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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI JMPM: Jurnal Matematika dan Pendidikan Matematika MONSU'ANI TANO Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi dan Multimedia Inferensi Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Natural
FAHREZAL ZUBEDI
Program Studi Statistika, Universitas Negeri Gorontalo, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Mathematics Neuroscience Physics Social Sciences Other

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Analisis Cluster Hasil Try Out Siswa MTS AlHuda Gorontalo dengan Chi-Sim Cosimilarity dan K-Means Clustering Zubedi, Fahrezal
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 5 No 1: March - August 2020
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v4i2.1706

Abstract

Tujuan penelitian ini yaitu menemukan kelompok siswa dan kelompokmata pelajaran yang homogen sehingga bisa memantau ataumengetahui kinerja akademik siswa. Langkah pertama yaitumentransformasi data dengan menggunakan normalisasi min-max.Setelah itu, diterapkan ? -Sim co-similarity untuk menghasilkanmatriks similaritas siswa (SS) dan similaritas pelajaran (SP). MasingmasingSS dan SP dikelompokan menggunakan algoritma k-meansclustering dan menggunakan Silhouette untuk menentukan banyaknyakelompok yang terbaik. Pada pengelompokkan SS diperoleh nilaiSilhouette terbesar yaitu 0,9755781 pada iterasi keempat yangmempartisi menjadi 4 cluster sebagai berikut 67 siswa pada cluster 1, 9siswa pada cluster 2, 45 siswa pada cluster 3 dan 43 siswa pada cluster4. Pada SP diperoleh nilai Silhouette terbesar yaitu 0,5756133 padaiterasi keempat yang mempartisi menjadi 2 cluster sebagai berikutBahasa Indonesia dan Bahasa Inggris pada cluster 1 dan Matematikadan IPA pada cluster 2.
Pemodelan Stunting dan Gizi Kurang di Kabupaten Bone Bolango menggunakan Regresi Poisson Generalized Zubedi, Fahrezal; Oroh, Franky Alfrits; Aliu, Muftih Alwi
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 6 No 2 (2021): September 2021 - February 2022
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v6i2.2507

Abstract

Tujuan penelitian ini adalah untuk menentukan model kasus Stunting dan Gizi Kurang dengan Regresi Poisson Generalized dan faktor-faktor yang berpengaruh terhadap kejadian tersebut. Analisis Data menggunakan Regresi Poisson Generalized karena untuk menangani masalah overdispersi pada data. Hasil yang diperoleh yaitu variabel yang berpengaruh signifikan terhadap kejadian Stunting 2018 adalah Jumlah penduduk miskin dan untuk kejadian Stunting 2019 adalah Persentase balita diberi ASI eksklusif dan Jumlah penduduk miskin. Variabel yang berpengaruh signifikan terhadap kejadian Gizi Kurang 2018 adalah Persentase balita diberi ASI eksklusif dan Jumlah bayi mendapatkan vitamin A dan untuk Gizi Kurang tahun 2019 adalah variabel Persentase balita diberi ASI eksklusif dan Persentase berat badan lahir rendah.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI STUNTING PADA BALITA DI KOTA GORONTALO MENGGUNAKAN REGRESI BINOMIAL NEGATIF ZUBEDI, FAHREZAL; ALIU, MUFTIH ALWI; RAHIM, YOLANDA; OROH, FRANKY ALFRITS
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.10284

Abstract

This study aims to model stunting cases in children under five in Gorontalo city in 2018. In this model, it can be seen that the significant factors that affect stunting cases in children under five in Gorontalo city in 2018.  This study uses data on stunting cases in 9 (nine) districts in the city of Gorontalo and the factors that influence it. The research data were obtained from the Public Health in Gorontalo city. This study used one response variable, namely the number of cases of stunting and four predictor variables, namely number of toddlers who received exclusive breastfeeding, the percentage of low birth weight (LBW), the percentage toddlers who received complete basic immunization, and number of proper sanitation. The results obtained were the variables of number of toddlers who received exclusive breastfeeding and the percentage toddlers who received complete basic immunization which had a significant effect on stunting cases in children under five in the city of Gorontalo in 2018. This was indicated by the P-value of the variable for number of toddlers who received exclusive breastfeeding of 0.00283 and P-value of variable the percentage toddlers who get complete basic immunization is 0.06564. 
Analisis Komparatif Firefly Algorithm dan Particle Swarm Optimization dalam Optimasi K-Means untuk Pengelompokan Ketimpangan Pendapatan Antarprovinsi di Indonesia Adinda Adinda; Fahrezal Zubedi; Nisky Imansyah Yahya
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1026

Abstract

Income inequality among provinces in Indonesia reflects differences in welfare levels influenced by the social and economic characteristics of each region. This study aims to compare the performance of Par-ticle Swarm Optimization (PSO) and Firefly Algorithm (FA) in optimizing the number of clusters in K-Means clustering to group Indonesian provinces based on seven factors related to income inequality, namely Human Development Index (HDI), Number of Poor Population, Open Unemployment Rate, In-flation, Provincial Minimum Wage, GDP per Capita, and Labor Force Participation Rate. The data used are secondary data from 2024 covering 38 provinces. The analytical methods include data standardi-zation using Z-Score, K-Means clustering, and cluster number optimization using PSO and FA evalu-ated by Silhouette Coefficient (SC). The results show that both optimization methods improved clus-tering quality compared to K-Means without optimization, which yielded an SC of 0.23. PSO produced an SC of 0.28 with an optimal cluster number of 5, while FA produced an SC of 0.39 with an optimal cluster number of 3. FA proved superior in generating a more optimal and representative clustering structure. The clustering results reveal distinct characteristics among clusters that can serve as a ba-sis for formulating more targeted income inequality reduction policies in accordance with the charac-teristics of each regional group.
Perbandingan Metode Biclustering untuk Pengelompokan Wilayah Berdasarkan Faktor Penyebab Kusta di Sulawesi Revo Asiki; Fahrezal Zubedi; Armayani Arsal
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1028

Abstract

Leprosy is one of the health issues that spread due to various social and environmental factors. It is a public health issue whose spread is influenced by various social and environmental factors. The aim of this study is to group districts/cities on the island of Sulawesi based on the factors that influence the spread of leprosy using a biclustering approach. The methods used were Cheng & Church (CC) and Iterative Signature Algorithm (ISA), which can cluster data simultaneously on the dimensions of area and variables. The data used are secondary data from 2024 covering eight variables and a number of districts/cities as the units of observation. The analysis stages included pre-processing, missing value handling using mean imputation, data standardisation, and application of the two biclustering methods. The performance was evaluated using Mean Squared Residue (MSR), the Liu & Wang index, and variance. The results of the study show that the CC method produces an average MSR value of 0.006015, which is lower than the ISA method's value of 0.015006. The average Liu & Wang index value for the CC method was 0.2905, lower than the ISA method's value of 0.7356. Furthermore, the average variance in the CC method was 0.07737, which was lower than the ISA method at 2.7859. Based on these three evaluation criteria, the Cheng & Church method is more effective in grouping regions based on factors influencing the spread of leprosy in Sulawesi.
COMPARISON OF XGBOOST AND SVM FOR SENTIMENT ANALYSIS MERAH PUTIH COOPERATIVE POLICY Wahyu Pratama Lasaleng; Fahrezal Zubedi; Siti Nurmardia Abdussamad
Jurnal Statistika dan Aplikasinya Vol. 10 No. 1 (2026): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.10105

Abstract

Large volumes of textual data have been generated by the rapid growth of social media, making sentiment analysis an effective approach for understanding public perceptions of government policies. However, text classification still faces challenges such as high feature dimensionality, class imbalance, and non-linear relationships within the data. This study used data from the X social media platform to evaluate the performance of the Support Vector Machine (SVM) and XGBoost algorithms in classifying public sentiment toward the Koperasi Desa Merah Putih policy. The dataset consisted of 1,074 tweets collected through a scraping technique between July 21 and November 4, 2025, comprising 800 positive tweets and 274 negative tweets. The research process included data preprocessing, feature extraction using TF-IDF, data splitting with an 80:20 ratio, and hyperparameter tuning using GridSearchCV with 5-fold cross-validation. The models were evaluated using accuracy, precision, recall, and F1-score. Hyperparameter tuning successfully enhanced the performance of both models, with SVM benefiting the most from the optimisation process. The findings demonstrated that both models achieved strong classification performance; however, SVM outperformed XGBoost. The SVM model achieved an accuracy of 95%, with more balanced precision, recall, and F1-score values across both sentiment classes, whereas XGBoost achieved an accuracy of 91% and showed limitations in detecting negative sentiment as the minority class. The data exploration results also indicated that most users expressed positive sentiment toward the Koperasi Desa Merah Putih policy. Nevertheless, this study has several limitations, particularly the use of TF-IDF-based feature representation, which does not capture semantic relationships or sarcasm in textual data. The novelty of this study lies in the comparison of SVM and XGBoost with hyperparameter tuning using GridSearchCV in the context of sentiment analysis of the Koperasi Desa Merah Putih policy, a topic that has received limited attention in previous studies.
Development of generalized principal component analysis using multiple imputation genetic algorithm Fahrezal Zubedi; I Made Sumertajaya; Khairil Anwar Notodiputro; Utami Dyah Syafitri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp454-468

Abstract

In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.
Co-Authors Adinda Adinda ALIU, MUFTIH ALWI Armayani Arsal Bagus Sartono Franky Alfrits Oroh I Made Sumertajaya Khairil Anwar Notodiputro Lailany Yahya Mahmud, Sri Lestari Muftih Alwi Aliu MUFTIH ALWI ALIU Muliyani Mahmud Nisky Imansyah Yahya Novianita Achmad Novianty Djafri RAHIM, YOLANDA Revo Asiki Rusli Mowuu Siti Nurmardia Abdussamad Utami Dyah Syafitri Wahyu Pratama Lasaleng