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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year) This Journal accepts research articles in these following fields: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 187 Documents
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Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students Tanuwijaya, Padmavati Darma; Tjahjadi, Jhonatan Laurensius; Riti, Yosefina Finsensia
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1789

Abstract

Higher education institutions are educational units located at a higher level after high school or vocational school. Catholic University Darma Cendika Surabaya (UKDC) faces challenges in managing the admission of new students due to variations in the number of prospective students applying to each department, which is also influenced by changing trends in interests and job needs in Indonesia. The use of Artificial Neural Network with Backpropagation and Random Forest Regression algorithms for comparing the prediction of new student admissions in the following year will be beneficial for the administration of Catholic University Darma Cendika Surabaya (UKDC) to gain a clearer understanding of the dynamics of admissions and to support decision making in the future development of the university. The predicted number of students joining Catholic University Darma Cendika Surabaya  (UKDC) in the 2024 period using Artificial Neural Network is 219 students with a Mean Squared Error (MSE) of 0,1046 and Root Mean Square Error (RMSE) of 0,32.
Expert System for Diagnosing Covid-19 Disease Using Method Forward Chaining Fadli, Sofiansyah; Ashari, Maulana; Septiani, Ria; Saikin, Saikin; Sudyana, Didik
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.1786

Abstract

December 2019 was the beginning of cases that hit the Wuhan area, increasing cases of Covid-19 in China every day and increasing from January to February 2020. Initially reports came from the Hubei area and surrounding provinces, and the reports that came increased to the provinces around China, there were 86 other cases reported from various parts of the country, including Indonesia. Indonesia's first Covid-19 disease reportedly entered on March 2, 2020, there were two cases. The latest information is published on the official WHO website (World Health Organization) it was recorded that from January 3, 2020 to March 18, 2022 in Indonesia, there were 5,948,610 people who were recorded as positive for Covid-19 and 153,411 people were confirmed to have died. Diagnosing Covid-19 is the job of experts or specialists who have experience and knowledge in this field. An alternative that can help people who are not experts in diagnosing Covid-19 is an expert system. The forward chaining method was chosen because it is a forward tracking technique that is sorted according to the number of facts and ends with a conclusion. Forward Chaining is a method inference engine where this method compares facts and statements and will start from the left first (IF). Where, reasoning will start from the facts and then test the validation of the hypothesis (THEN). This research was conducted to make it easier for non-experts to diagnose Covid-19 with this expert system, and to be able to provide solutions after a successful diagnosis.
Implementation of the K-Means Clustering Algorithm in Determining Productive Oil Palm Blocks at Pt Arta Prigel Anggriani, Yesi Pitaloka; Arif, Alfis; Febriansyah, Febriansyah
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2008

Abstract

The purpose of this study is to implement the K-Means Clustering method to determine the patterns of productive oil palm production based on their blocks at Pt Arta Prigel. The research is motivated by issues within the oil palm blocks, such as the absence of productive block summaries, insufficient plantation land analysis, and erroneous decision-making. The development method utilizes CRISP-DM, with data spanning 2 years from October 2021 to October 2023. From the 1275 production records, after cleaning, 1015 records remain. Filtering the initial 51 blocks results in 37 blocks for the years 2021 and 2022, and 46 blocks for the year 2023. After clustering, the production outcomes for the year 2021 are as follows: cluster_0 has 34 blocks, cluster_1 has 10 blocks. For the year 2022, cluster_0 has 24 blocks, cluster_1 has 37 blocks. In the year 2023, cluster_0 has 44 blocks, cluster_1 has 27 blocks. The testing method employs the silhouette coefficient, and the silhouette score testing results indicate the formation of 2 clusters (K=2) with a value of 0.62, the results obtained from testing with 2 clusters indicate that the formed clusters are accurate. The findings of this study include patterns, graphs, and production tables generated using the K-Means Clustering method at Pt Arta Prigel.
Empowering Diagnosis: A Review On Deep Learning Applications for COVID-19 and Pneumonia in X-Ray Images Yaqoub, Karin Younis; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2024

Abstract

The emergence of COVID-19, a highly contagious virus capable of infecting both the upper and lower respiratory tracts, has led to one of the deadliest pandemics in modern history, claiming millions of lives worldwide. Early and accurate detection of this rapidly spreading disease is crucial for effective containment and saving lives. Chest X-ray (CXR) stands out as a promising diagnostic tool due to its accessibility, affordability, and long-term sample preservation. However, distinguishing COVID-19 pneumonia from other respiratory ailments poses a significant challenge. This article delves into various approaches utilized for COVID-19 detection and the hurdles encountered in this endeavor. The imperative for developing automated detection systems to mitigate virus transmission via contact is underscored. Notably, deep learning architectures such as ResNet, Inception and Googlenet have been deployed for COVID-19 detection, albeit with a focus on identifying pneumonia cases. Discriminating between COVID-19-induced pneumonia and pneumonia caused by other pathogens remains a formidable task, demanding innovative solutions for accurate and timely diagnosis.
Application of CNN in the Classification of Chili Varieties for Agricultural Efficiency Pamungkas, Febrian Trio; Muttaqin, Irsyad Zainal
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2062

Abstract

This research focuses on the problem of classifying chili harvests which is still done manually by farmers. This manual classification process will of course take a long time, require a lot of energy and will feel tedious. This research aims to develop a classification system for chili types using the Convolutional Neural Network (CNN) method. By utilizing CNN technology, it is hoped that the chili grouping process can be carried out automatically with a high level of accuracy, thereby increasing work efficiency and reducing errors in chili grouping. The data used in this research is primary data with a total of 500 images of chilies divided into 4 classes. These images were taken using a Samsung A7 smartphone camera under consistent conditions: all photos were captured during daylight hours with the same camera angle. The training and testing results of the CNN model in classifying types of chili showed an accuracy of 99.5% in the training stage and reached an accuracy of 94% in the testing stage. Based on these results, it shows that the application of the CNN method in classifying chili types can work very well and effectively.
Bio-Inspired Algorithms in Healthcare Tato, Firdaws Rizgar; Ibrahim, Ibrahim Mahmood
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2116

Abstract

Exploring hidden patterns in medical data sets is made possible by the huge potential of medical data mining. A clinical diagnosis can be made with the help of these patterns. Research on bio-inspired algorithms is a recent development. Its primary benefit is its ability to weave together social behavior, emergence, and connectionism subfields. In a nutshell, it involves modeling live phenomena using computers while studying life to make better computer applications. This chapter describes the application of five bio-inspired algorithms, including metaheuristics, to classify seven distinct real health-related information sets. While the other two of these methods rely on random population creation to create classification rules, the other two rely on the computation of similarity between the data used for training and testing. The outcomes demonstrated that bio-inspired supervised medical data classification methods were incredibly effective.
Vehicular Ad-Hoc Networks for Intelligent Transportation System: A Brief Review of Protocols, Challenges, and Future Research Bintoro, Ketut Bayu Yogha
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2125

Abstract

Vehicular Ad Hoc Networks (VANET) play an essential role in the advancement of intelligent transportation systems, facilitating real-time communication between vehicles (V2V), infrastructure (V2I), and surrounding environments (V2X). This systematic review analyzes a range of VANET routing protocols, highlighting the strengths and weaknesses of topology-based, position-based, cluster-based, and hybrid methods. Additionally, this review explores core challenges in VANET, including high mobility, data security, Quality of Service (QoS) requirements, and connectivity issues in dynamic and high-density traffic environments. The paper also provides insights into simulation tools and performance metrics employed in VANET research alongside practical applications in modern transportation systems, such as autonomous driving, traffic management, and safety-related communication. Furthermore, this review emphasizes the need for ongoing research to address the identified challenges and optimize VANET performance. Integrating emerging technologies, including 5G, artificial intelligence (AI), and edge computing, offers promising avenues for enhancing system efficiency and sustainability. This review establishes a comprehensive foundation for further advancements in VANET by highlighting key findings and research gaps. Ultimately, the effective implementation of VANET has the potential to significantly improve transportation safety, efficiency, and sustainability, contributing to the realization of smart city initiatives and innovative mobility solutions. This work aims to guide future research directions, ensuring that VANET continues to evolve in alignment with the demands of modern transportation systems and the broader context of intelligent mobility.
Development of the Story of Life: A Narrative and Educational Game Using the Godot Engine for Android Kusheryanto, Andhika; Mirza, Anis; Syaripudin, Ari; Hutagalung, Deanna Durbin
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2094

Abstract

Godot Engine, as an open-source platform, offers flexibility and ease of use for developing both entertaining and educational games. This research showcases the creation of Story of Life, an educational game aimed at engaging players in learning through interactive storytelling and character-driven quests. The game features a character named Ucup, who explores various environments, clearing questions, and interacts with NPCs to gain knowledge and complete learning-based challenges. The development process followed the Agile Software Development Life Cycle (SDLC), with rigorous testing conducted through black-box and white-box methodologies. Testing results indicate a 97% success rate in functionality and performance on low-spec devices, confirming the game’s compatibility and responsiveness. The findings demonstrate that Story of Life successfully combines narrative with educational content, providing a meaningful and accessible learning experience.
Development of Mobile GIS Based Digital Map Location Marking Application for Navigation Purposes Ghifari, Alif; Sutarman, Sutarman
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2117

Abstract

In today's digital era, map and navigation applications have become essential tools for users to find locations, obtain the fastest routes, and explore new places. Google Maps is one of the most popular and comprehensive mapping applications that offers various features, such as navigation. However, despite being highly beneficial, Google Maps has several limitations, particularly regarding map markers, personalized, creating individual routes, and offline map usage on navigation. To address these issues, this research develops an innovative application called StellarPath. StellarPath is a mobile-based digital map location marker application that adopts Location Based Service (LBS) methods for more effective navigation. This application focuses on offline usability as a digital map marker and includes features such as offline maps stored indefinitely, offline navigation, manual route creation, and higher personalization, allowing users to save and manage markers with additional information offline. Test results show a success rate of 92.5%, indicating the application's effectiveness. The conclusion drawn is that the application can download and store maps offline, although it has shortcomings in managing downloaded maps. The offline navigation feature allows users to draw, save, and manage manual routes on the map. Users can create and save location markers with additional information offline. Navigation is flexible, with the option to start navigation by tapping on the map and features that can be enabled or disabled. This research aims to provide a more flexible and personalized navigation solution compared to existing standard map and navigation applications, such as Google Maps.
Development of a Cashier Business Transaction System using the Android Based Agile Method Junanda, David Bagus; Mardhiyyah, Rodhiyah
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2127

Abstract

The grocery retail sector in Indonesia plays an important role in meeting people's basic needs, with grocery merchants in urban and rural areas serving as the main middlemen in the distribution of staples to end consumers. Grocery merchants in Indonesia often face challenges in improving sales efficiency due to the use of conventional methods that are slow, unstructured, and prone to recording errors. These challenges can potentially lead to loss of customers, financial losses, and hinder business growth. This research aims to develop an Android-based cashier application specifically designed for grocery merchants to improve transaction efficiency and stock management. Using a qualitative approach, in-depth interviews were conducted to understand the needs and constraints faced by merchants in the transaction process. The findings were used to design the main features of the application, such as CRUD-based item management (Create, Read, Update, Delete), barcode scanning with a smartphone camera, digital receipt sending via WhatsApp, and payment support via QRIS E-wallet and cash transactions. This application is assumed to be used on Android devices with adequate internet access. The results show that this application is able to reduce recording errors, speed up transactions, and increase customer satisfaction compared to the manual method. The solution is expected to improve productivity, drive business growth, service quality and customer satisfaction as well as the competitiveness of grocers in the digital era. Future recommendations include the development of analytics features to monitor sales performance and support strategic decision-making.

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