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Aswi, Aswi
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Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
GEOGRAPHICALLY WEIGHTED LASSO (GWL) MODELING TO IDENTIFY FACTORS INFLUENCE STUNTING INCIDENTS IN SOUTH SULAWESI Novianti, Andi Rima; Aswi, Aswi; Mar'ah, Zakiyah
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09109

Abstract

The Geographically Weighted Lasso (GWL) method is a technique that employs the Lasso approach within the Geographically Weighted Regression (GWR) model, which can also simultaneously select non-significant variables by shrinking the regression coefficient values to zero. Consequently, any variable assigned to a zero coefficient is considered statistically insignificant. In 2022, stunting remained a significant public health issue in South Sulawesi, ranking 10th nationwide with a prevalence of 27.2%. This underscores the urgent need for spatially sensitive analytical methods that can address regional heterogeneity and reveal key determinants at the district level. Notably, the application of GWL to analyze stunting in South Sulawesi using data from the Indonesian Nutrition Status Survey (SSGI 2022) represents a significant contribution that addresses an important research gap. This study aims to model stunting prevalence and identify its influential factors using GWL. The analysis yielded a tuning parameter λ = 0.04, achieving a model goodness of fit of R² = 0.957, demonstrating GWL’s effectiveness in mitigating multicollinearity. Four primary predictors of stunting emerged: low birth weight (LBW), access to safe drinking water, the human development index (HDI), and the average length of parental schooling.
Pemberdayaan Masyarakat Sekolah Melalui Pelatihan Pemanfaatan Data Tracer Study sebagai Upaya Mempercepat Keterserapan Alumni Poerwanto, Bobby; Aswi, Aswi; Rahmat, Abdul
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 5 No. 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v5i2.4339

Abstract

Kegiatan ini bertujuan untuk meningkatkan kompetensi literasi data untuk memanfaatkan data tracer study sehingga dapat dijadikan dasar penyusunan program kerja sehingga dapat mempercepat penyerapan alumni di dunia industri, selain itu kegiatan ini juga bertujuan untuk membantu alumni dan mahasiswa tingkat akhir untuk mempersiapkan diri menghadapi tes wawancara kerja. Peserta pelatihan pada kegiatan ini berjumlah 20 orang guru dan tenaga kependidikan, serta 20 orang mahasiswa tingkat akhir dan alumni. Materi yang diberikan merupakan standar pelaksanaan dan pemanfaatan data tracer study, pengolahan data dan persiapan wawancara kerja. Hasil diukur dari respon peserta berupa kesesuaian materi dengan kebutuhan peserta, tingkat kesulitan materi yang diterima, sistematika narasumber dalam menjelaskan, dan tingkat penguasaan materi oleh narasumber. Hasilnya, mayoritas memberikan respon sangat jelas. Dari kegiatan ini, pihak sekolah sudah mengetahui standar pelaksanaan tracer study, sudah mampu mengolah data tracer study menjadi informasi untuk membuat program kerja, dan sudah mampu membuat laporan pelaksanaan tracer study untuk kebutuhan akreditasi.
Pemberdayaan Masyarakat Sekolah Melalui Penguatan Kompetensi Numerasi dan Tuntutan Membelajarkan Numerasi Lintas Mata Pelajaran di SD Inpres Sero Poerwanto, Bobby; Aswi, Aswi; Arwadi, Fajar
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 1 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v6i1.4912

Abstract

Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi numerasi guru sehingga guru dapat mengajarkan numerasi pada lintas mata pelajaran. Kegiatan ini dilaksanakan di SDI Sero dengan peserta yang terdiri dari para guru berjumlah 16 orang. Pelaksanaan kegiatan dilakukan pada tanggal 14 September 2024 dengan menghadirkan 3 narasumber dari Universitas Negeri Makassar. Narasumber pertama membahas terkait pentingnya numerasi dalam pemecahan masalah kehidupan sehari-hari, narasumber kedua berfokus pada identifikasi capaian pembelajaran sehingga memungkinkan integrasi numerasi pada lintas mata pelajaran. Terkahir, narasumber memberikan materi terkait pentingnya alat peraga numerasi untuk menstimulasi siswa agar cepat memahami numerasi. Setelah pelaksanaan kegiatan, berdasarkan wawancara, observasi dan tes yang dilakukan terdapat perubahan pada peserta pelatihan yaitu sekarang guru sudah dapat memuat kompetensi numerasi dalam mata pelajaran berdasarkan capaian pembelajarannya dimana sebelumnya mereka belum memahami. Selain itu, peningkatan pengetahuan numerasi juga terlihat dari penyelesaian modul ajar dimana sebelum pelatihan rata-rata nilai yang didapatkan adalah 55 menjadi 83 ketika selesai pelatihan. Untuk kepuasan, dapat disimpulan bahwa para peserta sangat puas dan merasa materi ini penting dan sesuai untuk kebutuhan sekolah
Rainfall Forecasting Using the Singular Spectrum Analysis (SSA) Method Nurhikmawati, Nurhikmawati; Aswi, Aswi; Ahmar, Ansari Saleh
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.4571

Abstract

This study aims to evaluate the accuracy and performance of rainfall data forecasting in the city of Parepare using the Singular Spectrum Analysis (SSA) method. Situated in South Sulawesi Province, Parepare City is characterized by high rainfall intensity, which increases the likelihood of natural hazards such as flooding and landslides. These disasters have the potential to negatively impact key sectors, including economic activity, tourism, and transportation. Therefore, reliable rainfall prediction plays a crucial role in establishing a robust disaster early warning system. Monthly rainfall measurements from two stations, Bukit Harapan and Bulu Dua, are analyzed. The results reveal a Root Mean Square Error (RMSE) of 191.0566 for Bukit Harapan station and 346.023 for Bulu Dua station, underscoring the method's forecasting accuracy. A 12-month forecast predicts consistently high monthly rainfall in Parepare City, with the highest rainfall expected in December 2024 at Bukit Harapan station and in January 2024 at Bulu Dua station. Conversely, the lowest rainfall at both stations is anticipated in July 2024. Forecasts predicting increased rainfall during certain periods, especially in December and January, provide critical insights for strengthening disaster preparedness and informing mitigation strategies. This information also plays a key role in minimizing adverse effects on the economic, transportation, and tourism sectors, while promoting more efficient and sustainable management of water resources.  
Implementation of the Bayesian Spatial Model for Mapping the Relative Risk of HIV Cases in Makassar City Aisyah Putri , Siti Choirotun; Aprilia Wardani Syam , Dewi; Aswi, Aswi; Hidayat , Rahmat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9770

Abstract

Human Immunodeficiency Virus (HIV) remains a major public health challenge in Indonesia, including Makassar City. This study aims to estimate and map the relative risk (RR) of HIV cases in Makassar City using the Bayesian spatial Conditional Autoregressive (CAR) Leroux model. The dataset comprises the number of HIV cases and the population of each district, with covariates including distance to the city center and population density. Results of Moran's I test indicated significant spatial autocorrelation in HIV cases across Makassar City. Model selection based on the Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) identified the optimal model as the CAR Leroux with an Invers-Gamma (IG) hyperprior (0.5;0.0005) and distance as a covariate, yielding the lowest DIC and WAIC values. The estimation results demonstrated that distance is negatively associated with HIV incidence. The highest RR was observed in Ujung Pandang district, while the lowest was in Biringkanaya District. These findings may provide a basis for identifying priority intervention areas and support the development of more targeted and effective HIV elimination strategies.
MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION Aswi, Aswi; Tiro, Muhammad Arif; Sudarmin, Sudarmin; Sukarna, Sukarna; Awi, Awi; Nurwan, Nurwan; Cramb, Susanna
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.148-159

Abstract

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
Comparison Of Bayesian Spatial Car Models For Estimating The Risk Of Diarrhea Cases In Makassar City Bakri, Nurul Aulya; Yudi, Wanda; Aswi, Aswi; Hidayat, Rahmat
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774752025

Abstract

Diarrhea continues to pose a significant public health challenge in Makassar City, with incidence varying across sub-districts. Mapping diarrhea risk is essential for public health planning, as it helps identify high-risk areas and allocate resources efficiently. Accurate spatial risk assessment supports targeted interventions and informs evidence-based health policies. This study aimed to identify areas with high and low relative risks (RR) of diarrhea cases using Bayesian spatial Conditional Autoregressive (CAR) models, specifically the Besag–York–Mollié (BYM) and Leroux approaches. The analysis was based on case data from 15 sub-districts in Makassar City in 2023. Model performance was assessed using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). The CAR-Leroux model with an Inverse Gamma (IG) hyperprior (0.5; 0.0005) was identified as the best-fitting model, providing the most reliable estimation of relative risk. Kepulauan Sangkarrang exhibited the highest RR, indicating a markedly elevated risk of diarrhea relative to the city average, while Biringkanaya District showed the lowest RR, reflecting a substantially lower risk compared to the average.Keywords: Bayesian spasial models, CAR BYM, CAR Leroux, Diarrhea, Relative risk.
ANALISIS REGRESI SPASIAL PENDEKATAN AREA INDEKS PEMBANGUNAN PEMUDA TAHUN 2022 DI INDONESIA Muttaqin, Imam Akbar; Aswi, Aswi; S, Muhammad Fahmuddin
Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo Vol 10, No 2 (2024)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35906/jep.v10i2.2278

Abstract

ABSTRAK Indeks Pembangunan Pemuda menjelaskan tentang bagaimana kemajuan kualitas pemuda pada suatu daerah dengan menggunakan 15 indikator penyusun. Capaian angka IPP di Indonesia dalam 5 tahun terakhir menunjukkan fluktuasi yang mencerminkan kualitas pemuda yang belum konsisten mengalami peningkatan. Penelitian ini bertujuan untuk mengetahui model terbaik regresi spasial dengan pendekatan area pada Indeks Pembangunan Pemuda tahun 2022 di Indonesia dan mengetahui indikator yang berpengaruh secara signifikan pada Indeks Pembangunan Pemuda tahun 2022 di Indonesia. Metode analisis yang digunakan pada penelitian ini adalah analisis regresi spasial. Data yang digunakan adalah data sekunder. Hasil penelitian menunjukkan bahwa model regresi terbaik adalah Spatial Error Model (SEM) dengan indikator yang signifikan memiliki pengaruh adalah rata-rata lama sekolah pemuda memberi pengaruh penurunan angka IPP sebesar 0.339, angka partisipasi kasar sekolah menengah memberi pengaruh peningkatan angka IPP sebesar 0.274, angka partisipasi kasar perguruan tinggi memberi pengaruh peningkatan angka IPP sebesar 0.870, dan angka kesakitan pemuda memberi pengaruh penurunan angka IPP sebesar 0.223.Kata Kunci: Regresi Spasial; Spatial Error Model; Indeks Pembangunan Pemuda ABSTRACT The Youth Development Index explains how the quality of youth in a region is progressing using 15 constituent indicators. The achievement of IPP figures in Indonesia in the last 5 years shows fluctuations which reflect the quality of youth which has not consistently improved. This study aims to determine the best spatial regression model with an area approach on the 2022 Youth Development Index in Indonesia and to determine the indicators that have a significant influence on the 2022 Youth Development Index in Indonesia. The analytical method used in this research is spatial regression analysis. The data used is secondary data. The results of the research show that the best regression model is the Spatial Error Model (SEM) with indicators that have a significant influence are the average length of youth schooling had the effect of decreasing the IPP figure by 0.339, the gross secondary school enrollment rate had the effect of increasing the IPP figure by 0.274, the gross college enrollment rate had the effect of increasing the IPP figure by  0.870, and the youth morbidity rate had the effect of decreasing the IPP figure by 0.223.Keywords: Spatial Regression; Spatial Error Model; Youth Development Index
Co-Authors Abdul Rahmat Abidin, Muh. Zulkifli Abidin, Muhammad Rais Ahmar, Ansari Saleh Aisyah Putri , Siti Choirotun Annas, Suwardi Annas, Suwarni Aprilia Wardani Syam , Dewi Assagaf, Said Fachry Awaluddin Awaluddin Awi Dassa, Awi Awi, Awi Bakri, Nurul Aulya Cramb, Susanna Diana Eka Pratiwi Fahmuddin, Muhammad Fajar Arwadi Folorunso, Serifat Adedamola Haekal, Muh. Fahri Herman, Nur Taj Alya’ Hidayat , Rahmat Hisyam Ihsan Ikhwana, Nur Isnaini, Wulan Maulia Lalu Ramzy Rahmanda M. Miftach Fakhri Mahadtir, Muhamad Mangkona, Andi Ilham Azhar Mar'ah, Zakiyah Muhammad Arif Tiro, Muhammad Arif Muhammad Fahmuddin Muttaqin, Imam Akbar Natalia, Derliani Novianti, Andi Rima Nurhikmawati, Nurhikmawati Nurhilaliyah, Nurhilaliyah Nurwan, Nurwan Oktaviana Oktaviana Palarungi, Andi Gagah Poerwanto, Bobby Poewanto, Bobby Putri Ananda, Elma Yulia Putri, Siti Choiratun Aisyah Putri, Siti Choirotun Aisyah Rahmat Hidayat Rais, Zulkifli Ramadani, Reski Aulia Risma Mastory Ruliana Ruliana, Ruliana S, Muhammad Fahmuddin Saleh, Andi Rahmat Shanty, Meyrna Vidya Sudarmin Sudarmin Sukarna Sukarna Sukarna, Sukarna Sulistiawaty Sulistiawaty, Sulistiawaty Suwardi Annas Taufik, Andi Gagah Palarungi Vivianti Wahidah Sanusi Yassar, La Ode Salman Yudi, Wanda Yunus, Sitti Rahma