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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Journal of Innovation and Applied Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Statistics and Its Applications STATISTIKA
Nur Silviyah Rahmi
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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PERAMALAN INFLOW UANG KARTAL BANK INDONESIA KPW TASIKMALAYA JAWA BARAT DENGAN METODE KLASIK DAN MODERN Nur Silviyah Rahmi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.166-174

Abstract

Ketersediaan uang kartal di Bank Indonesia (BI) dapat ditinjau melalui arus keluar masuknya uang kartal yang disebut dengan istilah inflow. Banyaknya uang yang beredar di masyarakat akan berpengaruh pada kondisi perekonomian suatu negara, sehingga Bank Indonesia (BI) menyusun perencanaan kebutuhan uang rupiah. Penelitian ini bertujuan untuk meramalkan inflow uang kartal di KPw Bank Indonesia (BI) Tasikmalaya dengan menggunakan pemodelan ARIMA, ARIMAX, Metode Dekomposisi, Metode Winter’s, MLP (Multilayer Perceptron) atau FFNN (Feed Forward Neural Network), Regresi Time Series, Metode Naïve dan Model Hybrid. Dari delapan metode runtun waktu tersebut baik klasik maupun modern akan dicari metode mana yang memberikan hasil akurasi ramalan yang terbaik dengan kriteria RMSE, MAPE dan MAD. Kesimpulan yang dihasilkan yaitu Hybrid ARIMA-NN yang merupakan gabungan dari model ARIMA dengan neural network tidak menjamin kinerja hasil peramalan yang lebih baik. Seperti yang disebutkan dalam hasil M3 Competition, semakin kompleks metode yang digunakan belum tentu metode tersebut menghasilkan akurasi yang lebih baik dibandingkan metode sederhana (klasik). Pada ramalan data inflow KPw BI Tasikmalaya Jawa Barat ini, menghasilkan kesimpulan bahwa metode regresi time series memiliki nilai kriteria pemodelan paling kecil dibandingkan dengan metode lainnya.
Identification of Social Support and Knowledge of Covid-19 Survivors with Structural Equation Modeling in R Nur Silviyah Rahmi; Laila Masruro Pimada; Reza Yesica; Devi Nur Cahaya Ningsih
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
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.v6i2p287-295

Abstract

COVID-19 cases in Indonesia have finally reached a second peak amounting to 4 million cases. A number of the death rate was 3.4 percent, yet the recovery rate was 95.9 percent. The Health Ministry of Republic Indonesia through the Covid-19 Task Force has issued guidelines for preventing and controlling Covid-19 to decrease the death rate and increase the recovery rate. According to the guidelines, a person who undergoes quarantine needs to be provided with health care, and social and psychosocial support. This study seeks to identify the influence of external factors including social support, as well as internal factors including patient motivation, and knowledge on the recovery rate of Covid-19 survivors. The research methods use Structural Equation Modelling to determine the indicators that have the most significant influence on the latent variables of social support, knowledge, and motivation for healing Covid-19. Primary data collection was carried out online with a sample of 176 Covid-19 survivors across Indonesia in August 2021. The methods of the Shapiro-Wilk test for normal multivariate show the p-value at 0.00 significantly satisfies the assumption. The result shows that social support has a significant effect on knowledge with a regression coefficient is 0.263. Knowledge has a regression coefficient is 0.645 for the Healing of Covid-19. In conclusion, the higher social support provided by the patient's external parties: family, surrounding environment, and public health center officers, will impact the higher patient's knowledge and healing of Covid-19 disease. Meanwhile, social support has no significant effect on healing actions.
Peningkatan Kualitas Pelaporan DHKP (Daftar Himpunan Ketetapan Pajak dan Pembayaran) Desa Kedungsolo Porong Sidoarjo Tahun 2022 Nur Silviyah Rahmi; Ni Wayan Surya Wardhani; Adji A. Rinaldo Fernandes
Journal of Innovation and Applied Technology Vol 9, No 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

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Abstract

Pemungutan Pajak Bumi dan Bangunan (PBB) dalam pelaksanaannya masih terdapat permasalahan. Salah satunya di Desa Kedungsolo Porong, yaitu pencatatan dilakukan secara klasik dalam daftar buku oleh petugas desa, sehingga membutuhkan waktu lama apabila melakukan proses pencarian nama wajib pajak. Selain itu terdapat kesalahpahaman pencatatan distribusi SPPT PBB oleh petugas, mengenai status terakhir. Solusi yang dilakukan adalah dengan mengembangkan sebuah sistem informasi dalam bentuk dashboard dengan bahasa pemrograman Hypertext Preprocessor (PHP) yang terintergrasi dengan MySQL untuk meningkatkan kualitas pelaporan Daftar Himpunan Ketetapan Pajak dan Pembayaran (DHKP). Aparat desa dan masyarakat setempat sangat terbantu dengan adanya sistem informasi Dashboard Data Entry SPPT PBB-P2. Koordinator bertugas memonitoring data pemungutan PBB yang diinput oleh petugas penarikan pajak, memberikan kemudahan petugas penarikan pajak yang berperan sebagai admin dalam pencatatan PBB secara online dan terstruktur, serta memudahkan wajib pajak untuk mengakses status SPPT PBB secara individu
Confirmatory Factor Analysis pada Indikator Kesembuhan Pasien Isolasi Mandiri Covid-19 di Indonesia Nur Silviyah Rahmi; Luthfatul Amaliana; Laila Masruro Pimada; Reza Yesica; Devi Nur Cahaya Ningsih
Statistika Vol. 22 No. 1 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i1.477

Abstract

ABSTRAK Jumlah kasus terkonfirmasi positif Covid-19 di Indonesia pada September 2021 telah mencapai angka 4.204.116 Jiwa dengan pasien meninggal sebanyak 141 ribu orang atau tingkat kematian sebesar 3,4 persen. Kementerian Kesehatan Republik Indonesia melalui Satgas Penanganan Covid-19 telah menerbitkan pedoman pencegahan dan pengendalian Covid-19 untuk menekan angka kematian dan meningkatkan angka kesembuhan. Menurut pedoman tersebut, seseorang yang menjalani isolasi mandiri perlu diberikan perawatan kesehatan, dukungan sosial dan psikososial, serta kebutuhan dasar termasuk makanan, air dan kebutuhan pokok lainnya. Penelitian ini berusaha mengidentifikasi pengaruh faktor eksternal yang meliputi dukungan, serta faktor internal yang meliputi motivasi dan pengetahuan pasien terhadap tingkat kesembuhan penyintas Covid-19 isolasi mandiri. CFA digunakan apabila pengetahuan mengenai struktur suatu faktor laten diketahui. Struktur tersebut diperoleh berdasarkan kajian teoritis, hasil penelitian mengenai hubungan antara variabel yang diobservasi dengan variabel laten. Pendekatan CFA diterapkan untuk mengetahui indikator yang paling besar berpengaruh terhadap variabel laten dukungan sosial, pengetahuan dan motivasi kesembuhan. Pengambilan data primer dilakukan secara online dengan jumlah sampel sebanyak 148 responden penyintas Covid-19 di seluruh Indonesia pada bulan Agustus 2021. Hasil analisis CFA menunjukkan bahwa indikator DS1 (mendapatkan ungkapan empati, kepedulian, dan perhatian yang diberikan petugas terhadap penderita Covid-19) memiliki nilai loading factor terbesar yaitu 0,741. Pada variabel laten pengetahuan, indikator P7 (Tujuan pengobatan Covid-19 adalah menyembuhkan penderita, mencegah penularan dan kematian serta menurunkan tingkat penularan) memiliki nilai loading factor terbesar yaitu 0,767. Sedangkan pada variabel laten tindakan sembuh Indikator TS1 (Bersedia menjalani masa pengobatan dengan pemberian dalam waku pengobatan kurang lebih 14 hari) memiliki nilai loading factor terbesar yaitu 0,924. ABSTRACT The number of positive confirmed cases of Covid-19 in Indonesia in September 2021 has touched 4,204,116 people with the number of patients dying as many as 141,000 people or a death rate of 3.4 percent. The Ministry of Health of the Republic of Indonesia through the Covid-19 Handling Task Force has issued guidelines for the prevention and control of Covid-19 to reduce mortality and increase recovery rates. Based on these guidelines, people who are in self-isolation need to be provided with health care, social and psychosocial support, as well as basic needs including food, water and other basic needs. This study seeks to identify the influence of external factors which include support, as well as internal factors which include the motivation and knowledge of patients on the recovery rate of Covid-19 survivors in self-isolation. CFA can be used when knowledge of the composition or structure of a latent factor is known. The structure is obtained from theoretical studies, the results of research on the relationship between the variables studied and the latent variables. By using the CFA approach, it can be seen that the indicators that have the greatest influence on the latent variables of social support, knowledge and motivation for healing are known as loading factors. Primary data was collected online with 148 COVID-19 survivors across Indonesia in August 2021 as the sample. From the CFA analysis, it was found that the Social Support 1 (DS1) indicator, which is getting expressions of empathy, concern, and attention given by officers to Covid19 sufferers is the largest loading factor value, which is 0.745. This means that the influence of the DS1 indicator on the latent variable of Social Support is 0.745 or 74.5% is influenced by the DS1 indicator compared to the other 4 indicators, namely DS2, DS3, DS4, DS5. In the knowledge latent variable, the Knowledge 7 (P7) indicator, namely the purpose of Covid-19 treatment is to cure the patient, prevent transmission and death and reduce the transmission rate, which is the largest loading factor value, which is 0.767. Then on the latent variable of healing action, Healing Action Indicator 1 (TS1), namely being willing to undergo a treatment period with administration within a treatment time of approximately 14 days) is the largest loading factor value, which is 0.924.
GENERALIZED CONFIRMATORY FACTOR ANALYSIS FOR KNOWING IMPACT OF KNOWLEDGE, ATTITUDES, AND BEHAVIORAL FACTORS HIV/AIDS IN INDONESIA Nur Silviyah Rahmi; Suci Astutik; Ani Budi Astuti; Alifiandi Rafi Muhammad; Ulfah Maisaroh; Sri Handayani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0695-0706

Abstract

The cumulative number of detected HIV/AIDS cases in the January – March 2021 period is 9,327, consisting of 7,650 HIV and 1,677 AIDS reported by 498 districts and cities from 514 districts and cities in Indonesia. Human Immunodeficiency Virus (HIV) is the virus that causes Acquired Immunodeficiency Syndrome (AIDS). Several factors that influence the spread of HIV/AIDS include knowledge, attitudes and behavior about HIV/AIDS. Someone who gains knowledge about HIV/AIDS will have high self-confidence and a positive outlook on life and be more optimistic in taking HIV/AIDS prevention actions. The main objective of this study is to determine the influence of external factors which include demographic, social and economic aspects, as well as internal factors which include knowledge, attitudes and behavior to the level of transmission of HIV/AIDS. By using the CFA approach, it can be seen which indicators have the greatest influence on the latent variables of knowledge, attitudes, and behavior or called loading factors. The data used is secondary data from a 5-year survey from the Central Statistics Agency, namely the 2017 Indonesian Demographic and Health Survey (IDHS) published at the end of 2018. The CFA results show that the P11 variable (about known infections) has the largest loading factor value, which is 0.613 in the variable. . hidden. knowledge. In the latent variable of attitude, the S1 variable (about identifying how the respondent knows someone is infected with HIV-AIDS) has the largest loading factor value of 0.514. While the behavioral latent variable, the variable R8 (whether men have been infected with sexually transmitted diseases (STI) with symptoms) has the largest loading factor value, which is 0.954.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Suci Astutik; Nur Silviyah Rahmi; Diego Irsandy; Fang You Dwi Ayu Shalu Saniyawati; Fidia Raaihatul Mashfia; Evelin Dewi Lusiana; Intan Fadhila Risda; Mohammad Hilmi Susanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
Co-Authors Adji A. Rinaldo Fernandes Alifiandi Rafi Muhammad Ani Budi Astuti Devi Nur Cahaya Ningsih Devi Nur Cahaya Ningsih Diego Irsandy Evelin Dewi Lusiana Fang You Dwi Ayu Shalu Saniyawati Fidia Raaihatul Mashfia Intan Fadhila Risda Laila M. Pimada Luthfatul Amaliana Mohammad Hilmi Susanto Ni Wayan Surya Wardhani Reza Yesica Reza Yesica Sri Handayani Suci Astutik Suci Astutik Ulfah Maisaroh