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Model Vector Autoregressive-Generalized Space Time Autoregressive Winata, Hilma Mutiara; Puspita, Entit; Agustina, Fitriani
Jurnal EurekaMatika Vol 5, No 2 (2017): Jurnal EurekaMatika
Publisher : Mathematics Program Study, Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.848 KB) | DOI: 10.17509/jem.v5i2.9598

Abstract

ABSTRAK. Data volume kendaraan yang masuk ke Kota Bandungmelalui gerbang tol yang berada di Kota Bandung adalah data runtun waktumultivariate berpola musiman. Untuk memperoleh prediksi volumekendaraan yang masuk melalui gerbang tol dimasa yang akan datangdibutuhkan suatu model peramalan. Salah satu model runtun waktumultivariat yang menghubungkan keterkaitan antara waktu dan lokasi,dimana data runtun waktu tersebut berpola musiman adalah model VectorAutoregressive-Generalized Space Time Autoregressive (VAR-GSTAR).Model ini terdiri dari 2 orde yaitu orde waktu yang diperoleh dari modelVAR dan orde spasial yang diperoleh dari model GSTAR. Keterkaitanantar ruang pada model ini ditunjukkan dengan pembobotan lokasi. Dalampenelitian ini digunakan bobot lokasi normalisasi korelasi silang. Hasilramalan yang diperoleh dari model VAR-GSTAR pada data volumekendaraan yang masuk ke Kota Bandung melalui gerbang tol yang beradadi Kota Bandung adalah mengikuti pola data yang sebelumnya, yaituberfluktuasi dengan kecenderungan yang naik.Kata Kunci: VAR-GSTAR, Bobot lokasi normalisasi korelasi silang,Peramalan.ABSTRACT. Volume of vehicles coming into the city of Bandung throughtoll gates in the city of Bandung is the seasonal multivariate time seriesdata. To obtain a prediction volume of vehicles that go through the tollbooths in the future requires a forecasting model. One of modelmultivariate time series that connects between the time and the location,where the data of the time series data is seasonally namely VectorAutoregressive-Generalized Space Time Autoregressive (VAR-GSTAR)models. This model has two orders, the order of the time obtained from theVAR model and order the space obtained from GSTAR. connectionbetween the space on this model is indicated by the weighting of thelocation. This research used a weight normalized cross correlation.Forecast results obtained from the VAR-GSTAR model on the data volumeof vehicles coming into the city of Bandung through toll gates in the cityof Bandung is to follow the pattern of previous data, which fluctuates withrising tendency.Keywords: VAR-GSTAR, Weights location normalized cross correlation,Forecasting.
MENGATASI OVERDISPERSI DENGAN REGRESI BINOMIAL NEGATIF PADA ANGKA KEMATIAN IBU DI KOTA BANDUNG Hilma Mutiara Winata
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.616-622

Abstract

The maternal mortality rate in the city of Bandung is still a concern for the government, even though various health programs have been held to handle it. The very slight reduction in maternal mortality is a reason for further research to look for factors that have a significant effect. The data on maternal mortality cases usually contain a lot of zeros and follow the Poisson distribution so that they are solved with a Poisson regression model, however the model formed cannot be used because the model shows overdispersion with a deviation value of more than one. Therefore, to overcome this problem, negative binomial regression is used as a solution. This negative binomial regression model produces three predictor variables out of seven variables that have a significant effect on maternal mortality in the city of Bandung including pregnant women receiving FE1 (30 tablets), deliveries assisted by health personnel and postpartum service coverage. Then tested the goodness of the model from the negative binomial regression model by looking at the AIC value. The true negative binomial regression model is better because the AIC value is 109.4 which is smaller than 121.65 which is the AIC value of the Poisson regression model.
Pengaruh Inovasi Pada Aplikasi Access by KAI Terhadap Kualitas Pelayanan Rima Nurmalah; Muhibudin Wijaya Laksana; Hilma Mutiara Winata
PUBLIKA : Jurnal Ilmu Administrasi Publik Vol. 10 No. 1 (2024): Publika : Jurnal Ilmu Administrasi Publik
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jiap.2024.16859

Abstract

Innovation in the Access by KAI application aims to provide better services to the public. However, this innovation encounters several issues such as limited technological access by the public, policy non-inclusivity, and lack of effectiveness in socialization efforts. Therefore, this study aims to investigate the impact of innovation in the Access by KAI application on service quality. Additionally, it aims to determine the effective contribution of innovation dimensions in the Access by KAI application to service quality. This research utilizes an associative method with a quantitative approach. Data was collected through observation and questionnaire results, while data analysis employed robust M-estimation regression. The results indicate that the quality of public service innovation is very high, and the quality of m-service is high. Furthermore, public service innovation obtains significant variable coefficient values at a significant level (α=0.05). This is indicated by the p-value of the public service innovation variable being 0.000<0.05, showing the influence of public service innovation on m-service quality in the Access by KAI application. The effective contribution of each dimension of public service innovation to m-service quality is as follows: relative advantage dimension 0.48%, compatibility dimension 29.03%, complexity dimension 14.22%, testability dimension 9.83%, and observability dimension 8.39%.
Comparative Study Between Pre and Post Implementation of Several Policies: Air Quality Index in DKI Jakarta Hilma Mutiara Winata; Agung Satrio Wicaksono
Jurnal Administrasi Publik Vol 14, No 2 (2023): JURNAL ADMINISTRASI PUBLIK
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31506/jap.v14i2.22689

Abstract

Air quality in DKI Jakarta is currently trending. On August 10, 2023, Jakarta was ranked as the city with the worst air quality in the world. This condition causes a lot of anxiety among the public and disrupts health. Therefore, people in the capital city are asking the government to act quickly and precisely to overcome the problem of poor air quality. The response given by the government to deal with air pollution in DKI Jakarta is to provide several policies. Starting from August 11 until the end of August 2023, the government will issue up to 7 policies that are expected to overcome the pollution problem in the capital city. This research wants to see whether there is a difference in air quality in DKI Jakarta before and after the follow-up provided by the government. Daily recorded air quality index data is taken from a database, namely AQAIR. The data obtained was analyzed using the Mann-Whitney Test to see whether there were differences before and after the government issued the policy. After analyzing the data, the results showed that there was not sufficient evidence to state that there were significant differences related to air quality in DKI Jakarta before and after the policy issued by the government. Based on this, it seems that the policies issued by the government to overcome pollution are still not enough or not appropriate. Therefore, other alternative solutions are needed so that air quality problems can be resolved as soon as possible.
PENGARUH HARGA BROILER DAN HARGA JAGUNG TERHADAP HARGA KARKAS DENGAN PENAMBAHAN CALENDAR EFFECTS MENGGUNAKAN METODE VECM-X Hasnita; Hilma Mutiara Winata
Journal of Social and Economics Research Vol 6 No 1 (2024): JSER, June 2024
Publisher : Ikatan Dosen Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/jser.v6i1.508

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui pengaruh harga broiler dan harga jagung terhadap harga karkas dengan menerapkan analisis Vector Autoregressive (VAR) serta menerapkan metode VAR dengan ditambahkan calendar effects (VAR-X) dan jika data tidak stasioner pada level dan terdapat kointegrasi maka digunakan vector error correction model (VECM-X). Hal ini didasarkan pula pada pertimbangan untuk melihat apakah ada perbedaan harga ketika terdapat kejadian hari raya tertentu dan hari biasa. Hasil analisis dengan VECM pada lag 13 untuk harga karkas menyatakan bahwa terdapat beberapa hubungan kausalitas, diantaranya harga broiler mempengaruhi harga karkas. Hari-hari khusus seperti awal tahun, akhir tahun, awal Ramadhan, idul fitri dan idul adha mempengaruhi harga karkas. Sedangkan harga jagung tidak mempengaruhi harga karkas. Uji kelayakan model menunjukkan hasil bahwa sisaan model VECM bersifat whise noise pada tingkat kepercayaan 95%. Oleh karena itu dapat disimpulkan bahwa model VECM layak digunakan.
The Role Of The Tanggap Karawang Application (Tangkar) In Enhancing Public Satisfaction In Karawang Regency: A Pls-Sem Approach Hilma Mutiara Winata; Wulan Najla Attala
Jurnal Dialektika: Jurnal Ilmu Sosial Vol. 22 No. 3 (2024): Jurnal Dialektika: Jurnal Ilmu Sosial
Publisher : Pengurus Pusat Perkumpulan Ilmuwan Administrasi Negara Indonesia (PIANI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/dialektika.v22i3.344

Abstract

The rapid development of information technology has significantly improved access to public services, including at the regional government level. In response, the Karawang Regency Government introduced the Tanggap Karawang (TANGKAR) application in 2019, designed to increase public participation by allowing citizens to report various issues such as infrastructure problems and natural disasters. This application enables faster government responses to public reports, thereby improving service delivery. Despite its potential, the implementation of TANGKAR faces several challenges, particularly in terms of accessibility for non-Android users and the lack of comprehensive public outreach. This study examines the effectiveness of the TANGKAR application in improving public satisfaction with government services in Karawang Regency. Using a quantitative approach, specifically through survey methods, this research applies the Partial Least Squares - Structural Equation Modelling (PLS-SEM) technique for data analysis. The findings reveal that the quality of electronic services provided by TANGKAR has a significant positive impact, with a 58.9% influence on public satisfaction. The measurement instruments used were proven to be valid and reliable, ensuring accurate assessments of the targeted variables. The study concludes that improving the quality of digital services is crucial for building public trust and satisfaction with local government initiatives, emphasizing the importance of continuous improvement in e-governance solutions
PENINGKATAN AKURASI KLASIFIKASI INTERAKSI FARMAKODINAMIK OBAT BERBASIS SELEKSI PASANGAN OBAT TAKBERINTERAKSI Hilma Mutiara Winata; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.327

Abstract

Identifying the pharmacodynamics drug-drug interaction (PD DDI) is needed since it can cause side effects to patients. There are two measurements of drug interaction performance, namely the golden standard positive (GSP) which is the drug pairs that interact pharmacodynamics and golden standard negative (GSN), which is a drug pairs that do not interact. The selection of GSN in the previous which studies were only selected randomly from a list of drug pairs that do not interact. The random selection is feared to contain drug pairs that actually interact but have not been recorded. Therefore, in this study the determination of GSN was carried out by, first, grouping drug pairs included in the GSP using the DP-Clus algorithm with certain values of density and cluster properties. Then the drugs in different group would be paired and only the drug pairs in the GSN list are selected. It was found that our new proposed classification method increases the AUC value compared to the results obtained by random selection of GSN.