital er
APPLICATION OF BACKPROPAGATION FOR FORECASTING OPEN UNEMPLOYMENT IN MAKASSAR CITY
No full textBased on data from the Statistics Bureau of South Sulawesi Province, the open unemployment rate in Makassar City has remained consistently high over the past ten years, averaging 11.41%. This highlights a persistent labor market issue and positions Makassar as the leading contributor to the open unemployment rate in the province. To support effective policymaking and early intervention strategies, it is essential to forecast future unemployment trends based on historical data. Therefore, this study aims to forecast the open unemployment rate in Makassar City over the next five years using a machine learning approach. Among the available forecasting methods, the Backpropagation Artificial Neural Network (ANN) was selected due to its proven ability to model complex, non-linear relationships often found in socio-economic data. ANN is particularly effective in handling temporal dynamics without assuming linearity or stationarity, unlike traditional statistical models. In this study, the forecasting process involved data normalization, scenario-based data partitioning, ANN architecture design, and model training and testing. The model with the best performance consisted of 11 neurons in the input layer, 55 neurons in the hidden layer, and 1 neuron in the output layer, using 80% of the data for training and 20% for testing. This configuration yielded a forecasting accuracy of 91.896%, with a MAPE of 8.131% and an MSE of 0.003. The denormalized results forecast a steady decline in the open unemployment rate from 9.078% in 2023 to 7.248% in 2027, indicating a positive trend in employment. Nevertheless, it is important to acknowledge the limitations of forecasting models and the potential influence of external factors that may affect actual outcomes
MODELLING AND NUMERICAL ANALYSIS FOR CRACK PROPAGATION IN COMBINING CONCRETE WITH 25% FEATHER SHELL POWDER USING FINITE ELEMENT METHOD
No full textThis study explores the application of the Extended Finite Element Method (XFEM) for modeling fracture behavior, utilizing COMSOL Multiphysics 5.6 to simulate a homogeneous concrete medium without embedded reinforcement. The computational model incorporates key parameters such as stress ratio (Young’s modulus of 137.9 MPa), lateral strain from axial loading (Poisson’s ratio of 0.17), concrete density of 2.4 g/cm³, and a crack growth rate governed by Paris’ law. The simulation results show a maximum stress intensity factor ( ) of 66.2 and a failure point occurring after approximately 22,568 load cycles. A mixture comprising 25% clamshell ash and lime was used as a sustainable cement substitute, achieving a maximum compressive strength of 20.53 MPa—meeting the structural concrete standard. These findings contribute to enhancing predictive fracture models and promoting sustainable material innovation in civil engineering
BIRESPONSE SPLINE TRUNCATED NONPARAMETRIC REGRESSION MODELING FOR LONGITUDINAL DATA ON MONTHLY STOCK PRICES OF THREE PRIVATE BANKS IN INDONESIA
No full textThis study investigates the application of a truncated spline nonparametric regression model for biresponse analysis of longitudinal data, focusing on modeling monthly stock prices specifically opening and closing prices of three private banks in Indonesia: Bank Mayapada, Bank Mega, and Bank Sinar Mas. The data used in this research are secondary data sourced from the website Id.Investing.com and monthly financial statement publications of three private banks in Indonesia. Longitudinal data, combining cross-sectional and time-series dimensions, are utilized to capture trends and patterns not detectable in traditional cross-sectional analysis. The truncated spline method is selected for its adaptability to nonlinear relationships and abrupt data behavior changes. The model incorporates three predictor variables traded stock volume, total assets, and total liabilities and evaluates their influence on stock prices. Assumptions of longitudinal data are validated using the Ljung-Box autocorrelation test, Bartlett’s sphericity test, and Pearson correlation. Results confirm significant within-subject correlations, independence between subjects, and strong interdependence between response variables. The optimal configuration is determined using Generalized Cross Validation (GCV), with up to three knots considered for segmentation. Weighted Least Squares (WLS) is employed for parameter estimation, accounting for within-subject correlations. Model evaluation based on Mean Absolute Percentage Error (MAPE) indicates high accuracy, with all MAPE values below 5%. The highest MAPE value is 4.41% for the closing price of Bank Mayapada, while the lowest is 2.65% for the opening price of the same bank. The segmentation analysis reveals that traded stock volume and total assets positively influence stock prices, while total liabilities exhibit a predominantly negative impact. The model is limited to internal financial indicators and does not include external macroeconomic factors such as interest rates or inflation. This study is the first to apply a biresponse truncated spline nonparametric regression approach to analyze stock prices of private banks in Indonesia by simultaneously modeling both opening and closing prices, providing a flexible and effective method for capturing complex patterns in longitudinal financial data
AN EXAMINATION OF THE GREEN STOCK PORTFOLIO IN CONNECTION WITH THE 2024 INDONESIAN REPUBLIC PRESIDENTIAL GENERAL ELECTION
No full textThe presidential election of the Republic of Indonesia occurs on a frequency of once every five years. The present work investigated the impact of the 2024 Presidential Election on the performance of the optimal stock portfolio constructed by K-Means Clustering during the first phase of stock selection. Subsequently, the portfolio will be evaluated using two distinct approaches, namely Mean Absolute Deviation (MAD) and Mean-Variance Efficient Portfolio (MVEP). Both techniques were employed to construct several portfolios throughout three time periods: before the Presidential Election (13 August 2023 to 13 February 2024) and after the Presidential Election (15 February to 15 April 2024 and 20 April 2024 to 20 May 2024). This was done by implementing a mechanism to manage the allocation of shares in order to optimize the portfolio. The analyzed data is historical data on daily green stock closing prices indexed on the SRI-KEHATI index. A portfolio was constructed and subsequently evaluated for its performance using the Sharpe Index. The findings of this study suggest that the upcoming 2024 general election for the presidency of the Republic of Indonesia had a favorable impact on the Indonesian capital market, particularly for stocks that are indexed by SRI-KEHATI. This criterion was proposed based on the observation that the average Sharpe ratio index for Period II and Period III exceeds the average Sharpe ratio index for Period I (prior to the election day). The most optimal portfolio examined in this study was the MVEP portfolio, mostly composed of assets in the primary consumer products industry, with a Sharpe ratio of 0.53586. Furthermore, the performance of portfolios in period III (after the election result release) was far superior to that of other portfolios examined in previous periods
THE EFFECT OF SAMPLE SIZE ON THE STABILITY OF XGBOOST MODEL PERFORMANCE IN PREDICTING STUDENT STUDY PERIOD
No full textStudent success can be defined based on the period of study taken until graduation from college. Machine learning can be used to predict the factors that are thought to influence student success. To achieve optimal machine learning model performance, attention is needed on the sample size. This study aims to determine the effect of student sample size on the stability of model performance to predict student success. This research is quantitative. The data used is student data from a university in Yogyakarta from 2014 to 2019, totaling 19061 students. The target variable is the student study period in months, while the predictor variables are college entrance pathways, GPA from semester 1 to semester 6, and family socioeconomic conditions based on the father’s and mother’s income. This research uses the XGBoost model with the best hyperparameters and the bootstrap approach. Bootstrapping was performed on the original data by sampling twenty different sample sizes: 250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250, 3500, 3750, 4000, 4250, 4500, 4750, and 5000. The resulting bootstrap samples were replicated ten times. Model performance evaluation uses the Root Mean Square Error (RMSE) value. The result of this research is the XGBoost model with the best hyperparameters, obtained through the training data division scheme of 90% and testing data of 10%, which has the smallest RMSE value of 8.318. The model uses the best hyperparameters: n_estimators of 75, max_depth of 8, min_child_weight of 5, eta of 0.07, gamma of 0.2, subsample of 0.8, and colsample_bylevel of 1. The XGBoost model with optimal hyperparameters demonstrates peak performance stability at a sample size of 1750 students, as evidenced by consistent RMSE values across 10 bootstrap replications, confirming that this data quantity provides the ideal balance between prediction accuracy and stability for estimating study duration.
 
COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE
No full textThe heart is a vital organ in the human body that functions to pump blood throughout the body and to the lungs. The heart is located in the chest cavity. The heart is the main force that drives human life. Therefore, if there is a disturbance in heart function, this can cause a decrease in quality of life to death, one of which is heart failure. Heart failure, if not diagnosed and treated quickly, will result in death. Based on findings showing the high death rate due to heart failure, a classification is needed to predict heart failure using machine learning methods. Machine learning can help predict this disease to improve early detection and more accurate medical decision-making. This study focuses on predicting the likelihood of a patient experiencing heart failure. The machine learning algorithm method used is supervised machine learning classification, including decision trees, random forests, naïve bayes, SVM, and K-NN. The results showed that the best method for predicting heart failure was Random Forest with an accuracy of 74.35%, followed by SVM with an accuracy of 69.23%. Meanwhile, Naïve Bayes had the lowest accuracy of 51.28%. Based on these findings, Random Forest is recommended as the best method for heart failure prediction due to its ability to handle data complexity and provide more stable results. Once the best algorithm is obtained, the prediction results and early detection of heart failure will be more accurate
Biskuit Bebas Gluten menggunakan Bahan Baku Tepung Mocaf dan Blondo
No full textThis study evaluated the effects of substituting modified cassava flour (mocaf) with blondo coconut oil on the moisture, fat, protein, carbohydrate, and ash content of gluten-free cookies. A completely randomized design with a one-factor arrangement was applied, consisting of five substitution levels of macaf to blondo (300:0 g; 250:50 g; 200:100 g; 150:150 g; 100:200 g). The highest moisture content (4.84%) was obtained at 100 g of mocaf and 200 g of blondo, while the lowest (4.64%) occurred with 300 g of mocaf and 0 g of blondo. The highest ash content (1.29%) was recorded at 200 g blondo, and the lowest (0.99%) was with no blondo. Protein content peaked at 4.59% (200 g of mocaf, 100 g of blondo) and was lowest at 3.88% (300 g of mocaf, 0 g of blondo). Fat content ranged from 7.83 (300 g mocaf, 0 g blondo) to 9.51% (200g mocaf, 100 g blondo), while carbohydrate content was highest at 69.24% (300 g mocaf, 0 g blondo) and lowest at 67.11% (100 g mocaf, 200 g blondo). This result demonstrates that partial substitution of mocaf with blondo significantly alters the nutritional profile of gluten-free cookies