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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) ESTIMASI: Journal of Statistics and Its Application MATHunesa: Jurnal Ilmiah Matematika Indonesian Journal of Statistics and Its Applications
Sahriman, Sitti
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Transportation Other

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RAINFALL FORECASTING OF SALT PRODUCING AREAS IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH LINEARIZED RIDGE REGRESSION DUMMY Sahriman, Sitti; Randa, Eunike Laurine; Surianda, Sitti Aisyah; Hisyam, M. Zaky Gozhi; Taufik, Muh. Ikbal; Putra, Guntur Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0483-0492

Abstract

Pangkep Regency is one of the regions in South Sulawesi that is the center of national salt production. Salt production in the area is still dependent on sea water evaporation so that rainfall is one of the determining factors for the success of salt productivity. Statistical downscaling is an accurate method for rainfall forecasting by linking the local scale rainfall in Pangkep Regency (response variable) with the global scale of the global circulation model/GCM output (predictor variable). However, the GCM output rainfall has a large dimension, which is an 8×8 grid (64 predictor variables), causing multicollinearity. The linearized ridge regression (LRR) method is used to overcome this problem. This method combines the performance of generalized ridge regression and Liu-type methods to reduce multicollinearity. In addition, dummy variables based on the K-means clustering technique were added to the model to overcome heteroscedasticity. The purpose of this study is to obtain the results of rainfall forecasting in Pangkep Regency using the LRR method in the statistical downscaling model. The model generated from the LRR method with dummy variables is better at explaining the variability of rainfall in Pangkep Regency. The value is higher (72%) than without dummy variables (57%). The addition of dummy variables in the LLR model has better accuracy in forecasting rainfall. The actual rainfall correlation of Pangkep Regency with has the largest correlation (0.76) with the smallest mean absolute percentage error value (0.49). The results obtained are that the months of May - November tend to have relatively low rainfall, so that salt farmers can produce salt with good quantity and quality.
Statistical Downscaling Model with Jackknife Ridge Regression and Modified Jackknife Ridge Regression to Forecast Rainfall Sahriman, Sitti; Upa, Dewi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, 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.v8i2p155-165

Abstract

Statistical downscaling (SD) is a transfer function that connects local scale rainfall data with global scale rainfall. Global-scale rainfall can be obtained from the Global Circulation Model (GCM) output. GCM simulates climate variables in the form of large-scale grids, causing a high correlation between the grids (multicollinearity). The methods used in SD modeling to overcome multicollinearity are Jackknife Ridge Regression (JRR) and Modified Jackknife Ridge Regression (MJR). The method is the development of the Ridge Regression (RR) method. This study aims to predict local rainfall data in Pangkep Regency (response variables) based on local scale GCM output rainfall data (predictor variables) with the JRR and MJR approaches. In addition, K-means cluster technique is used in determining dummy variables to overcome the heterogeneity of the various remaining models. Results using training data (1990-2017 period) show that the MJR method is better at explaining the diversity of data based on a higher R2 value (68%) and a lower Root Mean Square Error / RMSE value (165.57) than the JRR method (R2 amount is 67 and RMSE amount is 167.72). Model validation using data testing (2018 period) also shows the same results, namely MJR is better than JRR. Other than that, the addition of dummy variables can improve the accuracy of the model in estimating rainfall data. Adding a dummy variable to the model results in a high R2 (range between 94% -95%) with a lower RMSE value (range between 66.60-67.69).
PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN KERNEL ADAPTIVE BISQUARE PADA ANGKA KEMATIAN BALITA DI INDONESIA Wunarso, Laura; Sahriman, Sitti; Sirajang, Nasrah; Busrah, Zhulmuhqsith; Patabang, Girbsan Ananta
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p1-10

Abstract

Angka Kematian Balita (AKB) merupakan indikator utama yang mencerminkan kualitas sistem kesehatan serta tingkat kesejahteraan masyarakat. Variasi kondisi demografis, sosial, dan lingkungan menyebabkan pola AKB berbeda antarwilayah sehingga analisis yang mempertimbangkan aspek spasial menjadi penting. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi AKB dengan menggunakan Mixed Geographically Weighted Regression (MGWR). Data yang digunakan mencakup 34 provinsi di Indonesia pada tahun 2020. Variabel dependen dalam penelitian ini adalah AKB, sedangkan variabel independen terdiri atas rata-rata lama sekolah, persentase perempuan pernah kawin usia 15–49 tahun yang melahirkan anak pertama sebelum usia 20 tahun, persentase rumah tangga yang menggunakan bahan bakar utama arang atau kayu untuk memasak, serta persentase penduduk miskin. Pemilihan bandwidth optimum menghasilkan kernel adaptive bisquare yang digunakan dalam pembentukan model MGWR. Hasil analisis menunjukkan bahwa rata-rata lama sekolah dan penggunaan bahan bakar kayu atau arang berperan sebagai parameter global yang berpengaruh signifikan terhadap AKB. Sementara itu, persentase perempuan yang melahirkan sebelum usia 20 tahun dan persentase penduduk miskin bertindak sebagai parameter lokal dengan tingkat pengaruh berbeda antarprovinsi. Perbandingan nilai AICc dan R-Squared mengindikasikan MGWR memiliki performa terbaik (AICc = 191.82; R-Squared = 0.91), dibandingkan dengan Regresi Linear Berganda (AICc = 212.80; R-Squared = 0.76) dan GWR (AICc = 197.90; R-Squared = 0.91). Temuan ini menegaskan bahwa pendekatan spasial memberikan pemahaman lebih komprehensif mengenai determinan AKB dan dapat menjadi dasar dalam merumuskan intervensi kesehatan yang lebih tepat sasaran sesuai karakteristik wilayah.
PERBANDINGAN METODE HOLT-WINTERS ADITIF DAN MULTIPLIKATIF DENGAN OPTIMASI GENETIC ALGORITHM PADA PERAMALAN PRODUKSI KOPI INDONESIA TAHUN 2023 Mahza, Syahza; Sahriman, Sitti
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p38-47

Abstract

Abstrak Latar Belakang. Produksi kopi Indonesia menunjukkan pola musiman yang dipengaruhi kondisi iklim sehingga fluktuasinya cukup besar setiap tahun. Ketidakpastian ini menuntut metode peramalan yang akurat untuk mendukung pengambilan keputusan terkait produksi dan distribusi. Metode Triple Exponential Smoothing Holt-Winters mampu menangkap pola tren dan musiman, tetapi penentuan parameter pemulusannya sering dilakukan secara trial and error sehingga akurasinya kurang optimal. Tujuan. Penelitian ini bertujuan memperoleh parameter optimal α, β, dan γ menggunakan Genetic Algorithm (GA) serta membandingkan performa pendekatan aditif dan multiplikatif untuk menentukan metode yang paling sesuai dalam peramalan produksi kopi Indonesia. Metode. Data produksi kopi Indonesia periode 2016–2023 dari Badan Pusat Statistik (BPS) dianalisis menggunakan Holt-Winters aditif dan multiplikatif, kemudian parameter pemulusan dioptimasi menggunakan Genetic Algorithm untuk meningkatkan akurasi. Hasil. Identifikasi pola menunjukkan bahwa data memiliki karakter musiman multiplikatif. Sebelum optimasi, pendekatan multiplikatif menghasilkan MAPE sebesar 62,23% (korelasi 0,9740), lebih baik dibandingkan aditif dengan MAPE 95,00%. Setelah optimasi GA, diperoleh parameter terbaik α 0,0230; β 0,2851; dan γ 0,9175, yang menurunkan MAPE pendekatan multiplikatif menjadi 0,5024% dan aditif menjadi 12,2092%, dengan korelasi keduanya mencapai 0,9997. Kesimpulan. Pendekatan multiplikatif Holt-Winters merupakan metode yang paling sesuai untuk pola musiman produksi kopi Indonesia. Optimasi Genetic Algorithm terbukti mampu meningkatkan akurasi peramalan secara signifikan pada kedua pendekatan, dengan performa terbaik ditunjukkan oleh Holt-Winters multiplikatif hasil optimasi. Kata Kunci: Holt-Winters, Genetic Algorithm, peramalan, produksi kopi, optimasi parameter. Abstract Background. Coffee is one of the leading plantation commodities in Indonesia. Its production exhibits a strong seasonal pattern influenced by climatic factors, resulting in substantial fluctuations from year to year. This uncertainty creates a need for accurate forecasting methods to support decision-making in production and distribution. The Triple Exponential Smoothing Holt-Winters method is capable of capturing trend and seasonal components; however, its smoothing parameters are often determined through trial and error, which may lead to suboptimal accuracy. To address this issue, an optimization approach based on the Genetic Algorithm (GA) is employed to obtain more precise and efficient parameter estimates. Objective. To obtain the optimal smoothing parameters α, β, and γ using the Genetic Algorithm and to determine whether the additive or multiplicative Holt-Winters approach performs better for forecasting Indonesia’s coffee production. Methods. The study used monthly Indonesian coffee production data from 2016–2023 sourced from Statistics Indonesia (BPS). Holt-Winters forecasting was evaluated using both additive and multiplicative forms, followed by parameter optimization using the Genetic Algorithm. Results. The multiplicative approach reflected the seasonal pattern more accurately, yielding an initial MAPE of 62.23% with a correlation of 0.9740. After optimization, the GA produced optimal parameters α = 0.0230, β = 0.2851, and γ = 0.9175. These parameters significantly improved model performance, reducing MAPE to 0.5024% and increasing correlation to 0.9997, indicating highly precise forecasting results. Conclusion. The multiplicative Holt-Winters method optimized with the Genetic Algorithm is the most appropriate and accurate approach for modeling Indonesia’s coffee production pattern. Keywords: Holt-Winters, Genetic Algorithm, coffee production forecasting, time series, parameter optimization.
Meramalkan Curah Hujan di Kabupaten Maros dengan Menggunakan Metode Adaptive Neuro Fuzzy Inference System Tandirerung, Rael Hofni; Herdiani, Erna Tri; Sahriman, Sitti
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.26433

Abstract

The adaptive neuro-fuzzy inference system or ANFIS method is a hybrid of the fuzzy time series method and artificial neural networks. This algorithm maps the input data in the input layer to the target in the output layer via neurons in the hidden layer using time series data. The working principle of ANFIS has layers that function as input and output. This study aims to obtain the results of rainfall forecasting using the ANFIS method in Maros Regency, South Sulawesi. This research is divided into training data and testing data with details of 292 training data and 73 test data. Then the forecasting results were obtained using 73 test data, namely the period October 20 - October 31, which obtained a value of 0.384% from the calculation of MAPE (Mean Absolute Percentage Error) in the very good forecasting category. The correlation coefficient was obtained by 0.99 with a strong correlation category. So, it can be concluded that the ANFIS method can predict rainfall in Maros Regency with a good degree of accuracy.
Co-Authors Aidawayati, Aidawayati Amir Kamal Amir Andi Kresna Jaya Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Anwar, Andi M. B., Hasmawati Busrah, Zhulmuhqsith Edy Saputra Rusdi Georgina Maria Tinungki Herdiani, Erna Tri Hisyam, M. Zaky Gozhi Jaya, A. Kresna Jusmawati Jusmawati, Jusmawati Jusmawati Massalesse Kasbawati Kasbawati Mahza, Syahza Naimah Aris Nirwan Nirwan Nur Erawaty Nurhidayatullah Nurhidayatullah, Nurhidayatullah Patabang, Girbsan Ananta Putra, Guntur Dwi Ramadhan, Muh. Nur Bahri Randa, Eunike Laurine Rangkuti, Aidawayati Raupong, Raupong Sirajang, Nasrah Sri Astuti Thamrin Surianda, Sitti Aisyah Tandirerung, Rael Hofni Taufik, Muh. Ikbal Thoha, Syamsuddin Upa, Dewi Wahda, Wahda Wahyuni, Alfiana Waibusi, Hendriete Tiur Marowi Wunarso, Laura Yulianti, Andi Sri Yusuf, Anisa Haura Salsa Fatih