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Arief Hidayat
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INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Wahid Hasyim Semarang
ISSN : 26562855     EISSN : 26855518     DOI : http://dx.doi.org/10.36499/jinrpl
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 222 Documents
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Penerapan Metode Dbscan untuk Identifikasi Kluster Gempa Bumi di Daerah Yogyakarta Wahyu Ajitomo; Irfan Pratama
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.v6i1.9214

Abstract

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.
Analisis Tingkat Penggunaan Gadget pada Anak Usia Dini dengan menggunakan K-Mean Khaerul Anam; Rizal Rusyana; Bani Nurhakim; Denni Pratama
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.v6i1.10317

Abstract

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).
Sistem Perizinan Santri Berbasis Web dengan Fitur Whatsapp Menggunakan Framework Laravel (Studi Kasus pada Pondok Pesantren Luhur Wahid Hasyim Semarang Putri) Zuhrof Karima Hamidah; Arief Hidayat
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.9051

Abstract

Luhur Wahid Hasyim Islamic Boarding School is one of the huts located in the Semarang area, to be precise, in the Sampangan Village. The Islamic boarding school is part of the Wahid Hasyim Semarang University (UNWAHAS) foundation. In implementing the student licensing system, Luhur Wahid Hasyim Islamic Boarding School still applies the conventional system, students must meet the management and fill in the data in the permit book when applying for a permit. The licensing process using this method raises problems such as the large number of students who do not carry out permits according to the procedure, the data of students whose permits cannot be managed validly due to negligence. The purpose of this research is to produce a system that can speed up the process of managing, inputting, and storing student licensing data more effectively and efficiently. In making this information system the author uses the waterfall method which is the development of a system or software with the process of working on a system carried out sequentially and systematically. The results of this study are a web-based licensing system at the Luhur Wahid Hasyim Islamic Boarding School using the Laravel Framework. This licensing system can assist students in applying for permits at PPLWH more effectively and efficiently, assisting administrators in viewing lists of permit applications and recapitulating permits according to the type.
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.
Prediksi Harga Mobil Bekas Menggunakan Algoritma Regresi Linear Berganda Dea Miftahul Huda; Gifthera Dwilestari; Ade Rizki Rinaldi; Iin Solihin
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.v6i1.10266

Abstract

The lack of information regarding used car prices creates obstacles for people in buying and selling vehicles because they don't understand the market prices that are used as a reference. This information is very important to know price predictions with the range of variables that can be considered. The aim is to process an algorithm model that is capable of carrying out statistics using appropriate techniques to make predictions. Prediction is a very important technique in decision making. The linear regression algorithm is a model building technique used to predict the value of a given dataset. In this study, a multiple linear regression algorithm was used to predict used car prices. The dataset used to create a prediction model with a linear regression algorithm was sourced from the Kaggle repository for used car prices and then the results were visualized in Rapminer. The prediction process uses a comparison of testing data and training data with a ratio of 90 training data and 10 testing data in the process of building the model and evaluating the model that has been produced. The result of the prediction process using the linear regression algorithm is a prediction model of Price 1637.49. The prediction model will be evaluated with 2 assessment indicators, namely RMSE and Relative Error. The results obtained from this model, in the Price category, the RMSE value is 1637.49 and the Relative Error value is 11.89%. And the application of the regression model to new data from the independent variables used is the attribute Age (Age) 24 X1, Kilometers (KM), 783764 X2, Horse power (HP) 100 X3, Transmission (Automaitc) 0 X4, Engine capacity (CC) 1500 regression equation Y = b1 + b2X1 + b3X2 + b4X3 + b5X4 +b6X5 +b7X6.
Sebuah Penerapan Metode Naïve Bayes dalam Klasifikasi Masyarakat Miskin pada Desa Tanjungsari Tundo Tundo; Mesra Betty Yel; Veri Arinal; Bobby Arvian James; Andi Saidah
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.v6i1.9799

Abstract

The main problem in efforts to reduce poverty today is related to the fact that economic growth is not spread evenly. The research will carry out classification based on data on poor residents obtained from Tanjungsari Village, Kajen District using data mining techniques. The attributes that will be used in classifying residents are Education, Occupation, Income, Dependents, Electricity Power, Home Ownership Status. The method that will be used is the Naïve Bayes Classifier method, which is one of the classification techniques in data mining. The expected result of this research is to obtain information/data regarding determining poverty in the Tanjungsari Village community which can be used by the district government to design strategies to improve community welfare. The classification system for the poor population of Tanjungsari Village is based on the results of confusion matrix testing, using the Naïve Bayes classification method based on test data taken from the research object, obtaining an accuracy rate of 83%, a recall value of 100%, a precision of 83%, and an error rate of 17%.
Komparasi Algoritma K-Nearest Neighbor dan Naive Bayes pada Klasifikasi Tingkat Kualitas Udara Kota Tangerang Selatan Avira Budianita; Nurul Iman; Fida Maisa Hana; Cikita Berlian Hakim
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.v6i1.10956

Abstract

The growth of technology and the impact of industrial activities on the earth have an influence on environmental changes, including changes that are felt are a decrease in air quality or air pollution which has an impact on the health of the human body. Based on this, this research aims to produce a model for solving air quality classification problems based on parameter indicators. A comparative evaluation was also carried out on the classification of the K-Nearest Neighbor and Naive Bayes algorithm methods on the air quality dataset in South Tangerang in 2022. At the same ratio in the classification process, the K-Nearest Neighbor algorithm got an accuracy value of 94.44% and the Naive Bayes algorithm got an accuracy value of 94.44%. Accuracy value 86.11%. From the results of testing the data, it can be concluded that the K-Nearest Neighbor algorithm has high accuracy compared to the Naive Bayes algorithm in air level classification.
Analisis Perbandingan Klasifikasi Citra Genus Panthera dengan Pendekatan Deep learning Model MobileNet Waeisul Bismi; Deny Novianti; Muhammad Qomaruddin
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

Abstract

Wildlife conservation is increasingly becoming a top priority as several species in the Panthera genus have experienced significant declines in their populations since the 1970s, due to illegal hunting activities, loss of natural habitat, and reduced prey. Their protection is therefore of paramount importance. In today's digital age, image processing and artificial intelligence (AI) technologies have changed the way we view and protect wildlife. In this context, the approach of using MobileNet models in deep learning, which is a branch of artificial intelligence, has proven to be very effective in overcoming complex challenges in image processing. However, despite MobileNet's potential in classifying images of the Panthera genus, not many studies have specifically compared it with other existing methods. Therefore, in this study, a comparative analysis of Panthera genus image classification using the deep learning approach of MobileNet model with alternative models from previous studies is conducted. The dataset used consists of 6,460 images with 6 labels: Jaguar, Leopard, Lion, Lioness, Tiger, and Snow Tiger, which are divided into training, validation, and testing sets. Based on the evaluation results, the proposed method using the MobileNetV1 model achieved the highest accuracy of 89.93%, followed by the MobileNetV2 model with 89.78%. This research is expected to provide valuable insights in the development of system implementation in an application on various platforms for image detection, to support species conservation efforts in the Panthera genus.
Analisis Sentimen Opini Supporter Pengguna Youtube terhadap Sistem Pembelian Tiket Pertandingan Persib menggunakan Metode Naïve Bayes Adam Arifian Alamyah; Rini Astuti; Fadhil Muhamad Basysyar
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.v6i1.10310

Abstract

Reporting about Persib cannot be separated from the role of the media from the era of the union until today. The first news about Persib in the media was at least in November 1904, when the Priangan Association (PVB) was recorded as the first association in Bandung. Using Descriptive Analysis, in the form of a word cloud, which is used in this research to identify and form word patterns that can be associated with other words that are considered important. Naïve Bayes Classifier Method. used in this research to identify and form word patterns that can be associated with other words to obtain information that is considered important. YouTube has become one of the largest platforms for sharing visual content on the internet. One of the topics that is being widely discussed is the ticket purchasing system for Persib Bandung matches. This has invited a lot of reactions, especially from the community, especially residents of West Java. This causes the controversy to become a polemic. Therefore, a method is needed to classify reviews automatically by conducting sentiment analysis. In this research, 2129 comment data in several contents discussed the Persib Bandung match ticket system. The aim of this research is to classify the analysis. review of the polemic of the match ticket system using the Naïve Bayes algorithm.
Prediksi Jumlah Sampah pada Sektor Informal di Provinsi Jawa Barat MenggunakanAlgoritma Regresi Linear Nursyifa Puspa Ar-rahmi Slamet; Nana Suarna; Willy Prihartono
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.v6i1.10294

Abstract

Waste has become one of the most pressing global problems to be solved. Rapid population growth, urbanization, and consumerism have led to a significant increase in the volume of waste worldwide. This phenomenon not only affects the environment, but also touches the economic sector, health, and social life. West Java, as one of the provinces with the highest population density in Indonesia, faces great pressure regarding waste management. The province is experiencing a significant increase in the amount of waste that occurs due to population growth and high intensity of industrial activities. The method used in this research is linear regression algorithm. The application of linear regression algorithm can help the government to plan strategic measures in waste management. By using historical data on waste production, population growth, and other factors. This algorithm can provide an overview of future trends in waste generation. The purpose of this research is to implement a linear regression algorithm to predict the amount of waste data that goes to the informal sector, especially involving collectors or stalls in West Java province. The results of this study resulted in an increase in the accuracy level of the accuracy of the volume of waste in the informal sector in West Java Province can have a significant impact and make a major contribution to the understanding of the effectiveness of the application of linear regression algorithms. This increase in accuracy is expected to deepen the understanding of how the algorithm can be optimized for more efficient prediction and management of waste volume.

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