Jurnal Rekayasa Perangkat Lunak

CLOUD-BASED HEALTHCARE: INOVASI SISTEM INFORMASI MOBILE DENGAN FIREBASE SEBAGAI CLOUD DATABASE

Andhika Syaputra, Ratnasari Ratnasari — Thu, 12 Jun 2025 00:00:00 +0000

The development of information technology has driven the transformation of information systems in various sectors, including the health sector. One technology that is now widely used is Cloud Database, which allows online data storage and management without the need for physical storage media. This study discusses the application of Cloud Database using Firebase services in the development of a mobile-based Health Center Information System. The purpose of this study is to produce an information system that can improve the efficiency of services in the Health Center, especially in supporting recording, reporting, and service activities to the community more quickly and accurately. The method used in this study is Extreme Programming (XP) which emphasizes communication, feedback, and iterative software development. This study also uses the Unified Modeling Language (UML) approach for system modeling. The results of the study indicate that the use of Firebase as a Backend as a Service (BaaS) is effective in providing stable, secure, and easily integrated real-time database services with mobile applications. The system built is expected to be an alternative solution in the development of cloud-based health information systems in the future.

PERBANDINGAN KINERJA ALGORITMA NAÏVE BAYES DAN DECISION TREE UNTUK PREDIKSI PENYAKIT BATU GINJAL

Hafsah Mukaromah — Thu, 12 Jun 2025 00:00:00 +0000

Kidney stones consist of calcium crystals and are mineral deposits that can form in the urinary tract. The prevalence of kidney stones is estimated to be 21.11%, with the Standard Age Prevalence in men and women being 24.3% and 18.7%, respectively. This study involved participants with an average age of 52.15 years, and a higher prevalence of kidney stones was observed in women aged 40-50 years and individuals with a moderate socioeconomic status. Logistic regression results indicate that the likelihood of kidney stones is higher in individuals with diabetes, hypertension, fatty liver, and overweight. The Basic Health Research (Riskesdas) data for the year 2013 shows the prevalence of Kidney Failure and Kidney Stones in Indonesia. Therefore, a profound understanding of the factors causing kidney stone formation is crucial. This research was conducted to compare the performance of the naive Bayes and decision tree algorithm models. Based on the experimental results, the decision tree algorithm showed a higher accuracy rate of 72.50% and an AUC value of 0.740%, while naive Bayes had an accuracy rate of 68.57% and an AUC value of 0.697. These results support the conclusion that the decision tree has better predictive performance than naive Bayes.

Analisis Kepuasan Pengunjung terhadap Pelayanan Wisata dengan Menggunakan Algoritma C4.5 di Lanakila, Bendungan Way Sekampung

Aviv Fitria Yulia, Ahlan Wasalan, Tahta Herdian Andika — Thu, 12 Jun 2025 00:00:00 +0000

The tourism industry's share of regional assets consists of tourist destinations that the
locals want to visit and that have the ability to make money and advance the growth of the area. It
is imperative that the quality of tourism services be preserved and enhanced. Positive remarks and
recommendations for the location are common among contented tourists. On the other hand,
unhappy tourists may harm the reputation of the place and the travel sector as a whole.
The research objective was to analyze visitor-level satisfaction with tourism services in Lanakila
using the C4.5 algorithm and identify the factors that most influence visitor
satisfaction. The C4.5 algorithm successfully built a prediction model to identify potentially
dissatisfied visitors.
100 respondents completed the questionnaire that the researcher successfully distributed, and the
accuracy of the C4.5 model was 100.00%. Information service, staff attitude, food and beverage,
and support were the four primary elements that were shown to have an impact on satisfaction,
according to the analysis. With 100.00% accuracy, each of these categories was met

Pemodelan Artificial Neural Network Dalam Prediksi Penentu Penerima Beasiswa

Dita Septasari — Thu, 12 Jun 2025 00:00:00 +0000

Scholarships play a vital role in supporting high-achieving students who face financial constraints. However, the selection process is often time-consuming and prone to subjectivity. This study develops a predictive model for scholarship recipients using the Artificial Neural Network (ANN) approach to create a more efficient and objective selection system. The dataset includes academic performance, parental income, number of dependents, and non-academic achievements. The ANN model is designed through data preprocessing, training, and evaluation stages, using metrics such as accuracy, precision, recall, and F1-score. The results indicate that ANN can accurately and effectively predict scholarship recipients, making it a valuable tool to assist decision-making in scholarship distribution.

PENERAPAN DEEP LEARNING UNTUK PENGENALAN CITRA TULISAN TANGAN AKSARA LAMPUNG

Putri Dianing Ratri, Dwi Yana Ayu Andini — Thu, 12 Jun 2025 00:00:00 +0000

In the field of artificial intelligence, technology has opened new opportunities for preserving cultural heritage, including the Lampung script. One method that shows great potential in script recognition is Deep Learning. This research aims to develop a handwritten image recognition model for the Lampung script using deep learning methods with the ResNet- 50 architecture and transfer learning. Handwritten data of the Lampung script were collected from various sources and processed through image normalization and augmentation to improve the quality of the training data. The model was built using the ResNet-50 architecture with transfer learning, which allows the use of pre-trained models to enhance efficiency. The research results show that the model is capable of classifying 20 primary characters of the Lampung script with adequate performance. However, the study suggests further data collection and exploration of augmentation techniques to improve results. This research not only aims to enhance the recognition of the Lampung script but also contributes to efforts to preserve and introduce the Lampung script to the wider public through modern technology.

Data Mining untuk Klasterisasi dan Klasifikasi Pelanggan Berdasarkan Pendapatan dan Transaksi Pembelanjaan Menggunakan Algoritma K-Means

IKNA AWALIYANI — Thu, 12 Jun 2025 00:00:00 +0000

In the era of technology and business, handling customers is very important as consumers and a significant source of marketing information. Customer evaluations have a major impact on marketing and service quality can be a benefit or a loss to a seller's reputation. Knowing key customer profiles supports effective marketing strategies with a deep understanding of customer preferences and behavior. This approach is supported by clustering technology for better analysis of customer information, improving management, and designing IT technology according to current developments. The results of data processing form four clusters, forming customer profiles based on family structure and income/expense levels. Understanding cluster characteristics is the basis for an effective marketing strategy. By customizing marketing tactics, targeting specific offers, and optimizing promotions, companies can build strong relationships, increase retention, and achieve business success. This approach provides deep insight, enables targeted decision making, and is responsive to evolving market dynamics.