Jurnal Informatika dan Rekayasa Perangkat Lunak
Journal of Informatics and Software Engineering accepts scientific articles in the focus of Informatics.
The scope can be: Software Engineering, Information Systems, Artificial Intelligence, Computer Based Learning, Computer Networking and Data Communication, and Multimedia.
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<![CDATA[Klasifikasi Penentuan Tingkat Penyakit Demam Berdarah dengan menggunakan Algoritma Naïve Bayes (Studi Kasus Puskesmas Nagreg) ]]>
<![CDATA[Saeful Anwar]]>;
<![CDATA[Revita Lestari Faujiah]]>;
<![CDATA[Tuti Hartati]]>;
<![CDATA[Edi Tohidi]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10299
<![CDATA[The rapid development of science and technology, especially in the field of information technology, can give rise to new innovations for presenting and managing information to meet information needs. The role of technology in the health and medical fields has helped a lot in helping the human spirit and has shown its importance. Dengue Hemorrhagic Fever (DHF) is a disease that occurs in children and adults with the main symptoms of fever, muscle and joint pain, which usually gets worse after the first two days. DHF is a public health problem in Indonesia where the number of sufferers tends to increase and its spread causes bleeding. Dengue fever is characterized by sudden high fever lasting 2-7 days without a clear cause accompanied by manifestations such as petechiae, epistaxis sometimes accompanied by vomiting of blood, diarrhea, decreased consciousness, tendency to cause shock and death. The Naïve Bayes algorithm is a form of data classification using probability and statistical methods. The algorithm uses Bayes' theorem and assumes that all attributes are independent or not interdependent given the values of the class variables. Another definition says that Naïve Bayes is a classification using probability and statistical methods discovered by the British scientist Thomas Bayes, namely predicting future opportunities based on previous experience. Proceeding to the final stage, the final stage or step is to see the level of accuracy or how well the classification of the model we are using is.]]>
<![CDATA[Implementasi Algoritma Regresi Linear Berganda untuk Memprediksi Biaya Asuransi Kesehatan ]]>
<![CDATA[Bagas Al Haddad]]>;
<![CDATA[Agus Bahtiar]]>;
<![CDATA[Gifthera Dwilestari]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10262
<![CDATA[Technological developments such as telemedicine and big data analysis have had a significant impact on the health insurance industry. It is very difficult to make wise decisions if customers do not understand the cost of insurance. Age, gender, medical history, region, smoking, and body mass index (BMI) are a number of variables used to determine the variables that contribute to health insurance costs. Multiple linear regression was used to identify variables that contribute to predicting relative health insurance costs. Multiple linear regression analysis, also known as multiple regression analysis, is a regression model that involves more than one independent variable. This is determined by using statistical software to determine which independent variables have a significant influence on the dependent variable. The value of using multiple linear regression is primarily related to the need for prediction of insurance costs. In the RapidMiner tool, the linear regression operator is used to perform linear regression calculations. From a total of 1338 datasets, the data is divided into two parts. 90% is used as training data (with a total of 1204 data) and 10% is used as test data (with a total of 134 data). The results of the analysis show that independent factors such as smoking status, age, and body mass index have a significant correlation with insurance premium costs. The value 5891.019 was generated from model evaluation using Root Mean Squared Error (RMSE). The strong correlation between smoking status and premium costs, along with positive correlations with age and body mass index (BMI), suggests that premium costs increase with increasing age and weight category.]]>
<![CDATA[Penerapan K-means Clustering untuk Pengukuran Kinerja Programmer di Software House ]]>
<![CDATA[Alya Fitria]]>;
<![CDATA[Taufik Ardiansyah Putra]]>;
<![CDATA[Syahiduz Zaman]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.9701
<![CDATA[Performance evaluation of programmers plays a crucial role in maintaining efficiency within a software development company. This study proposes the implementation of K-means clustering as a method to measure and categorize programmer performance based on several criteria. The proposed approach involves assessing code quality, productivity, technical skills, team collaboration, and problem-solving abilities. By applying the K-means clustering method, programmers can be grouped into different performance clusters, allowing for the identification of high, moderate, and developmental performers. The K-means clustering method divides data into clusters related by calculating the distance between data points and cluster centers, and iterates until stable clusters are formed..]]>
<![CDATA[Development of Android Media in Learning Islamic Religious Education and Character by Believing in Allah's Books for Class VIII Students ]]>
<![CDATA[Mustagfirin Mustagfirin]]>;
<![CDATA[Achmad Munib]]>;
<![CDATA[Aris Abdul Ghoni]]>;
<![CDATA[Sumardi Sumardi]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10847
<![CDATA[A communication tool and source of information in learning, one of which is the use of Learning Media. This requires careful teacher position in selecting the use of media in the educational process to be able to increase student motivation in learning with the benefits of learning, namely: 1) the educational process becomes more interesting, 2) the learning process becomes more efficient and effective, 3) saves energy and time . In reality, in Islamic Religion and Character lessons with the material of Believing in Allah's Books, students often experience difficulties in learning this knowledge. Difficulties faced by students in studying Islamic Religion and Character lessons with the material of Believing in Allah's Books. Based on the results of surveys and observations at SMPN 2 Dempet Demak, it was revealed that the main challenges faced by students regarding understanding the material "Understanding the Books of Allah" at school were due to several factors. One of them is the difficulty in capturing the essence of the material presented in books and presentation slides. The traditional teaching approach by teachers is also a major factor in reducing students' enthusiasm and motivation for learning. The increasingly varied use of media is a challenge for teachers in carrying out their duties as teachers in schools in order to achieve learning goals. The use of smartphones which are easy to carry, easy to access and affordable as a medium for learning will have a big impact on students. Apart from facilities that are relatively new, students will be more interested in using facilities that are "current" and familiar to students' situations in everyday]]>
<![CDATA[Penerapan Algoritma Naive Bayes pada Analisis Sentimen Ulasan Aplikasi Whoosh – Kereta Cepat Di Google Play Store ]]>
<![CDATA[Tuti Hartati]]>;
<![CDATA[Rachmat Trikar Sohadi]]>;
<![CDATA[Edi Tohidi]]>;
<![CDATA[Edi Wahyudin]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10307
<![CDATA[Sentiment analysis of user reviews on mobile app distribution platforms is a complex and crucial issue, especially with the rapid growth of the number of users and the volume of reviews. This research focuses on the application of Naive Bayes Algorithm to analyze the sentiment of user reviews of WHOOSH app on Google Play Store. Naive Bayes algorithm was chosen due to its efficiency and easy implementation. Using a dataset of 500 cleaned and labeled reviews (positive or negative), the model was trained and achieved 81.25% accuracy. The high precision for the positive class (90%) demonstrates the model's ability to correctly identify positive reviews. Although the recall of the positive class is high (94%), the recall of the negative class still needs to be improved (64%). Overall, the Naive Bayes model is effective for classifying sentiment in WHOOSH user reviews, but needs to improve the accuracy and recall of negative classes.]]>
<![CDATA[Analisis Data Stok Alat Kesehatan menggunakan Metode Regresi Linier Berdasarkan Nilai RMSE ]]>
<![CDATA[Trian Nurmansyah]]>;
<![CDATA[Rudi Kurniawan]]>;
<![CDATA[Yudhistira Arie Wijaya]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10275
<![CDATA[Inventory of healthcare equipment, whether in hospital or clinic settings, represents a significant investment requiring substantial cost allocation. However, estimating these equipment needs often relies solely on the overall available stock, as monthly or yearly requirements tend to fluctuate. Consequently, this approach leads to an inability to meet all necessary equipment needs, resulting frequently in surplus inventory. Therefore, anticipating this issue requires predicting healthcare equipment stock at Klinik Pembina Sehat. This study aims to forecast equipment stock using the linear regression algorithm method. The selection of this algorithm is due to its suitability in handling the linear relationship between dependent and independent variables. Research findings demonstrate the developed model's ability to predict healthcare equipment stock with a reasonably high level of accuracy, with a Root Mean Square Error (RMSE) value of 93.359. This value signifies a relatively low prediction error, indicating the model's precision in estimating stock requirements. Thus, this research holds the potential to enhance operational efficiency in managing healthcare equipment stock within the clinic and serves as a foundation for further studies to improve stock planning processes in similar healthcare institutions.]]>
<![CDATA[Analisis Asosiasi Data Penjualan Produk Mega Baja Cipondoh Kota Tangerang Menggunakan Algoritma Fp-Growth ]]>
<![CDATA[Kamelia Faridah]]>;
<![CDATA[Nining Rahaningsih]]>;
<![CDATA[Raditya Danar Dana]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10221
<![CDATA[Mega Baja Cipondoh, a construction material store in Tangerang, is experiencing suboptimal sales and revenue decline. This research addresses the issue through Fp-Growth algorithm application in sales data analysis using RapidMiner. With a minimum Support of 0.002, minimum Confidence of 0.8, and lift value of 1, six significant association rules are identified. For instance, 10.4% of transactions involve purchasing both Hollo Galvanis and Pipa Galvanis. Customers buying Hollo Galvanis have a 9.6 times higher chance of also purchasing Pipa Galvanis, achieving a 100% success rate. Implementation of these findings is expected to enhance Mega Baja Cipondoh's sales strategy, optimize sales, and improve customer satisfaction, laying the groundwork for future developments in construction material sales data analysis.]]>
<![CDATA[Penerapan Metode Dbscan untuk Identifikasi Kluster Gempa Bumi di Daerah Yogyakarta ]]>
<![CDATA[Wahyu Ajitomo]]>;
<![CDATA[Irfan Pratama]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.9214
<![CDATA[Earthquakes are one of the natural disasters that frequently occur in Indonesia, including the Yogyakarta region. A profound understanding of earthquake patterns and characteristics in this area is crucial for risk mitigation efforts and disaster preparedness. Clustering methods, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), can provide an effective approach to identifying earthquake clusters with high density in the Yogyakarta region. This research used the DBSCAN method to identify earthquake clusters with specific magnitude strengths in the Yogyakarta region. Earthquake distribution data from 2017 to 2022 was used as the research sample. The clustering process considered the epsilon parameter and the minimum number of samples within a cluster. The analysis results revealed the existence of earthquake clusters with high density concentrated in specific locations in the Yogyakarta region. These clusters reflect clear spatial patterns and indicate significant seismic activity in the area. The conclusion of this study confirms the presence of earthquake patterns and clusters that can be identified using the DBSCAN method. These clusters provide further insight into the distribution of earthquakes in the Yogyakarta region and can serve as a reference for earthquake risk mitigation in the future. The findings of this research offer valuable insights for stakeholders in decision-making and planning responsive actions to earthquakes in the Yogyakarta region.]]>
<![CDATA[Analisis Tingkat Penggunaan Gadget pada Anak Usia Dini dengan menggunakan K-Mean ]]>
<![CDATA[Khaerul Anam]]>;
<![CDATA[Rizal Rusyana]]>;
<![CDATA[Bani Nurhakim]]>;
<![CDATA[Denni Pratama]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10317
<![CDATA[The use of gadget devices in early childhood has become an increasing concern in recent years. influence on human thinking patterns because devices can find data quickly for children. Research on the consequences of using gadgets in early childhood has its own significance in understanding its impact on their development. In this study, an analysis was carried out on the level of gadget use in early childhood by applying the K-Means algorithm. The K-Means algorithm is used to group the level of gadget use in children, allowing the identification of groups that have similar characteristics. The aim of this research is to evaluate and understand the level of gadget usage by young children in response to technological developments, as well as to develop an effective method or approach in classifying their gadget usage patterns by utilizing the K-Means algorithm. Thus, this research aims to provide in-depth insight into gadget use patterns in young children, which can be the basis for developing better strategies or policies regarding technology use in this age group. From a total of 332 questionnaire responses, 14 groups were found based on the best DBI scores with different category distributions, namely "very often", "often", "sometimes", "rarely", and "never" with each percentage of 1 % (2 people), 24% (80 people), 0%, 71% (235 people) and 5% (15 people).]]>
<![CDATA[Peningkatan Efisiensi Pemantauan Kehadiran Siswa Melalui Analisis K-Means Clustering di Sekolah Menengah Pertama Negeri 3 Rancaekek, Kabupaten Bandung ]]>
<![CDATA[Fitriani Agustina]]>;
<![CDATA[Rudi Kurniawan]]>;
<![CDATA[Tati Suprapti]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret ]]>
Publisher : <![CDATA[Universitas Wahid Hasyim ]]>
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DOI: 10.36499/jinrpl.v6i1.10300
<![CDATA[Student attendance at school is a key factor in determining the quality of education and the effectiveness of the learning process. Therefore, student attendance data can be one of the indicators for schools in managing and improving the quality of education. The problem is that the analysis process has not been carried out to group potential student activeness based on similar characteristics and the school still has difficulty in processing large data so that the quality of education is not optimal. This research involves student attendance data from SMP Negeri 3 Rancaekek for one academic year as the main dataset. The research method includes the stages of data collection, pre-processing, and analysis. The collected student attendance data was processed to remove outliers and create a dataset suitable for Clustering analysis. The K-Means Clustering method is used to group students into groups based on their attendance patterns. K-Means means an iterative clustering solution procedure that performs partitioning to classify or group a large number of objects. K-Means as a popular data mining method, is a solution procedure that is often used to identify natural groups in a case. This method focuses on grouping data that has similarities, so that the results can be analysed in more depth. The research results show that.]]>
