PROTOTYPE METHOD PADA APLIKASI SCHEDULER BERBASIS MOBILE

PT United Tractors is currently implementing digitalization in its various activities. In its implementation, delivering information and archiving documents in the PSD4 Service Division still have obstacles due to the lack of effective and efficient access to information needed to do work. To overcome these problems, the solution that can be provided is to develop a mobile-based document filing system using the Flutter framework to maximize the development of mobile-based systems. For developers to interact directly with teams related to application capabilities, this system development method uses the Prototype method, which has 5 phases: Communication, Quick Plan, Modelling Quick Design, Construction of Prototype, and Development, Delivery, and feedback phases. The result of this research is an Android-based Scheduler Mobile document archiving application that has been made based on the steps in the prototype method. Testing using Black Box and Flutter DevTools is done to find out the results of the application that has been made. Black Box and Flutter DevTools testing results show that the application can run as expected

DATA QUALITY ASSESSMENT: A CASE STUDY ON ASSET VALUATION COMPARISON DATA

To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework to measure data quality. The results of measuring all dimensions, 29 out of 58 business rules cannot be fulfilled completely. The business rules that can be fulfilled in each dimension are 47.06% in the completeness dimension, 60% in the validity dimension, and 44.44% in the accuracy dimension. The main factor causing the existence of data attributes that have not met the data quality business rules is because the asset valuation comparison data comes from various data sources. In addition, there are methods or standards for recording data from data source units that are not uniform, so an evaluation of the uniformity of data standardization and the implementation of data governance is needed. The results of this study can be used as material for organizational consideration to be more aware of the current state of data quality. In addition, it can be used by organizations to design strategies and steps to improve data quality so that it can support leaders in making the right decisions

INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN DIGITAL LIBRARIES: A SOFTWARE ENGINEERING DESIGN

This research aims to design software engineering that integrates blockchain technology in digital libraries to improve system security and reliability. This integration is expected to overcome challenges related to data security, service reliability, and efficiency in digital library management. The research methodology involves collecting data through literature, expert interviews, and observations, on the implementation of blockchain technology in digital libraries, then analyzing data to support data design such as etherum, smart contracts, address, node.js, solidity, metamask, and sublime text, then using the Agile Extreme Programming (XP) method for software development. The research results include the design of a decentralized blockchain architecture, the use of smart contracts, and the application of cryptographic techniques to enhance security. Immutability testing in the context of blockchain involves verifying data consistency, validating the process of adding data, testing the ability to delete data, testing against attacks, and activities on immutable data. These tests were conducted using the Truffle framework. The results show that the system is able to maintain data integrity well

PENERAPAN METODE APPLICATION DEVELOPMENT DALAM PERANCANGAN PROGRAM PENJUALAN TANAH KAVLING BERBASIS WEBSITE

Currently, property developer agencies are progressing rapidly, the supporting elements of home ownership are also facilitated by the presence of home ownership loans. In the process of selling land property, it still uses conventional methods so that customers must be present in person to find and order land plots. The sales process is still manual, causing errors in reporting and making it difficult for employees to find documents. The purpose of this research is to design a website-based land plot sales program that can facilitate the process of providing online land availability information, the land plot sales process which is equipped with a payment scheme calculator feature and a track booking feature. This research uses the Rapid application Development (RAD) model with the aim that the application can be efficient in processing time, and uses the black-box testing method in its testing. The result of this research is a land plot sales website with a payment scheme calculator feature and a track booking feature, so that the sales process becomes efficient, reporting data becomes accurate and data search becomes easier

ENTERPRISE ARCHITECTURE PLANNING BAGI DESA DIGITAL DI KABUPATEN SUMEDANG

Penerapan Enterprise Architecture (EA) telah menjadi keharusan strategis yang penting untuk menjawab tantangan transformasi digital di Desa Digital. Penelitian ini bertujuan untuk mengembangkan Enterprise Architecture Desa Digital di Kabupaten Sumedang. Permasalahan utamanya terletak pada tidak adanya kerangka terstruktur atau tata kelola dalam pengelolaan transformasi digital yang efektif di tingkat desa. Metodologi yang digunakan dalam mengembangan Enterprise Architecture pada Desa Digital menggunakan Enterprise Architecture Planning (EAP), EAP dilakukan secara cermat, dimulai dengan analisis mendalam mengenai tantangan spesifik yang dihadapi desa dalam proses transformasi digital. Tahapan perancangan EAP meliputi inisiasi perencanaan, pemodelan bisnis dan teknologi saat ini, perancangan arsitektur data, arsitektur aplikasi, arsitektur teknologi, dan perencanaan implementasi. Temuan penelitian ini bahwa partisipasi aktif pemerintah daerah, masyarakat, dunia usaha dan pakar teknologi dianggap penting dalam merumuskan rencana EAP yang komprehensif. Penekanannya adalah pada pendekatan inklusif untuk memastikan keselarasan antara tujuan bisnis desa dan infrastruktur teknologi yang direncanakan, sehingga memberikan panduan praktis bagi pengambil keputusan dalam mengarahkan transformasi menuju Desa Digital yang lebih adaptif dan inovatif. Penelitian ini menghasilkan panduan berupa roadmap pengembangan teknologi informasi 5 tahun ke depan yang terdiri dari 14 data kandidat, 7 kandidat aplikasi, dan peta jaringan terkait, yang dapat memberikan wawasan berharga bagi kepala desa dalam merencanakan dan melaksanakan perubahan arsitektur yang ada apakah sudah sesuai dengan kebutuhan, kekhususan dan potensi Desa Digital

PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR

Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%

A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results

DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684

PERBANDINGAN KRITERIA DECISION TREE PADA PENGETAHUAN MASYARAKAT PADA PEMILIHAN UMUM PRESIDEN INDONESIA

In the Presidential election, gen z as a new voter, must know in advance who the presidential candidate will be in the 2024 election as well as the election process, because if voters do not know and do not understand, it will cause the wrong choice will result in their votes cannot be used even to abstain. What factors cause milennials and gen z generations to not know about elections can be determined using a decision tree. Therefore, in this study, a questionnaire was given to millennials and gen z generation to find out whether voters know the presidential candidate to be elected. The data from the questionnaire is processed to become training data and testing data with a ratio of 70:30. Then measure the accuracy level using the C4.5 algorithm with a comparison of splitting criteria, namely gain ratio, information gain and gini index.  By knowing the right splitting criteria, the decision tree model can help overcome the problem of overfitting in the data. Overfitting occurs when the model is too complex in memorizing training data, thus failing to generalize well to read new data. The calculation results show the difference in accuracy values between Gain ratio, Information gain and Gini index, namely 81.67%, 83.33% and 83.33%. It can be concluded that for the use of Algorithm C4.5 splitting criteria Gain ratio and Gini index have the same accuracy value for accuracy measurement in this study

THE EFFECT OF IMMERSIVE TECHNOLOGY ON ENHANCING STUDENT LEARNING: A SYSTEMATIC LITERATURE REVIEW

Education is one of the fundamental factors influencing national development. Educators are required to constantly explore the newest and creative strategies to deliver topics to students and promote students’ engagement in the learning process. One technology that has numerous potentials to facilitate the education process is immersive technology. Immersive technology has the potential to improve student achievement by enabling better learning outcomes. This study aims (1) to analyze the trends and impact of immersive technology usage in education; (2) to identify immersive technology that supports students’ learning processes in the pandemic era; and (3) to identify the usage of learning theories in educational application development that implement immersive technology to enhance students’ learning outcomes. This study examined seventeen selected studies after adopting the selection process of the systematic literature review process by Kitchenham. The selected studies were published between 2018 and 2022 by Emerald Insight, IEEE Xplore, and Scopus. There are three kinds of immersive technology identified in this study: virtual reality, augmented reality, and mixed reality. The study showed the nine types of students’ learning outcomes, and the results showed that using immersive technology significantly improved student learning compared to traditional methods. Furthermore, thirteen learning theories were adopted as the basis for developing educational applications. Future research directions are also suggested to continue developing promising and limited new technologies to enhance the variety of education-oriented applications. Immersive technologies designed for learning are still on a long journey, including considerations of readiness and potential effects that may arise for users such as privacy and security concern