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All Journal BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Sarwahita : Jurnal Pengabdian Kepada Masyarakat Insan Cita Jurnal Pengabdian Kepada Masyarakat Parameter: Journal of Statistics
Dadan Kusnandar
Department Of Statistics, Tanjungpura University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Parameter: Journal of Statistics

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STOCK PRICE FORECASTING USING THE HYBRID ARIMA-GARCH MODEL Oprasianti, Risky; Kusnandar, Dadan; Andani, Wirda
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17162

Abstract

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
ADAPTIVE SYNTHETIC IMPLEMENTATION ON RANDOM FOREST IN ARCHIPELAGIC FISHING PORT OF PEMANGKAT NESSYANA DEBATARAJA, NAOMI; Kusnandar, Dadan; Anugrahnu, Joannes Fregis Philosovio
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17279

Abstract

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
WATER QUALITY ANALYSIS IN THE RESIDENTIAL AREAS OF PONTIANAK CITY USING THE GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION METHOD NESSYANA DEBATARAJA, NAOMI; Kusnandar, Dadan; Lestari, Fika Dian; Anugrahnu, Joannes Fregis Philosovio
Parameter: Journal of Statistics Vol. 5 No. 2 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i2.17900

Abstract

Water quality is a key indicator of a community’s health and welfare, yet it has deteriorated significantly due to pollution caused by human activities. This study aimed to evaluate Geographically Weighted Logistic Regression’s (GWLR) ability to handle spatial nonstationarity in the relationship between explanatory factors and water quality status in Pontianak City, and to compare its performance with logistic regression. Three modelling approaches were applied to classify water as polluted or non-polluted: (i) logistic regression with spatially invariant) parameters; (ii) GWLR with a fixed Gaussian kernel, producing spatially varying parameters using a fixed bandwidth; and (iii) GWLR with an adaptive Gaussian kernel, producing spatially varying parameters using an adaptive bandwidth. Model performance was compared using Akaike’s Information Criterion (AIC) and classification accuracy. The GWLR model with a fixed Gaussian kernel produced an AIC of 22.52, whereas the logistic regression model produced a slightly lower AIC of 22.39; both models achieved a classification accuracy of 92.86%, with the adaptive-kernel GWLR showing comparable classification performance. These results indicate that, for the parameter settings considered, GWLR offered performance comparable to, but not substantially better than logistic regression for modelling the factors affecting water quality, despite its capacity to address spatial nonstationarity.
Co-Authors Aipassa, Ezra Amarya Andani, Wirda Anugrahnu, Joannes Fregis Philosovio Ayuni, Anisa Putri Cahyani, Cristy Framedia Debataraja, Naomi Nessyana Evy Sulistianingsih Fadhillah, Rahmi Febriani, Nindy Febryanti, Winda Fiqriah, Isnaini Handayany, Indry Hendra Perdana HUDA, NUR’AINUL MIFTAHUL Hutami, Bintang Ratna Imro'ah, Nurfitri IMRO’AH, NURFITRI Lestari, Fika Dian Martha, Shantika NESSYANA DEBATARAJA, NAOMI Neva Satyahadewi Novi Safitri Oprasianti, Risky Prianti, Sabrina Eka Rahmawati, Fenti Nurdiana Riswanda, Giovani Parasta Rizki, Setyo Wira Rossie Wiedya Nusantara Simanjuntak, Martina Tamtama, Ray Utari, Shindy Wuri, Hastri Sastia Yusril, Alqaida