INFORMATION SYSTEM USING WEB-BASED RAPID APPLICATION DEVELOPMENT METHOD

The service of recording and borrowing goods at the Teacher Learning Center agency is currently still being carried out manually. parties who will borrow goods must come to the teacher learning center directly and record borrowing data and item data is still done manually, whereas at this time it has entered the era of the industrial revolution 4.0, of course, everything will have efficiency and very fast access. With this problem, of course, the right solution is to have a system that can borrow and record goods based on a website. The process of creating and designing a website uses the Rapid Application Development model, which is a method that starts with the requirements planning stage, RAD design workshop (work with users, build system) and implementation. Testing on the application uses alpha and beta testing, alpha testing uses blackbox testing while for beta testing uses the mean opinion score (MOS). Alpha testing obtained the website running according to what was expected to be successful while beta testing obtained a measurement result score with 10 respondents, namely 88.60% based on alpha testing and beta testing, it was concluded that this website was able to meet and support the needs of users and agencies

ELICITIATION OF INFORMATICS ENGINEERING INFOTECH WEBSITE NEEDS USING A USER PERSONA APPROACH

Requirements elicitation is the most important activity in the software development process, because it has a direct impact on the success of the development process. The success of a software is determined from the involvement of a user persona at the design stage. The involvement of user personas in software design is critical in the requirements engineering process, because if the process is done incorrectly, the resulting software will also have poor quality. This study focuses on using the approach of the user persona in collecting information related to the needs of the informatics engineering infotech website at the University of Muhammadiyah Malang, so that the results obtained can be in accordance with the needs of the user. The final result that is expected in this research is a design of the plagiarism check feature for the practitioner's program automatically on the infotech website which is implemented in the form of use cases and prototypes. This feature will later make it easier for assistants in assessing program results collected by practitioners, so that the time needed to correct the program can be done faster

DESIGN OF I-SLA (ISLAMIC LEARNING APPLICATION) AS TAJWEED LEARNING MEDIA BY USING THE SPEECH RECOGNITION TECHNOLOGY

Indonesia is a country with the majority of the population converting to Islam, which is more than 87% of the total population of Indonesia. As Muslims who adhere to the teachings of Islam, the teachings that must be understood are tajweed lessons. Tajweed science is the science that studies how to read the Qur'an properly and correctly. Adherents of Islam in Indonesia are still many who do not understand and cannot read the Qur'an properly and correctly. Research noted that there are still about 65% of Indonesian Muslims still blind to the writings of the Qur'an. The importance of learning tajweed science is that it can read precisely, if there are errors in reading the Quran can change its true meaning. Tajweed lessons are commonly obtained through non-formal educational institutions that focus on learning Islam. The current pandemic period causes all learning activities to be limited and difficult, including learning al quran education.  Online learning applications today are still rare that develop Quran Education including tajweed science, so people who want to learn the science have not found the right tools. We are planning an application called I-SLA (Islamic learning application). I-SLA is a tajweed learning application that utilizes speech recognition to correct the pronunciation of the user's Quran and provide justification if in pronunciation there is still something wrong, this technology has the ability to exchange information using acoustic signals. In addition, there is a consulting feature of tajweed experts if they feel they want to deepen tajweed knowledge. The design of the application in this study was carried out in a direct manner. The mechanism of this research is made by conducting a literature study for the process of making software needs specifications, followed by the creation of software design with UI / UX, followed by the creation of applications and closed with testing. This process is carried out continuously in accordance with the planning period. The result of this study is the application of I-SLA (Islamic Learning Application) with the aim of users of children, adolescents, and adults who want to deepen tajweed science to improve its pronunciation

MULTI-DOCUMENT SUMMARIZATION USING A COMBINATION OF FEATURES BASED ON CENTROID AND KEYWORD

Summarizing text in multi-documents requires choosing important sentences which are more complex than in one document because there is different information which results in contradictions and redundancy of information. The process of selecting important sentences can be done by scoring sentences that consider the main information. The combination of features is carried out for the process of scoring sentences so that sentences with high scores become candidates for summary. The centroid approach provides an advantage in obtaining key information. However, the centroid approach is still limited to information close to the center point. The addition of positional features provides increased information on the importance of a sentence, but positional features only focus on the main position. Therefore, researchers use the keyword feature as a research contribution that can provide additional information on important words in the form of N-grams in a document. In this study, the centroid, position, and keyword features were combined for a scoring process which can provide increased performance for multi-document news data and reviews. The test results show that the addition of keyword features produces the highest value for news data DUC2004 ROUGE-1 of 35.44, ROUGE-2 of 7.64, ROUGE-L of 37.02, and BERTScore of 84.22. While the Amazon review data was obtained with ROUGE-1 of 32.24, ROUGE-2 of 6.14, ROUGE-L of 34.77, and BERTScore of 85.75. The ROUGE and BERScore values outperform the other unsupervised models

SENTIMENT ANALYSIS ON E-LEARNING UNIVERSITY XYZ WITH NAÏVE BAYES CLASSIFIER METHOD

The covid-19 pandemic forced students and lecturers to carry out teaching and learning from home. Therefore, XYZ    University focuses its students on using e-learning. E-learning that has been running and used by students must be evaluated, so that teaching and learning activities can run well. Evaluation can be done by collecting opinions based on the features of XYZ University E-learning on students through questionnaires. All opinions can be analyzed using classification method called Naïve Bayes and Support Vector Machine for comparison.  The research started by collecting data, preprocessing data, labeling using polarity, calculating the frequency that often from each e-learning feature, and calculating the accuracy of the Complement Naïve Bayes model and Support Vector Machine model. The research results conducted on 1995 dataset testing, in student opinions with 1289 positive values, 372 negative values, and 364 neutral values. Reinforced by the comparison result of Complement Naive Bayes and Support Vector Machine. When Complement Naïve Bayes model accuracy of 89%, recall 85,3%, and the f1-score 85%. While Support Vector Machine accuracy is lower 11,1% than Complement Naïve Bayes Model with only 74,4%. These results indicate that of the 12 features on XYZ University E-learning, 8 features have a good opinion, 2 features have a bad opinion, and 2 feature have a neutral opinion

IMPLEMENTATION OF ANIMAL FACE’S RECOGNITION BY CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM

The purpose of this research is to apply the Convolutional Neural Network (CNN) method in the field of Computer Vision. The CNN algorithm is a combination of Neural Network and Multilayer Perceptron which uses a convolution approach to extract features. The CNN technique was used to identify an animal dataset that has 16,130 images divided into three categories: Cats, Dogs and Wild. This study aims to recognize facial images of animals belonging to the category of Cats, Dogs or Wild Animals which resemble the derivatives of cats or dogs such as Lions, Tigers, Hyenas, Wolves and so on. Comparing to learning rate and epoch, the results are 10e-4 and 60 respectively. Utilizing random images from the datasets, learning rate and epoch may achieve an accuracy of about 97.22% or 116.33 out of 120 images. When using images taken outside of the datasets, the accuracy may be as high as 77.78% or 93.33 out of 120 images

AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD

SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%

DC-SAM: DILATED CONVOLUTION AND SPECTRAL ATTENTION MODULE FOR WHEAT SALT STRESS CLASSIFICATION AND INTERPRETATION

Salt stress can impact wheat production significantly and is difficult to be managed when the condition is critical. Hence, detecting such stress whet it is at an early stage is important. This paper proposed a deep learning method called Dilated Convolution and Spectral Attention Module (DC-SAM), which exploits the difference in spectral responses of healthy and stressed wheat. The proposed DC-SAM method consists of two key modules: (i) a dilated convolution module to capture spectral features with large receptive field; (ii) a spectral attention module to adaptively fuse the spectral features based on their interrelationship. As the dilated convolution module has long receptive fields, it can capture short- and long dependency patterns that exist in hyperspectral data. Our experimental results with four datasets show that DC-SAM outperforms existing state-of-the-art methods. Also, the output of the proposed attention module reveals the most discriminative spectral bands for a given wheat stress classification task

ANALYSIS THE EFFECT USER READINESS ON ACCEPTANCE OF PEDULILINDUNGI APPLICATIONS USING METHOD TECHNOLOGY READINESS ACCEPTANCE MODEL (TRAM) BASED ON THE PERSPECTIVE OF GENERATION Z USERS

The PeduliLindung application is a new application released on March 28, 2020 by the Ministry of Communications and Informatics to be used to track and collect data to stop the spread of the Covid-19 virus in Indonesia. Judging from the Google Play Store, the PeduliLindung application received a rating of 3.6 as of October 2021 and received many negative responses from users. Therefore, to measure the success of the adoption of the PeduliLindung application, an evaluation of the level of readiness and acceptance of PeduliLindung application users will be carried out. This study aims to analyze the effect of user readiness on acceptance of the PeduliLindung application using the Technology Readiness Acceptance Model (TRAM) method based on the perspective of generation Z users. This study involved 349 generation Z respondents who were users of the PeduliLindung application and had vaccinated at least dose 1 as a sample of the population of application users. CareProtect. The data obtained will be analyzed using SmartPLS. The results of this study indicate that the level of readiness of users of the PeduliLindung application based on the perspective of generation Z users is in the high readiness category, and 4 of the 10 proposed hypotheses are accepted, and the remaining 6 are rejected hypotheses

KEYWORD IDENTIFICATION IN SCIENTIFIC JOURNAL PUBLICATION CONTENT FOR CASE STUDY ITS ONLINE PUBLICATION (POMITS) SEARCHING

ITS Online Publication (POMITS) is a publication journal for ITS undergraduate students. Many articles are published in it, and they are often needed as reference material for other student research. The search process is still based on title, abstract, author's name, and keywords. The data is still entered manually by the author. This process allows the selection of less appropriate keywords. So an effort is needed so that the choice of these keywords can be more precise and represent the article.The purpose of this research is to identify keywords in articles automatically. These keywords are distinguished into the software used, methods, and other representative keywords. With this identification, article searches can return more precise search results. This problem can be solved by using Named Entity Recognition (NER). However, the Indonesian language NER model owned by SpaCy is still not available, so it is necessary to develop the NER model.This study identifies each keyword annotation in POMITS content into metadata by detecting named entities in the form of software, methods, and representative keywords using the NER model. The NER annotation results are stored as triplet pairs in the Apache Jena Fuseki triple store. Furthermore, the triple store can answer searches about software, methods, and keywords. Based on the test results, the system successfully detects NER entities and saves annotations as triplet pairs on Apache Jena Fuseki. Keywords identification produce an average value of 84.76% precision and 63.59% recall.