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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Journal of Science and Technology Jurnal Riset Statistika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Journal of Authentic Research Indonesian Journal of Statistics and Its Applications
Salma, Admi
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Languange, Linguistic, Communication & Media Mathematics Physics Public Health Social Sciences Other

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Process Capability Analysis of OPC Cement Production Using Statistical Process Control and IMR Method: Blaine Test Evaluation Wafiq Alya Aufa; Yenni Kurniawati; Admi Salma; Darwas
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-iss3/379

Abstract

The main challenge in cement production at PT Semen Padang is maintaining consistent product quality, particularly the fineness of cement particles measured by the Blaine test. Variations in raw materials and the production process can cause fluctuations in quality, which affect the performance of the final product. Therefore, it is crucial to monitor and control process stability and capability to consistently meet product specifications. Based on the Statistical Process Control (SPC) analysis using Individuals and Moving Range (I-MR) control charts on 28 observations of Ordinary Portland Cement (OPC) Blaine values from February 2025, one out-of-control point was detected on the Moving Range chart between observations 16 and 17, indicating a significant variation. However, all points on the Individuals chart remained within control limits, suggesting that the individual process values were still under control. After revising the outlier data, the process was confirmed stable. Process capability analysis showed a Cp value of 2.17 and a Cpk value of 1.98, indicating that the production process is not only statistically stable but also highly capable of meeting quality specifications. Therefore, despite some variation between data points, the cement production process at PT Semen Padang can be considered stable and capable. Nevertheless, periodic evaluations are recommended to maintain consistent product quality and provide strategic recommendations for the Quality Assurance division in implementing data-driven quality control.
Comparison Performance of SARIMA and Exponential Smoothing Holt-Winter’s models for Forecasting turnover PT. Indah Logistik Cargo Padang Silvia Triana; Dina Fitria; Yenni Kurniawati; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/432

Abstract

Forecasting is an important part of corporate decision making. With forecasting, companies can predict future conditions and demand so that they can make appropriate and strategic decisions. PT. Indah Logistik Cargo Padang's turnover data contains trend and seasonal elements that are forecasted using a time series model. This study was conducted to determine the best model for forecasting PT. Indah Logistik Cargo Padang's revenue in the coming period. The methods used in this study are the SARIMA method and Holt-Winter's Exponential Smoothing. The best model was obtained from the results of a comparative analysis of the two methods, as seen in the forecasting error rate determined by the mean absolute percentage error value. For forecasting the revenue of PT. Indah Logistik Cargo Padang, the best model used was SARIMA with a MAPE value of 3.9%.
K-Means Clustering of Jambi Province Based on Economic Growth in 2023 Fathina Nafisa Putri; Dina Fitria; Admi Salma
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (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-iss1/434

Abstract

  Economic growth describes a region’s economic condition. In Jambi Province, although recovery after the COVID-19 pandemic has been visible, gaps between districts and cities still exist due to income inequality, poverty, unemployment, and differences in human capital quality shown by the Human Development Index. This study aims to group districts/cities in Jambi Province based on economic growth and its determinants using the k-means clustering method. The analysis resulted in five clusters with distinct characteristics. Cluster 1, located in the central region, is characterized by relatively low economic growth and human capital, along with a high poverty rate. Cluster 2, covering areas in the western highlands and eastern region, shows strong human capital and a low poverty rate. Cluster 3, in the western part of the province, is marked by low poverty and unemployment rates. Cluster 4, situated in the northeastern coastal area, has the highest Gross Regional Domestic Product (GRDP) per capita and the lowest unemployment rate but struggles with a high poverty rate and weak human capital. Meanwhile, Cluster 5, representing the provincial capital area, demonstrates robust economic growth and strong human capital, although unemployment remains a key issue. These findings highlight the heterogeneity of regional conditions, suggesting that development policies must be tailored to each cluster to promote inclusive growth and equitable welfare.
Handling Unbalanced Data with SMOTE Algorithm for Unemployment Classification in Lima Puluh Kota Regency Using CART Method Aldwi Riandhoko; Nonong Amalita; Dodi Vionanda; Admi Salma
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.v8i2p166-177

Abstract

Unemployment is a problem that occurs in the labor force, where high unemployment is caused by the low ability of the labor force. A region that is still experiencing unemployment problems in West Sumatera is Lima Puluh Kota Regency. Unemployment in Lima Puluh Kota Regency is caused by the low competence of human resources to fulfill employment market requirements. Based on the results of the Sakernas survey in August 2023, Lima Puluh Kota Regency has more employed labor force than unemployed labor force, so this results in unbalanced data. A method that can overcome unbalanced data is Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with addition of synthetic data in minority class so that the proportion is balanced. Data imbalance conditions need to be handled so as to improve the performance of the classification model. Classification and Regression Trees (CART) is a classification technique with a decision tree method that can obtain the characteristics of a classification. The purpose of this research is to compare the CART model before and after applying SMOTE which can be measured by comparing the highest Area Under Curve (AUC) value. The AUC value in the CART method before SMOTE applied has a value of 62.1% while the AUC value in the CART method after SMOTE applied has a value of 70.2%. Therefore, it can be concluded that the CART classification analysis after SMOTE applied is able to provide better performance compared to the CART classification analysis before SMOTE applied.
Metode Subtractive Fuzzy C-Means dalam Pengelompokan Provinsi di Indonesia Berdasarkan Keluarga Risiko Stunting Fathina Nafisa Putri; Admi Salma
JOSTECH Journal of Science and Technology Vol 6, No 1: Maret 2026
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v6i1.13745

Abstract

Stunting merupakan permasalahan gizi kronis yang masih dihadapi Indonesia saat ini. Salah satu upaya dalam percepatan penurunan stunting yang dilakukan pemerintah yaitu melalui pendekatan keluarga berisiko stunting (KRS). Penelitian ini bertujuan untuk mengelompokkan provinsi di Indonesia berdasarkan KRS. Metode yang digunakan yaitu metode Subtractive Fuzzy C-Means dengan 8 variabel penelitian diperoleh dari data KRS yang terdiri dari keluarga sasaran dan faktor risiko. Hasil analisis menggunakan metode Subtractive Fuzzy C-Means dengan jari-jari 0.5 menghasilkan 2 klaster. Klaster pertama terdiri dari 35 provinsi dengan jumlah keluarga berisiko stunting yang relatif rendah dibandingkan klaster 2 dengan jumlah keluarga berisiko stunting tinggi sehingga memerlukan perhatian lebih dan menjadi prioritas dalam pelaksanaan program percepatan penurunan stunting melalui pendekatan keluarga berisiko stunting.
Mapping Area of Nagari Tanjung Gadang Sijunjung Regency Yenni Kurniawati; Dina Fitria; Admi Salma
Pelita Eksakta Vol 8 No 1 (2025): Pelita Eksakta, Vol. 8, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss01/281

Abstract

Developing a digital village as a government point of view supports Nagari Tanjung Gadang as one of Sicantik (a village loving statistics). The village and server got the up to date data about the village and its sub-village. The problem for the village is presenting and analysing the data to publish as it is used. They also found difficulties in writing it into a publication format. The server gave an assistance to write Lumbuang Data Nagari Tanjung Gadang. The result is a book which explains the demographic condition of the village.
Multidimensional Poverty Clustering using K-Means Algorithm with Dimensionaly Reduction by Principal Component Analysis Admi Salma; 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.
Peramalan Curah Hujan Kabupaten Padang Pariaman dengan Menggunakan Metode Fuzzy Time Series Singh Riskiani Lubis; Zamahsary Martha; Syafriandi; Admi Salma
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.v8i1.10465

Abstract

Abstrak Penelitian ini bertujuan untuk meramalkan curah hujan di Kabupaten Padang Pariaman, Provinsi Sumatera Barat, menggunakan metode Fuzzy Time Series Singh. Penelitian ini dilatarbelakangi oleh fluktuasi curah hujan yang tinggi di wilayah tersebut, yang menyebabkan bencana seperti banjir dan tanah longsor, yang merugikan sektor pertanian, infrastruktur, kesehatan, dan perekonomian masyarakat. Data yang digunakan adalah data curah hujan bulanan dari Januari 2020 hingga Desember 2024. Metode Fuzzy Time Series Singh dipilih karena sederhana namun efektif dalam meramalkan data runtun waktu berbasis logika fuzzy. Tahapan dalam metode ini meliputi pembentukan himpunan semesta, penentuan interval, fuzzifikasi data, pembentukan hubungan logika fuzzy, dan defuzzifikasi. Berdasarkan hasil penelitian diperoleh bahwa metode ini mampu menghasilkan estimasi curah hujan yang mendekati nilai aktual, dengan MAPE 7,67%. Hasil penelitian dapat digunakan sebagai alat bantu dalam perencanaan mitigasi bencana seperti tanah longsor dan banjir. Kata kunci: Curah Hujan, Peramalan, Fuzzy Time Series Singh Abstract This study aims to forecast rainfall in Padang Pariaman Regency, West Sumatra Province, using the Fuzzy Time Series Singh method. The research is motivated by the high fluctuation of rainfall in the area, which often leads to disasters such as floods and landslides, adversely affecting the agricultural sector, infrastructure, public health, and the local economy. The data used in this study consists of monthly rainfall records from January 2020 to December 2024. The Fuzzy Time Series Singh method was chosen due to its simplicity and effectiveness in forecasting time series data based on fuzzy logic. The stages of this method include the formation of the universe of discourse, interval determination, data fuzzification, formation of fuzzy logical relationships, and defuzzification. The results of the study show that this method is capable of producing rainfall estimates that closely match the actual values, with a MAPE of 7.67%. The findings can be used as a supporting tool for disaster mitigation planning, particularly for landslides and floods. Keywords: Rainfall, Forecasting, Fuzzy Time Series Singh
Peramalan Jumlah Curah Hujan di Kota Pariaman Menggunakan Metode ARIMA Putri, Deya Junida; Martha, Zamahsary; Salma, Admi
Journal of Authentic Research Vol. 5 No. 3 (2026): August
Publisher : LITPAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jar.v5i3.6451

Abstract

Curah hujan merupakan salah satu unsur iklim yang berpengaruh terhadap berbagai sektor, seperti pertanian, perikanan, kesehatan, transportasi serta pengelolaan sumber daya di Indonesia. Kota Pariaman yang merupakan wilayah pesisir dengan tingkat kerentanan banjir yang memerlukan sistem peramalan jumlah curah hujan yang akurat sebagai antisipasi dampak bencana yang ditimbulkan dan untuk mendukung perencanaan pembangunan. Penelitian ini bertujuan untuk melakukan peramalan data deret waktu jumlah curah hujan di Kota Pariaman dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA). Penelitian ini menggunakan data sekunder yang diambil dari website Badan Pusat Statistik (BPS) Kota Pariaman terkait jumlah curah hujan dari Januari 2020 sampai Desember 2024. Berdasarkan hasil analisis, diperoleh model terbaik yaitu  model ARIMA (2,0,2) dengan nilai MSE terkecil dan residual yang berdistribusi normal. Hasil analisis juga menunjukkan nilai MAPE sebesar 6,81% artinya model ARIMA (2,0,2) sudah sangat akurat digunakan. Hasil peramalan ini diharapkan dapat dijadikan dasar pengambilan keputusan bagi pemerintah dan masyarakat dalam mengantisipasi dampak bencana dan mendukung perencanaan pembangunan di Kota Pariaman. Rainfall is one of the climate elements that affect various sectors, such as agriculture, fisheries, health, transportation and resource management in Indonesia. Pariaman City, which is a coastal area that has a high vulnerability to flood disasters, requires an accurate rainfall forecasting system to anticipate the impact of disasters caused and to support development planning. This study aims to forecast time series data on the amount of rainfall in Pariaman city using the Autoregressive Integrated Moving Average (ARIMA) method. This research uses secondary data taken from the Pariaman City Statistics Agency (BPS) website regarding the amount of rainfall from January 2020 to December 2024. Based on the results of the analysis, the best model is the ARIMA (2,0,2) model with the smallest MSE value and normally distributed residuals. The analysis results also show a MAPE value of 6.81%, meaning that the ARIMA (2,0,2) model is very accurate to use. The results of this forecasting are expected to be used as a basis for decision making for the supporting development planning in Pariaman City.
Co-Authors Ade Eriyen Saputri Aldwi Riandhoko Amalia, Nonong Anita Fadila Atus Amadi Putra AULIA YUSWITA Aulia, Yuke Bahri Annur Sinaga Belia Mailien Chairina Wirdiastuti Darwas Denia Putri Fajrina dhea afrila harelvi Dina Fitria Dina Fitria Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina Fathina Nafisa Putri Fathina Nafisa Putri Faulina Fitria Panca Ramadhani Futiah Salsabila Gilang Ibnul farizi Hanifa Hasna Hariati Ainun Nisa Ihsan Dermawan Ihsanul Fikri Indah Lestari Jimmi Darma putra Kurnia Andrea Diva Mardhiatul Azmi Maulidya Hernanda Mawaddah, Nurul Melda Safitri Muhamad Rayhan Nanda P Muthia Sakhdiah Nahda Maesya Tsani Nonong Amalita Nurdalia Okia Dinda Kelana Permana, Dony Putri fajriyanti nur Putri, Deya Junida rahmad revi fadillah Reny Wulandari Rheizma Dewi Adiningtiyas Riska Ramadani Riskiani Lubis Riwi Dyah Pangesti Seif Adil El-Muslih Sherly Amora Jofipasi Silvia Agustina Silvia Triana Suliswati, Yeni Susi Jumiati Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Ulya Syafitri.J Vidhiya Addini Wafiq Alya Aufa Wilia Sondriva Winartha, Mardia Wulan Septya Zulmawati Yenni Kurniawati Yenni Kurniawati Yulia Pertiwi Zamahsary Martha Zilrahmi, Zilrahmi