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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kesehatan Manarang J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Contemporary Mathematics and Applications (ConMathA) Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Pendidikan Matematika Limits: Journal of Mathematics and Its Applications
Ardi Kurniawan
Airlangga University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Analisis Hubungan Media Pembelajaran Konvensional dan Digital terhadap Learning Gain dan Partisipasi Aktif Siswa pada Pelajaran Matematika dengan Uji Chi-square dan Cramer’s V Hanny Valida; Ardi Kurniawan
Jurnal Pendidikan Matematika Vol. 3 No. 1 (2025): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v3i1.2193

Abstract

Penelitian ini bertujuan untuk menganalisis hubungan antara media pembelajaran konvensional dan digital terhadap learning gain dan partisipasi aktif siswa pada mata pelajaran Matematika di SD Kusuma Putra Surabaya. Penelitian menggunakan pendekatan kuantitatif dengan jenis komparatif. Sampel terdiri atas 40 siswa kelas 5 yang dibagi menjadi dua kelompok berdasarkan jenis media pembelajaran yang digunakan. Data dikumpulkan melalui tes (pre-test dan post-test) serta observasi partisipasi aktif. Analisis dilakukan menggunakan uji Chi-square untuk mengetahui signifikansi hubungan dan koefisien Cramer’s V untuk menentukan kekuatan hubungan antarvariabel. Hasil penelitian menunjukkan adanya hubungan yang signifikan. antara media pembelajaran dan learning gain siswa (p-value = 0,004; Cramer’s V = 0,27), namun tidak terdapat hubungan signifikan antara media pembelajaran dan partisipasi aktif siswa (p-value = 0,924; Cramer’s V = 0,0039). Dengan demikian, media digital memiliki hubungan yang signifikan dengan peningkatan hasil belajar, namun tidak menunjukkan hubungan yang berarti dengan tingkat partisipasi aktif siswa.
TRAINING ON EARLY STUNTING DETECTION USING WEB AND R-SHINY APPLICATIONS FOR COMMUNITY HEALTH WORKERS (POSYANDU) IN THE SONGGON COMMUNITY HEALTH CENTER CATCHMENT AREA, BANYUWANGI REGENCY Nur Chamidah; Ardi Kurniawan; Toha Saifudin; Raaulia Gita Nafsi; Mia Khoirunnisa; Fa’iqotus Zuqna Dwi Syauqie; Dwika Maya Harsanti; Verina Tita Nabila; Naufal Ramadhan Al Akhwal Siregar
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 10 No. 1 (2026): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

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

Abstract

Stunting is a condition that reflects the nutritional status of toddlers and serves as a crucial indicator for monitoring their growth and development. The prevalence of stunting in East Java Province was recorded at 19.2% in 2022, indicating that the province still faces serious challenges requiring sustained intervention. However, monitoring efforts at the local level, particularly within the Songgon Public Health Center (Puskesmas) working area, still encounter technical obstacles such as inconsistent and inaccurate nutritional data recording systems, which risk compromising the validity of early detection. To address these issues, this community service activity aimed to equip Posyandu cadres with nutritional knowledge and technical skills in utilizing a Web-based and R-Shiny early detection application. The application allows users to input toddler anthropometric data (Weight-for-Age, Height-for-Age, and BMI-for-Age) and automatically generates growth charts based on reference standards. It also integrates National Identification Number (NIK) inputs to ensure data validity and prevent duplication. The activity was conducted on August 2, 2025, involving 49 cadres from the Songgon Health Center working area. Evaluation results showed a significant increase in competence, marked by a higher average post-test score (90.204) compared to the pre-test score (77.007), with a paired t-test p-value of 0.000. Participants' satisfaction levels were also categorized as excellent, with average scores exceeding 85 across all indicators. Through intensive mentoring and an accurate local data-driven approach, this program is expected to serve as an adaptive, modern community service model that can be replicated to accelerate stunting reduction.
Modeling Risk Factors of Acute Respiratory Infections using Logistic Regression and Multivariate Adaptive Regression Splines Ardi Kurniawan; Nathania Fauziah; Arinda Mahadesyawardani; Syifa’ Azizah Putri Gunawan; Aurellia Calista Anggakusuma
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33833

Abstract

Acute Respiratory Infections (ARI) remain a leading cause of morbidity among toddlers, partic ularly in regions with limited healthcare access. This study aimed to model the risk factors of ARI in toddlers using Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS). Using secondary data from Southeast Aceh, seven predictor variables were analyzed, including ma ternal characteristics, breastfeeding status, and household conditions. Both models were statisti cally significant in identifying key predictors. Logistic regression showed superior performance with 86.96% accuracy, 85.00% precision, 91.89% recall, 81.25% specificity, and 88.30% F1-score. In contrast, MARS achieved a higher recall (97.30%) but lower specificity (62.50%), indicating higher sensitivity but a greater likelihood of false positives. Exclusive breastfeeding, home ventilation, and housing density were significant predictors in both models. Overall, logistic regression was found to be the more reliable and interpretable method, offering better balance in classification metrics. These f indings support the use of logistic regression for identifying ARI risk factors in similar contexts and contribute to improved data-driven public health strategies aimed at reducing ARI incidence among vulnerable populations.
Bayes estimation of a two-parameter exponential distribution and its implementation Ardi Kurniawan; Johanna Tania Victory; Toha Saifudin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26015

Abstract

Life test data analysis is a statistical method used to analyze time data until a certain event occurs. If the life test data is produced after the experiment has been running for a set amount of time, the life time data may be type I censored data. When conducting observations for survival analysis, it is anticipated that the data would conform to a specific probability distribution. Meanwhile, to determine the characteristics of a population, parameter estimation is carried out. The purpose of this study is to use the linear exponential loss function method to derive parameter estimators from the exponential distribution of two parameters on type I censored data. The prior distribution used is a non-informative prior with the determination technique using the Jeffrey’s method. Based on the research results that have been obtained, application is carried out on real data. This data is data on the length of time employees have worked before they experienced attrition with a censorship limit based on age, namely 58 years, obtained from the Kaggle.com website. Based on the estimation results, the average length of work for employees is 6.29427 years. This shows that employees tend to experience attrition after working for a relatively long period of time.
Comparing Multivariate Adaptive Regression Splines and Machine Learning Methods for Classifying Pneumonia in Indonesian Toddlers Ardi Kurniawan; Nur Azizah; Sheila Sevira Asteriska Naura
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32418

Abstract

Pneumonia is a type of infectious and contagious respiratory disease that causes death in toddlers. According to the Indonesian Ministry of Health (2024), the coverage of pneumonia among toddlers in 2023 was 36.95% with a total of 416,435 cases. This study aims to model and classify the pneumonia status of toddlers in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method and several machine learning methods, such as logistic regression, K-NN, random forest, and SVM. This study uses secondary data from the Survei Kesehatan Indonesia in 2023 and the Profil Kesehatan Indonesia in 2023, including variables such as the percentage of toddlers health service coverage, low birth weight babies, population density, percentage of malnutrition in toddlers, prevalence of smoking in the population aged ≥10 years in the last 1 month, percentage of toddlers who are exclusively breastfed, and percentage of toddlers who have incomplete basic immunization. The best model obtained using the MARS method is with BF = 14, MI = 2, and MO = 3. This model produces a GCV value of 0.122 and R-Square of 82.9%, which shows good prediction performance. The classification results show that the MARS method is superior to the logistic regression, K-NN, random forest, and SVM methods with an accuracy rate of 97.06%.
MODELING AND SEGMENTATION OF FACTORS AFFECTING HUMAN DEVELOPMENT IN ISLANDS OF JAVA USING FIMIX PLS METHOD WITH MEDIATION EFFECT Muhammad Rosyid Ridho Az Zuhro; Ardi Kurniawan; Dita Amelia; Idrus Syahzaqi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0397-0412

Abstract

Human development is a key indicator used to assess the quality of a country's human resources. Although Indonesia's HDI has experienced a significant increase of 75.02 in 2024, inequality is still a pressing issue, especially in terms of gender representation in the workforce. This study aims to identify the influence of poverty, economic, health, employment and education factors on human development in Java Island by considering gender equality as a mediating variable. The data used in the study is limited to 119 districts/cities in Java Island and sourced from BPS publications, the Health Office and the Education Office. The novelty of this study lies in the use of the Finite Mixture Partial Least Square (FIMIX-PLS) approach with mediation effects which is rarely applied in human development research in Indonesia, as well as allowing the identification of latent population heterogeneity and region-based segmentation. The results of this method reveal two distinct district/city segments in Java, with Segment 1 dominated by the variables in this study that have significant direct and indirect effects through the mediation of gender equality on human development, while Segment 2 has characteristics that emphasize the effect of gender equality. Given these differences in characteristics, it is important that contextual and regional segmentation-based development policies are designed by local and central governments. Statistical segmentation approaches such as FIMIX-PLS make a significant contribution to more targeted policy making. By changing the type of intervention according to specific problems, the government can allocate resources more effectively. This supports the achievement of SDG-10 in reducing inequality.
BAYESIAN ESTIMATION OF THE SCALE PARAMETER OF THE WEIBULL DISTRIBUTION USING THE LINEX AND ITS APPLICATION TO STROKE PATIENT DATA Tentri Ryan Rahmanita; Ardi Kurniawan; Elly Ana; Sediono Sediono; Dita Amelia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0413-0426

Abstract

Survival analysis is used to study the timing of an event, such as recovery or death, in the context of medical data. One of the diseases that many people suffer from is stroke. Based on the survey results, the number of stroke sufferers in Indonesia reached 8.3% of 1000 people in Indonesia continues to increase every year, especially among the elderly. The research conducted aims to model the estimation of the type III censored Weibull distribution parameters with the Bayesian Linear Exponential Loss Function (LINEX) method. This study uses secondary data on stroke patients in the period January-November 2024 with a sample of 62 patients at the Haji Surabaya Regional General Hospital. Weibull distribution model with Bayesian approach using Linear Exponential Loss Function (LINEX) was applied to estimate the distribution parameters and survival function. The estimation results show that the parameter α is 6.32342 with an average hospitalization time of 5.9151646 days. MSE value is 0.000270555, which indicates that the estimation model is more accurate in predicting data for the length of hospitalization for stroke patients at the Haji Surabaya Regional General Hospital. The probability value of the survival function of stroke patients who have been hospitalized on the 5th day shows a probability of 82.4% so that no further hospitalization is needed, which indicates that the patient's health condition is improving. In addition, the hazard function analysis shows that the longer a patient is hospitalized, the greater the risk of the patient not recovering.
Co-Authors Adelia Frielady Yosifa Adelia Frielady Yosifa Ana, Elly Aprilia Prastyaningrum Arinda Mahadesyawardani Aurellia Calista Anggakusuma Azizatul Kholidiyah Christabel Lee Angie Sugianto Deby Victoria Dita Amelia Dita Amelia Dwika Maya Harsanti Elly Ana Fa’iqotus Zuqna Dwi Syauqie Hanny Valida Idrus Syahzaqi Johanna Tania Victory M. Fariz Fadillah Mardianto Made Riyo Ary Permana Mia Khoirunnisa Muhammad Rosyid Ridho Az Zuhro Nabila Shafa Aflaha Nathania Fauziah Naufal Ramadhan Al Akhwal Siregar Ni Wayan Widya Septia Sari Nur Azizah Nur Chamidah Putri Fardha Asa Oktavia Hans Putri Nur Farida Raaulia Gita Nafsi Ruth Hosana Sabrina Salsa Oktavia Salsabylla Nada Apsariny Sediono, Sediono Sheila Sevira Asteriska Naura Suliyanto Syifa’ Azizah Putri Gunawan Tentri Ryan Rahmanita Toha Saifudin Vanisia Suci Ananda Verina Tita Nabila Victoria Anggia Alexandra Vrisca Natalia Putri Wardhani Yovita Karin Kalista