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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Contemporary Mathematics and Applications (ConMathA) ESTIMASI: Journal of Statistics and Its Application Basis : Jurnal Ilmiah Matematika
Andi Kresna Jaya
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pemodelan Regresi Binomial Negatif menggunakan Estimator Jackknife Negative Binomial Ridge Regression pada Data Angka Kematian Bayi Provinsi Sulawesi Selatan Palinoan, Kezia Agra; Jaya, Andi Kresna; Islamiyati, Anna
Basis : Jurnal Ilmiah Matematika Vol. 3 No. 2 (2024): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v3i2.1140

Abstract

Analisis regresi Binomial Negatif adalah metode yang digunakan untuk menganalisis hubungan antara variabel prediktor terhadap variabel respon yang berdistribusi Poisson. Namun, regresi Poisson tidak dapat digunakan untuk memodelkan data dengan overdispersi maupun terdapat multikolinearitas. Untuk menyelesaikan masalah tersebut digunakan regresi Binomial Negatif dengan estimator Jackknife Negative Binomial Ridge Regression. Dalam penelitian ini, estimasi parameter regresi Binomial Negatif dengan estimator Jackknife Negative Binomial Ridge Regression diterapkan pada data tingkat kematian bayi di Sulawesi Selatan tahun 2017. Metode Jackknife berperan untuk mereduksi bias sehingga dapat diperoleh penaksiran parameter dengan bias yang kecil sedangkan metode ridge untuk menangani multikolinearitas. Metode pemilihan parameter ridge menggunakan nilai MSE terkecil. Model terbaik terbentuk pada model dengan parameter ridge k = 0.0081. Berdasarkan estimasi parameter yang terbentuk menunjukkan bahwa variabel jumlah bayi dengan berat badan lahir rendah (X1), jumah bayi yang diberi ASI eksklusif (X2), jumlah bayi yang mendapatkan vitamin A (X3), jumlah cakupan pelayanan K4 pada ibu hamil (X4), jumlah ibu hamil yang menerima imunisasi TT2+ (X5), dan jumlah kelahiran (X6) signifikan mempengaruhi jumlah kematian bayi.
Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation Hermawan, Agus; Siswanto, Siswanto; Jaya, Andi Kresna
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3199

Abstract

Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% Fmeasure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% Fmeasure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.
Co-Authors Agus Hermawan Amni, Wa Ode Sitti Amni Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Azis, Moh. Ivan Fajri, Ainul Haryanto, Loeky Herdiani, Erna Tri Ika Pratiwi Haya Jusmawati Massalesse Mahie, Andi Galsan Marsya Anggun Prisila Najamuddin, Fadhila Febriyanti Nasrah Sirajang Nirwan Ilyas Niswar Aliansa Nur Erawati Nur Fuadil Maqnun Wahab Nurtit Sunusi NURTITI SUNUSI Palinoan, Kezia Agra Reski Amalah S Siswanto Sahriman, Sitti Sunarti Sunarti Sutrianah Burhan Talangko, La Podje Yusuf, Anisa Haura Salsa Fatih