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Arief Hidayat
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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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Sistem Informasi Suhu dan Kelembaban Inkubator Telur Ayam Menggunakan Sensor Dht22 Berbasis Mikrokontroler Ihsanulfu'ad Suwandi
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.v5i2.8047

Abstract

Measurement of temperature and humidity generally uses a tool that can determine the value of two physical quantities of a material or system (thermometer or hygrometer). When it comes to hatchery incubators, success and protection are priorities. The microcontroller is a small computer in the form of a chip, DHT22 temperature and humidity sensor with a range of (-40)-80°C. Formulation of the problem, (1) How to get digital data of hatching incubator room temperature using a microcontroller? (2) How is the application of the system to hatching incubators?. Methods of research, analysis, implementation, and simulation (a) Analysis, the lowest ideal temperature for hatching eggs shows a figure of approximately 38°C and the highest is 38.5–39°C. (b) Implementation, starting with the use of DHT22 giving a temperature signal according to whether or not when the condition of the incandescent lamp as a heater will turn on or off, the display will be displayed on the LCD. (c) Simulation, the first stage is the DHT22 schematic to Arduino, the second stage is the relay schematic to Arduino, the third is I2C LCD to Arduino, the fourth is relay to lights and indicators. Based on the analysis, implementation and simulation, conclusions are drawn (1) Digital data related to temperature from the DHT22 sensor displayed on the LCD can be applied to help monitor the hatching process of chicken eggs using an incubator. (2) Arduino board-based microcontrollers can be applied as controllers related to system flow in chicken egg hatching incubators.
Sistem Pakar Deteksi Dini Tingkat Kecanduan Gadget pada Anak Menggunakan Fuzzy Tsukamoto Fernando Bayu Andika; Agus Sidiq Purnomo
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.v5i2.8750

Abstract

Information and communication technology continues to develop and progress which demonstrated by the presence of gadget technology. Gadgets are smart electronic devices that assist in making it simple for users to accomplish various task. The use of gadget technology in children are unable to be separated. According to the 2020 KPAI survey, approximately 71,3% of school-age children own and have played with gadgets for a longer time. As a result, it is expected that early detection of gadget addiction can be carried out to ensure that mental and  emotional disorders in children who use gadgets can be properly addressed. The aim of this research is to create a prototype expert system for early detection of gadget addiction levels in children using the fuzzy tsukamoto. The fuzzy tsukamoto method was used in this study. This study included 74 respondents aged 9 to 12 years old. The DAS (Digital Addiction Scale : For Children) was used as the data collection method in this study. The system’s as performance will be evaluated using 74 respondents data by comparing the result of expert calculations and fuzzy tsukamoto method calculations. Fuzzy Tsukamoto reasoning with 64 rule bases in used to build this expert system. According to evaluation with 74 respondent data, this expert system has a system acurracy rate of 87,83%, which indicates that it proceeds succesfully.
Klasifikasi Citra Genus panthera Menggunakan Pendekatan Deep learning Berbasis Convolutional Neural network (CNN) Waeisul Bismi; Muhammad Qomaruddin
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.v5i2.8931

Abstract

This research aims to develop an image classification method for the panthera genus using a deep learning approach based on Convolutional Neural network (CNN). The panthera genus includes large species such as tigers, lions, leopards, and jaguars, which share similarities in appearance but also differences in fur patterns, body size, and habitat. Image classification of the panthera genus is important in various applications, including wildlife conservation and biological research. In this study, image datasets of tigers, lions, and leopards were collected from various sources to a total of 6,290 images. The proposed method involves image pre-processing, such as resizing, converting and normalization, and the use of a Convolutional Neural network (CNN) model to perform classification. The CNN model is implemented and trained using training data to recognize specific visual patterns in the images of each species. The results of this study show that the CNN-based deep learning approach can achieve high accuracy in the classification of panthera genus images of 85.21%. This method can correctly distinguish between tiger, lion, and leopard images based on unique visual features. In addition, the deep learning approach also offers advantages in efficiency and scalability to cope with the large number of images in the dataset. This research makes an important contribution to the development of wildlife image classification methods using a CNN-based deep learning approach.
Rekomendasi Paket Mata Pelajaran Pilihan (MPP) pada SMA Negeri 1 Kebumen Menggunakan Algoritma K-means Gustina Alfa Trisnapradika; Wildanil Ghozi; Yuminah Yuminah
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.v5i2.8514

Abstract

Curriculum changes are needed to adapt education to the times. Since the covid-19 pandemic, face-to-face learning has been suspended. Online learning is an alternative used during a pandemic. This has an impact on learning loss so that the quality of learning decreases. Recovery of learning during the pandemic and post-pandemic Covid-19 is important to reduce the impact of learning loss on students. After the pandemic, the independent curriculum was launched which was a refinement of the 2013 curriculum which had only been implemented in several schools. The subject structure of the Merdeka curriculum for SMA level in Fese E or grade 10, all students get the same subjects. While in Phase F (grades 11 and 12), the subject structure is divided into 2 main groups, namely general subjects and elective subjects. Based on the provisions of the SMKA 2021-2022 curriculum structure, SMA Negeri 1 Kebumen prepares elective subjects (MPP) which are made up of 7 MPP packages. This study uses a clustering technique of student scores using the K-Means algorithm to obtain MPP package recommendations that suit student abilities. For each MPP package, clustering is carried out into 2 clusters with features in the form of predetermined subject scores. The result of this clustering is that each student gets a "yes" or "no" recommendation for each MPP package.
Classification Model Analysis of ICU Mortality Level using Random Forest and Neural Network Lymin Lymin; Alvin Alvin; Bodhi Lhoardi; Darwis Darwis; Joseph Siahaan; Abdi Dharma
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.v5i2.8749

Abstract

Based on the results of previous studies, research on machine learning for predicting ICU patients is crucial as it can aid doctors in identifying high-risk individuals. A high accuracy in machine learning models is necessary for assisting doctors in making informed decisions. In this study, machine learning models were developed using two models, namely Random Forest and Artificial Neural Network (ANN), to predict patient mortality in the ICU. Patient data was obtained from The Global Open Source Severity of Illness Score (GOSSIS) and underwent preprocessing to address issues of missing values and imbalanced data. The data was then divided into training, validation, and testing sets for model training and evaluation. The results of the study indicate that the Random Forest model performs better with an accuracy of 93% on the testing data compared to the ANN which only achieved an accuracy of 86% on the testing data. Consequently, the Random Forest model can be utilized as a solution for predicting patient mortality in the ICU.
Analisis Sentimen terhadap Penyelenggaraan Sea Games 2023 Kamboja pada Twitter Menggunakan Algoritma Naive Bayes Farah Fadila Rahman; Frise Anesha Lutia; Ultach Enri
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.v5i2.8946

Abstract

Southeast Asian Games or SEA Games is a Southeast Asian sporting event held every 2 years, where the participants are 11 member countries of the Association of Southeast Asian Nation (ASEAN). Cambodia was chosen as the host for the 2023 SEA Games. The implementation of the SEA Games in Cambodia experienced many controversies ranging from the inverted Indonesian flag to leaking lodging rooms for athletes. Social media Twitter became one of the places for netizens to express their opinions about the implementation of the SEA Games in Cambodia. This study aims to determine the level of tendency of positive, negative and neutral opinions through the sentiment analysis process. The sentiment analysis process is carried out using the Naive Bayes method, through five main stages, namely Data Selection, Preprocessing, Transformation, Data Mining, and Evaluation. The data used comes from Twitter users who use the hashtag "SEA Games Cambodia" then obtained data as many as 1595 tweets. The results of this study describe the results of Naive Bayes implementation and performance testing using confusion matrix obtained accuracy 66%, precision 70%, recall 66%, and f1-score 61%. and also obtained the results of the tendency of public opinion sentiment on Twitter with positive results as much as 49%, then negative results as much as 40% and neutral results as much as 11%.
Analisis Manfaat Investasi Teknologi Informasi Nirmala Hadir pada Koperasi Nirmala Luh Ketut Kartika Candra Dewi; I Gusti Agung Pramesti Dwi Putri; Linda Yupita
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.v5i2.8562

Abstract

The rapid development of Information Technology (IT) is currently creating new habits and facilitating life activities from all aspects which is a challenge for various parties, including non-bank financial companies, to be prepared to face business competition through the application of information technology. One of the companies investing in information technology is the Nirmala Cooperative by presenting Nirmala Hadir e-commerce to support its business units and prioritize convenience for its members. When developing digital technology, companies need a large capital investment with an uncertain percentage of return and the measurement is quite difficult to do so an analysis of the benefits of technology investment is needed to ensure that the benefits obtained are higher than the negative impacts that arise. The method applied in this research is a qualitative descriptive approach which in its analysis uses the Ranti's Generic IS/IT Business Value and Cost Benefit Analysis (CBA) methods. The results of research with Ranti's Generic IS/IT obtained 7 categories and 9 sub-categories of benefits and CBA calculations obtained an ROI value of 7.93%, NPV of IDR 11,731,529, Payback Period of 0.929 (339 days) and BCR of 9.87 which indicates that the technology investment made by the Nirmala Cooperative has a good profitability value with a payback period of less than one year.
Penyeleksian Beasiswa Berprestasi pada Universitas XYZ Menggunakan Metode MOORA Tundo Tundo; Panji Wijonarko
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.v5i2.8761

Abstract

XYZ University has a scholarship program intended for outstanding students. Determination of recipients of outstanding scholarships is still constrained by the unclear system for determining scholarship recipients. This can affect the fairness of receiving scholarships. Students who really deserve to get a scholarship do not receive the scholarship. The solution to overcome this problem, the researcher came up with an idea in the form of a Multi-Objective Optimization by Ratio Analysis (MOORA) Decision Support System method, with the aim of being able to solve the problem of selecting an objective scholarship acceptance process. The MOORA method was chosen because it can solve the problem with a fairly effective method. After conducting research, it was found that 5 (five) candidates were eligible to receive scholarships for outstanding students, namely Alt7, Alt10, Alt37, Alt49, and Alt35. Furthermore, from the results of this study, it was concluded that students who get achievement scholarships are students with the highest scores from the results of the MOORA method, there is no element of subjectivity.
Sistem Informasi Pelayanan Kesehatan Hewan Berbasis Website Menggunakan Metode Rational Unified Process (RUP) Ninda Sofiatun Ni’mah; Daniel Alfa Puryono; Moh Rofi
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.v5i2.9040

Abstract

Puskeswan adalah pusat kesehatan hewan yang menyediakan jasa pengobatan untuk hewan ternak. Selama ini proses pelayanannya di puskeswan pengelolaan data masih manual yaitu menggunakan media buku besar, sehingga banyak pencatatan data yang dilakukan secara berulang. Selain itu pemilik hewan ternak juga harus datang ke puskeswan atau telepon untuk melakukan konsultasi terkait dengan hewannya. Maka penelitian ini dilakukan untuk merancang sistem informasi pelayanan kesehatan agar dapat membantu puskeswan wedarijaksa dalam mengelola data dan mempermudah pemilik hewan mendapatkan pelayanan kesehatan untuk ternaknya. Sistem ini menggunakan metode Rational Unified Process (RUP) dalam pengembangan yang berkaitan dengan perangkat lunak pada sebuah sistem yang interaktif. Proses pengembangan metode Rational Unified Process ini ada 4 tahapan yaitu Inception, Elaboration, Construction dan Transition. Hasil dari penelitian ini menunjukkan bahwa sistem informasi pelayanan kesehatan hewan yang dibangun memiliki kegiatan pelayanan mulai dari registrasi pendaftaran, pemeriksaan, konsultasi, pelaporan online dan pemetaan hewan ternak dapat berjalan dengan baik.
Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms Sugeng Winardi; Mohammad Diqi; Arum Kurnia Sulistyowati; Jelina Imlabla
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.v5i2.8632

Abstract

This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribute to the growing body of evidence supporting deep learning methods in sentiment analysis and underscore the potential of AI models like Transformers in handling complex natural language processing tasks. This study broadens the scope for AI applications in social media sentiment analysis.

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