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The conceptual model to improve failure risk management water distribution system using ordinary differential equation model to support water resilience in military residential facilities Fulkan Kafilah Al Husein; Muhamad Syazali; Suhaila Saidat; Nursyiva Irsalinda; Fajri Farid
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 2 No. 3 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def..v2i3.341

Abstract

Water resilience is still big problem in Indonesia.  In border and underdeveloped areas in Indonesia, the use of water sources is still considered not resilience. Especially in military context, where water needs are bigger and also more fundamental, this water resilience problem demanding a comprehensive solution. To address this issue, this research proposes the use of ordinary differential equations as a mathematical tool to model the dynamics of system damage over time, consumption, maintenance scheme, water crisis scheme, and other factors affecting water distribution resilience in military facilities. This journal presents a conceptual model of failure risk management water distribution system using a differential equation model approach to support water resilience. Specifically, the derivation of failure equation in the “reliability and maintenance system technical” textbook will be the basic reference for generating mathematical model. It is used because our model will be focused in improving failure risk management. By using the model, there are a lot of problem will be tackled such as Identify and manage failure risks in the water supply system, design an efficient water distribution maintenance scheme, and predict how strong the system to face water crisis. But before the model applied, the prediction of model will be tested by applying it in form of computer program. The case study of this research will be focused in testing the model in form of computer program with some simplicity and assumption. Through this approach, it is expected to find solutions that improve water usage efficiency, support the well-being of military personnel, and contribute to national water resilience to bolster national defense especially in case of water crisis happened. This research holds significant benefits for scientific advancement by providing a conceptual model that can serve as a reference for future research. It has the potential to make a tangible contribution but also still need so much development especially for application in real data, adding others variables that can be included for next research, conducting the interpretation, and better defining the measurement boundaries.
THE APPLICATION OF MULTINOMIAL NAIVE BAYES FOR SENTIMENT ANALYSIS OF CULTURAL TOURISM REVIEWS: A CASE STUDY OF BOROBUDUR AND PRAMBANAN TEMPLES Irvan Alfaritzi; Baruna Abirawa; Smertniki Javid Ahmedthian; M. Syamsuddin Wisnubroto; Fajri Farid; Meida Cahyo Untoro
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 20 No 1 (2026): Mei 2026
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/dj70zr92

Abstract

Abstract : Candi Borobudur dan Candi Prambanan dikenal sebagai dua situs warisan dunia UNESCO, yang memiliki nilai sejarah dan budaya tinggi. Penelitian ini berfokus pada analisis komparatif sentimen wisatawan berdasarkan 10.000 ulasan yang diambil dari Google Maps. Tujuannya adalah mengidentifikasi persepsi wisatawan terhadap dua destinasi sekaligus mengevaluasi aspek kekuatan dan kelemahannya untuk memberikan umpan balik berbasis data bagi pengelola wisata. Metodologi yang diterapkan menggunakan pendekatan hibrida: data ulasan dikumpulkan melalui web scraping, diterjemahkan ke Bahasa Inggris dan dilabeli secara otomatis menggunakan VADER (Valence Aware Dictionary and sEntiment Reasoner). Setelah tahap preprocessing dan pembobotan fitur menggunakan TF-IDF, model klasifikasi Multinomial Naive Bayes dilatih untuk memprediksi polaritas sentimen. Hasil analisis menunjukkan bahwa Candi Prambanan memiliki proporsi sentimen positif sebesar 82,5%, lebih tinggi dibandingkan Candi Borobudur sebesar 73,7%. Model klasifikasi mencapai akurasi 81,9% untuk Prambanan dan 74,7% untuk Borobudur. Visualisasi word cloud mengindikasikan keluhan negatif yang berulang, seperti “panas”, “harga tiket”, dan “parkir”. Analisis ini menunjukkan adanya perbedaan signifikan dalam persepsi pengunjung terhadap kedua destinasi dan memberikan kontribusi praktis dalam pengembangan strategi peningkatan kualitas layanan wisata berbasis analisis sentimen. English Abstract: Borobudur Temple and Prambanan Temple are known as two UNESCO world heritage sites, possessing high historical and cultural value. This research focuses on a comparative sentiment analysis of tourists based on 10,000 reviews taken from Google Maps. The objective is to identify tourist perceptions of the two destinations while also evaluating their strengths and weaknesses to provide data-driven feedback for tourism management. The methodology applied uses a hybrid approach: review data is collected via web scraping, translated into English, and automatically labeled using VADER (Valence Aware Dictionary and sEntiment Reasoner). After the preprocessing stage and feature weighting using TF-IDF, a Multinomial Naive Bayes classification model is trained to predict sentiment polarity. The analysis results show that Prambanan Temple has a higher proportion of positive sentiment at 82.5%, compared to Borobudur Temple at 73.7%. The classification model achieved an accuracy of 81.9% for Prambanan and 74.7% for Borobudur. Word cloud visualizations indicated recurring negative complaints, such as “panas” (hot), “harga tiket” (ticket price), and “parkir” (parking). This analysis indicates a significant difference in visitor perceptions of the two destinations and provides a practical contribution to developing strategies for improving tourism service quality based on sentiment analysis.