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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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Data Mining untuk Memprediksi Animo Masyarakat terhadap Proses Penerimaan Peserta Didik Baru Berek, Anggelina Bete; Isa, Sani Muhamad
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.v6i1.563

Abstract

Data Mining techniques can now be implemented in all aspects of society because of development in technology. Data Mining can be used in education. There is the possibility of being employed for educational purposes to predict achievements. The involvement of the community is one of the factors that influence the quantity of students at the school. The research project is using Data Mining to predict the factors that impact social engagement. The demographic information used in this study came from the parents of potential candidates. The techniques used are Decision tree, Naïve bayes, and Support Vector Machine. Their accuracy scores are evaluated by a confusion matrix. The results of this study are below: Decision tree 80.16%, Naïve Baye 79.94%, and Support Vector Machine 86.02%.  Based on the comparison results, it can be concluded that the highest accuracy is achieved by using the Support Vector Machine algorithm, while the factor that affects public sentiment is ayah penghasilan.
Rancang Bangun Sistem Informasi Penjualan Produk Berbasis Web Menggunakan Metode Agile Pada Risti Promotion Lisanti, Sekar Wulan Ayu; Diartono, Dwi Agus
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.v6i1.554

Abstract

Risti Promotion is a business engaged in the production and sale of souvenirs and raw materials. Risti Promotion is located on Jl. Sawah Besar RTO6 RW 03, Kaligawe Village, Gayamsari District, Semarang City. This home production focuses on making and selling products in the form of bags, wallets, dysgribs, and raw materials in the form of cloth. The problem with Risti Promotion is that the sales system still uses a manual system in the form of selling from seller to seller and the sales system has not been computerized. The existence of these problems causes various obstacles, such as difficulty in managing sales data, limited access to information about products, service delays, limited range of sales. So it took a while, vulnerable to problems. From this problem, a web-based sales system will be built using the Agile method. The development of systems with this method is because Agile methods are dynamic, so they can develop rapidly. The goals and results of building the system are to make it easier for business owners in Risti Promotion to manage product sales data and orders made by buyers, to make it easier for buyers to search for related product information, to reduce errors that may occur in the process of reporting and extracting product sales data.
POS Android untuk Transaksi Offline-Online di Bengkel Remaja Motor Imanda, Ridho Novan; Listyorini, Tri; Supriyati, Endang
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.v6i1.568

Abstract

Bengkel Remaja Motor, a workshop that has been operating for more than 45 years, faces challenges in managing transactions due to its manual processes. These challenges include difficulties in checking spare part prices, issuing receipts, and the absence of digital payment options. This study focuses on the development of a digital cashier application designed for the Android platform. The application is tailored to meet the specific needs of end-users, featuring an offline mode for cash transactions and an online mode for digital payments. The system is built using Laravel for the backend and Flutter for the user interface, with Bluetooth Printer integration to enable receipt printing. The Rapid Application Development (RAD) approach, which emphasizes user feedback, was adopted to accelerate the development process. Testing results demonstrate that the system facilitates transaction management, supports digital payments via Midtrans, and operates effectively in both offline and online environments. This system is expected to enhance the operational efficiency of Bengkel Remaja Motor.
Analisis Kepuasan Pengguna Terhadap Wesbite Layanan Perbaikan Komputer Lev Computer Pratama, Adhitya Fajar Risqi Djati; Sampetoding, Eliyah Acantha Manapa
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.v6i1.559

Abstract

Lev Computer is a computer repair service that provides solutions to various problems faced by users. To provide the best possible service, Lev Computer has created a website where users can access information about Lev Computer profiles, available services, facilities, ratings from patients using Lev Computer services, and Lev Computer address if they are interested in repairs. However, for continued growth, the Lev Computer website still needs a lot of optimization. This study aims to assess the level of user satisfaction with the Leave Computer website and what factors influence it. This study uses the System Usability Scale (SUS) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). This study included 100 respondents, namely Lev Computer website users. The survey using the system usability scale method resulted in a score of 68.3. The mean score on the D grading scale falls into the marginal (high) category and is within the excellent range. Meanwhile, the analysis results of the UTAUT2 model show that there are variables that influence user interest, such as the change in effort expectancy. The results of the conducted research show that users are satisfied with the Lev Computer website is considered acceptable by users as a website that can help users, provide convenience, and provide a pleasant experience in using the Lev Computer website.
Impelementasi Payment Gateway untuk System Management Member Gym Menggunakan Framework Laravel 10 Wibowo, Beautifully Margaretha Putri; Asmiatun, Siti
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.v6i1.550

Abstract

Public interest in sports is increasing from time to time. People are starting to realize the importance of maintaining body health and encouraging themselves to exercise more regularly and maintain a healthier lifestyle. Various types of sports can be done ranging from light exercise to competitive sports. One of the sports that has become a trend lately is exercising indoors with modern sports equipment facilities, such as gyms. Even though gyms have provided various needed facilities, people still have some obstacles experienced, such as lack of free time to be able to book sports facilities and pay for facilities directly. Therefore, the researcher developed a gym member management system by utilizing Laravel 10 as the back-end system and Midtrans as an online payment provider, as well as applying the Agile Software Development method which includes the stages of planning, design, development, testing, deployment, and review as a benchmark in system design. Thus, the gym member management system can help users book sports facilities anywhere and anytime through membership and simplify payment transactions by implementing online payments, as well as provide a new experience for users in booking gym facilities and improve operational efficiency.
Comparative Evaluation of CNN, LSTM, and GRU Architectures for Tsunami Prediction Using Seismic Data 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.v6i1.573

Abstract

Tsunamis are among the most catastrophic natural disasters, often triggered by seismic events such as earthquakes. Accurately predicting tsunami occurrences based on seismic parameters is critical for mitigating their devastating impacts. This study investigates the application of three advanced deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs) for binary classification of tsunami events using seismic data. The dataset comprises earthquake records from 1995 to 2023, including features such as magnitude, depth, latitude, longitude, Modified Mercalli Intensity (MMI), and Community Internet Intensity (CDI). The models were evaluated using stratified 10-fold cross-validation and assessed across precision, recall, F1-score, accuracy, and ROC-AUC metrics. Results indicate that CNN outperformed the other architectures, achieving the highest accuracy (72.5%), precision (0.5987), and ROC-AUC (0.7838). GRU demonstrated moderate performance, balancing computational efficiency and predictive accuracy with an accuracy of 71.7% and ROC-AUC of 0.7709. LSTM, while theoretically adept at modeling temporal dependencies, showed the lowest performance due to challenges in capturing the dataset’s characteristics. The findings emphasize the importance of selecting architecture suited to the dataset’s features and task requirements. CNN’s superior performance highlights its effectiveness in spatial pattern extraction, while GRU offers a computationally efficient alternative. Future work will explore hybrid models and the integration of additional features to enhance prediction robustness. This study contributes to advancing tsunami prediction methodologies, supporting early warning systems for disaster preparedness.
Klasifikasi Gempa Bumi Berdasarkan Magnitudo Menggunakan Metode Logistic Regression Mar’atuzzulfa, Salma; Prathivi, Rastri; Susanto, 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.v6i1.564

Abstract

The purpose of this study is to categorize areas in Indonesia that are potentially prone to earthquakes using the logistic regression algorithm. Variables such as latitude, longitude, depth, and magnitude are used to analyze 118 data points of natural disasters that occurred in Indonesia in 2023. As much as 40% of the data is used for testing, while 60% is used for training. The magnitudes are high, medium, and low. The logistic regression method is used to determine the level of health in the area and assess the relationship between variables. The study's findings indicate that the model has an accuracy of 93.62%, precision of 94%, recall of 93%, and F1 skor of 93% overall. In addition, the evaluation of the model's kinerja using the confusion matrix indicates that algorithms might associate a given category with a high sensitivity to error. By identifying data points and creating Logistic regression can assist in developing more effective bencana mitigation strategies by identifying data points and producing accurate predictions. As a result, it is believed that the general public can reduce the amount of dampak gempa bumi.
Aplikasi Pemesanan Buket Berbasis Web Pada Toko Anastha_Craft Pusvita, Ester Ayuk; S, Natalia . Setyaningrum
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.v6i1.555

Abstract

The bouquet ordering system at Anastha_Craft, a small business located in Nabire, faces several main issues. These include limited customer reach due to the store's physical location being far from the city, restricted operating hours, and a manual ordering system that does not provide complete and real-time product information. To address these problems, the research uses the Rapid Application Development (RAD) method to develop a website-based ordering application. RAD was chosen for its flexibility in adapting to changing requirements during the development process. This ordering system is built using PHP programming language and a MySQL database, with three main stages: Requirements Planning, Design Workshop, and Implementation. The developed application allows customers to place orders online, extends the store's service reach, and facilitates product data management. The results of the study show that the implementation of this website-based system successfully improved operational efficiency, made product information more accessible to customers in real-time, and supported the business growth of Anastha_Craft
Klasifikasi Citra Dalam Identifikasi Jeruk Nipis dan Jeruk Mandarin Menggunakan Convolutional Neural Network (CNN) Dan Optimalisasi Median Filter Erlanda, Hadrian; Saputra, Randy; 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.v6i1.546

Abstract

With the rapid advancement of technology in the field of image processing, farmers are increasingly supported in identifying types of citrus fruits. This study aims to differentiate between lime (Citrus aurantiifolia) and mandarin orange (Citrus reticulata) using image processing methods and morphological analysis. Image processing is employed to examine visual differences based on factors such as color, texture, and size of the fruit. Additionally, chemical analysis is conducted to distinguish the composition of compounds found in both types of citrus. The results of the study show that this approach is effective in identifying the differences between lime and mandarin orange with high accuracy, and can be applied in various industries, including agriculture and food processing
Efektivitas Digital Marketing LinkedIn Ads dan Email Blast untuk Meningkatkan Partisipasi Webinar Mappable Syawalido, Luky; Widodo, Suprih
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.v6i1.569

Abstract

This study aims to analyze the effectiveness of LinkedIn Ads and Email Blast as digital marketing strategies to increase participation in the Mappable webinar organized by PT. Indi Teknokreasi Internasional. The findings reveal that LinkedIn Ads excel at reaching new professional audiences by leveraging features such as Sponsored Content, Message Ads, and Lead Gen Forms, which allow targeting based on job titles, industries, and locations. A total of 32.8% of respondents reported learning about the webinar through LinkedIn, indicating the platform's relevance as a primary information channel for professional audiences. Meanwhile, Email Blast demonstrates high effectiveness in converting existing audiences into webinar participants. Data analysis from eight audience segments recorded performance variations in open rates, click rates, and bounce rates, highlighting the importance of segmentation and content personalization. The combination of LinkedIn Ads and Email Blast provides significant synergy, with LinkedIn Ads expanding the reach to new audiences while Email Blast optimizes conversions through more targeted messaging. This study recommends validating email recipient databases, optimizing delivery times, and utilizing analytics to enhance future campaign effectiveness. With this integrated approach, both strategies are proven to significantly boost engagement and participation in webinars.

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