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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Variance : Journal of Statistics and Its Applications Jurnal Pengabdian Masyarakat Indonesia Leibniz: Jurnal Matematika
Jauhara Rana Budiani, Jauhara Rana
Jurusan Statistika Institut Teknologi Sepuluh Nopember
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Pemanfaatan Sampah Organik Menjadi Pupuk Kompos di Desa Ngeper Kabupaten Bojonegoro Sahri, Sahri; Budiani, Jauhara Rana; Malik, Syaifudin; Aqiyyah, Zahrotul
Jurnal Pengabdian Masyarakat Indonesia Vol 5 No 1 (2025): JPMI - February 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.3352

Abstract

Desa Ngeper memiliki potensi besar dalam pertanian, namun pengelolaan sampah organik sebagai sumber daya lokal karena masih minim pengetahuan,ketrampilan dan kurangnya kesadaran masayrakat.. sehingga sampah organik menumpuk di pembuangan sampah. Melalui program ini, dilakukan serangkaian kegiatan yang meliputi sosialisasi, pelatihan pembuatan pupuk kompos, dan pendampingan penerapannya pada budidaya sayuran. Kegiatan pengabdian masyarakat ini bertujuan untuk mengedukasi dan meningkatkan keterampilan masyarakat Desa Ngeper, Kecamatan Padangan, Kabupaten Bojonegoro, dalam memanfaatkan sampah organik menjadi pupuk kompos guna mendukung budidaya sayuran.  Metode yang digunakan adalah pendekatan partisipatif, di mana masyarakat dilibatkan secara aktif dalam setiap tahap kegiatan. Hasil kegiatan menunjukkan bahwa masyarakat mampu memanfaatkan sampah organik rumah tangga menjadi pupuk kompos yang berkualitas, yang selanjutnya diaplikasikan pada lahan sayuran. Pemanfaatan pupuk kompos ini tidak hanya mengurangi volume sampah, tetapi juga meningkatkan hasil panen sayuran secara signifikan. Selain itu, kegiatan ini berhasil meningkatkan kesadaran masyarakat terhadap pentingnya pengelolaan lingkungan yang berkelanjutan.
THE DESIGN OF STANDARD GRAPH FOR TODDLER GROWTH USES NONPARAMETRIC PENALIZED SPLINE REGRESSION Kartini, Alif Yuanita; Budiani, Jauhara Rana; Arifat, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp917-926

Abstract

One way to carry out early detection of toddler growth is through the Healthy Way Card (KMS). The KMS used in Indonesia does not describe the growth behavior of toddlers. The KMS used is the standard from the World Health Organization (WHO). Apart from that, the growth chart for toddlers at each age will show different patterns. This pattern does not form a linear graph or a particular pattern. Therefore, the Nonparametric Regression method was used using a penalized spline estimator which produces a local Indonesian standard KMS which is used to assess the growth of toddlers. Designing KMS with a confidence interval approach to nonparametric regression values using a penalized spline estimator. Data was obtained from the results of the recapitulation of Posyandu in Bojonegoro from January to December 2023, totaling 120 data. The variables used in this research are the toddler's weight (y) as the response variable and the toddler's age (x) as the predictor variable. In nonparametric regression modeling using a penalized spline estimator with several combinations of numbers and knot point locations. Selection of optimal knot points using minimum Generalized Cross Validation (GCV). Based on the results of the analysis, it shows that there are different times of weight change for male toddlers and female toddlers in Bojonegoro. The weight of male toddlers in Bojonegoro has 3 patterns of change, namely the weight of male toddlers increases drastically until the age of 16 months, then increases slowly until the age of 55 months. Then the weight of male toddlers will increase again drastically after the age of 55 months. Meanwhile, the weight of female toddlers in Bojonegoro has three patterns of change, namely the weight of female toddlers increases drastically until the age of 5 months, then increases slowly until the age of 15 months, and again increases drastically after the age of 15 months. This can be caused by physical differences in babies based on gender. To create a standard chart for toddlers' weight growth based on age, it was analyzed by calculating the percentile values consisting of P3, P15, P50, P85, and P97 for each toddler age category.
MODELING THE NUMBER OF POOR POPULATION IN EAST JAVA USING QUANTILE REGRESSION Kartini, Alif Yuanita; Huda, Tisa Dwi Julianti; Budiani, Jauhara Rana
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page105-112

Abstract

The economic development of East Java continues to increase every year. However, this increase is not directly proportional to a significant decrease in poverty rates. Therefore, research is needed to determine the factors influencing poverty in East Java. This is important because it can be used as a consideration for the East Java Provincial Government in designing strategies to reduce poverty. In the case of the number of poor people in East Java, there are outlier data, so the quantile regression method is used to overcome this. This study uses several quantile values, namely 0.25, 0.50 and 0.75. Based on the results of the quantile regression parameter estimation, one significant category at all quantile levels is the Average Length of Schooling variable. From the quantile regression model, four categories of Poor Population are obtained: low, medium, high, and very high. Based on the classification of the Poor Population in East Java in 2023, there are four districts/cities with a low number of poor people, 18 districts/cities with a moderate number of poor people, and 16 districts/cities with a high number of poor people.
COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE Budiani, Jauhara Rana; Mahmudah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2739-2750

Abstract

The heart is a vital organ in the human body that functions to pump blood throughout the body and to the lungs. The heart is located in the chest cavity. The heart is the main force that drives human life. Therefore, if there is a disturbance in heart function, this can cause a decrease in quality of life to death, one of which is heart failure. Heart failure, if not diagnosed and treated quickly, will result in death. Based on findings showing the high death rate due to heart failure, a classification is needed to predict heart failure using machine learning methods. Machine learning can help predict this disease to improve early detection and more accurate medical decision-making. This study focuses on predicting the likelihood of a patient experiencing heart failure. The machine learning algorithm method used is supervised machine learning classification, including decision trees, random forests, naïve bayes, SVM, and K-NN. The results showed that the best method for predicting heart failure was Random Forest with an accuracy of 74.35%, followed by SVM with an accuracy of 69.23%. Meanwhile, Naïve Bayes had the lowest accuracy of 51.28%. Based on these findings, Random Forest is recommended as the best method for heart failure prediction due to its ability to handle data complexity and provide more stable results. Once the best algorithm is obtained, the prediction results and early detection of heart failure will be more accurate.
Pemodelan Kasus Kemiskinan di Provinsi Jawa Timur dengan Algoritma Geographically Weighted Logistic Regression Budiani, Jauhara Rana; Hendarsyah, Tantra Pratama; Mahmudah, Nur
Leibniz: Jurnal Matematika Vol. 6 No. 01 (2026): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v6i01.693

Abstract

Kemiskinan di Jawa Timur pada tahun 2024 mencapai 9,79% dan menunjukkan variasi spasial antar kabupaten/kota. Penelitian ini bertujuan untuk mengklasifikasikan status kemiskinan menggunakan pendekatan Geographically Weighted Logistic Regression (GWLR). Data bersumber dari Badan Pusat Statistik Provinsi Jawa Timur tahun 2024 dengan 38 kabupaten/kota sebagai unit analisis. Variabel prediktor meliputi pengeluaran per kapita, tingkat pengangguran terbuka, rata-rata lama sekolah, tingkat partisipasi angkatan kerja, dan angka harapan hidup. Analisis diawali dengan pengujian autokorelasi spasial dan heterogenitas wilayah, kemudian dilanjutkan dengan pemodelan regresi logistik biner dan GWLR menggunakan pembobot Adaptive Gaussian Kernel dengan pemilihan bandwidth melalui cross validation. Kinerja model dievaluasi menggunakan nilai Akaike Information Criterion (AIC) dan akurasi klasifikasi. Hasil menunjukkan bahwa model GWLR memiliki kinerja lebih baik dibandingkan regresi logistik global dengan nilai AIC sebesar 47,99 dan tingkat akurasi sebesar 74%. Variabel rata-rata lama sekolah menjadi faktor paling dominan di sebagian besar wilayah, sementara pengaruh variabel lainnya bersifat spasial dan tidak homogen. Temuan ini menegaskan pentingnya pendekatan klasifikasi berbasis spasial dalam analisis kemiskinan.
Classification Of Student Depression Using Support Vector Machine Modelling and Backward Elimination Sabar, Rohmat Abidin; Pajri, Afril Efan; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12203

Abstract

Depression among university students has become a serious mental health concern that can negatively affect academic performance and overall well-being. Early detection of Depression is essential to provide timely support and preventive interventions. This study proposes a machine learning approach to classify student Depression using a Support Vector Machine (SVM) combined with Backward Elimination (BE) for feature selection. The dataset used in this research was obtained from a public repository and consists of 502 student records with multiple psychological and demographic attributes. Data preprocessing included categorical encoding and Min–Max normalization, followed by an 80:20 split for training and testing. Experimental results show that the baseline SVM model achieved an accuracy of 0.9208, while the application of Backward Elimination improved the performance to 0.9604. In addition, precision, recall, and F1-score also showed notable improvements, indicating a reduction in misclassification, particularly for non-depressed students. These findings demonstrate that integrating feature selection with SVM can enhance classification performance and provide a more efficient model for supporting early Depression detection among university students.
Sentiment Analysis of the Free Nutritious Meal Program (MBG) on Social Media X (Twitter) Using K-Nearest Neighbor and Artificial Neural Network Hakim, Fernanda Amri; Prastya, Ifnu Wisma Dwi; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12205

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) is a national policy initiated by the Indonesian government to improve public nutritional status, particularly among children and vulnerable groups. Since its implementation, the program has generated extensive public discussion on social media, reflecting diverse opinions, support, and criticism. This study aims to analyze public sentiment toward the MBG program on social media X (Twitter) using machine learning-based text classification methods. A total of 9,038 Indonesian-language tweets were collected and processed through text preprocessing, semi-automatic sentiment labeling with manual validation, and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. Sentiments were classified into three categories: positive, neutral, and negative. The performance of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and ANN with class balancing using Synthetic Minority Over-Sampling Technique (ANN + SMOTE) was evaluated using accuracy, precision, recall, and F1-score metrics supported by confusion matrix analysis. The results indicate that the ANN + SMOTE model achieved the highest performance with an accuracy of 93.58%, outperforming ANN (92.59%) and KNN (86.28%). The sentiment distribution indicates that public opinion toward the MBG program is predominantly neutral (52.1%), followed by positive (40.0%) and negative (7.9%) sentiments. These findings suggest that while the MBG program is generally well received, negative sentiments provide important feedback related to program implementation and governance.
Co-Authors Afril Efan Pajri Aqiyyah, Zahrotul Arifat, Muhammad Hakim, Fernanda Amri Hendarsyah, Tantra Pratama Huda, Tisa Dwi Julianti Kartini, Alif Yuanita Malik, Syaifudin Nur Mahmudah Prastya, Ifnu Wisma Dwi Sabar, Rohmat Abidin Sahri Sahri