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All Journal J@TI (TEKNIK INDUSTRI) ComTech: Computer, Mathematics and Engineering Applications Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) Spektrum Industri Journal of Integrated System
Lumintu, Ida
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics

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MINIMIZING PATIENT LENGTH OF STAY IN THE EMERGENCY DEPARTMENT AT ANNA MEDIKA GENERAL HOSPITAL, MADURA Lumintu, Ida; Rohman, Hidayatur; Annisa, Rullie
J@ti Undip: Jurnal Teknik Industri Vol 19, No 3 (2024): September 2024
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.19.3.115-121

Abstract

Prolonged length of stay (LOS) in emergency departments (EDs) negatively impacts patient care and operational efficiency. This study applies simulation modeling using ARENA software to analyze and optimize ED operations at Anna Medika General Hospital in Madura, Indonesia. Three improvement scenarios were evaluated: adding one nurse, one bed, and one general practitioner. The results show that adding a general practitioner reduced LOS significantly, from 184.64 minutes (3.08 hours) to 154.37 minutes (2.57 hours), making it the most effective intervention. However, the findings emphasize the importance of a holistic approach, as standalone interventions may only address isolated bottlenecks. Combining targeted staffing increases with process optimizations provides the most sustainable improvements. This study highlights simulation’s value in evaluating operational strategies, enabling hospitals to make data-driven decisions that balance cost, resource allocation, and patient satisfaction.
Studi Eksperimen Pengeringan Cabe Jawa Menggunakan Metode Rancangan Acak Kelompok Lengkap Nurul Azizah, Siti; Lumintu, Ida; Widiaswanti, Ernaning
Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi Vol. 5 No. 1 (2025): Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi 2024
Publisher : Fakultas Teknik dan Teknologi - TANRI ABENG UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/snarstek.v2i1.766

Abstract

Piper Retrofractum Vahl., yang juga dikenal sebagai cabe jawa, memiliki manfaat kesehatan dan potensi ekonomi yang tinggi dengan harga pasar berkisar antara Rp. 80.000 hingga Rp. 100.000 per kilogram dalam bentuk kering. Peluang ekspor melibatkan negara-negara seperti Singapura, Malaysia, Cina, Timur Tengah, Eropa, dan Amerika. Untuk meningkatkan kualitas dan nilai ekonomi komoditas herbal ini, penelitian ini berfokus pada perbaikan proses pengeringan menggunakan metode oven, dengan mempelajari pengaruh pre-treatment berbasis ekstrak kulit jeruk dan kulit nanas yang kaya asam askorbat terhadap kualitas cabe jawa. Studi ini menggunakan tiga tingkat kematangan cabe jawa yaitu mentah (hijau), setengah matang (jingga), dan matang (merah). Penelitian dilakukan menggunakan rancangan acak kelompok lengkap dengan pilihan perlakuan meliputi tanpa perlakuan, blanching dengan air panas 70°C, perendaman dalam ekstrak kulit jeruk, blanching dengan ekstrak kulit jeruk pada suhu 70°C, perendaman dalam ekstrak kulit nanas, dan blanching dengan ekstrak kulit nanas pada suhu 70°C. Proses pengeringan menggunakan suhu oven konstan 70°C selama 18 jam menggunakan rancangan acak kelompok lengkap dengan parameter yang dinilai termasuk kadar air, kadar piperin, dan parameter warna seperti kecerahan (L*), kemerahan (a*), dan kekuningan (b*). Hasil optimal diperoleh dengan pra-perlakuan ekstrak kulit nanas, menghasilkan kadar air sebesar 8,79%, kadar piperin sebesar 0,218%, kecerahan (L*) sebesar 43,78, kemerahan (a*) sebesar 8,17, dan kekuningan (b*) sebesar 12,48.
Optimizing Quality Attributes of Piper Retrofractum Vahl. Through Partial Least Squares Regression: Insights from Pretreatment and Drying Experiments with Fruit Peel Infusions Lumintu, Ida
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12246

Abstract

The research aimed to optimize the quality attributes of Piper retrofractum Vahl.—piperine content, color brightness, and water content—using Partial Least Squares Regression (PLSR) to evaluate the pretreatment effects with fruit peel infusions and drying conditions. The research urgency lied in addressing the challenges of achieving consistent product quality while promoting sustainable food processing practices. Around 30 samples of Piper retrofractum Vahl. were subjected to varying pretreatment concentrations, soaking durations, drying durations, and peel types (orange and pineapple). The PLSR model was employed to identify key factors influencing the quality attributes and assess predictive performance based on Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) values. As a result, the PLSR model explains 43.22% of the variance in piperine content, highlighting the importance of shorter soaking durations and higher pretreatment concentrations in preserving piperine levels. For water content, the model captures 75.08% of the variance, emphasizing the critical role of drying duration in reducing moisture. However, the color brightness model explains only 18.5% of the variance, indicating the need to explore contributing factors further. The research introduces the innovative use of fruit peel-infused water as a sustainable pretreatment method, contributing to eco-friendly food processing practices and offering practical insights into optimizing production for improved product quality. The findings underscore the importance of balancing pretreatment and drying parameters to address inconsistencies in quality while promoting sustainability. Future research should expand experimental conditions, integrate additional variables, and explore advanced modeling techniques to enhance predictive accuracy and product quality.
Optimalisasi Strategi Product Bundling melalui Pemetaan Pola Peminatan dan Pola Penjualan Produk menggunakan K-Means Clustering dan Apriori Oktavian, Bagas Dwi; Lumintu, Ida; Amar, Samsul
Journal of Integrated System Vol. 8 No. 1 (2025): Journal of Integrated System Vol. 8 No. 1 (June 2025)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jis.v8i1.11503

Abstract

Persaingan bisnis ritel yang semakin kompetitif menuntut strategi pemasaran yang lebih terarah dan berbasis data untuk mengatasi stagnasi penjualan, khususnya pada produk dengan permintaan rendah. Penelitian ini bertujuan mengembangkan pendekatan analitik untuk mengoptimalkan strategi product bundling di toko Aldrin Stocklot’s melalui pemetaan pola peminatan dan keterkaitan produk. Metode yang digunakan meliputi analisis Recency, Frequency, and Monetary (RFM) pada level produk, K-Means Clustering untuk segmentasi produk berdasarkan peminatan, dan algoritma Apriori untuk mengidentifikasi asosiasi antarproduk dalam transaksi pelanggan. Hasil menunjukkan bahwa 82% produk termasuk kategori kurang diminati, dan hanya 13 dari 33 aturan asosiasi yang efektif untuk bundling lintas klaster. Evaluasi clustering menggunakan Davies-Bouldin Index (DBI) sebesar 0.717 menandakan kualitas segmentasi yang cukup baik. Temuan ini memperkuat argumen bahwa integrasi klasterisasi dan association rule mining pada level produk dapat menghasilkan rekomendasi bundel yang lebih adaptif untuk meningkatkan visibilitas dan penjualan produk stagnan. Implikasi praktis dari pendekatan ini berpotensi direplikasi di sektor ritel UMKM lainnya.
Sentiment Analysis and Emotional Language as Predictors of Drug Satisfaction in User Reviews Lumintu, Ida
Spektrum Industri Vol. 22 No. 2 (2024): Spektrum Industri - October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v22i2.275

Abstract

This study investigates how emotional expressions in user-generated drug reviews predict satisfaction ratings using sentiment analysis and emotion detection. By analyzing over 370,000 reviews from the UCI Machine Learning Repository, the study aims to bridge gaps in understanding the emotional drivers behind user satisfaction across different drug categories. For sentiment analysis, VADER, a Python-based lexicon tool, was used to categorize sentiment polarity, while the NRC Word-Emotion Lexicon provided a nuanced mapping of emotions like joy, sadness, and anger. Results reveal that emotions such as joy and trust are positively correlated with higher ratings, while anger and disgust are linked to lower satisfaction. However, the R-squared value (~0.043) indicates that emotions alone do not fully predict ratings, highlighting the need to consider additional factors like drug efficacy and side effects. This low R-squared value suggests that while emotions significantly influence satisfaction, other elements play a substantial role. The study's findings have critical implications for pharmaceutical companies and healthcare providers, suggesting the need for emotion-driven marketing strategies and improved patient support systems. Future research could explore more advanced machine learning models, such as BERT or GPT-based approaches, and investigate specific user demographics or drug side effects to enhance predictive accuracy.
Co-Authors Amar, Samsul Annisa, Rullie Ernaning Widiaswanti Nurul Azizah, Siti Oktavian, Bagas Dwi Rohman, Hidayatur