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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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Implementasi Fetch API dalam pengembangan Backend Website Daftar Film dengan Next.JS Maulana, Irvan Dhimas; Susetyo, Yeremia Alfa
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.v6i1.560

Abstract

This study aims to improve the performance of a film list website by implementing the Next.JS framework along with its Fetching API to solve challenges in search efficiency and providing accurate and up-to-date information for users. The amount of outdated and irrelevant data on film websites often prevents users in finding current movie information. This research develops an application using Next.JS, integrated with the Fetch API, to dynamically retrieve data using Server-Side Rendering (SSR) and Static Site Generation (SSG), improving communication between server and client, while providing a responsive and SEO-friendly user experience. Testing results with Lighthouse and Chrome DevTools show improved performance, with an application score of 92 on the Vercel platform and 82 on the Local side. Cache optimization on Vercel also reduced data transfer size from 2.2 MB to 0.27 MB, significantly speeding up load times and stabilizing the application. These results indicate that the application successfully delivers relevant and up-to-date information with high speed and stable performance. However, this study is limited in terms of testing devices and focuses only on the Vercel hosting platform.
Optimalisasi Kentang Merah dan Kentang Biasa Secara Otomatis Menggunakan Median Filter dan Segmentasi Gambar Berbasis Warna dan Analisis Tekstur: Pendekatan K-Means Clustering Permata, Edo; Khomsi, Ahmad; 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.v6i1.551

Abstract

In this study, we propose an automated system to identify potato varieties (red and regular potatoes) using color-based image segmentation, median filter, and texture analysis. The system uses K-Means Clustering for color segmentation in Lab color space, followed by the application of median filter to reduce noise in the image, as well as texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM) to distinguish potato types. Experimental results show that the proposed method achieves more than 90% accuracy in identifying potato varieties, demonstrating its potential for industrial applications in tuber processing. Our findings show that the system is robust under various lighting conditions and can significantly reduce human error in the potato sorting process.
Penerapan Metode PSO-SMOTE Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Bencana Tanah Longsor Ariyadi, Dedy; Siswa, Taghfirul Azhima Yoga; Rudiman, R
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.v6i1.574

Abstract

Landslides are natural disasters that frequently occur in Samarinda City, with 45-80 affected areas in 2022-2023. The use of machine learning to classify landslide data faces the challenge of data imbalance, which can lead to bias towards the majority class. This study aims to address this issue by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and optimization using Particle Swarm Optimization (PSO). The data used comes from BMKG and BPBD Samarinda City, consisting of 11 features and 730 records. The results show that SMOTE successfully balanced the data, improving accuracy from 89.91% to 94.76%, an increase of 4.85%.
Analisis Preferensi Penonton Anime berbasiskan Genre Film menggunakan Metode K-Means Zalzabila, Niken; Prathivi, Rastri
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.v6i1.565

Abstract

This study aims to analyze anime audience preferences based on genres using the K-Means clustering algorithm. The dataset consists of 100 popular anime titles with features such as ratings, votes, and genres. The research steps include data preprocessing, clustering with the Elbow method to determine the optimal number of clusters, and applying the K-Means algorithm. The clustering results revealed four clusters with unique characteristics, highlighting differences in popularity and genre preferences. Evaluation using the Confusion Matrix shows a model accuracy of 95%, while the Silhouette score of 0.285 indicates adequate cluster separation. These findings are expected to provide insights for streaming platforms to deliver more personalized and relevant anime recommendations to viewers.
Desa Siaga Banjir Berbasis Masyarakat Sebagai Upaya Dalam Mitigasi Bencana di Kecamatan Rawas Ulu Kabupaten Musirawas Utara Purwani, Fenny; Nopriani, Fathiyah; Yudiani, Ema
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.v6i1.556

Abstract

The low level of community preparedness in facing disasters, especially floods, is often exacerbated by the lack of digital literacy in the community in utilizing information technology. This study aims to examine the level of digital literacy and community preparedness in Rawas Ulu District in facing flood disasters and to review whether there is a correlation between the ability to utilize information technology and increasing such preparedness. A qualitative approach was used in this study to analyze knowledge and use of information technology, obstacles faced in its use, and community understanding of flood mitigation. Data were obtained from two types of instruments, namely 1) Knowledge and use of information technology with 6 (six) variables; 2) Community knowledge related to flood disaster mitigation with 5 variables. The results of the study showed that the majority of respondents understood the risk of flooding 88%, but there were still limitations in knowledge of disaster mitigation. The use of information technology, especially social media, was quite high at above 90%, but digital literacy was still low. Another interesting finding was that there was a positive correlation of 70% between the ability to use information technology and community awareness of flood mitigation in Rawas Ulu District. The results of this study highlight the need to increase the potential use of information technology in improving community preparedness in flood mitigation and recommend the development of community-based mitigation programs by forming information communities.
Penilaian Mahasiswa PBSI Unsika Terhadap Keefektifan Penggunaan Google Colab Dalam Pembelajaran Coding Amal, Bahar; Damayanti, Silvia; Khonsa, Aisyah Nabila; Zahra, Mutia Haristi; Rahmadhani, Vissi Aulia; Anggraeni, Windi; Zendrato, Kezia Davina Putri
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.v6i1.547

Abstract

The skills of processing and analyzing data are very important in various sectors, including education. Students of Indonesian Language and Literature Education (PBSI) use google colab to study data processing. The purpose of this research is to understand and analyze the assessment of using google colab in coding learning for students of the Indonesian Language and Literature Education (PBSI) with the research object limited to students from the 2022 and 2023 batches. The research method used is quantitative with a questionnaire as the data collection tool. The questionnaire consists of open-ended and closed-ended questions. The results of the questionnaire show that 64.8% of respondents indicated that they have been using google colab for less than three months. Therefore, the majority of respondents do not have a long usage period. The experience of easy access to google colab is demonstrated by the majority of respondents. As many as 70.4% of respondents admitted that google colab speeds up the data processing process. The use of Google Colab can enhance collaboration, as shown by the uneven distribution of responses from the respondents, with 55 respondents answering that it plays a “significant” role. The use of google colab is considered quite effective in coding education, as evidenced by 51 respondents stating so. Ease of access is a prominent advantage of google colab. It can be concluded that the assessment of Indonesian Language and Literature Education (PBSI) students regarding the effectiveness of using google colab in coding learning is positive.
Business Intelligence Visualisasi Data Penerimaan Mahasiswa Baru Menggunakan Tableau di Universitas ABC Anhari, Tirta; Alim, Endy Sjaiful; Rizkiawan, M. Asep; Hasan, Firman Noor; Aulia, Muhammad Fathan
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.v6i1.570

Abstract

This study aims to analyze the application of Business Intelligence (BI) using Tableau in the new student admission process at ABC University. Tableau is used to visualize admission data for the period 2021 to 2023, including the number of applicants, geographic distribution, and course preferences. The research methodology involves data collection, cleaning, and integration which is then visualized in an interactive dashboard. The results showed a decrease in the number of applicants during the study period, with the lowest applicants in 2024. Geographic distribution analysis shows that DKI Jakarta and West Java provinces still dominate, indicating the need for expansion in conducting promotions and also data-based marketing strategies. In addition, the shift in the interest of applicants from Communication Science study programs to Pharmacist and Management Professions is an important finding, indicating a changing trend in prospective students' preferences for the fields of Communication Science and Business. This study concludes that the implementation of BI using Tableau provides significant benefits in improving the efficiency of decision-making, expanding the range of admissions, and strengthening the competitiveness of ABC University amid changing educational trends. The findings contribute to the literature related to BI implementation in the education sector and recommend further development to optimize university management in the future
Analisis Pengujian Database SQL dan NoSQL pada Aplikasi Berbasis Microservice dengan Spring Boot Fatich, Steven Arycena; Susetyo, Yeremia Alfa
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.v6i1.561

Abstract

Efficient database utilization is essential in application development. This study aims to analyze the performance of PostgreSQL (SQL) and MongoDB (NoSQL) in CRUD operations and data aggregation using the Spring Boot framework. The research stages include developing a test application, conducting 10 simulation tests for each operation, and analyzing response times. The results indicate that MongoDB excels in create operations with an average response time of 230.4 ms and delete operations at 2.6 ms. In contrast, PostgreSQL demonstrates better efficiency in read operations and aggregations (sum, average, min, max) with superior response times
Peran Chatbot GPT dalam Meningkatkan Kompetensi Menulis Kreatif pada Mata Pelajaran Bahasa Indonesia di kelas 12 SMA Mandalahayu Bekasi Amal, Bahar; Zein, Adika Ananta; Revalina, Adysty; Qothrunada, Faiha; Pratiwi, Naisya Rosy; Larasati, L
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.v6i1.552

Abstract

The study explesses the challenges faced by 12th graders at the mandcity of bekasi in teaching creative writing skills, as well as the potential use of artificial intelligence (ai) technology to enhance the learning process. Surveys indicate that the development of ideas, grammar, and consistency of story lines is a major obstacle students face. On the other hand, the use of ai based tools, such as chatgpt, has been shown to offer constructive feedback, correct mistakes, and provide a quality writing example, thus encouraging students to be more open to the technology of writing. These findings provide insight for educators to effectively integrate technology in creative writing learning
Enhancing Real Time Crowd Counting Using YOLOv8 Integrated with Microservices Architecture for Dynamic Object Detection in High Density Environments Prihandoko, P; Zufari, Faisal; Yuhandri, Y; Irawan, Yuda
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.v6i1.575

Abstract

This study presents the implementation of the YOLOv8 algorithm to enhance real-time crowd counting on the ngedatedotid application, which aims to provide accurate crowd density information at various locations. The proposed model leverages the advanced capabilities of YOLOv8 in detecting and localizing head-people objects within crowded environments, even in complex visual conditions. The model achieved a mAP of 85%, outperforming previous models such as YOLO V8'S (78.3%) and YOLO V7 (81.9%), demonstrating significant improvements in detection accuracy and localization capabilities. The custom-trained model further exhibited a detection accuracy of up to 95% in specific scenarios, ensuring reliable and real-time feedback to users regarding crowd conditions at various locations. By implementing a microservices architecture integrated with RESTful API communication, the system facilitates efficient data processing and supports a modular approach in system development, enabling seamless updates and scalability. This architecture allows for independent deployment of services, thereby minimizing system downtime and optimizing performance. The integration of YOLOv8 and the custom-trained model has proven to be effective in enhancing real-time monitoring and detection of crowd density, making it a suitable solution for diverse applications that require dynamic and accurate crowd information. The results indicate that the proposed model and system architecture can provide a robust framework for real-time crowd management, which is crucial for business owners, event organizers, and public safety monitoring. Future research should consider exploring newer versions of YOLO, such as YOLO V9-S, and expanding the dataset to address challenges related to varying lighting conditions, occlusions, and object orientations. Optimizing these factors will further improve the model’s accuracy and reliability, setting a new standard for crowd detection systems in public spaces and enhancing the overall user experience.

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