Article Per Year (5 Year)
p-Index From 2021 - 2026
5.092
P-Index
This Author published in this journals
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 Pendas : Jurnah Ilmiah Pendidikan Dasar Indonesian Journal of Cardiology NUR EL-ISLAM : Jurnal Pendidikan dan Sosial Keagamaan JURNAL PENDIDIKAN TAMBUSAI FIKRAH PALAR (Pakuan Law review) Jurnal Manajemen dan Administrasi Rumah Sakit Indonesia (MARSI) TRANSFORMASI: Jurnal Manajemen Pemerintahan J-Kesmas: Jurnal Kesehatan Masyarakat (Indonesian Journal of Public Health) ESTIMASI: Journal of Statistics and Its Application Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Jurnal Cahaya Mandalika Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Cokroaminoto Journal of Primary Education International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Merdeka Indonesia Jurnal International Cermin Dunia Kedokteran International Journal of Science and Society (IJSOC) Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Jurnal Ilmiah Mahasiswa Fakultas Hukum Universitas Malikussaleh Perwira Journal of Science and Engineering (PJSE) PEMA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat eProceedings of Engineering E-JRM Recht Studiosum Law Review Jurnal Pengabdian Dinamika Protech Biosystems Journal MAXIMAL Mahkamah: Jurnal Riset Ilmu Hukum Moral : Jurnal Kajian Pendidikan Islam Salaga Journal Fikrah : Jurnal Pendidikan Agama Islam Disable: Law Review
Muhammad Fadil
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
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 Adawiyah, Afifah Afif, Salwa Fadiah Ahmad Mulyadi Kosim Ahmad Munir Ahmad Tarmizi Aini, Azka Putri Nur Ainur Rofieq Aji, Arif Pramana Akbar Fahrezi Aldrein Amin Ananda, Tiara Andi Febriyanti Angga Rusdinar Anggita Tiara Pramadiaz Angriany, A. Muthiah Nur Anna Islamiyati Aprizal Ahmad Aulia, Puput Rizky Bambang Nur Fauzi, Bayu 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 Elroi, Manuel Fajri, Belia Nurul Farismah Agustin, Isnaeni Farkhan Abdurochim Alfarauq Fikri, Usnul Firmanda, Kemal Fitra, Deni Fitriana, Widya Gusmaneli Gusmaneli Hartono Hartono Hasbi Indra Hasibuan , Afif Haidar Henny Mulyani Idandi Limbong Ikramuddin, Ikramuddin Irfan Maulana Siregar Jamal, Rini Jullimursyida Jumadi Jumadi Kalondeng, Anisa Kartin Aprianti Lovely Lady Lucky Purwantini Lumban Gaol, Hamit Tantio M Zakry Ramadhan Mahendra, Yusril Mariyudi Marthalina Marthalina Masrul Syafri Matondang, Cyntia Theresia Mon, Muhammad Donal Muhammad Alfath Qaaf Muhammad Hakeem Muhammad Rusydi H. Muhammad Sidik Muhammad Tahir Sapsal Muhammad Zidan Amriza Muharni, Yusraini Muliatiningsih Muliatiningsih Musli Nahwah, Elvina Naufal Bachri, Naufal Nellia Fonna Nirwan Ilyas Nur Fajriah, Sahda Nurkarim, Irvan Nurul Kholis Pannu, Abdullah Paundra , Fajar Permatasari, Pradita Diah Purnama, Irwan Rahim, Rahmiati Rahman, Anugrah Nur Isnaeni Raja Muhammad Fahreza Ramda, Rimaysa 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 Saputra, M Rendi Saputra, Rizqi Purnama Trian Saraswati, Ety Satya Wydya Yenny Siagian, Sofia Sinambela , Yosua Sitanggang, Welson Siti Nuraini Sitti Sahriman Situmorang, Aston Samuel Sutono, Sugoro Bhakti Suwati, Suwati Syafriyadi Syahida Fadhilah Andini, Shannaz Syairul Bahar Tethuka, Balu Tri Widya Kurniasari Vallarino, Alfian Akbar Wajdi, Muhammad Farid Wasisto Utomo Widiyaningsih, Cicilia Wiryono, Budy Wulandari, A. Elisha Yanita Yanto Supriyatno Yulia Yulia Yunita, Andi Isna Zahra, Aliya Mumtaz Zilmi Haridhi