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INDONESIA
Journal of Applied Computer Science and Technology (JACOST)
Published by Indonesian Society of Applied Science
ISSN : -     EISSN : 27231453     DOI : https://doi.org/10.52158/jacost
Core Subject : Science,
Computer Science & IT
Fokus dan Ruang Lingkup Journal of Applied Computer Science and Technology (JACOST) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian bidang ilmu komputer dan teknologi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Ilmu Komputer dan Teknologi. Journal of Applied Computer Science and Technology (JACOST) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 91 Documents
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Analisis Algoritma Klasifikasi Untuk Mengidentifikasi Potensi Risiko Kesehatan Ibu Hamil Purnama, Jajang Jaya; Nina Kurnia Hikmawati; Sri Rahayu
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.809

Abstract

The health of pregnant women has an important aspect in efforts to achieve the birth of a healthy baby. So early detection of the health of pregnant women has important. In this study the author identified potential maternal health risks for pregnant women by classifying them used machine learning which aims to analyze maternal health datasets with several algorithms including Random Forest, Extra Trees, Extreme Gradient Boosting, Decision Tree, and Light Gradient Boosting Machine. From several classification results carried out analysis and evaluation shown that the Random Forest classification algorithm provided optimal performance with an accuracy of 82,15%. These findings confirmed that the model created could identify complex patterns and relationships between features relevant to the classification of potential health risks for pregnant women at high, medium and low levels. These results have important implications in maternal care, because they cann help doctors and medical personnel make more appropriate and effective decisions in dealing with maternal health risks and provide insight into pregnant women from an early age regarding their health conditions.
Implementasi Re-design Application Mobile MRT Jakarta Menggunakan Metode User Centered Design Rahma Fahriyah, Diana; Ikasari, Diana; Widiastuti
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.812

Abstract

MRT Mobile Application is used to access information easily and quickly for MRT users, such as schedule information, routes, stations, fares, and other additional services. Based on observations and questionnaires regarding the MRT mobile application, users often experience difficulties or obstacles in interacting with the application interface. Starting with a complex interface, confusing navigation of features to difficulty finding the information you need quickly. This affects the use of the application and reduces the level of user satisfaction. Based on these conditions, it is necessary to redesign the user interface of this application to make it easier to use. This research uses User Centered Design (UCD) method with four phases, which is Understand Context of Use, Specify User Requirements, Design Solution, and Evaluation Against Requirements in analyzing the UI/UX design process of the MRT mobile application. In the design stage, UCD principles are used to integrate insights from user data into UI/UX design process based on results of questionnaire analysis. The final result of this research is a prototype design and user interface design for the MRT mobile application that meets the expectations of application users.
Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke Danis Rifa Nurqotimah; Naseh Khudori, Ahsanun; Siwi Pradini, Risqy
Journal of Applied Computer Science and Technology Vol 5 No 2 (2024): Desember 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i2.817

Abstract

Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. The study used data sets from the data.world.com site with a total of 40,910 data. Using the Orange tool, the study managed to classify stroke disease well using the RBF kernel with cross validation techniques resulting in an accuracy of 94.8%. The results of this study can be concluded that the stroke classification model developed has excellent performance. Overall, these results indicate that the Stroke classification model developed is highly reliable and effective, with excellent ability to detect stroke cases and provide accurate predictions. Making better and quicker medical judgments can be aided by using this approach to diagnose strokes.
Konsistensi Model Regresi Empat Variabel Pada Populasi dan Sampel untuk Prediksi Temperatur Syakrani, Nurjannah; Naufal Athaya S. R
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.971

Abstract

The ability to predict future events or trends has become very important today. One method that can be used to predict the future is to use linear regression. Accurate regression modeling requires sampling representative data, especially when working with large datasets. This research takes a relatively large volume as a data set by looking at the accuracy and consistency of the coefficients of a multi-variable linear regression model for temperature prediction which is built based on all the data, and looks at the differences in the regression model built from the sample data. The number of sample data (n) is determined based on the Slovin formula which depends on the number of population data (N) and the level of confidence (ơ), so that as many as (N/n) new regression models can be built. Each group of sample data is divided into 75% for modeling and 25% testing data. The dataset used is weather information in the Szeged area which was measured in 2006 - 2016. So the regression model is in the form of Y (temperature value) which is influenced by Xi (weather factors), namely humidity, wind speed, wind direction and visibility. Using 96,453 data records and a 1% significance level in Slovin's formula, 10 samples were generated. Nine out of ten sample regression models agree with the population model, with positive coefficients for visibility and wind direction and negative values for humidity and wind speed. There is an abnormality in sample #4. While the other nine sample regression models are consistent with positive R2 values, Sample #1 displays an oddity with negative values. The RMSE interval values for each regression model in this study fall between 4.334 and 9.582.
Pengembangan Aplikasi Augmented Reality untuk Edukasi Keselamatan Kebakaran: Metode Prototyping dan Usability Setiawan, Ridwan; Rahayu, Sri; Komalasari, Iis
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1028

Abstract

This study aims to develop an Augmented Reality (AR) application to introduce fire safety equipment as part of Occupational Health and Safety (OHS) education for the general public. The research employed a prototyping method, which involved iterative stages from requirements analysis to user evaluation, as well as functional testing using Black Box Testing and usability testing with 20 respondents. The results showed that all application features functioned according to specifications, and the usability testing yielded a user satisfaction score of 80.1%. These findings indicate that AR is effective as an interactive educational medium to enhance public understanding of fire protection equipment. The implication of this study is the potential for wider use of AR technology in public education to support fire risk mitigation efforts.
Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation Utama, Eldio; Rudiawan Jamzuri, Eko
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1037

Abstract

In automated manufacturing, verifying material orientation is essential to ensure the product assembly proceeds without errors. For instance, in the beverage industry, incorrect orientation of materials, such as bottle caps, can lead to failures in the packaging process, resulting in improperly sealed bottles that may compromise product quality and safety. This study compares the performance of Support Vector Machine (SVM) and k-Nearest Neighbors algorithms for verifying material orientation verification through automated optical inspection. The images were processed using the Inception V3 Convolutional Neural Network (CNN) to extract relevant image features, which were then classified using SVM and kNN algorithms. As a result, SVM achieved high classification performance during testing, with classification accuracy, precision, recall, and F1 score of 1.0 compared to kNN, which achieved only 0.967. However, kNN demonstrated superior computational efficiency, with a training time of 1.126 seconds and a validation time of 0.713 seconds, compared to SVM's training time of 3.101 seconds and validation time of 1.479 seconds. These results indicate that while both methods are highly effective for material orientation verification, kNN offers significant advantages in terms of computational speed, making it more suitable for real-time applications. The implications of this study highlight the potential for integrating the proposed method in industrial applications, promoting enhanced efficiency and reducing error rates in automated assembly lines.
News Classification using Natural Language Processing with TF-IDF and Multinomial Naïve Bayes Nadira Alifia Ionendri; Feri Candra; Afdi Rizal
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1099

Abstract

Online news contains valuable insights into public phenomena that can support statistical analysis by institutions like BPS Riau. However, current methods of classifying news are manual, time-consuming, and prone to human error. This study proposes an automated news classification system using Natural Language Processing (NLP) techniques with Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and the Multinomial Naïve Bayes algorithm for classification. The dataset was collected via web scraping and manually labeled across five statistical categories: poverty, unemployment, democracy, inflation, and economic growth. The system achieved a validation accuracy of 83%, a test accuracy of 90%, with an average precision of 0.85, recall of 0.93, and f1-score of 0.87. These results demonstrate that the proposed system can significantly reduce the manual workload of news classification and be practically implemented by BPS Riau to support accurate and timely statistical reporting.
Analisis Penerapan Mutual Information pada Klasifikasi Status Studi Mahasiswa Menggunakan Naïve Bayes Situju, Sulfayanti; Nur, Nahya; Halal, Nursan
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1106

Abstract

Early identification of Student Study Status is essential for higher education institutions to implement proactive and strategic measures that facilitate timely completion of studies and mitigate dropout rates. This research intends to predict student study status with the Naïve Bayes method based on the features obtained from the implementation of Mutual Information. Feature selection through Mutual Information seeks to analyse the factors that most significantly impact the classification of student study status. The study status is categorized into three classes: dropout, enrolled, and graduate, based on 36 factors. The Mutual Information approach is employed to diminish data dimensions by discarding less relevant features while preserving critical information based on score values to achieve enhanced predictive accuracy. The selection of appropriate attributes enables the model to maintain simplicity while incorporating critical information aspects that significantly impact performance. Experiments were performed on a dataset comprising student academic variables, with data partitioning ratios of 80:20, 70:30, and 50:50 for training and testing datasets. The classification outcomes utilizing Naïve Bayes, without the use of Mutual Information across the three testing ratios, exhibited the accuracy of 68.29% in the 70:30 data split. Simultaneously, the classification outcomes utilizing Mutual Information across three test ratios are as follows: 71.64% accuracy at an 80:20 ratio with 10 selected attributes, 72.06% at a 70:30 ratio with 10 selected attributes, and the highest accuracy of 72.65% at a 50:50 ratio using 15 attributes. The utilization of the Naïve Bayes method for classifying student study status demonstrates enhanced accuracy when integrated with Mutual Information for feature selection. The findings of this study demonstrate that Mutual Information can streamline data by considering the quantity of attribute selections according to the ranking of their score values.
Penerapan Metode Profile Matching untuk Menunjang Keputusan Seleksi Penerimaan Anggota pada Perusahaan Marikator Widia Ramadhani; Yuliwanda Anggi Kusumastuti; Elvi Fetrina; Qurrotul Aini; Meinarini Catur Utami
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1109

Abstract

The process of placing members in the XYZ organization is not optimal due to errors in placement or mismatches between fields and the positions assigned to its members. This is evidenced by members lacking maximum competence in their designated divisions, leading to slow organizational performance. Therefore, optimal human resource management in an organization requires selecting members with the right profiles to enhance operational performance. This study aims to determine a ranking and identify the best candidates for new member selection by applying the profile-matching method in the recruitment process of the XYZ organization. This approach considers criteria such as CVs, short essays, and interviews, using prospective member data comprising 19 (nineteen) individuals as the population and 5 (five) as the sample. Profile matching is a systematic approach for comparing the suitability of prospective member profiles against predefined criteria, enabling a more detailed and objective evaluation. This method helps identify competence gaps between candidates and expected criteria and minimizes the risk of placement errors within the organization. The calculation process is carried out using Microsoft Excel and a simple program developed with Python, utilizing Google Colab as the code editor. The study results indicate that the profile-matching method effectively identifies prospective members who best align with the organization's qualifications. Among the five candidates evaluated, Candidate 1 achieved the highest final score of 4.0556, indicating the most optimal suitability with the established criteria.
Penentuan Faktor Pemicu Gejala Penyakit Mata Glaukoma, Astigmatis, Hipermetropi, dan Miopi Wiguna, I Kadek Arta; Divayana, Dewa Gede Hendra; Indrawan, Gede
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1113

Abstract

Expert systems support medical problem-solving, including the analysis of eye diseases. According to BPS RI (2022), over 8 million Indonesians suffer from visual impairments. In diagnosis, doctors often struggle to identify the primary causes of symptoms, impacting treatment effectiveness. This study proposes a system that combines Backward Chaining and Simple Additive Weighting (SAW) to systematically identify and prioritize causal factors of eye diseases. Backward Chaining is used to trace relationships between symptoms and the causes of glaucoma, astigmatism, hyperopia, and myopia. SAW is applied to assign weights to each causal factor and determine priority based on score ranking. Testing with 45 patient cases shows the system achieves 91% accuracy in identifying dominant causes. The 9% error rate stems from data limitations, subjective weighting in SAW, and inference rules in Backward Chaining. This system offers valuable support in early decision-making by helping doctors prioritize handling strategies based on the most significant underlying factors, thereby enhancing diagnostic efficiency and consistency.

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