Digital Zone: Jurnal Teknologi Informasi dan Komunikasi

Development of a Web-Based E-Survey Application for Assessing Good Corporate Governance Understanding at PT. XYZ

Cristian Richardo Anin, Nia Ambarsari, Sinung Suakanto — Thu, 16 Nov 2023 00:00:00 +0000

PT. XYZ is currently facing challenges in conducting their Good Corporate Governance (GCG) comprehension survey due to the manual distribution and collection of survey data using Google Drive and Microsoft Excel. To address this issue, the researcher proposes the development of a web-based E-Survey application to streamline the survey process. The development follows the waterfall model and utilizes the Laravel framework. To ensure its effectiveness, the application undergoes Load Testing to assess its performance with multiple users and User Acceptance Test (UAT) to evaluate its alignment with company requirements. The results indicate that the system operates optimally with up to 100 users and receives a favorable score on the System Usability Scale (SUS). This application provides a suitable solution for conducting comprehension surveys at PT. XYZ.

Pemodelan Spasial untuk Analisa Produksi Padi Integrasi Machine Learning

Thu, 16 Nov 2023 00:00:00 +0000

The growing human population has sparked interest in accurate rice production prediction. This research combines Geographic Information Systems and machine learning to model rice paddy production prediction with minimal error. Using linear regression, this research analyzes rice paddy production in 13 districts/cities in South Kalimantan with spatial and historical data as features, and rice paddy production as the target variable. Experimental results show that this approach produces accurate predictions, with the Linear Regression Algorithm having a consistent level of accuracy and R² of 0.992, explaining that 99.2% of the variation in the response variable can be explained by the predictor variable, and 0.8% is influenced by other factors. While Hulu Sungai Utara district has the highest rice production and Barito Kuala district has the lowest. This study makes an important contribution in spatial modeling for rice paddy production prediction, helping agricultural experts and decision makers identify rice paddy production potential and improve the accuracy of rice paddy production prediction.

Implementation of The Open Group Architecture Framework to See the Readiness of Smart Schools in Pekanbaru

Thu, 16 Nov 2023 00:00:00 +0000

Smart Schools are a derivative of smart people in the 6 pillars of Smart City. Smart Schools is also a school concept utilizing information technology used in the teaching and learning process in the classroom and school administration. One of the schools in Pekanbaru City that will implement smart schools is Junior High School 17 Pekanbaru. Currently, the school already has several infrastructures including servers, laboratories, and administrative rooms, but it is necessary to analyze the technology architecture aimed at implementing Smart Schools. The technological architecture would be analyzed using TOGAF (The Open Group Architecture Framework) Framework version 9. The TOGAF framework is a framework for enterprise architecture which is able to develop an enterprise architecture in an organization. Enterprise architecture is an explicit explanation and current documentation of the relationship between management, business processes, and information technology. This research describes the current architectural conditions and target architectures to include the rules, standards, and lifecycle of information systems to optimize and maintain the environment of organizations that want to create and maintain by managing the IT portfolios. The results of this study are to produce an IT blueprint that is used as a school guide in implementing technology architecture to support the implementation of Smart Schools in Pekanbaru City.

Pengenalan Kepribadian Melalui Tulisan Tangan Menggunakan Convulutional Neural Network Dengan LS Classifiers

Yusa Virginiawan Guntara, Syamsuryadi, Sukemi — Thu, 16 Nov 2023 00:00:00 +0000

A person's handwriting is different and unique, even though it looks similar it is certainly not the same as someone else's writing. One's personality traits can be identified based on handwriting. One of the implementations is (handwriting recognition). To identify a person's personality, it can be classified by handwriting using the 'Graphology' field. The computational system to identify handwritten images can use the Convulution Neural Network method. Using the CNN method is expected to produce good accuracy with a low error rate. The CNN method is able to predict a person's personality through manuscripts as images. In addition, to increase the diversity of classifications, the Least Squared Classifiers method is needed. . LS Classifiers are designed to increase the variety of CNN methods in feature extraction and classification. The LS Classifier method is a classification method that estimates the w parameter vector and takes the best linear classifier based on the w parameter vector. Research has functions for users, including to find out someone's personality, especially extrovert and introvert personality. In this study CNN serves as Feature Extraction to classify Image and Ls Classifiers serves to increase diversity into 2 personality groups. The level of accuracy of the performance of the CNN & Ls Classifiers method in carrying out feature extraction and classification of handwritten images in determining personality has a good level of accuracy.

Platform Monitoring Berbasis Web untuk Sistem Stabilitas dan Pelacakan Kapal Nelayan

Agus Tedyyana, Fajar Ratnawati, Danuri Danuri — Sat, 25 Nov 2023 11:50:54 +0000

Fishing activities in the waters of Bengkalis Island face a number of serious problems, including a lack of data during fishing activities and ship accidents caused by delays in information. In response to this problem, this research proposes the development of a "Web-Based Monitoring Platform for the Stability and Tracking System for Fishing Vessels on Bengkalis Island", or also known as the SMARST tool. Safety is the top priority. SMARST is equipped with an early warning system that uses predictive algorithms to identify potential dangers, such as bad weather conditions. If an emergency occurs, fishermen can immediately contact the authorities via the integrated emergency communications facility. This tool is designed to monitor fishing activities, including monitoring the safety of ships when operating at sea. The implementation of the SMARST tool is expected to increase the success of fishing activities at sea and ensure the safety of fishermen and their vessels. This innovation offers an integrated solution to support the sustainability and efficiency of the fishing industry on Bengkalis Island. The SMARST tool will be able to identify the location of vessels in real-time, ensure their safety from threats such as conflicts with Malaysian authorities and assist in mitigating the risk of accidents. The system was built using an Agile approach, which allows for iterative development and is responsive to user needs. Key functionalities of SMARST include real-time tracking of vessel locations, monitoring vessel conditions, and providing the latest information relevant to fishing activities. From a non-functional perspective, this system emphasizes reliability, ease of use, and scalability.

Penerapan Teknologi Augmented Reality Sebagai Media Promosi Sepatu

Muhammad Khoirul Anam, Yuli Asriningtias — Thu, 30 Nov 2023 00:00:00 +0000

Keberadaan toko online sebagai platform penjualan dianggap sangat penting dalam meningkatkan profit, terutama dengan perkembangan teknologi augmented reality (AR) yang dapat memperkaya proses promosi. Tujuan dari penelitian ini adalah untuk mengembangkan sebuah aplikasi Android yang memperlihatkan produk sepatu dalam format tiga dimensi menggunakan teknologi AR. Data penelitian diperoleh melalui observasi perilaku konsumen dalam berbelanja online dan penerapan teknologi AR. Metode yang digunakan dalam penelitian ini adalah Multimedia Development Life Cycle (MDLC). Aplikasi AR ini dibuat menggunakan Unity 3D dan dapat diakses di platform Android dengan menggunakan teknologi markerless, yang memungkinkan pengguna menggunakan objek dunia nyata sebagai marker tanpa perlu tambahan perangkat, sehingga meningkatkan fleksibilitas penggunaan aplikasi dan memastikan performa tinggi serta responsif. Hasil pengujian User Acceptance Testing (UAT) menunjukkan nilai rata-rata sebesar 87,19%, dengan kategori "sangat baik", maka dapat disimpulkan bahwa aplikasi AR ini memiliki potensi yang sangat baik untuk mendukung upaya promosi penjualan secara online.

Constellation of Football Players Determination Based on Cost and Performance History Using the K-Means Clustering

Eko Prasetyo, Almendaris Shandy Priyatama, Fardanto Setyatama — Thu, 30 Nov 2023 00:00:00 +0000

Determining the constellation of football players determines a team's success when competing on the field. Disassembling players is an option that must be made considering performance history and costs. This research experiments with K-Means to automate the search for groups of players based on performance and price history. Grouping can achieve a constellation of players with high-performance characteristics but at an affordable price. The dataset used in this research is 580 football players for the 2022/2023 season from Sofifa, Fbref, and SofaScore. The data is divided into four player positions: goalkeeper, defender, midfielder, and attacker. Data for each position is grouped into 3 clusters. Each cluster is analyzed to obtain dominant performance indicator values and determine the characteristics of the cluster. Experimental results using K-Means show that cluster 1 is a team with medium player prices but low performance. Cluster 2 has the cheapest price but with the best performance. Meanwhile, cluster 3 is the most expensive but performs similarly to cluster 2.

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Ozzy Secio Riza, Ari Nuryadi — Thu, 30 Nov 2023 00:00:00 +0000

This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.

Implementasi Teknologi Blockchain dalam Aplikasi E-Voting Berbasis Mobile

Setiawan Restu Aji, Wahyuningdiah Trisari Harsanti Putri — Thu, 30 Nov 2023 00:00:00 +0000

Voting is a key element in democratic processes and decision-making in various contexts. With the development of information technology, traditional voting methods have undergone a significant transformation with the introduction of e-voting systems. Although some previous research has successfully developed e-voting applications, some significant drawbacks have been identified, such as insufficient security issues, risk of manipulation, and reliance on a central authority. Blockchain technology is decentralized and immutable. Therefore, it is an attractive solution in addressing these challenges. This research aims to overcome the weaknesses of previous e-voting applications by developing a mobile-based e-voting application that uses blockchain technology. This research also enables wider voter participation through mobile devices, improving the security and integrity of votes. The use of React Native allows the application to be accessible on various mobile platforms, including Android and iOS. This research has three important aspects: the use of blockchain technology to address security and integrity issues, the development of Mobile-Based e-voting applications to improve accessibility, and the contribution to the development of blockchain applications in various election contexts.