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All Journal PROSIDING SEMINAR NASIONAL Jurnal Statistika Universitas Muhammadiyah Semarang Value Added: Majalah Ekonomi & Bisnis The Journal of Educational Development Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Square : Journal of Mathematics and Mathematics Education Jurnal Litbang Edusaintech Parameter: Journal of Statistics Journal of Data Insights Prosiding Seminar Nasional Unimus Jurnal Meteorologi dan Geofisika
Rochdi Wasono
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Nursing Physics Public Health Social Sciences

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Pemodelan Tingkat Kemiskinan di Papua Barat dengan Pendekatan Binary Logistic Regression Aini, Anissa Nur; Octario Ashar, Andika Udistiyan; Lestari, Talia Indah; Nur, Indah Manfaati; Wasono, Rochdi
Square : Journal of Mathematics and Mathematics Education Vol 5, No 2 (2023)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2023.5.2.17169

Abstract

Binary logistic regression model is a regression model used to predict the probability of a specific event occurring or not occurring based on predictor variable. In this model, the response variable is binary, meaning it has only two possible categories: a “failure”category and a “success” category. West Papua is included in the list of  seven provinces that are the main focus of efforts to combat extreme poverty. Therefore, it is necessary to monitor the factors that need to be considered in order to prevent an increase in the poverty rate. To identify the factors influencing the poverty rate in Papua Barat, the research method used is binary logistic regression modeling, which assesses the influence of independent variables on the poverty rate in West Papua. So the results obtained from this study are from three variables, namely the open unemployment rate, average per capita expenditure, and gross regional domestic product have a significant effect on the poverty rate in West Papua with a classification accuracy of 100%.
K-Nearest Neighbor Algorithm in Classification of Stunting Detection Dataset: Algoritma K-Nearest Neighbor dalam Klasifikasi Dataset Deteksi Stunting Lea Angelina; Saeful Amri; M Al Haris; Rochdi Wasono; Erna Julia Nanga; Faninda Aidina Fitri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.752

Abstract

Stunting is a nutritional problem that can affect children's physical growth and cognitive development and has a long-term impact on the quality of future generations. Early detection of stunting is crucial to enable timely and effective interventions. As technology advances, machine learning algorithms such as K-Nearest Neighbors (KNN) offer potential solutions to improve the accuracy of stunting risk classification. This study aims to design a classification model based on the K-Nearest Neighbors (KNN) algorithm in the early detection of stunting risk in toddlers. This research uses the 2024 stunting dataset obtained from Kaggle. The data is analyzed through the stages of cleaning, transformation, and division into training and testing data. The KNN model was tested with various K values to determine the optimal value. The results showed that the KNN model with a value of K=8 resulted in an accuracy of 93.80%, F1-Score of 93.65%, precision of 93.63%, and recall of 93.79%. This shows that KNN is reliable in classifying the nutritional status of toddlers and can be applied in stunting prevention efforts using more accurate data. This research contributes to developing machine learning-based classification systems that can support decision-making in public health programs, especially in reducing stunting rates.
Co-Authors Aini, Anissa Nur Alwan Fadlurohman Amri, Saeful Andayani Nurfaizah Anissatush Sholiha Arini Rizky Wahyuningtyas Aulia Fadhli Boer Chendy Nofiantika Rosulya Putri Devy Noviani Dhevi Ratna Fitriani Dominikus Leki Sogen Erna Julia Nanga Evi Ardiati Sazaen Faninda Aidina Fitri Fauzi, Fatkhurokhman Gabriella Hilary Wenur Husna, Rizqa El Iis Istiqomah Iis Widya Harmoko Imaroh Izzatun Nisa Indah Manfaati Nur Iqbal Kharisudin Isna Nur Azizah Iswahyudi Joko Suprayitno Joko Sutarto Juwita Rahayu Karim, Abdul Kurniawati, Eka Lea Angelina Lestari, Talia Indah M. Al Haris Millenia Winadya Putri Moh Yamin Darsyah Moh. Yamin Darsyah Monica Firda Agustina Nila Amelinda Putri Octario Ashar, Andika Udistiyan Prizka Rismawati Arum Rahman Hidayat Rena Muntafiah Samsudi - Samsudi -, Samsudi Siti Hajar Rahmawati Soesanto - Soesanto -, Soesanto Tiani Wahyu Utami Ujiati Suci Rahayu Wahyu Putri Pratiwii