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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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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 : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

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 : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

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%
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.
Multidimensional Poverty Clustering using K-Means Algorithm with Dimensionaly Reduction by Principal Component Analysis Salma, Admi; Zilrahmi, Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 2 (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.v4i2.101

Abstract

The level of Multidimensional poverty in each province in Indonesia varies, similar policies is ineffective to reduce the poverty. Several poverty indicators also influence other factors. General policies established to overcome poverty have proven ineffective, making it urgent to identify the needs of each province in overcoming this condition. Grouping provinces based on similar multidimensional poverty which use cluster analysis, will help address this situation. The aim of this study is to group provinces based on multidimensional poverty indicators using the k-means clustering method. Principal Component Analysis (PCA) was also used to reduce variables and multicollinearity. The clustering results showed seven clusters. The highest multidimensional poverty was found in cluster 2, which consisted of one province, namely Papua Pegunungan. This province shows deficiencies in education, health, and living standards compared to other clusters. Meanwhile, the lowest multidimensional poverty was found in cluster 7. There are three provinces in this cluster, namely Bali, Jakarta, and DIY Jogjakarta. These provinces experience minimal multidimensional poverty which is able to provide a better quality of life. The policies and development strategies in these provinces could serve as role models to develop other provinces based on their specific deficiencies and needs.   Each cluster is well separated, as Davies Bouldin Index (DB) is lover, at 0.4.
Implementation of Fuzzy C-Means Algorithm for Clustering Provinces in Indonesia Based on Micro and Small Industry Ratio in Village Areas Rahmanesta, Frandito; Martha, Zamahsary; Vionanda, Dodi; Zilrahmi, Zilrahmi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p178-190

Abstract

Post-economic crisis, the micro and small industries contribute the most labor compared to other industries. Regional development sourced from small micro industries is a strategic force in developing a country because the development of small micro industries leads to realizing equitable welfare to reduce income inequality. Development in village areas is an important factor for regional development, reducing inequality between regions, and alleviating poverty. However, based on the 2018 PODES survey, there are regional imbalances in Indonesia in the small micro industry which is centralized on Java Island. Therefore, clustering and characteristics of the province were carried out based on the PODES survey of the small micro industry sector. This research uses the Fuzzy C-Means algorithm to cluster 34 provinces in Indonesia based on the ratio of small micro industries in village areas in 2021, to see how the development of small micro industries in village areas in each province in Indonesia. Fuzzy C-Means is one of the data clustering techniques that uses a fuzzy clustering model, where cluster formation is based on a membership degree value that varies between 0 and 1. The Fuzzy C-Means algorithm generates 4 clusters, cluster 1 and 2 represents provinces with high and very high micro and small industry development in village areas and cluster 3 and 4 represents provinces with medium and low micro and small industry development in village areas. The Fuzzy C-Means algorithm produces a good cluster structure with a silhouette coefficient value of 0,6406.
Fostering Deep Learning in Students: An AI Empowerment Program for Mathematics Teachers zilrahmi zilrahmi; sri wahyu
Pelita Eksakta Vol 9 No 1 (2026): Pelita Eksakta, Vol. 9, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol9-iss1/322

Abstract

The advancement of Artificial Intelligence (AI) and deep learning offers significant opportunities for innovation in education. This program aimed to improve the understanding and skills of junior high school mathematics teachers in Padang City in utilizing AI to support creative and efficient learning. The training, held at SMP Negeri 25 Padang and attended by 55 MGMP Mathematics members, included lectures, hands-on practice using MagicSchool AI and ChatGPT, and the development of interactive learning media through Wordwall and Kahoot!. Evaluation results showed that all participants improved their understanding of AI and deep learning concepts, and more than 90% found the materials relevant and easy to apply. The training successfully fostered teachers’ motivation to adapt to AI-based learning innovations. Similar programs are recommended to continue with classroom mentoring to ensure optimal AI implementation in schools
STUDY ON EMD METHOD FOR PREDICTING THE PRICE OF CURLY RED CHILI IN INDONESIA Zilrahmi, Zilrahmi; Wijayanto, Hari; Afendi, Farit M; Bakri, Rizal
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.600

Abstract

The fluctuations of curly red chili price affect the inflation rate in Indonesia. So that, the basic characteristics of price movement and correctly prediction for curly red chili price become concern in various studies. Empirical Mode Decomposition (EMD) method helps to examine behavioral characteristics of curly red chili prices in Indonesia easily. Ensemble EMD (EEMD) and modified EEMD are the decomposition method of time series which is development of EMD method. The decomposed data with EMD methods can also used for price forecast. The forecasting with ARIMA and trend polynomial performed to assess the effect of decomposition with EMD methods for forecast stability of curly red chili price in Indonesia under various conditions. The results show the most influence factor for price fluctuation of curly red chili in Indonesia is season and growing season. In this case, the ability of a decomposition method to produce the actual components that describe the pattern of data signals affect the accuracy of the predicted value obtained using the model. The predicted value using the decomposed data by modified EEMD always better than EEMD on the overall condition.
A Predicting the Future: A Forecast of Bukittinggi's Original Local Revenue from 1996 to 2024 Fedisha Elfiri Fedisha; Fadhilah Fitri; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/473

Abstract

In the past decade, Bukittinggi City’s locally generated revenue (PAD) has experienced considerable instability. A significant decline occurred during the 2020 pandemic, followed by external disruptions such as the 2024 Mount Marapi eruption. These conditions complicate regional financial planning and highlight the importance of reliable forecasting. This study aims to forecast PAD for the 2025–2029 period using the ARIMA (Autoregressive Integrated Moving Average) method. Annual data from 1996–2024 were obtained from official publications of Indonesia’s Central Bureau of Statistics (BPS) Bukittinggi. The analysis procedure included exploratory data analysis, variance stationarity testing using Box-Cox transformation, mean stationarity testing through the Augmented Dickey-Fuller test supported by ACF and PACF plots, tentative model identification, parameter estimation, residual diagnostics using the Ljung-Box and Shapiro-Wilk tests, and model selection based on the smallest MAPE value. The results showed that the data became stationary after Box-Cox transformation and second-order differencing. Among the candidate models, ARIMA(3,2,0) was selected as the best model because all parameters were statistically significant (p-value < 0.05), the residuals satisfied the white noise assumption, and the model produced the lowest MAPE value. Forecasting results indicate an increasing PAD trend from approximately 240.23 million Rupiah in 2025 to 429.57 million Rupiah in 2029. However, prediction intervals widened over time, indicating increasing uncertainty in long-term forecasts. Therefore, the local government should implement adaptive fiscal policies and strengthen regional revenue sources to anticipate future PAD fluctuations
Co-Authors Abilya Amanda Adinda Dwi Putri Afendi, Farit M Afifa Lufti Insani Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Devi Yopita Sipayung Dila Sari Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fajri Juli Rahman Nur Zendrato Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fedisha Elfiri Fedisha Fitri Mudia Sari Fitri, Fadhilah Gilang Ibnul farizi Hadid Habiburrahman Hamida, Zilfa Hanifah Nazhiroh Hari Wijayanto Hari Wijayanto Hendrawan, Muhammad Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Lathifa Putri Manja Danova Putri Martia Rosada Meliani Maya Sari Meliani Putri Melin Wanike Ketrin Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Fadlan Rafly Muhammad Faisal Muslimah, Nailul Amani Mutiara Amazona Sosiawati Nilda Yanti Nonong Amalita Nurdalia Nurwijayanti Permana, Dony Putri, Fadhira Vitasha Rahmad Wanizal Pastha Rahmadani Iswat Rahmanesta, Frandito Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Salma, Admi Sepriano Sepriano silfia wisa fitri Sindy Amelia Putri Sri Wahyu suci Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Miftahurrahmi Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Ully Martha martha Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha