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Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Published by Universitas Lancang Kuning
ISSN : 20864884     EISSN : 24773255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning (Online ISSN 2477-3255 and Print ISSN 2086-4884) This journal publish two periode in a year on May and November.
Arjuna Subject : -
Articles 198 Documents
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Constellation of Football Players Determination Based on Cost and Performance History Using the K-Means Clustering Prasetyo, Eko; Priyatama, Almendaris Shandy; Setyatama, Fardanto
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.17106

Abstract

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.
Implementation of the Linear Regression Method to Determine Predictions of the Influence of Religion on General Election Participation fitria, Saniya Izza; Senja Fitrani, Arif; Eviyanti , Ade
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.18105

Abstract

This research aims to predict election participation in places of worship through statistical data analysis methods and predictive algorithms. Election participation, as a complex phenomenon, is influenced by various factors, with religion often being a crucial element that motivates or inhibits voter turnout. This study uses variables from Central Statistics Agency (BPS) data and recapitulation of previous general elections, based on historical patterns. Using a statistical approach, the relationship between religious variables and the level of voter participation in places of worship is identified. The linear regression method is used to predict the influence of religion on election participation. In this research, a series of scenarios were carried out, and the research results showed different variations in R-squared (R-Square) and Mean Squared Error (MSE) results. The best scenario, namely the R-squared scenario with a value of around 0.00012 and an MSE of 0.09934, highlights the potential relationship between religion and voter participation. These findings suggest the need for further considerations in this context, as well as demonstrating the need for model adjustments to improve the accuracy of future election predictions.
Enhancing Dental Image Segmentation Techniques: Edge Detection and Color Thresholding Susandri, Susandri; Sumijan , Sumijan; Zamsuri , Ahmad; Rahmiati, Rahmiati; Asparizal, Asparizal
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.18757

Abstract

Rapid advancements in medical technology, particularly in the field of dentistry, have led to significant progress in the application of medical imaging techniques to generate valuable image data. The resulting images often exhibit heterogeneous intensity distributions, with boundaries not always distinctly clear between the tooth roots and bone, along with variations in shape and pose. This study specifically aimed to identify the optimal image for segmenting specific parts of the dental structures. Image segmentation is crucial for ensuring effective diagnosis in the context of dental medicine. To achieve optimal dental image segmentation, this research combines edge detection methods with the determination of color thresholds, specifically grayscale and Hue, Saturation, Value (HSV). The research findings revealed that edge detection using the Sobel gradient operator yielded a relevant count of 17,099 pixels. Using RGB=3 and HSV=0.3 the color thresholds show an enhancement in the brightness of the resulting HSV-segmented image, while in the RGB-segmented image, the selected object appears more prominent. The findings of this study contribute significantly to the evolution of dental image segmentation techniques, potentially enhancing the accuracy and effectiveness of diagnoses within the realm of modern dental practice
Implementation of a CNN-trained model for coffee type detection in an Android app with photo input of beans, fruits, and leaves Hidayat, M. Taufik; Utomo, Pradita Eko Prasetyo; Hutabarat, Benedika Ferdian
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19563

Abstract

Coffee is the most consumed type of drink in the world. Each type of coffee has different physical characteristics from leaves, fruits to seeds. Now technology is needed in the world of agriculture in making decisions. To determine the type of coffee with fission characteristics, there are still many people who do not understand in distinguishing the physical characteristics of coffee plants. In this case, an application was developed using the RAD method by utilizing the flutter framework and the Convolutional Neural Network model that has been trained. The pre-train model used is NasNet Mobile with a dataset of 900 photos and 100 epochs with early-stopping utilization and heti at epoch 55 with an accuracy of 90.67%. In this study, implementing existing models into Android applications using the Flutter framework. With the implementation process carried out by the application can help the detection process using an android device. The implementation results get good test results with a score of 0.97. This application can help the process of identifying the type of coffee and minimize errors in identifying directly.
Comparative Study of the Effect of Datasets and Machine Learning Algorithms for PDF Malware Detection Wiharja, Salman; Pradeka, Deden; Suteddy, Wirmanto
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19744

Abstract

This research presents an innovative approach to detecting malicious PDFs through machine learning algorithms, focusing on the expansion of the Evasive-PDFMal2022 dataset. The objective is to enhance the accuracy of detecting malicious PDFs by enriching the dataset, augmenting its representation and diversity, and developing a practical tool—a website—for extracting and detecting malicious PDFs. The methodology involves updating and enlarging the dataset with additional malicious PDFs sourced from CVE and Exploit-db, along with non-malicious PDFs from diverse origins. Features are then extracted using the PDFID tool, and these 20 features serve as the foundation for implementing K-Nearest Neighbor (KNN), Random Forest, and Random Committee algorithms. The outcomes demonstrate that the model trained with the expanded dataset achieves a remarkable 99% accuracy, surpassing the performance of models relying solely on the Evasive-PDFMal2022 dataset. Additionally, this research significantly enhances the representation and diversity of the dataset while delivering a practical solution in the form of a website tailored for the extraction and detection of malicious PDFs.
Recommendation System to Determine Achievement Students Using Naïve Bayes and Simple Additive Weighting (SAW) Methods Jazaudhi’fi, Ahmad; Vitianingsih, Anik Vega; Kristyawan, Yudi; Lidya Maukar, Anastasia; Yasin, Verdi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19746

Abstract

Giving appreciation to outstanding students can motivate students to compete with each other in learning. MA Tanwirul Qulub Tanggungan often experiences difficulties in determining outstanding students due to There is no application that can assist school management in identifying outstanding students, the implementation is considered less than optimal. besides that, the determination of outstanding students is still based on report cards that are only ranked, and there are no criteria that refer to the K-13 curriculum. The purpose of this research is to offer a solution to create a recommendation system for selecting outstanding students using the parameters of midterm exams, final exams, assignments, attendance, attitude, extracurricular activities, organizations, and award certificates using decision support system techniques. Extracurricular grades are taken from Scouting activities only because students are generally required to participate in them. Naïve Bayes and Simple Additive Weighting methods are used in this research, where the Naïve Bayes method classifies the categories of outstanding students and not, while the SAW method is used for ranking. The contribution of this research has the potential to increase school efficiency in student assessment and support efforts to improve the quality of education by rewarding students appropriately. The validation test results of Naïve Bayes and SAW techniques get an accuracy value of 100%, which shows that the SAW method can produce the best alternative recommendations
Application of the Naïve Bayes Algorithm in Sentiment Analysis of Using the Shopee Application on the Play Store Sitorus, Rina Afriani; Zufria, Ilka
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19828

Abstract

This research aims to find out the opinions of users of the Shopee application on the Play Store using the Naive Bayes Naive Algorithm and to find out the suitability of the correct application of the Naive Bayes algorithm in carrying out sentiment analysis with the classification of three sentiment classes. The dataset used in this study consisted of 2000 customer reviews obtained from the Play Store in 2024 collected by the scraping process using the Python library. The dataset has 1,198 examples of negative attitudes, 583 examples of good sentiment, and 219 examples of neutral sentiment. The results of this study are expected to be used as evaluation material for Shopee Apilkation to improve the performance of Shopee applications. Research findings show that the Bayes naive approach reaches accuracy determined by various aspects, such as the quantity of data collections and positive and negative data distribution. This study shows that the Bayes naive algorithm can function properly as a technique to evaluate user sentiment for applications in the Play Store. However, with the classification of three classes, another algorithm is needed to produce higher accuracy.
Hyperparameter Optimization of the Perceptron Algorithm for Determining the Feasibility of Research Proposals and Community Service Lisnawita, Lisnawita; Guntoro, Guntoro; Costaner, Loneli
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.17812

Abstract

Higher education in Indonesia includes diploma, bachelor, master, specialist, and doctoral programmes organised by universities. The Institute for Research and Community Service (LPPM) is in charge of assessing lecturers' proposals. This research aims to optimise the Perceptron algorithm to assess proposal eligibility using Turnitin plagiarism scores and reviewer scores. The optimisation results show that Perceptron accuracy reaches 99.44% to 99.63% at various training data ratios. GridSearchCV achieved 100% accuracy, while RandomisedSearchCV recorded accuracy between 98.89% to 99.63%. GridSearchCV also had the lowest MSE , despite higher Loss values, indicating a sacrifice in generalisation ability. Perceptron Default and RandomisedSearchCV had higher MSE and Loss, but remained low. GridSearchCV's AUC reached 100%, while Perceptron Default and RandomisedSearchCV showed very high AUC, ranging from 99.25% to 99.98%. Overall, the Perceptron algorithm is effective in assessing proposal eligibility with high accuracy.
Digital Image Based DSS for Assessing Tomato Quality using AHP-TOPSIS Method Wulandari; Surianto, Dewi Fatmarani; Parenreng, Jumadi M.; Adiba, Fhatiah
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.19060

Abstract

Tomatoes are a major export commodity in the country's plantation sector. This increases the urgency of efforts to increase tomato productivity, both in terms of quantity and quality. Evaluation of tomato quality currently relies on the degree of ripeness and skin texture. The conventional method currently used involves manual inspection, which can allow for misjudgment and economic loss. This research aims to use a digital image-based approach by utilizing a decision support system that combines the AHP and TOPSIS methods to assess tomato quality based on color and texture criteria. This research evaluates and ranks nine tomato images that have good quality, by giving higher priority to skin texture than skin color. Evaluation results from three tests showed that the system was able to determine the quality of tomatoes with an average kappa value of 0.78, which interpreted the results of good agreement between the system and expert judgments.
Implementation of Virtual Try-On Product to Enhance Customer Satisfaction Dawis, Aisyah Mutia; Ardhani, Rahmad; Setiyanto, Sigit; Khasanah, Aulia Uswatun; Muqorobbin; Tantra, Handoko
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.20845

Abstract

In the digital age, customer experience is key to business success. Augmented Reality (AR) technology offers immense potential to enhance the shopping experience, particularly through virtual try-on features. This research aimed to analyze the impact of implementing AR Product, specifically the virtual try-on feature, on customer satisfaction at PT Dua Naga Kosmetindo. The study employed the System Development Life Cycle (SDLC) waterfall model to develop and implement the virtual try-on feature on the E-Colux website. Black Box testing and User Acceptance Testing (UAT) were conducted to assess the success of the implementation and user acceptance levels. Black Box testing revealed a 100% success rate, while UAT yielded an average score of 87.7%. The results indicated a significant positive correlation between the use of virtual try-on and customer satisfaction, particularly in the dimension of product satisfaction (86%-95%). Female respondents and younger generations exhibited higher satisfaction levels. This research demonstrates that the implementation of virtual try-on can significantly enhance customer satisfaction. However, further research with a larger and more diverse sample is needed to generalize these findings. Additionally, studies on the long-term impact of virtual try-on on customer loyalty and business performance are warranted.

Page 18 of 20 | Total Record : 198

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