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Sekretariat KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) Jln. Jendral Sudirman Blok A No. 1/2/3 Kota Pematang Siantar, Sumatera Utara 21127
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Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Published by Sekolah Tinggi Ilmu Komputer Tunas Bangsa
ISSN : -     EISSN : 2720992X     DOI : 10.30645
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) adalah sebuah jurnal peer-review secara online yang diterbitkan bertujuan sebagai sebuah forum penerbitan tingkat nasional di Indonesia bagi para peneliti, profesional, Mahasiswa dan praktisi dari industri dalam bidang Ilmu Kecerdasan Buatan. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) menerbitkan hasil karya asli dari penelitian terunggul dan termaju pada semua topik yang berkaitan dengan sistem informasi. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) terbit 4 (empat) nomor dalam setahun. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 40 Documents
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Search results for , issue "Vol 5, No 2 (2024): Edisi April" : 40 Documents clear
Penerapan Teorema Bayes Pada Sistem Pakar Untuk Mendeteksi Dini Penyakit Tuberkulosis (Studi Kasus Di Rs. Tentara Dr. Reksodiwiryo Padang) Fadil Idensia; Y Yuhandri; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.369

Abstract

Tuberculosis (TB) is an infectious disease that is still a global health problem, including in Indonesia. Early detection of this disease is crucial for effective treatment. In order to improve early detection of TB, this research aims to apply the Bayes Theorem method to the development of an expert system. The case study was conducted at Dr. Reksodiwiryo, Padang, where the percentage of Tuberculosis based on the method has been identified. The Bayes Theorem method is implemented in an expert system to provide early diagnosis to patients suspected of having TB. Expert system testing was carried out to evaluate the accuracy of the diagnosis, with an average calculation result using Bayes' theorem of 80%. The results of this research indicate that the application of Bayes' Theorem in an expert system can be an effective tool in early detection of Tuberculosis. The practical implication of this research is to increase the capabilities of the Dr. Army Hospital. Reksodiwiryo Padang in treating TB early and accurately, as well as contributing to efforts to prevent and control this disease more efficiently.
Penerapan Algoritma K-Means Dalam Pengklasteran Hasil Evaluasi Akademik Mahasiswa Fitri Safnita; Sarjon Defit; Gunadi Widi Nurcahyo
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.360

Abstract

Several institutions that have utilized computer-based information systems for many years certainly have quite large amounts of data. The data generated and stored in a computer system is designed to be fast and accurate in both operation and administration. This data is designed for reporting and analysis that uses that data. It turns out that there is a lot of data available, with so much data we are increasingly faced with the question, "What knowledge can we gain from this data?" The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is easy to implement and run, relatively fast, easy to adapt, commonly used in practice. The parameter that must be entered when using the K-Means algorithm is the K value. The K value is generally used based on previously known information regarding how many clusters appear in This research aims to group students based on academic evaluation results. The method used to manage student academic data uses the Data Mining method with the K-Means Clustering Algorithm. The dataset processed in this research comes from the Faculty of Engineering, Informatics Engineering Study Program, Islamic University of Riau. The dataset consists of 180 student data starting from semester 1 to semester 4. The results obtained from this research are in the form of grouping students based on the achievement student cluster, there are 104 students with a percentage of 57.72%, the student cluster with potential for achievement is 62 students with a percentage of 34 .41%, the potentially problematic student cluster has 10 students with a percentage of 5.55%, and the problematic student cluster has 4 students with a percentage of 2.22%. Therefore, it is hoped that the results of this research will provide new knowledge that can be used as a source of information and function as a reference model for academic planners to monitor and predict the development of each student's academic performance.
Analisis Kinerja Jaringan Menggunakan Metode PCQ pada Jaringan Internet Kelurahan Butuh Andika Wahyu Kurniawan; Rissal Efendi
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.351

Abstract

The aim of this research is to improve and analyze internet network performance by applying the PCQ (Per Connection Queue) method in the bandwidth management system. The PCQ (Per Connection Queuing) method is a network traffic management technique that allocates bandwidth fairly to each active connection. This method allows network administrators to control network traffic and prioritize low-bandwidth connections. The PCQ method is used to overcome problems that often occur in internet network performance, such as unfair distribution of bandwidth. In analyzing computer network performance using the PCQ method, research is carried out to measure network performance using various parameters such as throughput, delay, jitter, packet loss. This research often compares network performance with and without using the PCQ method, as well as comparisons with other network traffic management methods. Using the PCQ method can fix problems that often occur in internet network performance which has an imbalance in bandwidth usage between one network user and another. The results of this research can be concluded that the use of the PCQ method greatly influences network performance for the better due to the distribution of bandwidth evenly. For this reason, this research is an important contribution in improving bandwidth management that is more effective and efficient
Comparative Analysis of Machine Learning Models for Emotion Classification in Textual Data Gregorius Airlangga
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.383

Abstract

This research presents a comprehensive comparative analysis of various machine learning models for emotion classification within textual data, aiming to identify the most effective architectures for understanding and interpreting emotional undertones. With the increasing prevalence of digital communications, the ability to accurately classify emotions in text has significant implications across numerous domains, including social media analysis, customer service, and mental health monitoring. This study evaluates traditional algorithms, such as Logistic Regression, and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks combined with Recurrent Neural Networks (CNN-RNN), Autoencoders, and Transformers. Through meticulous cross-validation, hyperparameter tuning, and performance evaluation based on accuracy, precision, recall, and F1 scores, the research elucidates the strengths and weaknesses of each model. LSTM and GRU models demonstrated superior performance, highlighting the importance of sequential data processing capabilities. In contrast, the Autoencoder model underperformed, underscoring the necessity for careful model selection tailored to the task's specifics. Surprisingly, Logistic Regression showed notable efficacy, advocating for its potential utility in scenarios prioritizing computational efficiency. This study enhances the understanding of affective computing within natural language processing, offering insights into the strategic deployment of machine learning models for emotion recognition and paving the way for future advancements in the field.
UI/UX Design of Waste Management Application Using Design Thinking Method Regar Chairul Soleh; M Maukar
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.374

Abstract

Effective and sustainable waste management is a global challenge that requires innovative solutions. This study proposes optimizing waste management through UI/UX design approach using the design thinking method. The stages of Empathize, Define, Ideate, Prototype, and Testing are employed to design the "SISAKU" mobile application to enhance community participation in waste management. The prototype testing involved 30 respondents, with a System Usability Scale (SUS) result of 80.29%. Evaluation shows that the application design successfully provides intuitive and engaging guidance, increasing public awareness and involvement in waste management. The implementation of lean canvas as a business model provides strategic direction for sustainable development. This research highlights the importance of a user-centered design approach and design thinking method in addressing complex issues such as waste management.
Penerapan Framework Ltsa Untuk Mengembangkan Lms Berbasis Blended Learning Untuk Proses Pembelajaran Aflili Sari; Sarjon Defit; S Sumijan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.365

Abstract

Nowadays everyone is able to learn about anything, anywhere, and at any time because of the internet. Many schools have started implementing learning via E-learning. The development of E-learning as application software can be done using various methodologies or frameworks and one of them is the Learning Technology System Architecture (LTSA) framework which is standard 1484.1-2013 from the Institute of Electrical and Electronics Engineers (IEEE) for learning technology. This LTSA framework is applied with the aim of mapping the current and proposed systems, including mapping sub-systems and their relationships with external systems. The objectives to be achieved in this research include designing a Learning Management System at SMAN 2 Gunung Talang using the LTSA framework. Implementation of Blended Learning at SMAN 2 Gunung Talang by utilizing a Learning Management System designed with the LTSA framework to improve the quality of learning and Application of the LTSA framework to build a Learning Management System synchronized with learning at SMAN 2 Gunung Talang. There are 5 layers implemented in creating a Learning Management System using the LTSA framework. Layer 1: Learner interaction with the environment. Layer 2: The influence the learner has on the system. Layer 3: Component system. Layer 4: Identify stakeholder priorities and perspectives. Layer 5: Operational components and interoperability. The results of this research show that the existence of a Learning Management System based on the LTSA framework can create more focused, structured and well-archived learning at SMAN 2 Gunung Talang. The application of the LTSA framework in building a Learning Management System that is appropriate and in harmony with learning at SMAN 2 Gunung Talang has shown significant results in improving the quality of learning at SMAN 2 Gunung Talang.
E-Perpus SMA Negeri 1 Cengal Menggunakan Metode Extreme Programming Renaldi Agustian Hamzah; Syahril Rizal
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.356

Abstract

A library is an institution or place that has an important role in providing access to various types of information, knowledge and reading materials. High school libraries have a very crucial background as providers of learning resources which provide various types of learning resources including textbooks, references, journals, magazines and digital materials. These learning resources support students in various subjects and encourage them to understand the concept of the learning process better. Currently, the library at SMA Negeri 1 Cengal is currently searching for references in the form of books or other reading sources done manually, namely by involving library staff to look for books that visitors want to borrow or the borrowing process is still carried out using a manual recording process with large books. In this research the author used the extreme programming (XP) method. To overcome the problems mentioned above. Researchers used Extreme Programming techniques in the Agile Methods methodology to build this library information system. The development process is simplified with Extreme Programming, which makes it more adaptable and versatile. It also offers alignment with design and functionality changes, and is easy to handle. 
Implementasi Teorema Bayes Diagnosa Penyakit Ikan Lele Di Dinas Ketahananpangan Perikanan Rohul Hendri Maradona; Mi’rajul Rifqi; D Dona; Darmanta Sukrianto; Kiki Yasdomi; Khairul Sabri; Urfi Utami; Muhammad Romi Nst; M Muhammadyodi
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.388

Abstract

Catfish is a freshwater fish that is widely cultivated in almost all parts of Indonesia. This is because catfish is one of the leading commodities, and has good market prospects. In aquaculture, disease attacks can be a threat that causes economic losses, and can even experience large losses in terms of crop yields. Advances in expert systems can overcome the problem by designing a web-based computer system that uses databases and programming languages such as PHP-MySQL so that it can help catfish cultivators to diagnose fish diseases. This study aims to detect disease in catfish using the Bayes theorem method. In this study, there were 13 types of diseases, namely Cotton Wall Disease, White Spots, Yellow Catfish Disease (Jaundice), Itchy Catfish Disease (Trichodiniasis), Rupture of Intestines/RIS (Reptures Intestine Syndrome), Smallpox Disease, Ragged Catfish Disease tail fin, fungal attack, brown blood disease, Enteric septicemia of catfish, columnar disease, proliferative gill disease, channel catfish virus disease. The result of this research is the construction of an expert system that can be used as an early diagnosis of disease in catfish. This system is expected to be developed for diagnosis in other fish, in order to provide greater benefits for its users.
Perbandingan Performa dari Algoritma AES dan RSA dalam Keamanan Transaksi Ahmad Miftah Fajrin; Christoper Kelvin; Brian Owen; Bayu Aji
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.379

Abstract

Online transactions have become increasingly prevalent in the modern day. It is highly user-friendly and can be conveniently transported to any location. Nevertheless, internet transactions possess inherent security vulnerabilities that render them susceptible to assaults, hence enabling the retrieval of consumers' personal data. Hence, it is imperative to use encryption measures for safeguarding users' personal data, including PINs, CVVs, and card numbers. This study aims to evaluate and contrast the efficacy of AES and RSA algorithms within a website platform designed for online transactions. The study's findings indicate that there is minimal disparity in the performance of the two algorithms. However, it was observed that both algorithms exhibit enhanced security when employing longer keys.
Penerapan Algoritma K-Nearest Neighbor (KNN) Pada Klasifikasi Kualitas Biji Kopi Robusta Putri Ayu Lestari; Desi Puspita; Siti Aminah; Y Yadi
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.370

Abstract

The aim of this research is to produce a quality classification system for robusta coffee beans using the k-nearest neighbor algorithm (knn). This research is based on the process of classifying the quality of robusta coffee beans which is carried out conventionally and has not been computerized, namely the classification of coffee beans still uses the selection method of color and cleanliness of the beans. This of course takes a long time and errors often occur, so this research can help classify the quality of Robusta coffee beans using the k-nearest neighbor (knn) algorithm. The system was built using MATLAB software, which is used in this research. is the SDLC (Software Development Life Cycle) method, the stages of this research include analysis, design, coding and testing, for the testing method using a confusion matrix which is divided into 2, namely training data and test data. The results of this research are a seed quality classification system robusta coffee using the k-nearest neighbor (knn) algorithm with data used 80 images for training and 10 images for testing. It can be concluded that RGB extraction and the k-nearest neighbor (knn) method can be applied to classify the quality of robusta coffee beans from 10 test data Accuracy was obtained at 70 with image processing in coffee business management in Bandar Village, Pagaralam City, South Dempo District.

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