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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 10 Documents
 1 
Search results for , issue "Vol 4 No 1 (2023): Juni 2023" : 10 Documents clear
Evaluasi Kapabilitas Sistem Informasi Pasien ICU dan HCU Menggunakan COBIT 5 dengan Domain BAI Erick Fernando; Jullend Gatc; yuhefizar Yuhefizar
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.451

Abstract

The hospital's Intensive Care Unit (ICU) and High Care Unit (HCU) systems are very important systems and are always used, so it is necessary to know the extent of their performance. Thus, the study aims to provide reference for xyz hospital regarding the condition (performance) of the current ICU and HCU patient systems. COBIT 5 is an information system capability evaluation framework with the build, acquire and implement (BAI) domain. Domain BAI focuses on developing, acquiring, and implementing information systems, applications, and services. It covers the entire IT project life cycle, starting with the planning and requirements gathering phases and continuing through the design, development, testing, and deployment phases. This study also uses qualitative approach with literature studies and quantitative approach for data analysis to measure capability level. Research respondents were heads and staff from the IT, ICU, and HCU divisions. The results of this study describe the level of capability achieved by hospitals in the BAI Domain, with an average score of 3.30 at the established level. This level illustrates that the application of information technology can achieve process results that are in accordance with the wishes of raft xyz management. This stage also illustrates that the xyz hospital has an information technology process that has been standardized thoroughly to achieve all processes. The results of this evaluation can also be guideline for the xyz raft house to make improvements and improve the performance of the ICU and HCU patient systems in meeting the needs of intensively treated patients.
Optimalisasi UMKM di Kepulauan Mentawai Melalui Marketplace dan Digitalisasi Logistik Dwi Welly Sukma Nirad; Rika Ampuh Hadiguna; Ahmad Syafruddin Indrapriyatna; Wahyudi; Ricky Akbar; Hafizah Hanim; Andrew Kurniawan Vadreas
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.467

Abstract

Economic growth in West Sumatra's Mentawai Islands is relatively undeveloped due to geographical conditions and human resource capacity. The status as a 3T region (Disadvantaged, Frontier, Outermost) inhibits Micro, Small, and Medium Enterprises (MSMEs) growth. Despite MSMEs being economic pillars that stimulate regional development, their progress remains slow. To counter this, our study proposes "Bulagat", an integrated marketplace with digital logistics aimed at enhancing economic and MSMEs growth in the Mentawai Islands. Bulagat will streamline MSME product distribution, broaden market reach, and boost income. Constructed using the Waterfall Model, Bulagat facilitates transactions between MSMEs and customers on an easy-to-use platform. The integration of logistics digitization aims to improve delivery safety and efficiency, thereby expanding MSMEs product reach in the Mentawai Islands. A tracking feature ensures successful delivery. The study suggests Bulagat has the potential to be an efficient tech solution supporting MSMEs product sales in Mentawai, overcoming geographical barriers, and strengthening the local economy and community welfare. This application aims to mitigate MSMEs challenges, drive economic growth, and enhance local community welfare.
Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron Jaka Kusuma; Rubianto; Rika Rosnelly; Hartono; B. Herawan Hayadi
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.484

Abstract

Corn is one of the substitute staple foods in Indonesia after rice. Maize crops grown in Indonesia often experience considerable losses due to maize plant diseases. Generally, plant diseases are initially caused by morphological changes in the leaves. Accurate detection and classification of diseases that appear on the leaves will prevent the widespread spread of the disease. This study will compare classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron to find the best algorithm in the classification of leaf disease in corn plants, namely, cercospora leaf spot gray, common rust, and northern leaf blight using the VGG-16 deep learning model used as image feature extraction. The results showed that the Multilayer Perceptron algorithm produced the best values with accuracy, precision, and recall of 97.4% each.
Implementasi Sistem Reminder Jadwal pada eLearning Moodle Berbasis API Menggunakan Framework Flutter M. Yudha Putra; Dwi Ely Kurniawan
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.490

Abstract

This research aims to develop a Flutter-based mobile application that provides users with information regarding class schedules and assignments. The application includes a reminder feature that sends notifications about lectures and assignments through WhatsApp and in-app notifications. The development methodology employed is the waterfall model, while testing utilizes black-box testing and Firebase Test Lab. The test results demonstrate the successful development of the application. Additionally, this application facilitates efficient and easy access to class schedules and assignments. It is expected to enhance users' academic performance and be applicable across various educational institutions. Overall, the development of this application offers users the advantage of effectively and efficiently managing study time and completing tasks, thereby contributing to the improvement of educational quality within institutions.
Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah Evita Fitri
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.491

Abstract

The need for a place to live is one that many people prepare, both millennials and adults and the elderly. With the continued increase in population growth in Indonesia and increasing public interest in buying a place to live early on, this can make not all groups of people have a place to live or a house that is quite livable. Related to this, the public needs up-to-date information related to predictions of house prices both for housing and second-hand housing prices for planning purposes in the future. The purpose of this study is to carry out a comparative analysis of the prediction results of house prices with several Machine Learning algorithms consist of Linear Regression, Random Forest Regression and Gradient Boosted Trees Regression. Evaluation for all the method applying Cross-Validation. The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. The results were obtained from testing a dataset of 2011 records with a division of 80% for data training and 20% for data testing, the data has 6 attributes used in testing including house prices, land area, building area, number of bathrooms, number of bedrooms and the number of garages. In this study, prediction results using the Random Forest Regression method yielded the highest accuracy of 81.5% compared to the Linear Regression and Gradient Boosted Trees Regression methods.
Systematic Literature Review: Analisa Sentimen Masyarakat terhadap Penerapan Peraturan ETLE Syafrial Fachri Pane; Muhammad Syiarul Amrullah
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.493

Abstract

This study examines the efforts to develop a model for analyzing public sentiment regarding applying ETLE (Electronic Traffic Law Enforcement) regulations. The method used is the systematic literature review. A systematic literature review (SLR) consists of three stages: planning, conducting, and reporting. The planning stage is the determination of the SLR procedure. This stage includes preparing topics, research questions, article search criteria & inclusion and exclusion criteria. The conducting stage, namely the implementation, includes searching for articles and filtering articles. The reporting stage is the final stage of SLR. This stage includes writing the SLR results according to the article format. The explanation follows: First, hybrid is the most widely used method in developing sentiment analysis models. Apart from hybrid, several methods are used to develop sentiment analysis models, including multi-task, deep, and machine learning. Each has its advantages and disadvantages in the development of sentiment analysis models. Second, this study shows the development of a model with superior performance, namely using XGBoost as a sentiment analysis model, and the stages it goes through are preprocessing data, handling imbalanced data, and optimizing the model. Therefore, the model for analyzing public sentiment regarding the application of ETLE regulations can be an option for hybrid methods, multi-task learning, deep learning, machine learning, and the XGBoost model to obtain superior performance with preprocessing data stages, handling imbalanced data and optimization models.
Analisis Usability pada Human Capital Management System PT. XYZ menggunakan WEBUSE Model Alia Mutia Mayanda; Agnes Sondita Payani; Adenia Adiresta; Imairi Eitiveni
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.504

Abstract

Human Capital Management System (HCMS) has become a necessity for organizations to perform administrative personnel services. PT XYZ started implementing HCMS in 2019 as an online self-service for personnel administration, which was previously done manually. During this transition phase, not all employees understand how to use the HCMS system due to its confusing interface. As a result, many employees still prefer assistance from the HR/IT team to make changes to their data rather than doing it themselves. This research was conducted to analyze the usability of the HCMS system using the WEBUSE method. There are four categories used: Content, Organization, and Readability (COR); Navigation and Links (NAL); User Interface Design (UID); and Performance and Effectiveness (PAE). Each category serves as a basis for measuring usability. Data was collected through questionnaires and interviews with respondents to obtain values and levels of WEBUSE usability based on user satisfaction aspects In the case study of this research, it was obtained from the results of the questionnaire distributed as many as 13 respondents stated that in general, the usability of the HCMS system was at the Good level. This research provides recommendations for improving the self-service HCMS based on the needs or experiences of employees using the system. Also contributes to the scientific study of UI/UX and provides an ideal overview practitioner in developing an HCMS system.
Audit Keamanan Sistem Informasi Manajemen Rumah Sakit Dengan Framework COBIT 2019 Pada RSUD Palembang BARI Arief Algiffary; M. Izman Herdiansyah; Yesi Novaria Kunang
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.505

Abstract

This study examines the implementation of information system at RSUD Palembang BARI with the aim of enhancing information system security. In this context, a security audit is conducted using the COBIT 2019 framework. The COBIT 2019 domains and processes utilizing include EDM03, APO12, APO13, APO14, and DSS05. The research involves the identification and evaluation of information security risks, determination of necessary security controls, and ensuring compliance with the information security standards established by COBIT 2019. The findings indicate that the level of information system security at RSUD Palembang BARI is at level 3 (Defined), with a gap analysis difference of 1 level below the expected target. Based on the above results, efforts to improve and enhance the information system security at RSUD Palembang BARI are still needed. The use of information system security techniques such as vulnerability scanning, penetration testing, WAF, IDS and IPS, and data encryption, as well as improving security in terms of server physical aspects such as installing CCTV and restricting user access with access cards or fingerprints, can be implemented to ensure compliance with relevant information security standards. Consideration for obtaining security certifications, like ISO 27001, should also be taken. Additionally, the quality of human resources in terms of policy-making and the ability of employees to address threats and attacks on information system security should be improved through training and strengthening coordination among employees.
Sistem Pendukung Keputusan PPDB di SMAN Unggul Dharmasraya Evi Yulia Susanti
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.521

Abstract

Acceptance of New Students is the initial process in educational institutions to screen prospective students who meet the criteria set by the school. SMAN Unggul Dharmasraya screens prospective students through report cards and written tests. In terms of enthusiasts, this school is classified as a school that has a lot of enthusiasts because it is facilitated by boarding.Therefore, each committee selection process has difficulty in determining prospective students who will be accepted according to the criteria. From these problems the idea emerged to conduct research on the application of decision support systems in accepting new students using the Simple Additive Weighting method. This method is known as the weighted sum method, because the basic concept of SAW is to find the weighted sum of the performance ratings for each alternative on all attributes so that this method can assist in the ranking process based on the results of the assessment of predetermined criteria. The rating of each attribute must be dimension-free in the sense that it has passed the previous matrix normalization process. The purpose of this research was to assist the admissions committee for new students in making decisions to determine prospective new students according to predetermined criteria and weights. The system development model used is the waterfall model, because this model uses a systematic and sequential approach. The results of this study are a decision support system using the Simple Additive Weighting method from calculating the prevalence value, there are several alternatives that can be recommendations for the committee to get prospective students who meet the criteria determined by the school.
Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen Sharazita Dyah Anggita; Ferian Fauzi Abdulloh
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.524

Abstract

Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.

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