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All Journal Jurnal Gamma EKSAKTA: Journal of Sciences and Data Analysis JURNAL SAINS PERTANIAN EQUATOR Jurnal Ekonomi : Journal of Economic Paradigma Geoplanning : Journal of Geomatics and Planning Pro Bisnis Jurnal Dinamika Pengabdian (JDP) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Teknologi Sistem Informasi dan Aplikasi JSiI (Jurnal Sistem Informasi) Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Teknologi Informatika dan Komputer Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Enthusiastic : International Journal of Applied Statistics and Data Science Proceedings Series on Physical & Formal Sciences Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Blend Sains Jurnal Teknik Jurnal Informatika dan Teknologi Pendidikan Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Borneo Journal of Language and Education Innovative: Journal Of Social Science Research Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat STATISTIKA JRIIN :Jurnal Riset Informatika dan Inovasi Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Medic Nutricia : Jurnal Ilmu Kesehatan Jurnal Ilmu Komunikasi dan Sosial Politik Jurnal Kajian Islam dan Sosial Keagamaan Journal of Intelligent Systems and Information Technology Indonesian Journal of Islamic Law (IJIL) Jurnal Sains dan Teknologi Kesehatan Emerging Statistics and Data Science Journal Jurnal Hukum dan Ekonomi Syariah
Achmad Fauzan, Achmad
brawijaya university
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SPATIAL INSIGHTS INTO EARTHQUAKE STRENGTH: A SULAWESI CASE STUDY USING ORDINARY AND ROBUST KRIGING METHODS Humairah, Nanda Lailatul; Fauzan, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1283-1296

Abstract

The data from the Meteorology, Climatology and Geophysics Agency (BMKG) in the last 22 years shows that there have been 230 destructive earthquakes in Indonesia with the highest incidence in 2021. One of the islands frequently hit by earthquakes is Sulawesi Island. According to the 2020 Disaster Risk Index Book (IRBI), 63 of the 81 regencies/cities on Sulawesi Island have a high category earthquake risk index. Based on this, information is needed as a first step in disaster mitigation so that the government can take preventive and anticipatory actions to reduce risks associated with earthquakes and ensure the safety of people on the island of Sulawesi, one of which is obtained through spatial interpolation. In this study, the Kriging methods of interpolation, Ordinary Kriging (OK) and Robust Kriging (RK) were used. From the analysis with OK and RK, the best theoretical semivariogram model is the Exponential model with nugget, sill and range values of ​​respectively 0.40, 0.70, and 6.50 for OK and 0.35, 0.90 and 9.50 for RK. Both methods produced the results that most areas of Sulawesi Island have the potential for shallow earthquakes with a magnitude of around 3.2 to 4.0 on the Richter scale. The potential for earthquakes with high strength is more common around the seas to the east and north of Central Sulawesi Province. The highest estimation results are at the coordinates of 120,029° East Longitude, 1.159° North Latitude, namely in the sea north of South Dampal. According to the results of K-Fold Cross Validation and Leave One Out Cross Validation, the more accurate method for estimating earthquake strength on Sulawesi Island is the RK method because the RMSE and MAPE values ​​in the RK method are smaller than the OK method.
SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS Maulana, Ashabul Akbar; Fauzan, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1317-1332

Abstract

The East Java Province stands as one of Indonesia's regions grappling with a notably elevated poverty rate, accounting for 11.32% of the populace. A strategic approach employed to comprehend and redress this issue involves the application of spatial analysis, wherein spatial factors are intricately integrated into the modeling and cartographic representation of poverty data. The primary objective of this research is to discern the principal determinants influencing the incidence of poverty in East Java Province, employing data reflective of the population's poverty percentages within the province for the year 2021. The study incorporates six pivotal variables, namely: the population poverty rate, open unemployment rate, labor force participation rate, average years of schooling, adjusted per capita expenditure, and the gross regional domestic product (GRDP), predicated on adjusted expenditure. Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. Outcomes indicate that the variable wielding the most substantial influence on the poverty percentage in East Java Province is the average years of schooling. Specifically, an increase in the pursuit of formal education manifests as a negative correlate to the poverty percentage, implying an inverse relationship. Moreover, the SEM model adheres to the requisite assumptions, encompassing (1) the normality of residuals, (2) homogeneity of residuals, and (3) non-spatial autocorrelation of residuals. Comparative analyses reveal that the SEM model utilizing DBW yields diminished values for MAE, MSE, RMSE, AIC, and MAPE in comparison to its linear regression counterpart. Furthermore, the pseudo- values obtained from the SEM surpass those derived from the linear regression model. Rigorous likelihood ratio tests underscore substantial disparities between the SEM and linear regression models, with the former proving more efficient and markedly enhancing the model's explanatory prowess concerning variations in the dataset.
SPATIAL MODELING OF MATERNAL HEALTH: GEOGRAPHICALLY WEIGHTED POISSON REGRESSION ON MATERNAL MORTALITY FACTORS Yuliana, Alfa; Fauzan, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp557-570

Abstract

Data from the 2021 West Java Provincial Health Profile Report, accessed from the official website of the West Java Provincial Health Office, reveals a significant surge in maternal mortality cases, rising from 165 in 2020 to 460 in 2021. In support of efforts to reduce maternal mortality rates, this study investigates the contributing factors to this phenomenon across various districts in West Java Province. The data used is from the year 2021. This study aims to evaluate the effectiveness of Poisson regression, negative binomial regression, and Geographically Weighted Poisson Regression (GWPR) models in capturing the variability of maternal deaths in the study area for that year. A comprehensive analysis revealed that the distribution of maternal mortality fits the Poisson model, displaying significant spatial heterogeneity. Acknowledging this variability, the GWPR approach using an Adaptive Kernel Bisquare weighting was selected due to its capability to produce localized parameter estimates, which more accurately reflect the specific conditions of each location. The analyzed independent variables include the number of community health centers, coverage of antenatal services at the first (K1) and fourth (K4) visits, management of obstetric complications, and coverage of iron supplementation for pregnant women. Of the five variables, only three showed statistically significant effects; therefore, the study proceeded using these three variables. The results indicate that GWPR provides the best explanation for the variability in maternal mortality rates, with an adjusted R² value of 63.17% and a MAPE of 37.70%.
Spatial Pattern Analysis and Determinants of Stunting Prevalence in Central Sulawesi, Indonesia: Using Linear Regression, Local Moran’s I, and Random Forest Approaches Arifuddin, Adhar; Fauzan, Achmad; Hakim, Raden Bagus Fajriya; Nur, A Fahira
Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Vol. 11 No. 3 (2025)
Publisher : Faculty of Medicine, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/htj.v11i3.1863

Abstract

Background: Stunting remains a significant public health issue in Indonesia, particularly in Central Sulawesi, where socio-economic and environmental factors contribute to its prevalence. Understanding these determinants is crucial for effective intervention strategies. Objective: This study aims to analyze the spatial distribution and predictors of stunting prevalence in Central Sulawesi, focusing on socio-economic and environmental factors. Methods: An observational design was employed, utilizing secondary data from the Central Sulawesi Provincial Health Department. Spatial analysis, including Moran’s I and Local Moran’s I, assessed spatial autocorrelation and identified outliers. Regression analysis and Random Forest modeling examined predictors of stunting prevalence. Results: The study found significant spatial clustering in stunting prevalence. Key socio-economic factors identified were maternal education and household income, with poverty being the most influential predictor. Random Forest analysis highlighted sanitation and access to health facilities as important, although access to clean water did not show a significant effect. Conclusion: The findings provide valuable insights into the socio-economic determinants of stunting and emphasize the need for targeted, comprehensive intervention strategies focusing on improving maternal education and addressing poverty, along with enhancing healthcare access in Central Sulawesi
Sistem Informasi Monitoring Hafalan Al-Qur'an Pondok Pesantren Al-Madina Banjarnegara Berbasis Android Nugraha, Agasta Pratama; Muktiadi, Ridho; Badharudin, Abid Yanuar; Fauzan, Achmad
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.11060

Abstract

Pondok Pesantren Al-Madina Banjarnegara merupakan lembaga pendidikan Islam yang menyelenggarakan program hafalan Al-Qur'an secara intensif bagi santri. Namun, proses pencatatan hafalan yang masih dilakukan secara manual menimbulkan berbagai kendala, seperti lambatnya pendistribusian informasi, potensi kehilangan data, serta kesulitan wali santri dalam menghubungkan perkembangan anak secara rutin dan real-time . Hal ini berdampak pada rendahnya efisiensi serta transparansi dalam proses evaluasi pembelajaran hafalan. Untuk menjawab tantangan tersebut, dikembangkan sebuah sistem informasi monitoring hafalan berbasis Android dengan menggunakan pendekatan Rapid Application Development (RAD). Pendekatan RAD dipilih karena mampu mempercepat proses pengembangan sistem melalui iterasi prototipe dan keterlibatan langsung pengguna. Sistem yang dibangun dilengkapi dengan fitur pencatatan hafalan digital oleh ustaz, pengiriman notifikasi otomatis kepada wali santri melalui Firebase Cloud Messaging (FCM), serta laporan perkembangan hafalan berupa e-Rapor yang dapat diakses dan diunduh melalui aplikasi. Sistem ini juga mengintegrasikan halaman Al-Qur'an digital yang bersumber dari API EQuran.id. Dengan adanya sistem ini, proses pencatatan hafalan menjadi lebih sistematis, informasi dapat tersampaikan secara cepat dan akurat, serta komunikasi antara pesantren dan wali santri menjadi lebih efektif. Pengembangan sistem ini diharapkan tidak hanya menyelesaikan permasalahan administratif, tetapi juga menjadi langkah awal transformasi digital dalam pengelolaan pendidikan pesantren.
An ST-DBSCAN Approach to Spatio-Temporal Clustering of Earthquake Events in West Java, Indonesia Widyawati, Dwi Kartika; Fauzan, Achmad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.33079

Abstract

Earthquakes are among the most frequent and damaging natural disasters in Indonesia, particularly in West Java Province, where their unpredictable occurrence often causes casualties and severe infrastructure damage. This study aims to identify spatial and temporal patterns of earthquakes to support disaster risk mitigation efforts. A quantitative exploratory approach was applied using the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) method, which groups earthquake events based on their proximity in space and time while distinguishing random noise. The analysis utilized secondary earthquake data from the Meteorology, Climatology, and Geophysics Agency (BMKG) covering the period January 2022 to December 2023. The results revealed eight distinct clusters and several high-risk zones with strong internal similarity (silhouette coefficient = 0.721), indicating stable and stationary patterns over the observed period. These findings demonstrate that ST-DBSCAN is effective in detecting consistent earthquake-prone areas. More importantly, the study provides practical implications for disaster mitigation, including the development of targeted early warning systems, prioritization of high-risk areas such as Cianjur Regency, and more efficient allocation of resources to strengthen preparedness and community safety.
A Machine Learning Approach to Spatial Analysis of Paddy Field Conversion Using Multispectral Sentinel-2A Imagery Fauzan, Achmad; Kurnia, Anang
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3617

Abstract

The expanse of rice fields is a critical metric as it is intimately linked to agricultural productivity in a given locale. This study investigates the application of satellite imagery to quantify trice fields' acreage and temporal variations. The data utilized was acquired by the Sentinel-2A multispectral satellite. The variables employed are the image's baseband and spectral index. The research area encompasses the Sukamakmur sub-district in Bogor Regency, Indonesia. The types of machine learning models include Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and k-Nearest Neighbor (kNN). The simulation of class numbers is conducted to achieve the most stable and precise evaluation metric values. The XGBoost algorithm is used for the overall classification process of the region based on the optimal metric score. The model's accuracy, precision, recall, and F1-score are 92.37%, 92.3%, 92.38%, and 92.33%, respectively, indicating a very good performance. The model successfully captures a decline in rice field area between 2020 and 2023. Using the Modified Moran’s Index (MMI), the study reveals a positive spatial autocorrelation, indicating a clustered pattern in land-use change. Regions that experience either substantial or minor changes in land use are commonly situated near areas exhibiting similar characteristics. This study presents a spatially aware machine learning framework that enables the effective monitoring of agricultural land-use dynamics. In the future, this framework can be enhanced by integrating time-series forecasting and socio-economic data, supporting more informed decision-making in food security planning and agricultural policy development.
Deep Learning for Lung Disease Diagnosis: A CNN-Based Radiographic Approach: Deep Learning for Lung Disease Diagnosis Danang Bagus Wibowo; Fauzan, Achmad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art5

Abstract

Various types of lung diseases affect the human respiratory system, with pneumonia, tuberculosis, and Covid-19 being among the most common. Early detection plays a crucial role in improving treatment outcomes and reducing mortality rates. Chest X-ray imaging is one of the most widely used diagnostic methods; however, it typically relies on manual interpretation by medical professionals, which can be time-consuming and prone to inconsistencies. This study aims to apply the Convolutional Neural Network (CNN) method as an automated approach to classify chest X-ray images of lung conditions. The dataset consists of 460 X-ray images for each category: normal, pneumonia, tuberculosis, and Covid-19. The CNN model was trained using an input shape of 224×224 pixels, a 3×3 filter size, and 5 epochs. Evaluation results showed that the model achieved 97% accuracy on the validation and 93% on the testing data. These findings highlight the potential of CNN in supporting automated diagnosis of lung diseases. In the future, this technology is expected to assist healthcare professionals in delivering faster and more accurate diagnoses, particularly in areas with limited access to radiology experts. Moreover, this innovation aligns with Sustainable Development Goal (SDGs) 3: Good Health and Well-being, by promoting early detection, timely treatment, and more equitable access to quality healthcare services.
Data Mining untuk Klasifikasi Penerimaan Peserta Didik Baru dengan Menerapkan Algortima Decision Tree Apriyanti, Diana; Fauzan, Achmad; Wahyudin, M. Abyan; Najlah; Dafrian, Rafli; Yanti, Fitri
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 3 No 4 (2025): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) (INPRESS)
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi informasi menyebabkan meningkatnya volume data pada berbagai sektor, termasuk pada proses Penerimaan Peserta Didik Baru (PPDB). Data yang tersimpan dalam jumlah besar membutuhkan teknik analisis untuk menemukan pola tersembunyi guna menunjang pengambilan keputusan. Penelitian ini menerapkan metode data mining dengan algoritma Decision Tree untuk mengklasifikasikan hasil PPDB berdasarkan atribut prestasi dan zonasi. Perhitungan dilakukan melalui tahapan entropy dan information gain untuk menentukan atribut dengan kontribusi paling tinggi terhadap keputusan. Model diuji menggunakan aplikasi Orange sebagai pembangun pohon keputusan. Hasil menunjukkan bahwa atribut zonasi memiliki nilai information gain tertinggi sehingga dijadikan root node dalam pohon keputusan. Decision Tree terbukti mampu memberikan keputusan klasifikasi secara mudah, interpretatif, dan akurat pada dataset PPDB yang dianalisis. Temuan ini menunjukkan bahwa Decision Tree dapat diandalkan untuk mendukung proses seleksi berbasis data dalam bidang Pendidikan.
Mesin Jahit Untuk Meningkatkan Kapasitas Produksi di Ta’aj Collection Sumber Pasir Kabupaten Malang Daryono, Daryono; Fauzan, Achmad; Mamungkas, Mohamad Irkham
Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Vol. 2 No. 1 (2024): Februari 2024 - Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ahsana.v2i1.335

Abstract

Usaha kecil menengah setiap tahun semakin meningkat dimana usaha ini merupakan salah satu pendongkrak perekonomian dan kesejahteraan di suatu daerah. UKM Ta’aj Collection adalah salah satu usaha yang berada di daerah kelurahan Sumber Pasir Kabupaten Malang. Usaha ini didirikan oleh Ibu Yunitasari yang bergerak di bidang fashion. Alamat dari usaha ini adalah di Sumber Pasir Kec. Pakis Kabupaten Malang. Ta’aj Collection merupakan usaha konveksi di Sumber Pasir, Kabupaten Malang yang mengalami kendala dalam meningkatkan kapasitas produksi. Kendala utama adalah keterbatasan jumlah mesin jahit dan keterampilan penjahit dalam menggunakan mesin jahit. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas produksi Ta’aj Collection dengan memberikan bantuan mesin jahit dan pelatihan penggunaan mesin jahit kepada para penjahit. Hasil kegiatan menunjukkan bahwa bantuan mesin jahit dan pelatihan penggunaan mesin jahit berhasil meningkatkan kapasitas produksi Ta’aj Collection hingga 2 kali lipat. Kegiatan ini diharapkan dapat menjadi model bagi usaha konveksi lain di Kabupaten Malang.
Co-Authors Abid Yanuar Badharudin Adhar Arifuddin Adhi Pribadi Agung Dwi Ramadhan, Agung Agung Purwo Wicaksono Ahmad, A. Sri Sartika Sufiina Amalina, Nur Dina Anang Kurnia Andreas Wahyu Gunawan Ariyadi, Fandy Akhmad Ashari Ashari Ayu Wulandari Bariklana, Muhammad Buana, Arya Galih Cahyani, Laras Niken Dwi Christopher Yudha Erlangga Dafrian, Rafli Danang Bagus Wibowo Dany Hilmanto Daryono Daryono, Daryono Denta, Anggeria Dewi, Sawitri Diana Apriyanti, Diana Dwitra Gusti Alriscki Efi Miftah Faridli Elsa Pudji Setiawati Ermadi Satriya Wijaya, Ermadi Satriya Evicenna Naftuchah Riani Fachry Abda El Rahman Fadliansyah, Azhimy Fauzi, Fatkhurokhman Faza Izzatul Muttaqin Fazira, Nabila Dwi Febrianti, Nur Qadri Feri Wibowo Fitri Amalia Fitri Yanti Fitriani, Maulida Ayu Ghefira Nurhaliza, Celine Hafidz, Syauqi Jauzza Hakim, Raden Bagus Fajriya Harjono Harjono Humairah, Nanda Lailatul Lahan Adi Purwanto LUKMAN, LUKMAN Marisi Aritonang Marisma, Murni Maulana, Ashabul Akbar Maziyah Mazza Basya Mohamad Irkham Mamungkas Muhammad Hamka Mukhlis Prasetyo Aji Munandar Munandar Nabilah, Muna Faizatun Najlah Nugraha, Agasta Pratama Nur Faizin Nur Kusmiyati Nursidah, Dea Ratu Oktaviani, Nabila Palahudin Perani Rosyani Pradana, Wahyu Aji Pratiwi, Erlina Lutfiayu Primandari, Arum Handini Purwanto, Kalis Purwanto, Muhammad Idris Rahman, Nurut Aulia Rahmawati, Octavia Ramadhan, Muhammad Rizal Ramadhan, Rizki Fauzian Rantisi, Muhamad Zia Ridho Muktiadi Rinaldi Sjahril, Rinaldi Rosdiana, Siti Roza Azizah Primatika, Roza Azizah Sadat, Fauzan Safira, Nuzulia Nur Samudzky, Fahrul Santoso, Alfian Saputra, Aditia Setya Adhiwibawa, Marcelinus Alfafisurya Shenny Oktoriana Siregar, Ayu Lestari Siregar, Rania Febriyola Supriyono Supriyono Suri, Mulhamatus Latifatus Suria, Muh. Yunus Sutisna, Irwan Syahdony, Farrel Nolan Teguh Marhendi Wahyudin, M. Abyan Widiyanto, Rhendy K. P. Widyastuti, Galuh Widyawati, Dwi Kartika Wilis Dwi Pangesti Wiranti, Ridha Yanasari, Herlinda Yuliana Watiningrum, Rahayu Yuliana, Alfa Zahidah, RA Ghina Zakariyah Zakariyah