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Kartini, Alif Yuanita
Universitas Nadhatul Ulama Sunan Giri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Implementasi Machine Learning Model sebagai Sistem Prediksi Penyakit Breast Cancer Cahyani, Nita; Irsyada, Rahmat; Kartini, Alif Yuanita
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.5209

Abstract

Breast Cancer atau Kanker payudara adalah penyakit yang paling umum ditemukan pada wanita di seluruh dunia. Setiap perkembangan untuk prediksi dan diagnosis penyakit kanker merupakan modal penting untuk hidup sehat. Sehingga, akurasi tinggi dalam prediksi kanker penting untuk memperbarui aspek pengobatan dan standar kelangsungan hidup pasien. Teknik Machine Learning (ML) merupakan aplikasi dari Artificial Intelligence (AI) yang dapat memberikan kontribusi besar pada proses prediksi dan diagnosis dini kanker payudara, dan telah terbukti sebagai teknik yang kuat. Dalam penelitian ini, diterapkan algoritma Machine Learning yaitu metode single: Support Vector Machine (SVM), Random Forest, Logistic Regression, dan K-Nearest Neighbors (KNN) dan metode ensemble yaitu SMOTE-Boosting dan SMOTE-Bagging pada dataset Breast Cancer di Bojonegoro. Tujuan dari penelitian ini Mendaptakan ketepatan klasifikasi atau prediksi breast cancer khususnya studi kasus di Bojonegoro dengan tingkat kinerja yang lebih baik. Nilai akurasi yang terbaik pada metode single yaitu model Random Forest (RF) sebesar 95,65% untuk data testing, 100% untuk data training sedangkan untuk metode ensembel SMOTE-Boosting Random Forest (RF) sebesar 100% untuk data testing, 100% untuk data training dan SMOTE-Bagging RF sebesar 97% untuk data training dan 100% untuk data testing. Sehingga SMOTE-Boosting RF dapat dijadikan analisis prediksi yang terbaik dalam penelitian ini. Hasil ini dapat digunakan di masa depan untuk memprediksi penyakit lainnya.
IMPLEMENTATION OF MIXED GEOGRAPHICALLY WEIGHTED REGRESSION MODEL TO ANALYZE SOCIAL ASSISTANCE BUDGET IN EAST JAVA Utami, Putri; Nurdiansyah, Denny; Kartini, Alif Yuanita
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08204

Abstract

Background - Social assistance (BANSOS) is aid provided by the government to low-income communities in the form of money, goods, or services. Understanding the allocation and influencing factors of social assistance in East Java is crucial for effective distribution. Mixed Geographically Weighted Regression (MGWR) combines global and local regression models to address spatial variability in the data. Purpose – This study aims to develop an MGWR model with a fixed kernel weighting function for the social assistance budget in East Java for 2022. The specific objectives are to identify factors affecting the budget and determine the best model that represents these global and local relationships. Methodology – The study employs the Mixed Geographically Weighted Regression (MGWR) method with a fixed Gaussian kernel to analyze social assistance budget data and economic factors in East Java for 2022. Models OLS, GWR, and MGWR are applied and evaluated using the Akaike Information Criterion (AIC) to identify the best-performing model. Findings – The MGWR model with a fixed Gaussian kernel is the best for the social assistance budget in East Java, yielding a lower AIC compared to OLS and GWR models. The globally influential factor in this model is economic growth (
Analisis Kepuasan Pengguna Jasa Petugas Parkir Dinas Perhubungan Bojonegoro Menggunakan Regresi Logistik Ordinal Winarko, M Teguh Deddy; Kartini, Alif Yuanita
Jurnal Statistika dan Komputasi Vol. 1 No. 1 (2022): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v1i1.442

Abstract

Latar Belakang: Tidak semua petugas parkir Dinas Perhubungan Kabupaten Bojonegoro melaksanakan kerjanya baik dan sesuai dengan standard operational procedure (SOP). Bentuk pembekalan dan sosialisasi oleh dinas terkait sudah diberikan, namun pengguna layanan jasa parkir merasa kurang puas. Untuk menganalisis masalah ini, diterapkan pemodelan regresi logistik ordinal untuk menilai kepuasan pelanggan. Tujuan: Mengetahui tingkat kepuasan dan faktor-faktor yang secara signifikan berpengaruh terhadap tingkat kepuasan pengguna jasa petugas parkir Dinas Perhubungan Kabupaten Bojonegoro. Metode: Metode Penelitian yang digunakan adalah metode kuantitatif berupa analisis regresi logistik ordinal. Digunakan accidental sampling dengan mengambil sampel dari responden yang kebetulan memakai jasa parkir petugas Dinas Perhubungan Kabupaten Bojonegoro. Variabel dependen adalah tingkat kepuasan pengguna jasa petugas parkir yang berskala ordinal dan variabel-variabel independen meliputi tangibles, reliability, responsiveness, emphaty dan assurance. Hasil: Kepuasan pengguna terhadap pelayanan petugas parkir terbesar adalah 35% cukup puas dan terbesar kedua 29% kurang puas. Dari hasil odds ratio, semakin besar tangibles, responsiveness, dan emphaty petugas parkir masing-masing memiliki peluang 2,0719; 5,9793; dan 9,0802 kali lebih besar daripada variabel lainnya terhadap tingkat kepuasaan pengguna petugas parkir. Kesimpulan: Mayoritas pengguna pelayanan petugas parkir kurang puas dan cukup puas. Penerapan regresi logistik ordinal memberikan pengetahuan bahwa tangibles, responsiveness, dan emphaty petugas parkir mempengaruhi kepuasan pengguna.
Penerapan Metode Regresi Linier Berganda Pada Kasus Balita Gizi Buruk Di Kabupaten Bojonegoro Janah, Miftahul; Kartini, Alif Yuanita
Jurnal Statistika dan Komputasi Vol. 1 No. 2 (2022): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v1i2.1170

Abstract

Latar   Belakang: Balita merupakan kelompok paling rentan terhadap masalah gizi apabila ditinjau dari sudut masalah kesehatan dan gizi, dimana balita mengalami siklus pertumbuhan dan perkembangan yang relatif pesat. Salah satu metode untuk menentukan faktor-faktor yang signifikan berpengaruh terhadap terjadinya kasus gizi buruk adalah metode Regresi Linear Berganda. Tujuan: Mendapatkan statistik deskriptif untuk kasus balita gizi buruk beserta variabel prediktornya di kabupaten Bojonegoro tahun 2020, dan mengetahui variabel apa saja yang dianggap signifikan mempengaruhi terjadinya kasus gizi buruk di kabupaten Bojonegoro menggunakan metode Regresi Linier Berganda. Metode: Diberikan metode kuantitatif dengan statistik deskriptif, pen gujian asumsi klasik, dan pengujian parameter Regresi Linear Berganda untuk Persentase Kejadian Balita yang mengalami gizi buruk di kabupaten Bojonegoro. Hasil: Karakteristik kejadian balita gizi buruk di kabupaten Bojonegoro untuk persentase kejadian balita gizi buruk per kecamatan terrendah sebesar 1,03% dan tertinggi 7,22%. Diperoleh variable-variabel yang signifikan memberikan pengaruh negative terhadap Persentase Kejadian Balita yang mengalami gizi buruk Per Kecamatan, yaitu Persentase Balita Ditimbang Empat Kali atau Lebih dalam Enam Bulan Terakhir sebesar -2,117, dan Persentase Balita Kurus Mendapatkan Makanan Tambahan sebesar -0,438. Akurasi model regresi diperoleh R-Square sebesar 74,3%. Kesimpulan: Variabel yang berpengaruh signifikan terhadap kejadian balita yang mengalami gizi buruk adalah Persentase Balita Ditimbang Empat Kali atau Lebih dalam Enam Bulan Terakhir, dan Persentase Balita Kurus Mendapatkan Makanan Tambahan.  
Comparison of Decomposition and Triple Exponential Smoothing Methods to Improve Rice Production Forecasting in East Java Province Lathifah, Nur Aisyatul; Nurdiansyah, Denny; Kartini, Alif Yuanita
Vygotsky: Jurnal Pendidikan Matematika dan Matematika Vol 7 No 1 (2025): Vygotsky: Jurnal Pendidikan Matematika dan Matematika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/voj.v7i1.1119

Abstract

This study forecasts rice production in East Java using Triple Exponential Smoothing (Holt-Winters) and Decomposition. Data includes rice production in dry milled grain (GKG) from January 2018 until December 2023, sourced from the Central Statistics Agency (BPS) of East Java. The analysis identifies the Holt-Winters Multiplicative model as the most effective, with the lowest error values: Mean Absolute Percentage Error (MAPE) of 0.1452, Mean Absolute Deviation (MAD) of 0.1078, and Mean Squared Error (MSE) of 0.0286 during training, and MAPE of 0.1974, MAD of 0.1909, and MSE of 0.0858 during testing. The Holt-Winters Multiplicative model is recommended for future rice production predictions, providing reliable method for accurate forecasting, and aiding in future rice demand planning in East Java.
Pendampingan Legalitas BUM Desa Gunung Jaya Mori sebagai Upaya Pemenuhan Good Corporate Governance Kartini, Alif Yuanita; Anwar, Saeful; Hamdan, Ali
Jurnal SOLMA Vol. 14 No. 1 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

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

Abstract

Background: Desa Mori  mempunyai  BUM Desa Gunung Jaya Mori yang sudah  beroperasi  dengan  unit  usaha  pembenihan ikan air tawar serta usaha penampungan dan penyaluran air baku. Kendala  yang  dihadapi  oleh BUM Desa Gunung Jaya Mori adalah belum  adanya legalitas hukum. Tujuan dari kegiatan Pengabdian kepada Masyarakat ini   adalah   untuk   membantu   legalitas   BUM Desa Gunung Jaya Mori. Metode: yuridis  normatif  dengan pendekatan  perundang-undangan  (statute  approach)  dan  metode empiris  dengan  pendekatan  wawancara. Hasil: adanya penyelenggaraan Musyawarah Desa, penataan  dan  penyesuaian  AD/ART,  Perdes tentang  BUM Desa  dan  tata  lembaga  BUM Desa  sesuai  dengan ketentuan peraturan perundang-undangan, yang selanjutnya  dilakukan  pendaftaran  legalitas BUM Desa melalui  sistem  informasi  kementerian  desa. Kesimpulan: Dalam pendaftaran legalitas BUM Desa diperlukan sinergitas antara para pihak yang terlibat, yakni kepala desa, BPD, pengurus BUM Desa serta dukungan dari pendamping BUM Desa tingkat kecamatan.
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.
AUTOREGRESSIVE DISTRIBUTED LAG MODELING FOR RICE PRICE PREDICTOR ANALYSIS IN BOJONEGORO REGENCY Khoirina, Jami’atul; Nurdiansyah, Denny; Kartini, Alif Yuanita
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09108

Abstract

Rice price fluctuations in Bojonegoro Regency are driven by complex interactions of economic, social, and environmental elements. These dynamics have a direct impact on the welfare of low-income households, making it essential to understand the underlying factors to support effective price stabilization efforts. Addressing this issue requires a comprehensive econometric model capable of capturing both immediate and lagged effects of relevant variables. This study analyzes the main drivers of rice price changes in Bojonegoro Regency by applying the Autoregressive Distributed Lag (ARDL) model. It focuses on how variables such as dried corn prices, rice consumption, harvest area, rice production, and money exchange rates contribute to rice price volatility. The ARDL model is employed to explore both short-term and long-term relationships between selected variables and rice prices. Model selection is guided by performance indicators including the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), R-Square, as well as results from stationarity, cointegration, and classical assumption tests. The study utilizes secondary data sourced from the Bojonegoro Regency Food Security and Agriculture Office and the Bojonegoro Statistics Agency. The optimal model, identified as ARDL (3,4,4,4,4,0), produces an R-Square of 97.13% and the lowest AIC among alternatives. The analysis reveals that dried corn prices, rice consumption, harvest area, and rice production significantly influence rice prices, each with distinct lag structures. The money exchange rate, however, is found to have no significant effect. This study does not account for policy-specific variables or broader external factors such as global climate change or international trade regulations, which may also impact rice prices. Additionally, the availability and quality of secondary data may affect the model’s predictive accuracy. By incorporating lag structures and localized economic factors, this research offers a robust predictive framework tailored to Bojonegoro Regency. It provides practical insights for policymakers aiming to enhance rice price stability and protect household purchasing power.
HYBRID K MEANS-MULTIVARIATE ADAPTIVE REGRESSION SPLINES FOR DISTRIBUTION OF DENGUE FEVER RISK MAPPING IN BOJONEGORO DISTRICT Kartini, Alif Yuanita; Cahyani, Nita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.953 KB) | DOI: 10.30598/barekengvol17iss1pp0313-0322

Abstract

Dengue Hemorrhagic Fever (DHF) is a dangerous disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes’ bites. WHO data shows that almost half of the world's humans are exposed to Dengue Hemorrhagic Fever. The number of mortality caused by dengue disease is around 20,000 every year. In East Java, Bojonegoro District has the highest number of dengue hemorrhagic fever cases (416). To reduce this number, the causative factors need to be known. Additionally, it's important to pinpoint the region or cluster where the variables driving the spread are located so that prevention and treatment efforts are effective. Based on the elements contributing to the transmission of Dengue Hemorrhagic Fever, this study seeks to identify and categorize locations at risk for the spread of the illness. This study uses Hybrid K Means-Multivariate Adaptive Regression Splines (MARS) which is a combination of K-Means and MARS methods in the hope of providing better analytical results. This is because the data was divided into simpler parts by considering the Oakley distance. The results obtained from the K Means-MARS hybrid shows the relationship between response variables and predictor variables for each cluster. There are three clusters of risk for the spread of dengue hemorrhagic fever in Bojonegoro district with categories: high risk cluster, medium risk cluster and low risk cluster. The high risk cluster consists of 7 sub-districts (Baureno, Kepohbaru, Balen, Sumberrejo, Kedungadem, Bojonegoro and Dander). The variables affecting the DHF Sufferer in the high risk cluster were population density (X2), Altitude (X3) and Health Worker (X6). Meanwhile, the medium risk cluster consists of 10 sub-districts (Kalitidu, Kanor, Kapas, Ngasem, Ngraho, Padangan, Sugihwaras, Sukosewu, Tambakrejo, and Trucuk). The variables that affect the DHF Sufferer in the medium cluster are Number of Dead (X1), Population Density (X2) and Health Facility (X5). The low risk cluster consisted of 11 sub-districts (Bubulan, Gayam, Gondang, Kasiman, Kedewan, Malo, Margomulyo, Ngambon, Purwosari, Sekar, and Temayang). The variables affecting the DHF Sufferer rate in the low risk cluster were number of dead (X1) and population density (X2).
Co-Authors Abdul Manaf Jamiluddin Ahmad Tauchid Ali Hamdan Arifat, Muhammad Ayuni, Intan Sri Barata, Mula Bintari Anggi Dwi Sugiarti Denny Nurdiansyah Devy Wulandari Dewi Niswatul Fithriyah Dinda Intan Pramesti Edi Setyanto Hamidatun Nihayah Huda, Tisa Dwi Julianti Ifa Afiana Irsyada, Rahmat Jauhara Rana Budiani, Jauhara Rana Khoirina, Jami’atul Khoirul Wafa Laelatul Nur Ummah Lathifah, Nur Aisyatul M Teguh Deddy Winarko Miftahul Janah Miftahul Janah, Miftahul Moch Nur Faizin Mochamad Nizar Palefi Ma’ady Muhammad Ishlahuddin Nilna Himawati Ningtiyas, Putri Ayu Nita Cahyani Nur Mahmudah Priadana, Benny Widya Puji Aning Nur Nadhifah Putri Utami, Putri Saeful Anwar Sri Rahayu Sufiandi, Enrico Ummi Agustin Yuliana Winarko, M Teguh Deddy Yogi Prana Izza Yunita Dwi Setyoningsih Zakki Alawi