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All Journal JUSIM (Jurnal Sistem Informasi Musirawas) Riau Jurnal Teknik Informatika
Varla Octavia Ramadhani
Universitas Bina Sarana Informatika
Computer Science & IT Decision Sciences, Operations Research & Management

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Pengelompokkan Provinsi Berdasarkan Kelayakan Ruang Kelas dan Tenaga Kependidikan Sekolah Dasar Menggunakan Algoritma K-Means: Analisi Data Periode 2023-2024 Maidy Tri Wardani; Varla Octavia Ramadhani; Namira Anggreani; Sumanto; Andi Diah Kuswanto
Riau Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Juli 2025
Publisher : Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i2.3389

Abstract

Abstract This study analyzes the disparity in primary education quality across Indonesian regions using K-Means clustering methodology to group 38 provinces based on classroom adequacy indicators and teaching staff availability for the 2023-2024 period. Adopting the CRISP-DM methodology and utilizing datasets from the Ministry of Education, Culture, Research, and Technology, this research reveals significant educational gaps across Indonesian regions. The analysis results show three clusters reflecting educational conditions: Cluster 1 (low category) encompasses 23 provinces (67.6%) dominated by Eastern Indonesia regions such as Papua, Maluku, and other remote areas; Cluster 2 (medium category) consists of 13 provinces (38.2%) including South Sumatra, Riau, and DKI Jakarta; and Cluster 3 (high category) contains only 3 provinces: East Java, West Java, and Central Java. Clustering validity is confirmed through silhouette coefficient with the highest value of 0.795 for Cluster 2. These findings identify structural inequality between Java and outer Java regions, providing empirical foundation for the government to design more targeted educational equity policies, with priority on infrastructure rehabilitation and teaching staff augmentation in disadvantaged areas to achieve national educational justice. The research demonstrates that educational quality distribution in Indonesia remains heavily concentrated in Java, while remote and eastern regions face significant challenges in both physical infrastructure and human resources, requiring comprehensive government intervention strategies for sustainable educational development. Keywords: K-Means clustering, primary education, regional disparity, educational infrastructure, teaching personnel.
PERANCANGAN CHATBOT ARTIFICIAL INTELLIGENCE BERBASIS RETRIEVAL AUGMENTED GENERATION LAYANAN INFORMASI HOTEL DENGAN FLOWISE AI Varla Octavia Ramadhani; Ahmad Fauzi; Andi Sanjaya
JUSIM (Jurnal Sistem Informasi Musirawas) Vol. 11 No. 3 (2026): JUSIM:Jurnal Sistem Informasi Musi Rawas September
Publisher : LPPM UNIVERSITAS BINA INSAN

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Industri perhotelan menghadapi tantangan dalam menyediakan layanan informasi yang cepat, akurat, dan tersedia selama 24 jam bagi tamu terkait kamar, fasilitas, tarif, serta kebijakan operasional, sementara layanan konvensional yang bergantung pada staf tidak selalu mampu menjangkau seluruh kebutuhan tersebut secara optimal. Penelitian ini bertujuan merancang chatbot Artificial Intelligence berbasis Retrieval Augmented Generation (RAG) sebagai solusi layanan informasi hotel menggunakan platform Flowise AI. Metode penelitian dilaksanakan secara sekuensial meliputi identifikasi masalah, studi literatur, pengumpulan data, analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Basis pengetahuan chatbot disusun dari 50 dokumen product knowledge hotel yang dikelompokkan ke dalam enam kategori, kemudian diproses melalui tahap chunking dan embedding menggunakan model OpenAI text-embedding-3-small, lalu disimpan pada vector database Pinecone, dengan model bahasa GPT-4o Mini sebagai komponen generasi jawaban. Pengujian dilakukan menggunakan metode Black Box Testing terhadap 150 skenario pertanyaan yang dianalisis lebih lanjut menggunakan pendekatan Confusion Matrix. Hasil pengujian menunjukkan sistem chatbot memperoleh akurasi keseluruhan sebesar 88,00% (132 dari 150 jawaban benar), dengan nilai precision 100,00%, recall 87,76%, dan F1-score 93,48%. Performa tertinggi diperoleh pada kategori Suites (100,0%) dan performa terendah pada kategori Rooms (82,9%). Hasil ini menunjukkan bahwa chatbot berbasis RAG dengan platform Flowise AI mampu menjawab pertanyaan tamu secara akurat tanpa menghasilkan informasi di luar cakupan knowledge base, sehingga layak diterapkan sebagai media layanan informasi hotel yang efektif dan responsif.
Co-Authors Ahmad Fauzi Andi Diah Kuswanto Andi Sanjaya Maidy Tri Wardani Namira Anggreani Sumanto Sumanto