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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
Department Of Statistics, 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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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.
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
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
Comparison of District/City Clusters in West Sumatra Province 2019–2025 Based on Labor Indicators Using K-Means Method Naila Marettania; Zilrahmi; Mellisa Ayuningtyas
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-iss2/495

Abstract

This study is motivated by the differences in labor conditions among regencies/cities in West Sumatra Province, as indicated by the Open Unemployment Rate (OUR) and the Labor Force Participation Rate (LFPR). In addition, the impact of the COVID-19 pandemic and the economic recovery process during the 2019–2025 period are assumed to have caused changes in labor characteristics across regions. However, the patterns of similarities and differences in labor conditions among regions have not been clearly identified, making it necessary to conduct a regional clustering analysis based on labor characteristics. This study aims to analyze the clustering of regencies/cities in West Sumatra Province based on the OUR and LFPR indicators during 2019–2025. The data used were obtained from the Central Statistics Agency, covering 19 regencies/cities. The analytical method applied was K-Means clustering using Euclidean distance, while cluster validation was conducted using the Silhouette Coefficient. This study used two clusters to facilitate the interpretation of results. The findings show that the regencies/cities in West Sumatra Province were divided into two clusters with different characteristics. Cluster 1 represents regions with better labor conditions, characterized by lower OUR and higher LFPR, while Cluster 2 represents regions with relatively poorer labor conditions, characterized by higher OUR and lower LFPR. Cluster membership changed from year to year, indicating dynamic labor conditions across regions. The results of this study are expected to serve as a basis for formulating more targeted labor policies according to the characteristics of each region.
Pengelompokan Potensi Kebakarn Hutan/Lahan di Indonesia Berdasarkan Sebaran Titik Panas Mengunakan Metode CLARANS silfia wisa fitri; Zamahsary Martha; Yenni Kurniawati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/182

Abstract

Kebakaran hutan/lahan merupakan bencana yang sering terjadi di beberapa negara di dunia. Peristiwa ini mendapat perhatian lebih dari pemerintah karena menimbulkan banyak kerugian seperti ekonomi, ekologi dan sosial. Indonesia merupakan negara dengan tingkat bencana kebakaran hutan/lahan yang tinggi, hal ini menjadikan Indonesia sebagai negara penyumbang pencemaran terbesar ketiga di dunia. Sehingga diperlukan upaya penanggulangan sejak dini, salah satu upaya yang dapat dilakukan adalah dengan memanfaatkan data titik api dengan melakukan klasifikasi wilayah yang berpotensi terjadinya kebakaran hutan/lahan. Kebakaran hutan/lahan ditandai dengan terdeteksinya data titik api oleh satelit yang terindikasi sebagai titik api. Pada penelitian ini parameter yang digunakan adalah lintang, bujur, kecerahan, keyakinan dan FRP (fire power radiative) dengan menerapkan metode CLARANS. CLARANS merupakan varian dari algoritma k-medoid dan juga merupakan pengembangan dari algoritma sebelumnya, seperti PAM dan CLARA untuk menangani jumlah data yang lebih besar dan tahan terhadap outlier. Hasil penelitian ini menunjukkan bahwa penggunaan metode CLARANS dapat digunakan untuk proses clustering data hotspot dengan hasil koefisien siluet sebesar 0,896 pada penggunaan 2 cluster dengan jumlah data sebanyak 12,287. Hasil cluster menunjukkan bahwa cluster 1 termasuk dalam potensi tinggi dengan kecerahan rata-rata 340K dengan kepercayaan rata-rata 95% dan cluster 2 termasuk dalam potensi sedang dengan kecerahan rata-rata 327 K.
Metode Density Based Spatial Clustering of Applications with Noise (DBSCAN) dalam Mengelompokkan Provinsi di Indonesia Berdasarkan Kasus Kriminalitas Tahun 2022 Syifa Miftahurrahmi; Zilrahmi; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/203

Abstract

Based on Central Statistics Agency 2023 data, in 2022 there was a significant increase in the number of crime cases in Indonesia compared to 2021, from 239,481 cases to 372,965 cases. The increase in the number of criminal acts occurred along with community activities that began to loosen up after the Covid-19 pandemic. The types of crimes that occur in Indonesia themselves vary, ranging from murder, theft, drug-related crimes, and others. This research will cluster provinces in Indonesia based on crime cases with certain types of crimes in 2022 using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) method. The results of the study are expected to help the government and police in an effort to deal with crime in Indonesia. Clustering using the DBSCAN method produces 2 clusters with a silhouette coefficient value of 0,68. The resulting cluster is cluster 0 with noise category consisting of 5 provinces with a high number of crime cases, while cluster 1 consists of 29 provinces with a low number of crime cases.
Evaluasi Faktor-Faktor Yang Memengaruhi Indeks Pembangunan Manusia Tahun 2023 Menggunakan Metode SEM-PLS Sindy Amelia Putri; Zilrahmi; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/214

Abstract

The human development index (HDI) is a measure of the success of development in a country. Indonesia as a developing country in 2022 has an HDI value that ranks 112 out of a total of 193 countries in the world. This indicates that there is an urgent need for evaluation in increasing the HDI value in Indonesia which leads to an increase in the quality of human development. The evaluation can be done using the Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis method. With 34 Indonesian provinces as observations, there are three dimensions as variables analyzed in this paper, namely economy, education, and health. These variables are analyzed based on each indicator variable. The results of the analysis show that in the economic variable, the influential indicators are the Open Unemployment Rate, GRDP per Capita at Constant Prices, and Average Wage per Hour Worker. Then in the education variable, the influential indicators are the School Participation Rate Age 7-12, the School Participation Rate Age 13-15, the Pure Enrollment Rate for Elementary/Middle School/Package A, the Pure Enrollment Rate for Junior High School/MTs/Package B, and the Pure Enrollment Rate for Senior High School/SMK/MA/Package C. Furthermore, in the health variable, there are indicators of the Percentage of Households by Province and Source of Adequate Drinking Water, and the Percentage of Ever-Married Women Aged 15-49 Years whose Last Childbirth Processed in a Health Facility which affect the value of HDI in Indonesia in 2023.
Pemodelan Tingkat Partisipasi Angkatan Kerja Terhadap Persentase Penduduk Miskin di Jawa Timur Tahun 2023 Menggunakan Metode B-Spline Gilang Ibnul farizi; Zilrahmi; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): 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/vol2-iss4/215

Abstract

Poverty is a common issue in Indonesia. Data on the Percentage of Poor Population against the Labor Force Participation Rate (LFPR) per district/city, consisting of 38 districts/cities in East Java Province in 2023, indicates that the highest percentage of poverty in East Java Province in 2023 was 21,760. Employment is considered the most effective solution to alleviate poverty. The data in this study shows a distribution pattern that does not form a specific pattern, making it difficult to analyze using parametric methods. Therefore, the appropriate approach is Nonparametric Regression. In this study, the nonparametric regression used is the B-Spline regression model. The suitability of the model is based on the Mean Squared Error (MSE) value of the model. The analysis results indicate that the B-Spline regression model achieves an MSE value of 20.11447. The optimal MSE value is obtained from B-Spline estimation with order 2. This suggests that the B-Spline method provides a good explanation in addressing the issue
Optimization of Sentiment Analysis for MBKM Program using Naïve Bayes with Particle Swarm Optimization Diva Aliyah; Zilrahmi; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): 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/vol2-iss4/220

Abstract

In early 2020, Kemendikbudristek launched the MBKM program with the aim of improving the quality of higher education through a student-focused learning approach. The launch of this program triggered various reactions on social media, especially on Twitter, both positive and negative. This study aims to analyze the sentiment of Twitter users towards the MBKM program using the Naive Bayes algorithm optimized with Particle Swarm Optimization (PSO). The data used are Indonesian tweets containing the keywords "MBKM" and "Merdeka Campus" from the period July to December 2022. The research stages include data collection through crawling, manual labeling of data into positive and negative sentiments, data preprocessing, application of the Naive Bayes algorithm, and feature selection with PSO. The results showed that the group of tweets categorized based on positive and negative sentiments towards the implementation of the MBKM program in Indonesia in 2022, showed that the NB-PSO experiment achieved an accuracy of 90.87%, an increase of 7.12% compared to the Naive Bayes algorithm alone. Thus, the use of Particle Swarm Optimization algorithm in Naive Bayes classification algorithm is proven to improve classification performance, especially in the case of sentiment analysis. Keywords: Sentiment Analysis, Merdeka Belajar Kampus Merdeka, Twitter, Naive Bayes, Particle Swarm Optimization.
PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) hanifah nazhiroh; Dina Fitria; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): 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/vol2-iss4/223

Abstract

The movement of the share price of PT Telkom (Tbk) fluctuates so it is necessary to do a forecasting analysis. Forecasting the share price of PT Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN) method. In this study using PT.Telkom (Tbk) stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023). The purpose of this research is to determine the movement of PT.Telkom (Tbk) stock prices in 2024, to find out the difference in forecasting using PT.Telkom (Tbk) 2018-2023 stock price data with PT.Telkom (Tbk) stock price data after covid-19 2021-2023, and to determine the level of accuracy of forecasting PT.Telkom (Tbk) stock prices using the LSTM method. The results showed that both data have a small MAPE value. to forecast the share price of PT.Telkom for 1 year, PT.Telkom (Tbk) share price data for 2018-2023 is used which has more data to analyze long-term forecasting. From the analysis results obtained MAPE of 1.016% with the optimal parameter combination of neuron 4, batch size 64, and epoch 80. The results of forecasting the share price of PT.telkom (Tbk) in 2024 experienced very rapid fluctuations with an average share price of PT.Telkom (Tbk) in 2024 Rp 4,668 / sheet.
Co-Authors Abilya Amanda Adinda Dwi Putri Afifa Lufti Insani Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Devi Yopita Sipayung Dila Sari Dina Fitria Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dony Permana Dwi Sulistiowati Fadhilah Fitri Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhira Vitasha Putri Fajri Juli Rahman Nur Zendrato Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fedisha Elfiri Fedisha Fitri Mudia Sari Fitri, Fadhilah Frandito Rahmanesta Gilang Ibnul farizi Hadid Habiburrahman Hamida, Zilfa Hanifah Nazhiroh Hari Wijayanto Ichlas Djuazva Ihsanul Fikri Khoirun Nisa Lathifa Putri Listia Maharani M. Anfasa Prana Karil Manja Danova Putri Martia Rosada Meliani Maya Sari Meliani Putri Melin Wanike Ketrin Mellisa Ayuningtyas Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Fadlan Rafly Muhammad Faisal Muhammad Hendrawan Muslimah, Nailul Amani Mutiara Amazona Sosiawati Naila Marettania Nilda Yanti Nonong Amalita Nurdalia Nurviqotun Khasanah Nurwijayanti Permana, Dony Rahmad Wanizal Pastha Rahmadani Iswat Retno Lis Megawati Rita Diana 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 Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Miftahurrahmi Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Ully Martha martha Ulya Syafitri.J Vania Riski Afifah Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha Zamahsary Martha