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Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Published by Universitas Prima Indonesia
ISSN : -     EISSN : 25802879     DOI : 10.34012
Core Subject : Science,
Decision Sciences, Operations Research & Management
This journal is about information systems and computer science.
Arjuna Subject : -
Articles 222 Documents
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MODELING HEART DISEASE CLASSIFICATION USING ROUGH NEURAL NETWORK: A DATA-DRIVEN APPROACH TO THE CLEVELAND HEART DISEASE DATASET Nursyahrina, Nursyahrina; Sahri, Alfi; Irsyad, As’Ary Sahlul; Hafizhah, Nadia Aini
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4848

Abstract

This study implements a Rough Neural Network (RNN) intelligent system method, merging rough sets with neural networks to diagnose heart disease effectively. Using the Cleveland Heart Disease Dataset, rough sets identified nine relevant features for model training, simplifying data complexity. Comparative assessment against traditional neural networks revealed the RNN model's superior performance, achieving 88.52% accuracy, 88.14% F1 score, and 88.85% AUC. This hybrid approach improves predictive accuracy while enhancing efficiency and interpretability. The findings contribute to advancing intelligent systems for heart disease diagnosis, facilitating early detection, and improving patient outcomes. Future research may explore selected features' clinical significance and RNN applicability in different contexts. Keywords: Heart Disease Detection, Rough Neural Network, Rough Set Theory, Neural Networks, Hybrid Intelligent System
Implementation of UI/UX Using Design Thinking Method in Tomsufood Application Sunarya, Edwin Yohanes; Huda, Baenil; Hananto, Agustia; Hilabi, Shofa Shofiah
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5026

Abstract

Tomsufood is one of the online food ordering applications. To provide the best experience in ordering food, Tomsufood designed a new application product. An approach called Design Thinking was used in this study. The Design Thinking method is a software product design approach that is based on innovation and relies on problem-solving techniques. After identifying and understanding the problem obtained through the process of identifying the problem, describing the solution, empathizing with the user, and prototyping and testing. So that the Tomsufood application is able to solve problems that occur in society. The results of the study showed that the application of the Design Thinking method was able to produce a UI/UX design that was more intuitive and responsive to user needs. Users reported increased satisfaction in using the application, which included ease of navigation, clarity of information, and efficiency in the food ordering process. Keywords: UI/UX, Design Thinking, Tomsufood, User Experience
DESIGN AND BUILDING OF ADJUSTABLE OIL FILLER AUTOMATIC COOKING OIL MEASURING FLOW SENSOR METHOD BASED ON ARDUINO NANO Perangin Angin, Despaleri; Situmorang, Maizer; Samosir, Gilson Manahara
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.5129

Abstract

Cooking oil is one of the household needs, where its use is very embedded among the wider community, therefore the circulation and distribution of cooking oil plays an important role, to help its distribution through MSME businesses, whether traders, the author designed an automatic cooking oil measuring tool with the research title Design Build an adjustable oil filler that measures automatic cooking oil based on Arduino nano. In the research the tool is running normally, using Arduino as the main component and the flow sensor inputs the tool data from the amount of liquid output, in the final result of this research the tool produces data values ​​with output accuracy values 99.44% data taken from comparing the volume output of the instrument and Matt Glass measuring instrument as a reference for the original volume and the endurance test obtained a value of 93% with 30 trials with 2 failed trials. Therefore, it can be concluded that this adjustable oil filler tool functions well and can be used. Keywords: Arduino cooking oil meter, Adjustable cooking oil oil filler Keywords:   Penakar minyak Goreng Arduino, Oil filler Adjustable Minyak Goreng
Visual Attention Analysis of Perspective Images Using the Eye Tracking Method Purba, Andres Taruli; Br Purba, Laura Natalia; Haliza, Della; Siagian , Hendricus; Simanjuntak, Pransisko Oktavianus; Evta Indra
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5133

Abstract

In this research, eye tracking was applied to observe students' visual attention to three perspective images, each of which has a Region of Interest (RoI). In initial research, it was found that the majority of students faced difficulties in concentrating during the learning process. The aim of this research is to analyze the visual attention of perspective drawings in adolescents in an effort to increase learning concentration. Eye tracking was used as a research instrument to monitor the eye movements of 70 students objectively and in real-time who were guided by giving assignments to look for certain objects. This study showed that in terms of perception speed and focus duration, female participants outperformed male participants. However, overall the level of concentration of teenagers cannot be said to be good. These findings provide important knowledge for educators in creating more effective visual content to improve student concentration and understanding.
Herbal Leaf Image Classification Using Convolutional Neural Network (CNN) Mujahid, Putra Edi; Manik, Rosianni; Simbolon, Junpri Sardodo; Sinaga, Maria Riska Ratna Sari; Aisyah, Siti; Nababan, Marlince; Harmaja, Okta Jaya
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5145

Abstract

This research delves into the application of Convolutional Neural Networks (CNNs) to address the complexities of identifying herbal leaf species in Indonesia, often challenging due to the vast variations in shape, color, and texture. Utilizing a dataset of herbal leaf images acquired using the Bing Downloader Scrapping technique, a CNN model was trained to classify various plant varieties with a remarkable accuracy rate of 92.66%. Additionally, the analysis of low loss values indicates that the model not only effectively maps the intricate features of each image to the correct category but also efficiently reduces error rates. These findings offer a significant contribution to the context of herbal medicine development and biodiversity conservation, opening up avenues for technological integration in efforts to preserve Indonesia's natural and cultural resources.
Analysis And Prediction Of Global Population Using Random Forest Regression Banjarnahor, Jepri; JetaJones, Catherine; Gulo, Esthin Mitra; Sianturi, Angelia Chrismeshi Sheila
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5312

Abstract

This research evaluates the performance of the random forest regression algorithm in predicting global population growth from time series data. The findings indicate that population growth predictions remain stable, with an annual increase of less than 1%. Model analysis using evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and model scores demonstrates high quality, with average values below 0.5. These results imply that the model can deliver optimal and consistent outcomes. The model shows potential for accurate predictions when tested on datasets. Further analysis reveals a population increase of 0.88% in 2024, equating to an addition of approximately 70,206,291 people, and a rise of 0.91% in 2025, adding about 73,524,552 people
Application Of Yolo V8 For Product Defect Detection In Manufacturing Companies Jamal, Malikil; Faisal, Sultan; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One important aspect in the production process is maintaining product quality and avoiding defects that could harm the company. This research aims to improve quality and avoid product defects that are detrimental to the company, especially defects in the form of bubbles in the product, by using YOLOv8. The dataset consists of 100 data which is divided into 80 for training and 20 testing data with an epoch value of 100. To obtain optimal bubble detection results, this research chose the latest version of YOLOv8 and compared several models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. The research results show that YOLOv8m achieves the highest accuracy among other models with a mAP value of 0.712, precision of 0.764, recall of 0.659, and F1-score of 0.708. This research highlights the potential of detection models that can detect bubbles precisely and accurately. Keywords: Kecacatan Produk, Deteksi Gelembung, Perusahaan Manufaktur, Model YOLOv8
Utilization Of Website-Based Technology For Analysis And Prevention Of Stunting Using The Fuzzy Tsukamoto Methods Wijaya, Chandra; Asido, Elpri; Laia, Yonata; aisyah, siti; Radhi, Muhammad; amalia, Amalia; Fahmi, Mohammad Irfan Fahmi
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5319

Abstract

Stunting is a chronic malnutrition condition that has significant impact on inhibiting the growth of a child both physically and intellectually. The study aims toto analyze stunts using a web-based technology system using Tsukamoto’s Fuzzy method. This method was chosen for its ability to deal with the uncertainty and variability medical data. The system integrates various variables that affect stunts, such as nutritional intake and physical growth, to produce a more accurate diagnosis. The research was carried out by collecting data from various health sources and applying the fuzzy Tsukamoto method to process the data. The trial subjects in this developmental study were 30 children aged 1–60 months, or 0–5 years. Subjects were selected by random sampling, consisting of 6 children from 1–5 years of age each. Based on the results of the analysis, it appears that the fuzzy Tsukamoto-based system development trial can provide a better prediction of the risk of stunting in children compared to conventional methods. Using this approach, it is expected to help health workers take more accurate steps in the treatment and prevention of stunts. Keywords: Stunting, Fuzzy Tsukamoto Method, Nutritional Analyisis, Technology Systems, Child Heal
Comparison of K-Nearest Neighbors and Convolutional Neural Network Algorithms in Potato Leaf Disease Classification Nurmayanti, Trisya; Hartini, Dina; Rohana, Tatang; Lestari, Santi Arum Puspita; Wahiddin, Deden
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5337

Abstract

tatang.rohana@ubpkarawang.ac.id3, santi.arum@ubpkarawang.ac.id4, deden.wahiddin@ubpkarawang.ac.id5ABSTRACTPotato production in Central Java was recorded to have decreased by 10.77% by the Central Statistics Agency (BPS), from 278,717 tons in 2022 to 248,700 tons in 2023. This decline is due to the fact that potatoes are susceptible to diseases such as late blight and dry spot (early blight) which can significantly reduce yields. This study aims to evaluate the performance of Convolutional Neural Network (CNN) with VGG16 architecture and K-Nearest Neighbors (KNN) to find the best method for potato late blight classification. The dataset used consists of 1500 potato leaf images divided into training, validation, and testing. This research uses pre- processing including resizing, rescaling, and data augmentation. The results show that CNN with the VGG16 model is superior in classifying potato leaf diseases compared to KNN with the MobileNetV2 model. CNN produced an accuracy of 96% while KNN with the MobileNetV2 model obtained an accuracy of 93%. These results can be used as a powerful tool in supporting potato leaf disease identification. This model makes a significant contribution to the development of disease identification techniques through digital image processing.Keywords: Potato Leaf Disease, Convolutional Neural Network, VGG16, K-Nearest
Implementation of comparison of K-means algorithm with C4.5 algorithm to predict the feasibility of being a Catholic Hulu, Ricky Kristian Arifin; Ginting, Alwi; Laia, Yonata
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5345

Abstract

This research conducted a comparison between two methods, namely C4.5 and K-Means, with the aim of improving the work efficiency of the Catholic Church Secretariat in selecting data of prospective Catholic congregants. These methods were developed to assist secretariat employees in determining the eligibility of valid files and data of prospective Catholic congregants through a web-based application. The data used comes from the selection results of several files and data of prospective congregants that have been collected by the Catholic Church Secretariat. Data analysis was carried out using the K-Means and C4.5 algorithms to predict the feasibility of the prospective congregants' files. It is hoped that the results of this research can help the Catholic Church Secretariat to improve work efficiency, both in terms of time and effort, in the selection process of prospective Catholic congregants and increase accuracy in determining the eligibility of the data of each applicant at the Catholic Church Secretariat improve the efficiency of the selection process and enhance the accuracy in determining the suitability of potential members.

Page 19 of 23 | Total Record : 222

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