Parameter: Journal of Statistics

BUSINESS INTELLIGENCE (BI) PRESIDENTIAL CANDIDATES BASED ON SOCIAL NETWORK ANALYSIS (SNA) WITH TWITTER DATA

Ichsan Ali, Abba Suganda Girsang — Tue, 31 Dec 2024 12:24:56 +0000

The twitter social network is widely used to discuss all kinds of topics, including those related to politics. Analyzing online conversations on Twitter to map the popularity of political figures as candidates for the Indonesian presidential election is a popular and challenging research area. In the Twitter network, citizens can express themselves and communicate with political figures. The conversational data in Twitter is very complex, so Business Intelligence is needed to transform raw data into meaningful and useful information to see the popularity of Indonesian presidential election candidates. The analysis used is Social Network Analysis (SNA) by measuring Degree Centrality, Eigenvector Centrality, Betweenness Centrality, Closeness Centrality. The presidential candidates in this study, Ganjar Pranowo with a twitter account “ganjarpranowo”, Puan Maharani with a twitter account “puanmaharani_ri”, and Anies Baswedan with a twitter account “aniesbaswedan”. The actor "aniesbaswedan" excels in the value of degree centrality and betweenness centrality. The “aniesbaswedan” account is the actor who has the most influence on social network interactions based on the total number of interactions generated, then the account also becomes a bridge or liaison in the interactions of other actors in the network.

EXPLORATION OF STUDENTS INTERESTS IN MBKM AT RIAU UNIVERSITY USING A MACHINE LEARNING APPROACH

Tue, 31 Dec 2024 12:23:59 +0000

This study aims to analyze the factors that have a significant influence on the interest of Riau University students in the Merdeka Belajar Kampus Merdeka (MBKM) program using a machine learning approach. MBKM is an innovation initiated by the Ministry of Education and Culture with the aim of improving student competence through its various programs. The Riau University as one of the universities supports this program by providing opportunities for its students to participate in various activities provided in the MBKM program. This study will specifically use a machine learning approach by utilizing several methods to analyze significant factors that have not been analyzed in depth by previous studies. The methods used in this analysis are logistic regression, decision trees, random forests, and naive bayes by utilizing secondary data on the level of interest of Riau University students to participate in the MBKM program in 2023. The variables used in this study include gender, generation, faculty, knowledge, self-confidence, feeling benefits, family support, friend support, lecturer support, self-ability, and facilities as independent variables and MBKM interest as a dependent variable. The results of the analysis of several methods show that the logistic regression method provides the best performance in modeling with an accuracy level of 95%. Variables that have a significant influence on students' interest in the MBKM program have also been successfully identified. The variables that have a significant effect are self-ability and family support. The development strategy of MBKM at the University of Riau can be optimized by paying attention to and focusing on these variables. The optimization of this strategy aims to make the implementation of the program more effective and efficient. Supportive policies such as workshops for the development of students' soft skills can be one of the strategic steps to improve students' abilities to the maximum

ADAPTIVE SYNTHETIC IMPLEMENTATION ON RANDOM FOREST IN ARCHIPELAGIC FISHING PORT OF PEMANGKAT

Tue, 31 Dec 2024 12:25:30 +0000

Random Forest is one of the classification methods employed in data mining. One of the problems in data mining classification is the problem of unbalanced class data This phenomenon arises when the data classes utilized do not have identical instances. Imbalance class data causes the classification results to be biased towards the majority class. Adaptive Synthetic (ADASYN) can be used to deal with this problem. ADASYN generates synthetic data by assigning different importance of minority class samples and then producing synthetic data with similar characteristics. The implementation of ADASYN is suitable for fishery production data, which will experience the problem of unbalanced class data. Fish production is part of the measured fishery. This study aims to classify the value of measured fishery production at PPN Pemangkat through Random Forest Classification using ADASYN to handle the imbalance class data problem and compare the results with those without ADASYN implementation. This study uses four predictor variables which include fishing gear types (), number of trip days (), number of crew (), and the total weight of fish () with production value as response variable (). Accuracy, precision, recall, specificity, and G-mean are the model performance indicators used. The results showed that ADASYN successfully handles the problem of unbalanced class data in Random Forest classification. Accuracy is increased from to , Specificity is increased from to , Precision from to , and G-Mean from to . The decrease in recall is negligible due to the small amount, so the Random Forest classification with ADASYN is better than without ADASYN

SOCIAL VULNERABILITY ANALYSIS IN CENTRAL JAVA WITH K-MEDOIDS ALGORITHM

Alwan Fadlurohman, Nila Ayu Nur Roosyidah, Nafida Amalia Annisa — Tue, 31 Dec 2024 12:26:05 +0000

To address the limitations of the Social Vulnerability Index (SoVI) in only providing a general overview without pinpointing areas of social vulnerability, a correlational approach paired with a clustering method can be applied. This approach helps in identifying dominant factors and pinpointing socially vulnerable districts or cities in Central Java. The study employs the K-Medoids algorithm, which is advantageous when dealing with outliers in the dataset. Three different distance measures are considered: Euclidean, Manhattan, and Minkowski distances, to identify the optimal clustering of social vulnerability. The evaluation of the best cluster is conducted using the Davies-Bouldin Index, a metric for validating clustering models by averaging the similarity of each cluster to its most similar counterpart. Findings indicate that using the K-Medoids algorithm with Manhattan distance yields the most effective clustering, resulting in two distinct clusters. Cluster 1, comprising 25 districts/cities, is identified as the most vulnerable to natural disasters and challenges in education, demography, economy, and health. Meanwhile, Cluster 2, encompassing 10 districts/cities, includes urban areas with the highest social vulnerability, notably in the proportion of rental housing.

GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING ON LIFE EXPECTANCY RATE IN SOUTH SULAWESI

Tue, 31 Dec 2024 12:26:43 +0000

Geographically Weighted Panel Regression (GWPR) is one of the panel data regression approaches used in spatial data analysis. This study uses the global Fixed Effect Model (FEM) panel regression model and the local GWPR model to examine Life Expectancy Rate (LER) at the district/city level in South Sulawesi Province in 2019-2021. LER is an important indicator that reflects the health and welfare of the community. This research aims to develop a GWPR model that can explain variations in LER and identify factors that affect that variable, so that it can help stakeholders in allocating resources and designing effective intervention programs. Parameter estimation in the GWPR model is carried out in each observation area using the Weighted Least Square (WLS) method. The calculation of spatial weights in the GWPR model used weighting functions such as fixed bi-square, fixed gaussian, fixed exponential, adaptive bi-square, adaptive gaussian, and adaptive exponential. The results showed that the use of a fixed exponential weighting function gave optimal results with the lowest cross-validation (CV) value of 44,614. Parameter analysis of the GWPR model shows that the factors that affect LER are local and not the same in each district/city in South Sulawesi Province. Factors that have a significant influence include the number of health facilities and households that have access to proper sanitation. This GWPR model has a coefficient of determination of 97,7%. The FEM model has a coefficient of determination of 58,4%. Therefore, GWPR performs LER modelling more effectively than FEM.

FORECASTING TOTAL ASSETS OF PT. BPD KALTIM KALTARA USING THE SINGLE EXPONENTIAL SMOOTHING METHOD

Tue, 31 Dec 2024 12:27:15 +0000

PT. BPD Kaltim Kaltara is one of the regional development banks that plays a crucial role in supporting regional economic development in East Kalimantan and North Kalimantan. The company's total assets reflect significant financial stability and growth, making it an interesting topic to analyze in the context of strategic financial planning. The purpose of this study is to use the Single Exponential Smoothing (SES) approach to forecast PT. BPD Kaltim Kaltara's total assets. In the forecasting process, alpha 0,3, alpha 0,6, alpha 0,7, and alpha 0,8 are tested to determine the best value that gives the most accurate results. Based on the forecasting accuracy analysis, the SES method with alpha = 0,7 proved to be the most optimal in predicting the company's total assets, achieving MAE = 1454272,737, MSE = 4764920751283, and MAPE = 4,0433% (excellent forecasting ability). The forecasting results show an upward trend in assets, with total assets in September 2024 estimated to reach IDR 48.440.683,75. This method provides valuable guidance in thecompany's financial strategic planning, helping to anticipate future asset developments more precisely.These forecasting results also emphasize the importance of selecting the right parameters in the forecasting model to improve prediction accuracy.

STOCK PRICE FORECASTING USING THE HYBRID ARIMA-GARCH MODEL

Risky Oprasianti, Dadan Kusnandar, Wirda Andani — Tue, 31 Dec 2024 12:27:57 +0000

In the current era, many people have made investments, namely capital investment activities within a certain period to seek and get profits. One of the most popular investment instruments in the capital market is stocks, which consist of conventional stocks and Islamic stocks. Conventional stocks are shares traded on the stock market without adhering to Sharia principles. In contrast, Sharia-compliant stocks meet Islamic principles and are traded in the sharia capital market. One form of development of the Islamic capital market in Indonesia is the existence of the Indonesian Sharia Stock Index (ISSI), which projects the movement of all Islamic stocks on the Indonesia Stock Exchange (IDX). Stock prices change every day so modeling is needed that can be used by investors to determine decisions. The Autoregressive Integrated Moving Average (ARIMA) model is one of the forecasting models that is applicable. Stock prices have volatility that tends to be high, this results in variance that is not constant or there is a heteroscedasticity problem, at the same time the ARIMA model must fulfill the assumption of homoscedasticity. Therefore, it is necessary to combine the ARIMA model with a model that can overcome the problem of heteroscedasticity, namely the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This research aims to get the best hybrid ARIMA-GARCH model that will be used to forecast the stock price of the ISSI. The daily closing data of the ISSI stock price from May 4, 2020, to January 13, 2023, is the data that was used. The study’s findings suggest that ARIMA (0,1,3)-GARCH (2,0) is the best model among all possible models for ISSI stock price forecasting. By evaluating the predictive accuracy of the model using Mean Absolute Percentage Error (MAPE), the forecasting result for ISSI stock prices using the best model, ARIMA(0,1,3)-GARCH(2,0) at 0,6092%, shows a forecasting that is close to the actual data, which means that the model used is highly effective at forecasting stock priced