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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi 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 Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences 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.
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 Dwika Maya Harsanti Fa’iqotus Zuqna Dwi Syauqie Hanny Valida M. Fariz Fadillah Mardianto Made Riyo Ary Permana Mia Khoirunnisa Nabila Shafa Aflaha Nathania Fauziah Naufal Ramadhan Al Akhwal Siregar Ni Wayan Widya Septia Sari Nur Chamidah Putri Fardha Asa Oktavia Hans Putri Nur Farida Raaulia Gita Nafsi Ruth Hosana Sabrina Salsa Oktavia Salsabylla Nada Apsariny Sediono, Sediono Suliyanto Syifa’ Azizah Putri Gunawan Toha Saifudin Vanisia Suci Ananda Verina Tita Nabila Victoria Anggia Alexandra Vrisca Natalia Putri Wardhani Yovita Karin Kalista