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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Unnes Journal of Mathematics
Harwanti, Nur Achmey Selgi
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Computer Science & IT Education Mathematics

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Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP Sanusi, Ratna Nur Mustika; Wijaya, Galih Kusuma; Harwanti, Nur Achmey Selgi
Unnes Journal of Mathematics Vol. 14 No. 1 (2025): Unnes Journal of Mathematics Volume 1, 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25750

Abstract

This study evaluates Indonesia's Free Nutritious Food Program (MBG) through an innovative dual-method approach combining geospatial clustering and sentiment analysis. Cluster analysis of 38 provinces identified three distinct priority zones: high-priority (Eastern Indonesia), medium-priority (Central Indonesia), and low-priority (Java-Bali-West Sumatra), revealing significant regional disparities. Parallel sentiment analysis of 1,358 social media posts showed 76.6% negative perceptions dominated by food safety concerns ("poisoning," "toxic"), contrasting with 23.4% positive feedback highlighting nutritional benefits. The study makes three key contributions: First, it demonstrates the disconnect between regional needs and implementation quality. Second, it introduces an integrated monitoring framework combining cluster mapping with real-time sentiment tracking. Third, it proposes actionable solutions including a rapid-response task force and targeted communication strategies. These findings provide policymakers with evidence-based tools to simultaneously address geographical inequities and improve program execution in nutrition interventions.
A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means Harwanti, Nur Achmey Selgi; Hendikawati, Putriaji; Sanusi, Ratna Nur Mustika; Pratama, Alfian Adi
Unnes Journal of Mathematics Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25755

Abstract

This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.
PEMBELAJARAN BIG DATA DI PERGURUAN TINGGI: POTENSI MASA DEPAN, FAKTOR PENDUKUNG DAN PENGHAMBAT Hendikawati, Putriaji; Harwanti, Nur Achmey Selgi; Wardono, Wardono; Prabowo, Ardi; Zahra, Mega Dea; Saefurrochman, Wisatsana Roychan
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7711

Abstract

Penelitian ini bertujuan untuk menyelidiki pemahaman mahasiswa terhadap pembelajaran Big Data, mengidentifikasi faktor-faktor yang mempengaruhi keterlibatan dan keberhasilan mahasiswa dalam pembelajaran Big Data, serta memberikan wawasan yang diperlukan bagi institusi pendidikan untuk meningkatkan kurikulum dan pen-galaman pembelajaran, serta mempersiapkan mahasiswa menghada-pi tantangan dan peluang di dunia profesional yang semakin bergan-tung pada data. Metode penelitian meliputi survei, analisis data per-sepsi mahasiswa, dan wawancara mendalam. Hasil penelitian menunjukkan bahwa mahasiswa memiliki pemahaman yang me-madai mengenai konsep Big Data, dengan rata-rata tingkat keakra-ban mencapai 3.6 pada skala 1 hingga 5. Responden menilai pent-ingnya pembelajaran Big Data dengan nilai rata-rata 4.3, menunjuk-kan bahwa pembelajaran ini sangat relevan dalam pendidikan tinggi dan dunia profesional. Faktor penghambat utama yang diidentifikasi meliputi kurangnya sumber daya finansial, keterbatasan akses teknologi dan infrastruktur, perubahan kurikulum, serta minimnya kolaborasi dengan industri. Faktor pendukung dari institusi, ketersediaan teknologi yang memadai, program kursus yang ter-struktur, dan kerjasama dengan industri menunjukkan dampak posi-tif terhadap pembelajaran Big Data. Pengalaman praktis, termasuk kontribusi dari praktisi industri, memperkaya pengalaman belajar mahasiswa. Sebanyak 50 dari 53 responden menunjukkan minat yang tinggi untuk mendalami Big Data lebih lanjut, menandakan potensi besar untuk pengembangan kurikulum. Penelitian ini merekomendasikan pembaruan kurikulum agar sesuai dengan perkembangan terbaru di industri, peningkatan pelatihan bagi dosen, serta penyediaan akses teknologi dan perangkat yang lebih baik.
Co-Authors Prabowo, Ardi Pratama, Alfian Adi Putriaji Hendikawati Saefurrochman, Wisatsana Roychan Sanusi, Ratna Nur Mustika Wardono Wardono Wijaya, Galih Kusuma Zahra, Mega Dea