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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Mutiara: Multidiciplinary Scientifict Journal
Lutfiah Maharani Siniwi
Department Of Mathematics, Universitas Jenderal Soedirman, Indonesia
Arts Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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QUERY EXPANSION RANKING PADA ANALISIS SENTIMEN MENGGUNAKAN KLASIFIKASI MULTINOMIAL NAÏVE BAYES (Studi Kasus : Ulasan Aplikasi Shopee pada Hari Belanja Online Nasional 2020) Lutfiah Maharani Siniwi; Alan Prahutama; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32795

Abstract

Shopee is one of the e-commerce sites that has many users in Indonesia. Shopee provides various attractive promos on special days such as National Online Shopping Day on December 12. Shopee site was a complete error on December 12, 2020. Complaints and opinions of Shopee users were also shared through various media, one of them was Google Play Store. Sentiment analysis was used to see the user's response to the Shopee’s incident. Sentiment analysis results can be extracted to obtain information regarding positive or negative reviews from Shopee users. Sentiment analysis was performed using the Multinomial Naïve Bayes classification. the simplest method of probability classification, but it is sensitive to feature selection so that the amount of data is determined by the results of feature selection Query Expansion Ranking. The algorithm that has the highest accuracy and kappa statistic is the best algorithm in classifying Shopee’s users sentiment. The results showed that the classification performance using Multinomial Naïve Bayes with 80% of the features (terms) which have the highest Query Expansion Ranking value was obtained at the accuracy and kappa statistics values are 89% and 77.62%. This means that Multinomial Nave Bayes has a good performance in classifying reviews and the number of features used affects the performance results obtained.
Pengelompokan Kemiskinan Di Provinsi Jawa Timur Tahun 2024 Dengan Pendekatan Algoritma K-Means Dan K-Means++ Amalia, Suci; Nikmah, Annisatul; Siniwi, Lutfiah Maharani
Mutiara: Multidiciplinary Scientifict Journal Vol. 3 No. 8 (2025): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v3i8.407

Abstract

Kemiskinan merupakan masalah kompleks yang mencerminkan kesejahteraan masyarakat dan kinerja pembangunan suatu daerah. Di Provinsi Jawa Timur, walaupun persentase kemiskinan mengalami penurunan hingga mencapai 9,79% pada tahun 2024, namun kesenjangan antarwilayah masih menjadi tantangan. Penelitian ini bertujuan untuk mengetahui karakteristik dan mengelompokkan Kabupaten/Kota di Jawa Timur berdasarkan indikator kemiskinan, yakni jumlah penduduk miskin, tingkat pengangguran terbuka (TPT), harapan lama sekolah (HLS), rata-rata lama sekolah (RLS), tingkat partisipasi angkatan kerja (TPAK), dan gini ratio. Algoritma clustering yang digunakan adalah K-Means dan K-Means++, untuk membandingkan hasil dan performa keduanya. Penentuan jumlah cluster optimum didasarkan pada nilai Davies-Bouldin Index (DBI) dan Silhouette Score, untuk evaluasi performa algoritma dilihat melalui nilai inertia. Hasil penelitian menunjukkan bahwa jumlah cluster optimum yang terbentuk adalah tiga cluster. Cluster 1 terdiri dari wilayah dengan kemiskinan struktural, Cluster 2 terdiri dari wilayah dengan kemiskinan fungsional, dan Cluster 3 mencerminkan wilayah dengan kondisi indikator yang relatif sedang. Berdasarkan hasil evaluasi, algoritma K-Means++ menghasilkan nilai inertia yang lebih rendah dibandingkan K-Means, serta nilai DBI dan Silhouette yang lebih baik, sehingga dapat disimpulkan bahwa K-Means++ lebih optimal. Hasil penelitian ini diharapkan dapat menjadi acuan dalam merancang strategi kebijakan penanggulangan kemiskinan berbasis karakteristik wilayah yang lebih tepat sasaran.
ORDINAL XGBOOST FOR MULTICLASS NUTRITIONAL STATUS CLASSIFICATION WITH IMBALANCED DATA Luthfia Hanun Yuli Arini; Lutfiah Maharani Siniwi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.45-60

Abstract

Stunting is a major global health problem that affects children’s physical growth and cognitive development, particularly in developing countries. The classification of toddlers’ nutritional status to detect stunting risk often faces two primary challenges: the ordinal nature of the labels and the imbalance in class distribution, where minority classes such as stunted and tall are much smaller than the majority class (normal). This study aims to develop an Ordinal Extreme Gradient Boosting (Ordinal XGBoost) method using a Binary Decomposition approach to classify toddlers’ nutritional status in imbalanced ordinal data. Secondary data from 100 respondents were analyzed, with 80% allocatedfor training and 20% for testing. The Binary Decomposition approach transforms the three-class ordinal classification problem into two binary subproblems, each trained using XGBoost with weighted logistic loss to address class imbalance. Model performance was evaluated using four key metrics: accuracy, ordinal Mean Absolute Error (MAE), Quadratic Weighted Kappa (QWK), and Macro-F1. Results showed an accuracy of 70%, ordinal MAE of 0.30, QWK of 0.45, and Macro-F1 of 0.53. The MAE and QWK values indicate the model’s ability to preserve class order while reducing large prediction jumps, although performance on minority classes remains limited. These findings suggest that the proposed approach is effective for imbalanced ordinal data and has potential applications in toddler nutritional status monitoring systems.
Co-Authors Alan Prahutama Arief Rachman Hakim Luthfia Hanun Yuli Arini Nikmah, Annisatul Suci Amalia