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All Journal Paradigma : Journal of Science, Religion and Culture Studies Jurnal Kybernan Jurnal Mahupiki Jurnal Simetris Jurnal Online Mahasiswa (JOM) Bidang Ilmu Keperawatan Jurnal Kesehatan Andalas Gravitasi Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Indonesian Journal of Cardiology NUR EL-ISLAM : Jurnal Pendidikan dan Sosial Keagamaan FIKRAH Jurnal Manajemen dan Administrasi Rumah Sakit Indonesia (MARSI) TRANSFORMASI: Jurnal Manajemen Pemerintahan ESTIMASI: Journal of Statistics and Its Application Fikrah : Journal of Islamic Education Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Cokroaminoto Journal of Primary Education Cermin Dunia Kedokteran International Journal of Science and Society (IJSOC) Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Jurnal Ilmiah Mahasiswa Fakultas Hukum Universitas Malikussaleh PEMA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat E-JRM Recht Studiosum Law Review Jurnal Pengabdian Dinamika Protech Biosystems Journal Mahkamah: Jurnal Riset Ilmu Hukum Moral : Jurnal Kajian Pendidikan Islam Salaga Journal
Muhammad Fadil
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : ESTIMASI: Journal of Statistics and Its Application

 1 
Mengatasi Overdispersi Menggunakan Regresi Binomial Negatif dengan Penaksir Maksimum Likelihood pada Kasus Demam Berdarah di Kota Makassar Fadil, Muhammad; Raupong, Raupong; Ilyas, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i1.14552

Abstract

The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model  and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.
Perbandingan Metode Seasonal ARIMA dan Extreme Learning Machine dalam Prediksi Produksi Padi di Sulawesi Selatan Jamal, Rini; Baso, Andi M Alfin; Andi Febriyanti; Sitti Sahriman; Siswanto, Siswanto; Yunita, Andi Isna; Angriany, A. Muthiah Nur; Rahim, Rahmiati; Fadil, Muhammad
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45821

Abstract

South Sulawesi is one of the provinces that significantly contributes to national rice production. Therefore, accurate forecasting of rice production is crucial for food security planning and agricultural policy-making. This study aims to compare the performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Learning Machine (ELM) methods in predicting rice production in South Sulawesi. SARIMA is a statistical forecasting method effective for data with seasonal patterns, while ELM is a machine learning approach capable of handling complex relationships among variables with high computational speed. Rice production data from the Central Statistics Agency (Badan Pusat Statistik) were used to evaluate the accuracy of both methods. The evaluation was conducted using forecasting error metrics such as Mean Absolute Percentage Error (MAPE). The results show that the SARIMA(1,1,0)(1,1,0)12 model outperformed ELM in predicting rice production in South Sulawesi. This is indicated by a lower MAPE value of 19.937%, compared to 21.632% for the ELM method.
Pengelompokan Kemiskinan di Provinsi Sulawesi Selatan Tahun 2023 dengan Metode K-Means Clustering Wulandari, A. Elisha; Baso, Andi M. Alfin; Fajri, Belia Nurul; Kalondeng, Anisa; Islamiyati, Anna; Pannu, Abdullah; Fadil, Muhammad; Vallarino, Alfian Akbar; Rahman, Anugrah Nur Isnaeni
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45824

Abstract

Poverty remains a significant social and economic issue in South Sulawesi Province. This study aims to classify districts/cities in South Sulawesi based on poverty levels using the K-Means Clustering method. The data used were obtained from the Central Bureau of Statistics (BPS) for 2023, including indicators such as the percentage of poor population, education level, and employment sector. The Silhouette Index method was applied to determine the optimal number of clusters. The results indicate that South Sulawesi is divided into two clusters, representing high and low poverty levels. The scatter plot further reveals that cluster 1 is more varied, while cluster 2 is more concentrated. These findings can serve as a foundation for formulating more targeted policies to reduce poverty in South Sulawesi.
Co-Authors Abdullah Fahrieza Adam Afiz Ahmad Mulyadi Kosim Ahmad Munir Ainur Rofieq Aji, Arif Pramana Akbar Fahrezi Amin Ananda, Tiara Andi Febriyanti Anggita Tiara Pramadiaz Angriany, A. Muthiah Nur Anna Islamiyati Baso, Andi M Alfin Baso, Andi M. Alfin Chairul Azmi Nasution David Ramli Dina Tauhida Dwi Sabtika Julia Ekayana, Ega Danar El Akmaly, Azlif Zhibran Fajri, Belia Nurul Farismah Agustin, Isnaeni Fitriana, Widya Gusmaneli Gusmaneli Hartono Hartono Hasbi Indra Henny Mulyani Idandi Limbong Irfan Maulana Siregar Irvan Nurkarim Jamal, Rini Jumadi Jumadi Kalondeng, Anisa Kartin Aprianti Lovely Lady Lucky Purwantini Lumban Gaol, Hamit Tantio M Rendi Saputra Marthalina Marthalina Masrul Syafri Mon, Muhammad Donal Muhammad Hakeem Muhammad Rusydi H. Muhammad Tahir Sapsal Muhammad Zidan Amriza Muharni, Yusraini Muliatiningsih Muliatiningsih Musli Nahwah, Elvina Nellia Fonna Nirwan Ilyas Nurul Kholis Pannu, Abdullah Rahim, Rahmiati Rahman, Anugrah Nur Isnaeni Raja Muhammad Fahreza Ramdani Bayu Putra Raupong, Raupong Resya I Noer Ricvan Nandra Nindrea Rizka Pratama, Agung Rois Arifin Rusda Khairati S Siswanto Sabhan Sabhan Safri Safri Saiyidinal Fajrus Salam Salsa Azima Samuel Buha Raja Nadeak Sangadji, Ismail Saraswati, Ety Satya Wydya Yenny Siti Nuraini Sitti Sahriman Sutono, Sugoro Bhakti Suwati, Suwati Syafriyadi Tri Widya Kurniasari Usnul Fikri Vallarino, Alfian Akbar Wasisto Utomo Widiyaningsih, Cicilia Wiryono, Budy Wulandari, A. Elisha Yanto Supriyatno Yulia Yulia Yunita, Andi Isna Zilmi Haridhi