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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics GAUSS: Jurnal Pendidikan Matematika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Zilrahmi, Zilrahmi
Universitas Negeri Padang
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia Fajrin Putra Hanifi; Syafriandi; Chairina Wirdiastuti; Nonong Amalita; Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.75

Abstract

Inflation is defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable Inflation, defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable economic growth. The importance of controlling inflation is based on the consideration that high and unstable inflation hurts the socio-economic conditions of the community. In this context, government and economic agents must know the future inflation rate. The backpropagation algorithm forecasting method can be a mathematical tool to forecast future inflation rates. The best forecasting model is obtained from applying the backpropagation algorithm, namely ANN BP (12,2,1), with a mean square error value of 0.15 and an absolute percentage error value of 11.09%. Based on these results, the back-propagation algorithm in artificial neural networks can accurately forecast the inflation rate. Thus, it is hoped that this research can be used in economic decision-making.
Penerapan Metode Multivariate Adaptive Regression Spline untuk Memahami Dinamika Kemiskinan di Indonesia Khasanah, Nurviqotun; Zilrahmi; Syafriandi
GAUSS: Jurnal Pendidikan Matematika Vol. 8 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/gauss.v8i1.10569

Abstract

Abstrak Kemiskinan masih menjadi tantangan besar bagi pembangunan di negara berkembang, khususnya Indonesia. Berbagai faktor seperti pendidikan, kesehatan dan pendapatan masyarakat diketahui mempengaruhi tingkat kemiskinan, namun hubungan antar faktor tidak sederhana. Studi ini dilakukan untuk memprediksi Presentase Penduduk Miskin Di Indonesia berdasarkan faktor sosial ekonomi menggunakan metode Mulitivariate Adaptive Regression Spline yang mampu menangkap hubungan nonlinear dan interaksi antar variabel. Penelitian menggunakan Data dan Informasi Kemiskinan Kab/Kota di Indonesia Tahun 2023 dari publikasi Badan Pusat Statistik (BPS) yang telah melalui proses Pre-processing data. Model terbaik dibangun dari 0.8 data training dan 0.2 data testing dengan kombinasi BF=26, MI=3, MO=1 dengan Generalized Cross Validation (GCV) terkecil sebesar 0.160211 dan dari 13 variabel prediktor yang diteliti menunjukkan bahwa variabel Persentase Pengeluaran Rata-Rata per Orang untuk Makanan Kategori Miskin dan Tidak Miskin (X5) dan variabel Persentase Pengeluaran Rata-Rata per Orang untuk Makanan Kategori Miskin dan Tidak Miskin (X6) yang mempunyai skor tertinggi sebesar 100% untuk menurunkan nilai GCV model dan menurunkan Residual Sum of Squares (RSS) pada model. Selain itu, model MARS mampu menjelaskan variasi tingkat kemiskinan dengan nilai R-squared sebesar 83,7% yang mengidentifikasikan prediksi cukup akurat. Kata kunci : Kemiskinan, MARS, GCV Abstract Poverty remains a major challenge for development in developing countries, especially Indonesia. Various factors such as education, health and income are known to affect the poverty rate, but the relationship between factors is not simple. This study aims to predict the percentage of poor people in Indonesia based on socioeconomic factors using the Mulitivariate Adaptive Regression Spline method which is able to capture nonlinear relationships and interactions between variables. The research uses data and information on poverty in districts / cities in Indonesia in 2023 obtained from the Central Statistics Agency (BPS) which has gone through a process of cleaning, standardisation and handling outliers. The best model was built from 0.8 training data and 0.2 testing data with a combination of BF=26, MI=3, MO=1 with the smallest Generalised Cross Validation (GCV) of 0.160211 and of the 13 predictor variables studied showed that the variable Percentage of Average Expenditure per Person on Food for Poor and Non-Poor Categories (X5) and the variable Percentage of Average Expenditure per Person on Food for Poor and Non-Poor Categories (X6) which had the highest score of 100% to reduce the GCV value of the model and reduce the Residual Sum of Squares (RSS) in the model. In addition, the MARS model is able to explain the variation in poverty rates with an R-squared value of 83.7%, which identifies a fairly accurate prediction. Keywords: Poverty, MARS, GCV
Comparison of The Singular Spectrum Analysis and SARIMA for Forecasting Rainfall in Padang Panjang City Putri, Fadhira Vitasha; Fitri, Fadhilah; Kurniawati, Yenni; Zilrahmi, Zilrahmi
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p61-74

Abstract

Indonesia is an area with a tropical climate, so it has two seasons, namely the rainy season and the dry season. The rainy season lasts from November to March and during this period rainfall tends to be high in several areas. Padang Panjang City is one of the cities with the smallest area in West Sumatra Province, which has the nickname Rain City. This is because the city of Padang Panjang has cool air with a maximum air temperature of 26.1 °C and a minimum of 21.8 °C, so this city has a fairly high level of rainfall with an average of 300 to 400 mm/year. This article discusses rainfall forecasting for Padang Panjang City by comparing the Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average methods. The data used spans 8 years, from January 2016 to December 2023. Forecasting results are obtained from the best method selected based on the smallest Mean Absolute Percentage Error value. The Singular Spectrum Analysis method has a Mean Absolute Percentage Error value of 5.59% and Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average  has a value 7.43%. The best forecasting method is obtained by the Singular Spectrum Analysis method.
Application of Singular Spectrum Analysis in Predicting Rupiah Exchange Yuan Hendrawan, Muhammad; Zilrahmi, Zilrahmi; Kurniawati, Yenni; Fitria, Dina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p75-85

Abstract

The exchange rate between two countries is the price of the currency used by residents of these countries to trade with each other, the relationship between the Rupiah exchange rate and the Yuan is one of the important aspects in the dynamics of international trade. Therefore, forecasting the exchange rate is important as an effort to predict the exchange rate of Rupiah against Yuan in the future. The method used for forecasting is Singular Spectrum Analysis, namely decomposition and reconstruction. The accuracy of the resulting forecast is measured using the Mean Absolute Percentage Error criterion. The exploration results obtained are forecasting accuracy based on the Mean Absolute Percentage Error value of 2.15% with a window length of 23 which identifies that the forecasting results are accurate and effective. Forecasting is said to be accurate if the Mean Absolute Percentage Error value is lower than 10% and close to 10%
Forecasting Analysis of Total Coconut Production in Padang Pariaman Using the Double Exponential Smoothing Holt Della Amelia; Zilrahmi; Fitri Mudia Sari
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss2/367

Abstract

Kelapa merupakan buah khas daerah tropis yang memiliki banyak manfaat. Kelapa memiliki arti penting yang strategis bagi Indonesia. Sumatera Barat merupakan salah satu provinsi penghasil kelapa di Indonesia dengan total produksi sebesar 88 ribu ton pada tahun 2023. Dimana Kabupaten Padang Pariaman merupakan kabupaten penghasil kelapa terbesar di Provinsi Sumatera Barat dengan total produksi sebesar 38.794 ton pada tahun 2022. Kelapa merupakan salah satu komoditas utama dan sumber perekonomian di Kabupaten Padang Pariaman. Melihat pentingnya peranan kelapa di Kabupaten Padang Pariaman, maka perlu dilakukan peramalan produksi kelapa untuk mengetahui kondisi hasil perkebunan tersebut. Double Exponential Smoothing merupakan metode yang sesuai digunakan dalam peramalan jumlah produksi kelapa di Kabupaten Padang Pariaman. Hal ini dikarenakan metode ini sesuai dengan data yang memiliki pola trend. Hasil peramalan menunjukkan bahwa produksi kelapa pada tahun 2024 sampai dengan tahun 2028 adalah sebesar 39.506,16 ton, 39.943,43 ton, 40.380,7 ton, 40.817,97 ton, dan 41.255,24 ton. Dimana hasil tersebut menunjukkan bahwa produksi kelapa mengalami peningkatan setiap tahunnya sekitar 1% dengan nilai MAPE sebesar 16,19% yang menunjukkan bahwa hasil peramalan tersebut termasuk dalam kriteria akurat.
Forecasting Inflation Rate in Indonesia Using Autoregressive Integrated Moving Average Method Putri, Lathifa; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/377

Abstract

Inflasi merupakan salah satu indikator penting untuk menilai stabilitas ekonomi suatu negara. Peningkatan inflasi yang terus menerus akan memperlambat pertumbuhan ekonomi. Oleh karena itu, prakiraan tingkat inflasi yang akurat penting untuk perencanaan ekonomi jangka menengah hingga panjang. Penelitian ini dilakukan untuk meramalkan tingkat inflasi di Indonesia selama 12 periode mendatang, yaitu dari Januari 2025 hingga Desember 2025. Penelitian ini menggunakan metode ARIMA, karena model ARIMA bersifat fleksibel terhadap semua jenis pola data deret waktu, meskipun data tersebut bersifat non-stasioner. Hasil penelitian menunjukkan bahwa ARIMA (2,0,2) merupakan model terbaik dengan nilai akurasi MAPE sebesar 25,21%. Model ini dapat memprediksi tingkat inflasi yang stabil di Indonesia selama 12 periode mendatang, dengan rata-rata sebesar 1,861%. Hasil ini menunjukkan bahwa kenaikan harga umum barang dan jasa di Indonesia selama periode tersebut akan stabil tanpa fluktuasi, yang merupakan tanda positif bagi stabilitas makroekonomi dan daya beli masyarakat.
Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024 Rahmad Wanizal Pastha; Zilrahmi; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/411

Abstract

This paper aims to identify the determinants affecting the Human Development Index (HDI) in Lampung Province, Indonesia, during the periode 2022-2024 using panel data regression. Lampung consistenly ranks among the provinces with the lowest HDI scores in Sumatera, indicating developmental disparties across regions. The research employs secondary data from 15 districts/cities and includes variables such as life expectancy, expected years of schoolingm mean years of schooling, and expenditure per capita. Panel data regression models fixed effect, random effect, and common effect were evaluated using chow, hausman, and lagrang multiplier tests to select the most approriate model. The random effect model was chosen, supported by a high R-Squared value of 92,71% indicating strong explanatory power. The analysis found that life expectancy and mean years of schooling significantly influence HDI, while expected years of schooling and expenditure per capita were not statistically significant in this model. The analysis shows that ensuring equal opportunities in health and education significantly contributies to better human development. Future research is recomended to incorporate qualitative approaches and more recent variables to enrich the analysis.
Analisis Kinerja Model Long Short Term Memory dengan Adaptive Moment Estimation dalam Memprediksi Harga Crude Palm Oil Hamida, Zilfa; Amalita, Nonong; Permana, Dony; Zilrahmi, Zilrahmi
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.766

Abstract

Crude Palm Oil (CPO) merupakan salah satu minyak nabati terpenting dan paling signifikan yang di perdagangkan secara global. Harga CPO mengalami fluktuasi hampir setiap harinya yang memberikan resiko besar bagi pelaku industri kelapa sawit seperti petani, konsumen, produsen, serta investor. Sehingga diperlukan analisis prediksi untuk meminimalisir kerugian. Dalam penelitian ini, metode yang digunakan yaitu Long Short Term Memory (LSTM) yang dioptimasi dengan Adaptive Moment Estimation (Adam) untuk melakukan prediksi harga CPO berdasarkan data historis harga CPO tahun 2020-2024. Model LSTM yang dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan nilai Mean Absolut Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model LSTM dengan kombinasi parameter jumlah neuron 6, batch size 64, dan epoch 80 menghasilkan nilai MAPE 1,36%, yang menggambarkan hasil prediksi memiliki akurasi yang baik. Hasil ini menujukkan bahwa model LSTM yang dioptimasi dengan Adam telah menunjukkan efektivitasnya dalam melakukan prediksi harga CPO untuk aplikasi dalam penyediaan model prediksi bagi industri kelapa sawit.
Nagari Tanjung Balik Menuju Digitalisasi Data Syafriandi, Syafriandi; Amalita, Nonong; Vionanda, Dodi; Fitria, Dina; Zilrahmi, Zilrahmi; Yarman, Yarman
Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 22, No 3 (2022): Suluah Bendang: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sb.03280

Abstract

Kegiatan pembangunan hendaknya dilaksanakan dengan menggunakan perencanaan yang tepat. Perencanaan ini memerlukan informasi yang diperoleh dengan adanya data.  Nagari Tanjung Balik Kecamatan X Koto Singkarak merupakan salah satu nagari yang termasuk dalam Kecamatan X Koto Singakarak, Kabupaten Solok. Untuk perencanaan pembangunan, nagari ini memerlukan adanya informasi dan data. Namun, nagari ini tidak memiliki akses ke data sektoral yang terhimpun di BPS. Di sisi lain, nagari ini juga dihadapkan pada keterbatasan sumber daya yang memiliki pengetahuan tentang Statistika. Oleh karena itu, tim pengabdi melaksanakan Kegiatan Pengabdian kepada Masyarakat di Nagari Tanjung Balik untuk membantu mengatasi kedua masalah di atas.  Dari kegiatan pengabdian ini, pemerintah Nagari Tanjung Balik memiliki database yang terbaru, akurat, dan mudah diakses yang bisa digunakan untuk mengetahui informasi yang detail tentang masyarakat nagari ataupun untuk memetakan potensi dan masalah di nagari. Begitu pula, dari kegiatan ini,  pemerintah nagari telah memiliki kader yang bisa melakukan pengumpulan data di waktu yang akan datang dengan menggunakan aplikasi RSN dan mengelola database yang telah dibangun.
Peramalan Jumlah Uang Beredar di Indonesia Menggunakan Jaringan Saraf Tiruan Muslimah, Nailul Amani; Dony Permana; Syafriandi; Zilrahmi
JURNAL ILMU KOMPUTER Vol 9 No 1 (2023): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v9i2.253

Abstract

ABSTRACT Inflation is one of the economic problems that has a strong correlation with people's welfare, especially for people with a low income fixed income class. Inflation will have a complicated impact on people with a low economy as well as the government. The money supply is an indicator that influences the rise and fall of the inflation rate in Indonesia. Therefore, controlling the money supply needs to be done to determine strategic policies that can be implemented by the government when the money supply is outside the stability limit. This study aims to predict the money supply using Backpropagation Neural Networks. The results of the analysis show that the most optimal Backpropagation model has 12 input layer units, 6 hidden layer units and 1 output layer unit or is written as BP model(12,6,1). The MAPE value resulting from forecasting with the BP(12,6,1) model is 7.53% and an accuracy of 92.47%. The BP(!2,6,1) model is a very good model for forecasting. Keywords— Forecasting, Money Supply, Inflation, Neural Networks.
Co-Authors Adinda Dwi Putri Admi Salma Admi Salma Afifa Lufti Insani Amanda, Abilya Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Della Amelia Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Fadhilah Fitri Fadhillah Fitri Fadlan Rafly, Muhammad Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fitri Mudia Sari Fitri, Fadhilah fitri, silfia wisa Frandito Rahmanesta Hamida, Zilfa Hari Wijayanto Hendrawan, Muhammad Ibnul farizi, Gilang Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Manja Danova Putri martha, Ully Martha Martia Rosada Meliani Putri Melin Wanike Ketrin Miftahurrahmi, Syifa Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Faisal Mukhti, Tessy Octavia Muslimah, Nailul Amani Mutiara Amazona Sosiawati nazhiroh, hanifah Nilda Yanti Nonong Amalita Nurdalia Permana, Dony Putri, Fadhira Vitasha Putri, Lathifa Putri, Sindy Amelia Rahmad Wanizal Pastha Rahmadani Iswat Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Sepriano Sepriano Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Nabilah Wandira Tessy Octavia Mukhti Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha