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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan ARRUS Journal of Mathematics and Applied Science IQTISHADequity jurnal MANAJEMEN Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Jendela Matematika
Mahmuda, Siti
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Forecasting the Total of Paddy Production in Indonesia Using Time Series Regression Model Kesuma, Ahmad Rizky; Siringoringo, Meiliyani; Mahmuda, Siti
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3175

Abstract

Time series regression (TSR) is a forecasting model that can be used when there are trend and seasonal patterns. Paddy is a source of rice, which is a staple food for the Indonesian people. Paddy has a seasonal pattern because its cultivation depends on the rainy season. This study forecasts the amount of paddy production in Indonesia based on monthly data of paddy production in Indonesia to determine the production of paddy in 2023 and to assess the ability of the TSR model to forecast paddy production. The results showed that paddy production in 2023 is forecasted to have the same seasonal pattern as in previous years and reaches its peak production in April at 8.699 million tons. The TSR model of paddy production has a mean absolute percentage error (MAPE) of 8.654% indicating a very good forecasting accuracy.
ANALISIS KUALITAS PRODUK PAKAN KUCING DI PT. CPP Ardhyani, Ika Widya; Adriansyah, Gusti; Pramudita, Rezki Aulia; Mahmuda, Siti; Manafsetiawan, Dhany
IQTISHADequity jurnal MANAJEMEN Vol. 7 No. 1 (2024): Desember 2024
Publisher : FAKULTAS EKONOMI UNIVERSITAS MAARIF HASYIM LATIF

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51804/iej.v7i1.16862

Abstract

Penelitian ini menganalisis pengendalian kualitas produksi pakan kucing di PT.CPP Sidoarjo. Tingginya jumlah produk cacat menjadi dasar evaluasi terhadapproses produksi dari bahan baku hingga produk akhir. Metode yang digunakanadalah Seven Tools, termasuk check sheet, p-Chart, dan histogram, untukmengidentifikasi penyebab cacat. Berdasarkan data Januari–Desember 2023,persentase cacat tertinggi terjadi pada Maret (12,5%) dan terendah padaAgustus (5%). Jenis cacat utama adalah Mixed, Moisture, Size Over/Under, Colour,dan Keropos, dengan cacat Size Over/Under paling dominan (2.500 kasus).Analisis p-Chart menunjukkan bahwa meskipun masih dalam batas kendali,fluktuasi ini perlu dipantau untuk mencegah potensi ketidakterkendalianproses produksi, yang dapat menyebabkan peningkatan cacat di masamendatang. Oleh karena itu, perbaikan perlu dilakukan melalui perawatanmesin berkala, pengawasan bahan baku, dan kepatuhan SOP yang lebih ketatuntuk meningkatkan kualitas produk.
APPLICATION OF NONPARAMETRIC REGRESSION SPLINE TRUNCATED FOR MODELING THE HEIGHT OF YEOP CHAGI KICKS OF TAEKWONDO ATHLETES IN SAMARINDA CITY Sitohang, Frans Karta Sayoga; Sifriyani, Sifriyani; Mahmuda, Siti
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/barekengvol18iss2pp0657-0666

Abstract

Nonparametric regression is a model approach method that is used when the shape of the regression curve between the response variable and the predictor variable is assumed to have an unknown shape or pattern. One of the estimators in the nonparametric regression approach is the truncated spline which has the ability to handle data whose behavior changes at certain sub intervals. The purpose of this study was to obtain the estimated value of the parameters of the nonparametric regression model with a truncated spline approach at one knot point, two knot points, and three knot points for kick height data of yeop chagi taekwondo athletes in Samarinda City. The results showed that the truncated spline nonparametric regression model was the best in modeling high kick height data for yeop chagi taekwondo athletes in Samarinda City with three knot points. This model has the minimum Generalized Cross Validation (GCV) value of 7.94 with an R2 value of 94.72% and a Mean Square Error (MSE) value of 2.62. Based on the results of the model parameter significance test, it was concluded that the factors that influence the kick height of the yeop chagi taekwondo athlete in Samarinda City are flexibility, leg power, leg length, and waist circumference.
Implementasi Metode Random Forest pada Kategori Konten Kanal Youtube Mahmuda, Siti
JURNAL JENDELA MATEMATIKA Vol. 2 No. 01 (2024): Jurnal Jendela Matematika: Edisi Januari 2024
Publisher : CV. Jendela Edukasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57008/jjm.v2i01.633

Abstract

Random Forest adalah metode klasifikasi ensemble dalam algoritma pembelajaran mesin. Metode klasifikasi ensemble bertujuan untuk meningkatkan akurasi model dan kinerja klasifikasi. Berdasarkan ukuran akurasi, Random Forest menunjukkan performa terbaik diantara metode klasifikasi yang ada, seperti Support Vector Machine (SVM) dan AdaBoost. Oleh karena itu, penelitian ini menerapkan metode klasifikasi Random Forest pada kategori konten kanal Youtube. Variabel prediktor klasifikasi adalah jumlahsubscribers, jumlah video, jumlah penayangan video, dan lama kanal Youtube. Metode Random Forest menunjukkan jumlah pohon yang dipilih adalah 100 dan mtry adalah 1. Jumlah subscribers merupakan variabel yang paling berpengaruh dalam pengkategorian konten kanal Youtube dengan tingkat kepentingan sebesar 19,04%. Akurasi klasifikasi yang dihasilkan sebesar 77,27%.
Clustering Regency in Kalimantan Island Based on People's Welfare Indicators Using Ward's Algorithm with Principal Component Analysis Optimization Ningsih, Eva Lestari; Mahmuda, Siti; Hayati, Memi Nor
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5363

Abstract

Cluster analysis is used to group objects based on similar characteristics, so that objects in one cluster are more homogeneous than objects in other clusters. One method that is widely used in hierarchical clustering is Ward's algorithm. This method works by minimizing the sum of squared distances between objects in one cluster (within-cluster variance) to produce optimal clustering. However, one important assumption in using this method is that there is no high correlation between variables, or in other words, the data must be free from multicollinearity. Multicollinearity can cause distortion in distance calculation, resulting in less accurate clustering results. To overcome this problem, a Principal Component Analysis (PCA) approach is used to reduce the dimension and eliminate the correlation between variables by forming several mutually independent principal components. This research aims to cluster 56 districts/cities in Kalimantan Island based on 19 indicators of people's welfare in 2023, using Ward's algorithm optimized through PCA. Validation of clustering results is done using the Silhouette Coefficient value to assess the quality of clustering. This research method is a combination of Principal Component Analysis (PCA) and hierarchical clustering using Ward’s algorithm. PCA was applied to reduce 19 welfare-related indicators into four principal components that retained most of the essential information in the dataset. The clustering process based on these components resulted in two optimal clusters, as determined by a Silhouette Coefficient value of 0.651, which indicates a moderately strong cluster structure. The results of this research are that the first cluster consists of 47 districts/cities characterized by relatively low welfare levels, while the second cluster comprises 9 districts/cities with comparatively higher welfare conditions. These findings imply the existence of considerable disparities in welfare among regions on Kalimantan Island. The results can be used as a reference for policymakers in formulating more targeted and equitable development strategies
Co-Authors Gusti Adriansyah Ika Widya Ardhyani Kesuma, Ahmad Rizky Manafsetiawan, Dhany Memi Nor Hayati Ningsih, Eva Lestari Pramudita, Rezki Aulia Sifriyani, Sifriyani Siringoringo, Meiliyani Sitohang, Frans Karta Sayoga