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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 419 Documents
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Analisis Perbandingan Kinerja DWT dan SWT dalam Pengenalan Emosi Berbasis EEG Menggunakan XGBoost Prameswari, Sonia Anjani; Kusrini, Kusrini
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.479

Abstract

Emotion recognition from electroencephalography (EEG) signals is crucial for human-computer interaction and diagnosing emotional disorders. This study evaluates the impact of feature extraction methods on the performance of XGBoost in classifying emotions in game players using EEG data. It compares the efficacy of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) combined with XGBoost, aiming to identify the most effective feature extraction method for improving emotion classification accuracy. Using the GAMEEMO dataset, which includes preprocessed EEG signals from game players, three scenarios were analyzed: XGBoost without feature extraction, XGBoost with DWT, and XGBoost with SWT. The results demonstrate that DWT significantly enhances classification performance, achieving higher accuracy, precision, and recall compared to SWT and no feature extraction. DWT's ability to capture rapid frequency changes in EEG signals is a key factor in its superior performance. Future work should focus on refining data preprocessing techniques, exploring additional feature extraction methods, and optimizing XGBoost hyperparameters to further enhance emotion recognition accuracy. This research provides valuable insights into the comparative effectiveness of different wavelet transform methods for EEG-based emotion classification, emphasizing the potential of DWT for improved performance
Analisis User Interface Pada Website E-Learning2 Bina Darma Menggunakan Metode Evaluasi Heuristik Raihan, Mohammad; Panjaitan, Febriyanti; Jemakmun, H.; Sauda, Siti
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.511

Abstract

The development of information and communication technology has changed many aspects of life, including in the field of education. One of its implementations is the use of e-learning systems as a means of distance learning. E-learning offers flexibility and ease of access for students, so that more and more educational institutions are implementing it. Bina Darma University, as one of the higher education institutions in Indonesia, has also developed an e-learning system known as E-learning2 Bina Darma. This system is an important tool for lecturers and students in carrying out the online teaching and learning process. The success of e-learning implementation is not only determined by the availability of adequate features and content, but also depends on the design and usability of a good user interface. An easy-to-use and intuitive user interface will improve the user experience and motivate students to actively participate in e-learning. Therefore, it is very important to assess and improve the user interface to ensure that the e-learning system can be used optimally. One effective method for assessing the usability of a user interface is heuristic evaluation. This method involves examining the user interface based on a set of established heuristic principles, as proposed by Jakob Nielsen. Heuristic evaluation can identify problems and weaknesses in the user interface, and provide recommendations for improvements that can improve the user experience and overall usability of the system. This study aims to analyze the user interface of the E-learning2 Bina Darma website using the heuristic evaluation method. The results of this study are expected to provide useful input for Bina Darma University in improving the quality and usability of the e-learning2 system, so that it can support a more effective and interesting learning process for students.
Rancang Bangun Sistem Informasi Pemesanan Makanan Berbasis Web pada Deshake Coffee Darmawan, Rahmat; Marbun, Nasib
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.470

Abstract

This research aims to design and develop a web-based food ordering information system specifically tailored to the needs of the cafe, this research emphasizes the importance of technology adoption to improve customer experience. The system will allow customers to place orders online easily and conveniently through an intuitive user interface. In addition, the integration of a secure payment system, effective order management, and the availability of accurate product information are the main focuses in its development. In the context of Deshake Coffee, the system is designed to allow customers to place food orders easily from anywhere, through any internet-connected device. An intuitive user interface will guide users through the ordering process, while integration with a secure payment system will guarantee smooth and reliable transactions. Effective order management will help Deshake Coffee to manage orders more efficiently, minimize errors, and optimize product delivery to customers. In addition, the system will provide customers with accurate and up-to-date product information, ensuring that they always have the information they need to make informed purchasing decisions. By implementing this ordering information system, Deshake Coffee is expected to not only expand its market reach through an online platform, but also improve inventory management and overall order management. It is hoped that the results of this research can make a positive contribution to the development of technology in the food and beverage industry, as well as significantly increase Deshake Coffee's customer satisfaction.
Analisis Tata Kelola Sistem Informasi Menggunakan Framework Cobit 2019 Pada Dukcapil Kota Salatiga Handyan, Nelson Christopher; Rudianto, Christ
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.502

Abstract

Information technology is an important and fundamental asset in a company or organization because the presence of good information technology will certainly support work effectiveness and maximum utilization of resources. One of the government organizations operating in the field of Community Services, namely DUKCAPIL, utilizes information technology as support in its operational processes. The problem faced by DUKCAPIL is that information system governance is not running well, especially in conveying information. Therefore, research was conducted on DUKCAPIL information system governance using the COBIT (Control Objective for Information and related Technology) framework, which is an IT governance framework that defines ways and methods for an organization. The research results show that the average level of capability achieved is at the Assisted Process level. This means each sub domain has been well defined and standardized. Even though almost all processes meet the requirements, there are still weaknesses in the implementation of processes that can be maximized to achieve adequate results. DUKCAPIL still needs to carry out the level 5 process, namely Optimizing Process, by providing regular training to human resources (HR) on existing Information Systems. In addition, companies must meet user needs to achieve the desired targets.
Sistem Pakar Untuk Rekomendasi Pembelian Produk Jamu Menggunakan Forward Chaining (Studi Kasus Jamu Mbah Djitoen) Hidayatulloh, Marchasyah Arzulla Akbar; Ringo, Johny H. Siringo
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.493

Abstract

Jamu mbah djitoen is a shop that operates in the culinary sector selling homemade herbal medicine and juice. So far, the sales process has been conventional, customers come to the shop or send products using online motorcycle taxi services.  Based on these problems, researchers will design a system that is developed based on a website and is equipped with a forward chaining expert system. The system design adapts the waterfall method, using MySQL as a database and using UML as a form of documentation for the system created. After designing the program and web hosting, black box testing was carried out on the system and involved 31 respondents to evaluate the system. The evaluation results obtained were 98% for a positive response to the system that had been created. It can be concluded that the expert system designed is able to help owners and customers.
Penerapan Metode Linear Discriminant Analysis Dalam Mendeteksi Kematangan Buah Tomat Setiawan, Adil; Sumijan, S
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.539

Abstract

Tomatoes are fruits of various shapes and sizes originating from the American continent, the tree can reach a height of 2.5 meters, grows as a fruit plant in fields, yards or is found wild at an altitude of 1-1600 meters. above the sea level. Tomatoes are classified as fruits because they are the edible part of the plant and contain seeds or seeds visible through fiber which play an important role in improving digestion. Fiber facilitates bowel movements and prevents constipation, thereby improving the digestive system. Researchers examined the level of ripeness of tomatoes to obtain a classification of ripe tomato parts and unripe tomato parts by utilizing the Linear Discriminant Analysis method, one of the supervised learning algorithms used to carry out classification in machine learning. This technique is used to find the best linear combination of variables as an indicator of separating classes (classifying) in the dataset. LDA works by projecting data into a lower dimensional space that maximizes the separation between RGB, hue, saturation classes. Farmers usually sort them manually to determine the ripeness of tomatoes, so a system is needed that is capable of classifying the ripeness of tomatoes using the Linear Discriminant Analysis method. Based on the results of accuracy testing, the accuracy rate reaches 85%, this is a result that has become reference material for future researchers
Enterprise Architecture: Digital Transformation of BPRCCo SMEs Using TOGAF 10 Wafiyah, Afifah Hasna; Mulyana, Rahmat; Fajrillah, Asti Amalia Nur
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.484

Abstract

Digital Transformation (DT) is a challenge for banks in Indonesia. Previous research has shown that IT strategy and architecture is one of the key governance mechanisms that play an important role in the success of DT in the Indonesian banking industry. However, this research has not been proven for the small-scale banking sector, in particular BPRCCo as an SME bank. Enterprise architecture (EA) is a method that can be used to align business and IT strategy. This research uses the Design Science Research (DSR) method, which includes problem explication, requirement specification, design and development, demonstration, and evaluation. Data was gathered through semi-structured interviews, validated using document triangulation, and analyzed using the TOGAF 10 framework, from the preliminary phase through technology architecture, producing blueprint for BPRCCo roadmap 2024-2026. This research contributes to the knowledge base of EA in DT for SMEs and guidance for BPRCCo and similar organizations in implementing prioritized artifacts for successful DT.
Perancangan Sistem Pendukung Keputusan Rekomendasi Penentuaan Penerimaan Beasiswa Merdekawati, Agustiena; Azlina, Yunidyawati; Wulansari, Murwani
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.516

Abstract

The scholarship acceptance process at one of the universities in Jakarta is based on several criteria, including the distance from the residence to the campus, active organizations, participating in UKM, GPA, having parents or guardians, parents' or guardians' jobs, income level, number of family dependents, ownership of a residence. In 2020, there were 1000 students who registered with various majors, so the scholarship selection team had difficulty in determining the scholarship acceptance selection quickly and accurately. In addition, subjective selection determinations were still found so that they were not on target and caused errors in determining policies to increase. Recording and determining scholarship acceptance were still done manually, namely via Excel. By entering detailed data on scholarship applicants and then calculating the value of each criterion met by scholarship applicants, this process is very complicated in processing scholarship determination. With this problem, research was carried out with data mining, using a PSO-based decision tree algorithm model with a PSO-based naïve bayes algorithm. By comparing the accuracy results of the two models, the highest accuracy was obtained with the PSO-based decision tree algorithm model. Furthermore, designing a web-based Decision support system for scholarship selection.
Klasifikasi Timun Segar dan Busuk Menggunakan K-Means Clustering dengan Peningkatan Noise Reduction dan Median Filter Dila, Rahmah; Saputra, Riyan; Ramadhanu, Agung
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.549

Abstract

Cucumber is one of the agricultural commodities that is vulnerable to quality degradation due to the rotting process. Manual classification of fresh and rotten cucumbers can be time-consuming and inconsistent, thus requiring a more efficient automated method. The main objective of this research is to implement an automated image processing-based classification system to classify fresh and rotten cucumbers based on visual features such as color, texture, and shape, in order to improve efficiency and consistency in the cucumber quality selection process. The applied method involves image processing with color space conversion from RGB to LAB to separate brightness and color. Additionally, improvements were made using noise reduction techniques and a median filter to minimize noise interference in the images, resulting in more accurate analysis. Noise reduction is applied to reduce noise that appears during the image acquisition process, which can disrupt the recognition of important features in cucumber images. The use of a median filter helps smooth the images without reducing important details, which is essential to preserve relevant visual information for classification. The K-Means Clustering algorithm is used to group the images into two clusters, namely fresh and rotten cucumbers. The data used includes 70 test images, consisting of 35 fresh cucumbers and 35 rotten cucumbers. The results of this study indicate that this method, with the application of noise reduction enhancement and median filter, successfully classifies fresh and rotten cucumbers with an accuracy rate of 98.6%, where 69 out of 70 images are correctly identified. The K-Means Clustering method enhanced with noise reduction and median filter is proven to be effective and accurate in determining the types of fresh and rotten cucumbers
Machine Learning for Tsunami Prediction: A Comparative Analysis of Ensemble and Deep Learning Models Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.572

Abstract

Tsunamis, triggered by seismic activities, pose significant threats to coastal regions, necessitating accurate prediction models to mitigate their impact. This study explores the application of machine learning models, including ensemble methods (Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost) and deep learning (Neural Networks), for tsunami prediction based on seismic data. The dataset spans seismic events from 1995 to 2023, characterized by features such as magnitude, depth, and geographic location. A 10-fold cross-validation approach was employed to evaluate model performance using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlight that Gradient Boosting achieved the best balance between precision and recall, with an F1-score of 0.6544 and the highest ROC-AUC of 0.8606, demonstrating its strong discriminatory power. Random Forest excelled in precision (0.6920) and F1-score (0.6287), making it suitable for reducing false positives. Ensemble boosting models, such as CatBoost and LightGBM, offered consistent performance with low variability across folds. In contrast, Neural Networks underperformed, achieving an F1-score of 0.5497 and an ROC-AUC of 0.7936, indicating the need for further optimization. Despite promising results, challenges in recall scores underscore the need for enhanced detection of tsunami-triggering events. The findings establish ensemble methods, particularly Gradient Boosting and Random Forest, as robust tools for tsunami prediction, providing a foundation for early warning systems. Future work will focus on improving recall and exploring hybrid modeling techniques to optimize predictive accuracy and reliability.

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