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All Journal EKSAKTA: Journal of Sciences and Data Analysis Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Kreano, Jurnal Matematika Kreatif-Inovatif Geoplanning : Journal of Geomatics and Planning Jurnal Riset Pendidikan Matematika AJIE (Asian Journal of Innovation and Entrepreneurship) JURNAL PENGABDIAN KEPADA MASYARAKAT E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Abdimas Madani dan Lestari (JAMALI) Refleksi Pembelajaran Inovatif Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Bulletin of Applied Mathematics and Mathematics Education JNPM (Jurnal Nasional Pendidikan Matematika) STATISTIKA Advance Sustainable Science, Engineering and Technology (ASSET)
ACHMAD FAUZAN
Universitas Islam Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Implementasi Metode Runtun Waktu dalam Pemodelan Total Harga Alat Kedokteran dan Kesehatan Daniar Wahyu Laraswati; Achmad Fauzan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): 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.v4i1.17873

Abstract

Hospital is an institution or health service that provides total individual health care by providing outpatient, inpatient, and emergency services. The health services that will be provided are promotive, preventive, and rehabilitative services. One of the efforts to improve the quality of hospital services is to provide good health services. In terms of supporting the health services provided, a health management is needed. The high price of medical supplies and equipment is due to several other factors causing hospitals to be able to make plans in the procurement of medical equipment and hospital medicine. Therefore, the author uses the Autoregressive Moving Average (ARMA) method in this study to predict the Total Price of Medical and Health Equipment Needed at the Sleman Regional General Hospital in the coming period. Based on the analysis that has been found, one significant and best ARMA model is obtained with the AIC value of 223.92 with equation  and the MAPE value of 18.78%, which means the accuracy of the forecasting is 81.22%.
Implementasi n-Gram dalam Analisis Sentimen Masyarakat DIY terhadap PSBB Jawa-Bali Jilid II Menggunakan Naive Bayes Classifier Syifa Ayu Anjani; Achmad Fauzan
Statistika Vol. 21 No. 2 (2021): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v21i2.294

Abstract

Coronavirus Disease (COVID-19) telah menyebar dengan sangat cepat ke seluruh dunia, termasuk Indonesia. Dalam upaya pencegahan penularan COVID-19, pemerintah menerapkan Pembatasan Sosial Berskala Besar (PSBB) di seluruh provinsi di Pulau Jawa dan Bali dari tanggal 26 Januari 2021 s.d. 8 Februari 2021. Pemberlakuan PSBB Jawa-Bali menimbulkan tanggapan positif maupun negatif dari masyarakat dalam media sosial twitter, khususnya masyarakat yang berlokasi di Daerah Istimewa Yogyakarta. Sehingga penting untuk dilakukan analisis terhadap tanggapan tersebut agar dapat mengetahui secara umum hal apa yang sering dikeluhkan masyarakat DIY terkait PSBB Jawa-Bali jilid II dan menjadi bahan evaluasi bagi pemerintah DIY dalam menjalankan kebijakan PSBB yang telah dibuat. Penelitian ini menggunakan teknik crawling untuk memperoleh data tweet (tanggapan) masyarakat dalam twitter. Kemudian, dilakukan preprocessing pada data agar siap untuk dianalisis. Tokenization dilakukan menggunakan metode n-gram, yang terdiri dari unigram, bigram, dan trigram. Selanjutnya, data diklasifikasikan berdasarkan kategori sentimen positif dan sentimen negatif menggunakan Naïve Bayes Classifier (NBC). Setelah itu akan dilakukan evaluasi pada hasil klasifikasi menggunakan confusion matrix yang akan menghasilkan nilai akurasi. Berdasarkan hasilnya, klasifikasi dengan NBC memiliki tingkat akurasi sebesar 82,14%. Klasifikasi dengan NBC dan unigram menghasilkan akurasi sebesar 83,04%, dengan bigram menghasilkan akurasi sebesar 80,36%, dan dengan trigram menghasilkan akurasi sebesar 82,14%.
Eksplorasi dan Peramalan Jumlah Izin Tinggal Kunjungan di D.I Yogyakarta menggunakan Metode ARIMA Fitri Amalia; Achmad Fauzan
Statistika Vol. 22 No. 2 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i2.1118

Abstract

Izin tinggal kunjungan (ITK) adalah izin yang ditujukan untuk orang asing yang berada di kawasan kedaulatan Indonesia dengan durasi yang singkat. Memasuki wilayah Indonesia, orang asing akan mendapatkan izin tinggal kunjungan dengan diberikannya visa kunjungan. Tujuan penelitian ini adalah memperoleh model peramalan yang terbaik menggunakan metode ARIMA dan memprediksi untuk 6 periode yang akan datang dengan data yang digunakan adalah jumlah izin tinggal kunjungan yang dilaporkan di Kantor Imigrasi Kelas I TPI Yogyakarta pada periode Januari 2015 – Februari 2022. Dari hasil analisis, diperoleh model terbaik adalah model ARIMA (0,2,1) dengan nilai MAPE 16.6 atau 16.6%. Hasil peramalan jumlah izin tinggal kunjungan untuk bulan Maret hingga Agustus 2022, dimana nilai prediksinya adalah 1414.190, 1310.598, 1276.498, 1260.143, 1314.935, 1418.857 orang. Dari penelitian ini bisa menjadi informasi dan gambaran terkait penerapan metode ARIMA dalam peramalan izin tinggal kunjungan serta sarana untuk evaluasi kedepannya.
Cluster Mapping of Waste Exposure Using DBSCAN Approach: Study of Spatial Patterns and Potential Distribution in Bantul Regency Fauzan, Achmad; Fadillah, Ganjar; Fitria, Annisa; Adriana, Hannura; Bariklana, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2475

Abstract

High level of plastic waste production is a common issue in various places, including the Special Region of Yogyakarta province. It is proven that the Piyungan Integrated Waste Disposal Site (TPST) or Final Disposal Site (TPA) in Bantul Regency has been closed several times due to capacity exceeding the quota and some blockages from residents around the TPA. Issues related to microplastic contamination resulting from discarded plastic waste are fascinating to study, considering the long-term impact of microplastic contamination on human health. This research aims to map the distribution of locations with the potential for waste accumulation to reduce the negative consequences of microplastic contamination. The population used included TPS and markets in Bantul District, with the sample being the distribution of TPS and market points in Bantul District in 2023. The results of checking the point distribution pattern using the quadrant and nearest neighbor method showed that the distribution of waste accumulation points had a clustered design; thus, it could be continued with cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Meanwhile, the number of clusters was determined iteratively from a combination of DBSCAN parameters (MinPts and Epsilon). The best method was evaluated based on the Silhouette Coefficient (SC) value, which in this case was 0.78 (MinPts 7 and Eps 1500) and included in the strong category. Subsequently, exploration was carried out by reducing the MinPts value and the lowest limit value of strong SC.
The Forecasting Result Study of the Poverty Line and Number of Poor Population in DIY using DES and ARIMA Shazia Ayesha Azzahra; Wiranti Nugrah Andini; Achmad Fauzan; Irwan Sutisna
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.36734

Abstract

The poverty rate in DIY, based on BPS data, stands at 11.04%, which remains above the national average of 9.36%. This study aims to predict poverty patterns in the Special Region of Yogyakarta (DIY) using DES and ARIMA methods. The data utilized in this research is sourced from BPS, focusing on poverty line data and the number of impoverished individuals. The DES model is employed to estimate the increase in the poverty line, demonstrating good accuracy with a MAPE value of 2.968%. Meanwhile, the ARIMA(0,2,1) model is applied to forecast a reduction in the number of impoverished individuals, yielding a MAPE of 3.543% through 2028. The findings of this study indicate that government policies have had a positive impact on reducing poverty, although challenges remain. The results of this analysis are expected to guide policymakers in crafting more effective and targeted poverty alleviation strategies in the DIY region. These findings suggest that government policies have had a positive impact on reducing poverty, despite ongoing challenges.
Comparison of Inverse Distance Weighted and Thin Plate Spline Interpolation Methods in Projecting the Strength of the West Sumatra Earthquake Nabila Azzahra Haris Putri; Fauzan, Achmad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 5, ISSUE 2, October 2024
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol5.iss2.art9

Abstract

The Indonesian archipelago is situated in a highly active geological zone, making it prone to frequent earthquakes. West Sumatra, located on the west coast of central Sumatra, comprises lowland coastal areas and volcanic plateaus formed by the Barisan Mountains, covering a land area of 42,297.30 km² (2.17% of Indonesia's territory). This research aims to determine which interpolation method—Inverse Distance Weighted (IDW) and Thin Plate Spline (TPS)—provides more accurate predictions of earthquake strength in West Sumatra. The dataset consists of 229 earthquake events, divided into 90% for training (206 points) and 10% for testing (23 points). The training data was further subdivided into 80% training data 2 (164 points) and 20% validation data (42 points). The interpolation processes using the IDW and TPS methods were repeated 100 times, with the training 2 and validation data randomly shuffled in each iteration. Visualization of the interpolation results indicated that the earthquake magnitudes ranged from 2.0 to 4.5. Although the Mean Absolute Percentage Error (MAPE) values for the TPS method on the test and validation datasets were 16.42 and 14.29, respectively—slightly lower than the MAPE values for the IDW method—the t-test results showed no statistically significant difference between the two methods. Statistically, there is no significant difference between IDW and TPS in terms of predictive accuracy. However, researchers prefer the IDW method due to its computational efficiency and simplicity. Therefore, IDW is considered the most suitable method for analyzing earthquake strength in the West Sumatra region
Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective Achmad Fauzan; Kusman Sadik; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2282

Abstract

This study examines the effect of multicollinearity on ordinal regression through a two-stage Monte Carlo simulation. A synthetic population of 2,000,000 observations was generated with predictors drawn from a normal distribution, and responses simulated using an ordinal probit model. A Monte Carlo procedure was employed with 10 repetitions, each consisting of 100 random samples of 1,000 observations. Parameter estimation employed Maximum Likelihood Estimation (MLE) for univariate models and Pairwise Likelihood (PL) for multivariate models, with performance assessed using mean squared error (MSE), bias, and computation time. Results show that multicollinearity had negligible impact on estimator bias and MSE, confirming the robustness of both MLE and PL to correlated predictors. However, severe multicollinearity substantially increased computation time, indicating a trade-off between estimator stability and efficiency. These findings highlight PL as a viable approach for analyzing complex ordinal data, particularly in applications such as socio-economic surveys and health metrics where predictor correlation is unavoidable.
Spatial Classification of Sentinel-2 Satellite Images with Machine Learning Approach Dea Ratu Nursidah; Achmad Fauzan; Marcelinus Alfafisurya Setya Adhiwibawa
Geoplanning: Journal of Geomatics and Planning Vol 12, No 2 (2025)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.12.2.253-266

Abstract

Urban expansion and land use change are increasingly critical issues in developing regions, where rapid development often leads to unplanned growth and environmental challenges. Accurate and timely classification of built-up and non-built-up areas is essential for supporting sustainable spatial planning and resource management. This study aims to classify built-up and non-built-up areas from Sentinel-2 satellite imagery using a machine learning approach and to analyze their spatial distribution around the Universitas Islam Indonesia (UII) campus. Three machine learning algorithms—Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)—were applied to perform the classification, and their performances were evaluated using four metrics: accuracy, sensitivity, specificity, and Area Under the Curve (AUC). Among these, the SVM method demonstrated the best performance based on the highest average accuracy, the smallest variance difference between training and testing datasets, and consistent results across multiple iterations. Using the classification results from the best-performing model, a spatial density proportion analysis was conducted. The findings revealed a clear spatial trend: areas closer to the UII campus exhibited a higher proportion of built-up land, while areas located farther from the center had a greater share of non-built-up land. These results confirm the effectiveness of the SVM algorithm for land cover classification using Sentinel-2 imagery and offer valuable insights into urban development patterns in the study area. The outcomes of this research can inform urban planners and policymakers in developing data-driven strategies for sustainable land use, infrastructure development, and campus-centered regional growth planning.
Spatial clustering analysis of DKI Jakarta criminal cases in 2024 using DBSCAN and comparison with K-Means SINTIA AFRIYANI; WINARSI J. BIDUL; IMAM AL MAKSUR; ACHMAD FAUZAN; ROZA AZIZAH PRIMATIKA
Bulletin of Applied Mathematics and Mathematics Education Vol. 6 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v6i1.14910

Abstract

The high crime rate in DKI Jakarta requires spatial analysis to accurately identify vulnerable zones. Such information is essential for developing data-driven crime prevention strategies. Therefore, this study aims to map the spatial distribution of criminal cases in DKI Jakarta in 2024 and to evaluate two clustering methods Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-Means in order to determine the most effective approach for identifying crime-prone areas. Data came from the Jakarta Open Data portal, containing coordinates (latitude, longitude), crime types, and supporting details. Pre-processing involved removing duplicates, filtering 2024 records, and eliminating invalid coordinates. Spatial features were normalized using Standard Scaler. DBSCAN parameters (eps, min_samples) were tuned via grid search, while K-Means used the Elbow method to determine optimal clusters. Performance was evaluated using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. K-Means achieved a higher Silhouette Score (0.483), lower Davies-Bouldin Index (0.725), and higher Calinski-Harabasz Index (145.897), indicating more compact, well-separated clusters. DBSCAN formed more clusters (12) and identified noise points, showing its ability to capture spatial density variations and detect small-scale hotspots. In conclusion, K-Means is more suitable for macro-level mapping of security areas, supporting the allocation of police resources and administrative planning, while DBSCAN is more effective in identifying localized hotspots that require targeted surveillance and rapid response. These findings provide practical insights for policymakers and law enforcement agencies in developing data-driven strategies for urban crime prevention and security management.
Co-Authors Abdullah Ahmad Dzikrullah Adriana, Hannura Afdelia Novianti Afdelia Novianti Ameilea Chealsea Ekaputrie Anang Kurnia Annisa Fitria Asyharul Muala Bariklana, Muhammad Daniar Wahyu Laraswati Dea Ratu Nursidah Dini, Sekti Kartika Duhania Oktasya Mahara Duhania Oktasya Mahara Edy Widodo Elita Nurfitriyani Sulistyo Farhatun Nissa Fitri Amalia Ganjar Fadillah, Ganjar IKA MEIZA MAHARANI IMAM AL MAKSUR Irwan Sutisna Jaka Nugraha Kusman Sadik Malecita Nur Atala Singgih Marcelinus Alfafisurya Setya Adhiwibawa Marcelinus Alfafisurya Setya Adhiwibawa Mega Luna Suliztia Muhammad Hasan Sidiq Kurniawan Muthia Citra Safira Muthia Citra Safira Muthia Dishanur Izzati Nabila Azzahra Haris Putri Nanda Lailatul Humairoh Novendri Isra Asriny Novendri Isra Asriny Novendri Isra Asriny Raden Rara Mentari Ayu Ramadhani Rahmi Novika Harahap Riefyal Arshyzha Mustain Rohmatul Fajriyah ROZA AZIZAH PRIMATIKA Sekti Kartika Dini Shazia Ayesha Azzahra Sheilta Alphenia Sheilta Alphenia SINTIA AFRIYANI Syifa Ayu Anjani WINARSI J. BIDUL Wiranti Nugrah Andini