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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Jurnal Kesehatan Vokasional
Marthabakti, CitraWani
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Computer Science & IT Education Health Professions Mathematics Nursing Public Health

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Analisis Korespondensi Hasil Produksi Budidaya Perikanan Berdasarkan Jenis Budidaya dan Pembagian Wilayah di Indonesia Abdillah, Adrian Wahyu; Marthabakti, CitraWani; Budijono, Gabriella Agnes; Wulandari, Indana Zulfa; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
Jurnal Sains Matematika dan Statistika Vol 11, No 1 (2025): JSMS Januari 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i1.27913

Abstract

Indonesia dikenal sebagai negara maritim karena mayoritas wilayahnya terdiri dari perairan sehingga sektor perikanan menjadi bagian integral dari kehidupan dan ekonomi masyarakat Indonesia. Produk perikanan menjadi salah satu komoditas ekspor utama Indonesia. Adanya perbedaan faktor geografis dan topografis di berbagai wilayah Indonesia berpengaruh terhadap jenis budidaya yang paling cocok pada keberhasilan budidaya perikanan. Oleh karena itu, penelitian menganalisis kecenderungan dari jenis budidaya perikanan dengan wilayah Indonesia secara geografis. Hasil pencatatan dari Produksi Budidaya Perikanan Menurut Provinsi dan Jenis Budidaya pada tahun 2021 digunakan sebagai data sekunder yang akan dianalisis. Pendekatan statikstika yang dipilih yaitu analisis korespondensi dengan jenis budidaya perikanan dan pembagian wilayah Indonesia sebagai variabel analisis. Sebelum dilakukan analisis korespondensi, diperlukan uji independensi yang hasilnya adalah terdapat keterkaitan yang nyata antar kedua variabel. Dari hasil analisis korespondensi diperoleh bahwa jenis budidaya jaring apung tawar, jaring apung laut, tambak intensif, tambak semi intensif, kolam air tenang, kolam air deras, dan minapadi sawah lebih cenderung dikembangkan di wilayah barat. Sedangkan jenis budidaya jaring tancap tawar, tambak sederhana. karamba, dan rumput laut lebih cenderung dikembangkan di wilayah tengah. Dan jenis budidaya laut lainnya lebih cenderung dikembangkan di wilayah timur Indonesia. Dari hasil ini, para pelaku produksi perikanan budidaya dapat menggunakannya sebagai acuan dalam memilih jenis budidaya yang tepat sehingga hasil produksi dapat lebih maksimal.
Analysis of Geographically Weighted Logistic Regression Models with A Bisquare Weighting Matrix on Poverty Status in West Java Saifudin, Toha; Chamidah, Nur; Aldawiyah, Najwa Khoir; Marthabakti, Citrawani; Ramadhanti, Aulia; Nahar, Muhammad Hafidzuddin; Muzakki, Naufal
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36315

Abstract

This research addresses the first Sustainable Development Goal and aims to analyze poverty status in West Java Province, which has the second highest number of poor people in Indonesia. The study employs Geographically Weighted Logistic Regression (GWLR) and compares it with global logistic regression. Influential variables include GDP, unemployment, population density, access to safe water, and roof type (bamboo/wood). Results show that 55.6% of regions are classified as poor, with the GWLR model using a Fixed Bisquare kernel achieving 81.4% accuracy, outperforming global logistic regression at 66.7%. Significant variables vary by region: unemployment rate in Bogor, Depok, and Bekasi; population density in Bekasi, Karawang, and Purwakarta; water access in Sukabumi; and roof type in Indramayu and Bogor. These spatial variations suggest that poverty reduction requires a region-specific approach. Consequently, policies should be formulated considering the priorities and characteristics of each region in West Java Province.
Performance Evaluation of Machine Learning Algorithms for AIDS-Infected Patient Classification Kurniawan, Ardi; Marthabakti, Citrawani; Putri, Larisa Mutiara; Suyono, Billy Christandy; Alisiah, Rindiani Ahmada
Jurnal Kesehatan Vokasional Vol 10, No 3 (2025): August
Publisher : Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkesvo.107716

Abstract

Background: According to UNAIDS (2023), approximately 39.9 million people are living with HIV worldwide, with 1.3 million new cases and 630,000 AIDS-related deaths in 2023. This indicates that HIV/AIDS remains a serious global health threat. Machine learning methods have the potential to improve the accuracy of AIDS infection classification.Objective: This research is aimed to determine the best classification method based on prediction accuracy and to identify the method with the best performance for further analysis.Methods: This research used a quantitative approach by evaluating the performance of machine learning algorithms: Decision Tree, Random Forest, XGBoost, Naive Bayes, and Logistic Regression. Secondary data were obtained from the UCI Machine Learning Repository, comprising 2,000 observations of AIDS patients and 23 variables. Model evaluation used a confusion matrix to calculate accuracy, precision, recall, and F1-score. The best model, logistic regression, was further analyzed with parameter significance tests, odds ratios, and goodness of fit.Results: Logistic regression yielded an accuracy of 88.4%, precision and recall of 90%, and the highest F1-score. Variables significant to AIDS were: time, preanti, symptom, offtrt, and cd420. The model passed the Hosmer and Lemeshow test (p-value = 0.365) with a Nagelkerke R-Square of 0.642.Conclusion: Machine learning approaches, particularly logistic regression, support early detection of AIDS and data-driven medical decision-making.
Co-Authors Abdillah, Adrian Wahyu Aldawiyah, Najwa Khoir Alisiah, Rindiani Ahmada Ardi Kurniawan Budijono, Gabriella Agnes Dita Amelia Elly Ana M. Fariz Fadillah Mardianto Muzakki, Naufal Nahar, Muhammad Hafidzuddin Nur Chamidah Putri, Larisa Mutiara Ramadhanti, Aulia Suyono, Billy Christandy Toha Saifudin Wulandari, Indana Zulfa