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Rudi Kurniawan
STMIK IKMI Cirebon
Computer Science & IT

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Peningkatan Efisiensi Pemantauan Kehadiran Siswa Melalui Analisis K-Means Clustering di Sekolah Menengah Pertama Negeri 3 Rancaekek, Kabupaten Bandung Fitriani Agustina; Rudi Kurniawan; Tati Suprapti
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10300

Abstract

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.
Analisis Data Sentimen Ulasan Aplikasi Dana di Google Play Store Menggunakan Algoritma Naïve Bayes Windy Astuti; Rudi Kurniawan; Yudhistira Arie Wijaya
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10272

Abstract

In an era of rapid technological advancement, mobile applications, especially e-wallets such as Dana Apps, are becoming increasingly easy to use for digital payment transactions. This innovation drives research on how digital payment platforms build information systems and business strategies through digital platforms. To compete in a growing industry, companies must produce products and services that meet customer needs. Internet user opinions, especially about apps, are important in gathering information. This research focuses on analyzing the sentiment data of Dana App reviews on the Google Play Store using the Naïve Bayes classification algorithm which is well known for its accuracy and high data processing speed. The results show that the 80:20 ratio provides an accuracy rate of 50.21%, precision and recall of 0.00% each, This research has a significant impact on the information technology industry, providing guidance for practitioners and researchers in choosing sentiment data analysis methods. The implementation of the Naïve Bayes algorithm can improve the interaction between users and applications, support innovation, and contribute to the overall development of information technology.
Analisis Data Sentimen Pemain Game Role-Playing Game (RPG) Honkai Star Rail dengan Algoritma Naive Bayes Yudis Firmansyah; Rudi Kurniawan; Yudhistira Arie Wijaya
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10243

Abstract

In the rapidly evolving digital age, the gaming industry has experienced significant growth. One of the games that is currently popular is Honkai Star Rail. This research uses Honkai Star Rail game player reviews as the object of sentiment data research. However, the problem found in sentiment data analysis is the existence of reviews that provide positive or negative ratings as a response from players. There are situations where these labels do not fully reflect the true essence of whether the response is positive or negative, so it is necessary to analyze sentiment data. The purpose of this sentiment data research is to assess the performance of the naive bayes model in classifying sentiment by finding the best accuracy value and AUC value from 3 scenarios of sharing test data and data. The method in this study uses the Naive Bayes algorithm, this algorithm was chosen because it is suitable for classification problems. The test results with 3 dataset sharing scenarios (60:40, 70:30, and 80:20) show that the accuracy value reaches the highest value in scenario 2 (70:30) which is 86%. The precision value also reaches the highest value in scenario 2, which is 84%, the recall value reaches the highest value in scenario 2, which reaches 89%. And the highest AUC (Area Under the Curve) value is obtained from scenario 2 of 0.92 with the excellent classification category.
Analisis Data Sentimen Ulasan Pengguna Aplikasi Shopee di Google Play Store dengan Klasifikasi Algoritma Naïve Bayes Wartumi Wartumi; Rudi Kurniawan; Yudhistira Arie Wijaya
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10273

Abstract

This research focuses on analyzing sentiment data from users of the Shopee e-commerce application using the Naïve Bayes Algorithm method. From 1000 datasets obtained from web scraping, reviews were analyzed to classify sentiment into positive or negative. With a ratio of 80% training data and 20% test data, the model developed achieved an accuracy of 95.5%. The classification results show precision 86.76%, recall 1%, and f1-score 92.91%. Even though the recall is low, the high accuracy shows that the model has good performance in predicting sentiment data. Recommendations can be provided to Shopee developers to improve customer satisfaction based on this sentiment data analysis.
OPTIMIZATION IOT TECHNOLOGY IN WEATHER STATIONS FOR IMPROVE AGRICULTURAL SUCCESS DURING EL NIÑO ERA Dodi Solihudin; Odi Nurdiawan; Rudi Kurniawan; Cep Lukman Rohmat
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5851

Abstract

The El Niño phenomenon is significant to global weather patterns, particularly in Indonesia, which adversely affects the agricultural sector, especially rice production. El Niño causes drastic changes in rainfall patterns, making it difficult for farmers to determine the right planting time. Limited access to accurate weather information is a major obstacle for farmers in planning their agricultural activities. This research aims to develop an Internet of Things (IoT)-based weather station capable of providing real-time and accurate weather data to support farmers' decision-making in their land management. The research method starts with observation in Babakan Jaya Village, Gabuswetan District, Indramayu Regency, to understand the local weather conditions and specific challenges faced by farmers. Next, the construction and implementation of a weather station equipped with sensors to measure various weather parameters such as temperature, humidity, wind direction and speed, and rainfall. The weather data collected by these stations is then processed and presented in real-time through a cloud platform, which allows access from computer devices and smart phones. The observation results from 1 June to 27 July 2024 showed that the air temperature ranged from 29°C to 35°C, humidity between 55% to 90%, and wind speed ranged from 0 to 7 km/h, with sporadic rainfall patterns. The developed IoT weather station successfully provides relevant and accurate weather data, which can be accessed in real-time by farmers. With this data, farmers can make more informed decisions in their land management, hopefully improving the efficiency and success of farming practices, especially in the midst of erratic weather conditions due to El Niño.
Co-Authors Cep Lukman Rohmat Dodi Solihudin Fitriani Agustina Odi Nurdiawan Tati Suprapti Wartumi Wartumi Windy Astuti Yudhistira Arie Wijaya Yudis Firmansyah