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Journal of Engineering and Science Application
Published by Institute Of Advanced Knowledge and Science
ISSN : 30470544     EISSN : 30469627     DOI : https://doi.org/10.69693/jesa
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT
Journal of Engineering and Science Application (JESA) is published by the Institute Of Advanced Knowledge and Science in helping academics, researchers, and practitioners to disseminate their research results. JESA is a blind peer-reviewed journal dedicated to publishing quality research results in the fields of Applied Sciences, Engineering and Information Technology. All publications in the JESA Journal are open access which allows articles to be available online for free without any subscription. JESA is a national journal with e-ISSN: 3046-9627, and is have fee of charge in the submission process and review process. Journal of Engineering and Science Application publishes articles periodically twice a year, in April and October. JESA uses Turnitin plagiarism checks, Mendeley for reference management and supported by Crossref (DOI) for identification of scientific paper.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 23 Documents
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Harnessing Machine Learning for Stock Price Prediction with Random Forest and Simple Moving Average Techniques Priyatno, Arif Mudi; Ningsih, Lidya; Noor, Muhammad
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.1

Abstract

This paper explores the application of machine learning in predicting stock price trends, specifically for PT Bank Central Asia Tbk (BBCA) shares, using the Random Forest Regression model and Simple Moving Average (SMA) techniques. The SMA parameters ranged from 3 to 200 days, aiding in forecasting the price trends as either rising, sideway, or declining. To achieve accurate and generalizable predictions, the data normalization process was implemented using the MinMax scaler. The methodological framework adopted a time series cross-validation (CV) approach, executed 10 times with a future test window of 40 days, ensuring the robustness and reliability of the predictive model. The model's performance was systematically evaluated based on metrics of accuracy, recall, precision, and F1-score. Results from the cross-validation series indicated varied performance, with the most notable achievements in the 9th and 10th iterations, where both demonstrated an F1-score surpassing 0.745 and 0.808 respectively, and similar levels of accuracy and recall at 0.825. These high F1-scores signify a strong harmonic balance between precision and recall, underscoring the model's capability to effectively predict the stock price movements of BBCA. The findings affirm the potential of utilizing advanced machine learning techniques like Random Forest in conjunction with SMA indicators to enhance the predictability of stock market trends, offering valuable insights for investors and financial analysts.
Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts Priyatno, Arif Mudi; Prasetya, M. Riko Anshori; Cholidhazia, Putri; Sari, Resy Kumala
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.2

Abstract

A college's students are an essential component. The college always opens registration for new students each year. Every year, more than 1,000 prospective new students register. Because of this, the new student admissions committee is constantly overwhelmed when responding to campus-related questions. As a result, developing a chatbot to assist new students is necessary. The best similarity method is needed for the development of a chatbot using a retrieval-model approach. The New Student Admission Chatbot and the Similarity Method are compared in this study using the Retrieval-Based Concept. The cosine, Jaccard, dice, euclidean, Manhattan, Canberra, and Chebyshev similarity methods are compared. In the context of Universitas Pahlawan Tuanku Tambusai, the data used are information about new students as well as accreditation for study program. There are 41 pieces of information used. Labels and information make up data. According to the test results, the dice and cosine similarity methods are the most effective. On all tested thresholds, dice and cosine similarity achieved an f1-score above 80%. Recall produces extremely optimal results, including 100%.Over 75% of the time, good results are reliably achieved. This demonstrates that the retrieval-model concept can be applied
Information System Design of Sales Promotion and Production Inventory Management using Fifo Method Putra, Tri Andi Eka; Tamara, Selvi Yona; Mayefis, Reska
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.3

Abstract

Toko Kerupuk Sanjai Bintang Baru This company produces and sells its products to consumers, but starting from production, production and sales inventory management still fully uses a manual system, starting from calculating production profits, production sales policies based on expiration dates, as well as the sales system. still conventional. The methods used in this research are interviews, observation, literature study, analysis, system design, testing, and implementation. From this research it is hoped that the information system created can provide convenience in the implementation of the Sanjai Bintang Baru Store will improve its production methods and sales promotion, so that collaboration with information system technology can help and provide benefits to the Sanjai Bintang Baru Store itself.
Application Internet of Things in Controlling Fan Based Telegram Febriyana, Karisman; Saepuloh, Asep
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.4

Abstract

IoT is a technology that is currently developing. Iot is used to control an electronic device remotely through a certain device. One of the IoT supporting devices for controlling electronic devices is Telegram. This study aims to create an Internet of Things (IoT) Application System in Telegram-Based Fan Control. The method used to create this system is to apply IoT to fans using the Arduino esp8266 which is a wifi module that functions as an additional microcontroller such as Arduino so that it can connect directly to wifi and make TCP/IP connections. This tool is used as a tool to process input data that will be sent to the output device on the system. This system also consists of a relay prototype that is used to control the electric current path on the fan. The results of the study show that smart phones can also be used to control electronic devices such as fans and other electronic devices.
Design of Public Opinion Monitoring Information System in Online Media Pratama, Aryo; Ikhwan, Ali
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.5

Abstract

The Office of Communication and Information of North Sumatra has so far carried out the process of managing public information analysis manually using Microsoft Excel. This is of course less efficient considering the large number of incoming articles and the lack of data that may be out of sync. For this reason, an information system was created that can monitor public opinion in online media to facilitate the work of staff in carrying out the process of monitoring public opinion so that it is more efficient and effective so that it can improve service quality better. . This system is designed using Visual Studio Code as a text editor, PHP programming language as a web builder, Bootstrap as an interface design, and MySQL as a database on the system. This research produces a Public Opinion Monitoring Information System in Web-Based Online Media.
Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah and Wai Ruhu Rakuasa, Heinrich; Latue, Philia Christi
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.6

Abstract

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.
Prototype of a Virtual Assistant System Integrated with AI Rahardian, Rifky Lana; Sudiatmika, I Putu Gede Abdi; Dewi, Komang Hari Santhi
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.7

Abstract

This research focuses on the development of a prototype virtual assistant system integrated with artificial intelligence (AI) using the Rational Unified Process (RUP) method. The system is designed to improve user flexibility and efficiency by allowing interaction through voice commands, removing the need for traditional input devices. Python was selected as the primary programming language due to its robust capabilities in handling AI-driven applications. The system utilizes Whisper API for speech recognition, enabling the virtual assistant to accurately interpret voice inputs. Additionally, the integration of Chat GPT API allows the assistant to process and generate responses in a natural, context-aware manner. The combination of these technologies is expected to enhance user experience by making the system more intuitive and seamless, applicable to both daily tasks and complex business environments. The RUP method, structured into phases such as inception, elaboration, construction, and transition, was applied to ensure that the development process was iterative, flexible, and aligned with user needs. The results indicate that the integration of Whisper API with Chat GPT API significantly improves the quality and accuracy of voice-based interaction, streamlining system operation while minimizing the need for complex graphical interfaces. This research demonstrates the potential of voice-driven AI systems in increasing overall operational efficiency.
Heart Disease Prediction Using Decision Tree Analysis Clarite, Princess Clair C.; Palma, Inna Vita Grace Vanya G.
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.9

Abstract

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. The objective of the research is to use significant features and factors to design a prediction algorithm using Machine learning. The goal is to accurately classify whether a person has heart disease or not. The dataset contains 1025 records. The records are divided into two categories positive those who have heart disease and negative those who do not. It is currently standard practice to divide the data at random into roughly 70% for training and 30% for testing. The researcher used the MATLAB software tool to create a decision tree model of heart disease prediction. Using a decision tree model, it was determined that the best indicator that someone has heart disease is chest pain.
A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis Priyatno, Arif Mudi; Ningsih, Yunia; Vandika, Arnes Yuli; Muhammadong, Muhammadong
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.10

Abstract

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models
Enhancing User Experience in Private Bank Mobile Banking: Insights from Management Information System Analysis Hartawan, Muhammad Syarif; Jauhari , Burhanuddin; Vandika, Arnes Yuli; Guterres, Juvinal Ximenes
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.11

Abstract

This research explores the impact of Management Information Systems (MIS) on user experience in private bank mobile banking, focusing specifically on the effectiveness of m-BCA. Utilizing a descriptive quantitative approach, the study aims to understand how mobile banking features, including accessibility, user interface, security, and functionality, influence user satisfaction. Data were collected through direct experimentation with the m-BCA application, complemented by analysis from secondary data sources and a survey of 30 users. Results indicate that 93.3% of users find m-BCA effective for routine banking needs, attributing high ratings to its convenience and functionality. However, while 80% of respondents appreciate the current security measures, 20% cite the absence of advanced security features such as fingerprint and facial recognition as a drawback. The study also reveals mixed feedback on the user interface; while the straightforward design aids usability, 10% of users express a desire for a more visually appealing interface. Despite these areas for improvement, m-BCA’s comprehensive set of features is generally seen as effective, supporting users in performing essential transactions with ease and reliability. The findings suggest that enhancing interface aesthetics and integrating advanced security options could further boost user engagement and trust in mobile banking applications, underscoring MIS’s role in meeting evolving customer expectations in digital banking.

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