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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 Performa Metode Perceptual Color Transfer Dalam Peningkatan Kualitas Citra Ina, Osmanila Tamo; Himamunanto, AR; Budiati, Haeni
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.420

Abstract

The eye, as the sense of human vision, not only serves to see objects but also builds perceptions of the objects seen so that, in this case, it can judge images from different perspectives. Improved image quality is required because images often experience decreased quality caused by many factors, including being too dark, blurred, less sharp, too bright, and other factors. Perceptual Color Transfer is one of the most popular methods used in research. This method changes the color of an image to match the characteristics of another image, while maintaining the visual quality and naturality of the image. By considering the way humans perceive color, this method produces visual and consistent color adjustments that can contribute to improving the overall image quality. The color spaces used in this study are the lαβ and HSV color spaces using the MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) parameters. The results of the study show that the Perceptual Color Transfer method can be a good alternative to image processing techniques in light and dim light conditions, with the best average MSE and PSNR results in dark source image color transfer in the HSV color space of 0.0678021 and 21.43221, as well as the best mean results in light source image color transfer in Lαβ spaces of 0.0608865 and 20.03709.
Integerasi Kecerdasan Buatan Generatif Untuk Analisis dan Mitigasi Data CVE Abimata, Pradipta Putra; Setiawan, Mukhammad Andri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.452

Abstract

Penelitian ini mendalami integrasi teknik Kecerdasan Buatan terkini, khususnya GPT-4 dari OpenAI, untuk mengatasi tantangan yang ditimbulkan oleh analisis data CVE dan memfasilitasi manajemen kerentanan yang lebih efektif.Menganalisis dan memperbarui basis data CVE yang terus berkembang yang dikelola oleh Basis Data Kerentanan Nasional (National Vulnerability Database/NVD) merupakan tugas yang menantang bagi para profesional keamanan. Namun, kemampuan unik Kecerdasan Buatan Generatif, seperti pemrosesan bahasa alami dan penalaran pengetahuan, dapat menyederhanakan proses ini secara signifikan. Dengan memanfaatkan alat berbasis kecerdasan buatan, para peneliti dapat mengekstrak wawasan dari laporan CVE, mengidentifikasi pola dan tren, serta mengembangkan strategi proaktif untuk menangani ancaman yang muncul.Kerangka kerja yang diusulkan menggunakan kombinasi metode Waterfall dan pengujian Blackbox untuk mengintegrasikan kecerdasan buatan generatif ke dalam alur kerja analisis data CVE. Pertama, SERP API digunakan untuk mengumpulkan data CVE yang relevan dan metadata dari NVD, yang kemudian diproses dan disusun untuk analisis berbasis Kecerdasan Buatan. Model GPT-4, yang dilatih dengan korpus pengetahuan keamanan siber yang luas, kemudian digunakan untuk menghasilkan ringkasan komprehensif, penilaian ancaman, dan rekomendasi mitigasi untuk setiap CVE.
Dampak Penggunaan Media Sosial pada Perilaku Sosial Media Generasi Milenial Yunianto, Askar; Purwatiningtyas, P; Supriyanto, Aji; Listiyono, Hersatoto
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.v5i4.475

Abstract

The millennial generation is a demographic group born between 1980 – 2000. As a generation that has grown up with rapid technological development, millennials have unique patterns of digital entertainment and development, millennials have unique patterns of digital entertainment and social media consumption. Understanding their preferences in these area is importantan for entertainment industry in developing effective content and platform strategies. This Study aims to analyze the pattern of digital entertainment and social media consumption, as well as preference for interaktive content, trends, and potential involment in content creation among the millennial generation. This research used a quantitative approach by distributing online survey to 110 millennial respondens. The collected data includes the use of digital entertainment platforms, sosial media, preferences for interactive content and trends, as well as interst in content creation. This research result show that the most widely used digital entertainment platforms among millenials are YouTube, Netfix, and Instagram. Meanwhile the most popular social media platforms are instagram, TikTok, and twitter. Millennials also a high preference for interaktive content and following ternd. Futhermore, there is quite a large potential for millennials to be involved in entertainment conten creation, with a high interest in participating in relevant training. This study provides valuable insights into the patterns of digital entertainment and social media consumption, as well as the preference and potential for content creation among the millennial generatin. These findings can help the entertainment industry in designing content and platform strategies that are more tailored to the needs of the millennial audience.
Pemodelan Prediktif Menggunakan Metode Ensemble Learning XGBoost dalam Peningkatan Akurasi Klasifikasi Penyakit Ginjal Soelistijadi, R.; Wismarini, Th. Dwiati; Eniyati, Sri; Sunardi, S
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.v5i4.507

Abstract

Chronic Kidney Disease (CKD) is a serious global health problem. However, information about how many people are affected by CKD in several countries is not very abundant and is sometimes not the same from one source to another. This research aims to increase accuracy in classifying CKD patients using the XGBoost ensemble learning method. The XGBoost model was drilled using the CKD dataset of 400 data records which were divided into training data and test data with a ratio of 70% used as training data and 30% as test data. Then an optimization technique is carried out, namely the parameter tuning process using a grid search method to find the best value using 5 parameters, namely n_estimators, max_ depth, learning_rate, Subsample, Colsample bytree. The evaluation results using the confusion matrix, were obtained with an accuracy level of 99.16%, precision 98.17%, recall 99.16% and f1-score 99.16%. So the XGBoost algorithm implementing parameter tuning techniques is a good classification method that is good enough to be applied in CKD and Not CKD classification.
Metode Servqual Dalam Analisa Kepuasan Pengguna Aplikasi Jakone Mobile Merchant Prima, Harry; Ramanda, Kresna; Rusman, Arief; Sukmana, Sulaeman Hadi; Sikumbang, Erma Delima; Azizah, Qudsiah 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.v5i4.466

Abstract

Jakone Mobile Merchant is a financial service application that allows to make transactions for daily needs at merchants that collaborate with Bank DKI, such as transaction information at merchants and electronic money transfers to Bank DKI savings accounts. One of the keys to success is providing excellent service to customers when using the Jakone Mobile Merchant application. In order to understand the level of satisfaction of application users. This measurement uses the Servqual method with five service quality dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Emphaty. This measurement makes it possible to provide recommendations on the level of user satisfaction. Results based on measuring five dimensions of service quality show that the two dimensions that influence service quality, namely the empathy dimension, have a result of -0.82. This means that this aspect of service needs to receive attention and become a priority for evaluation and assessment. Further improvement of service quality should be carried out by PT Bank DKI as the owner of the Jakone merchant mobile application
Penerapan Metode Preference Rangking Organization Method For Enrichment Evaluation Dalam Pemilihan Perguruan Tinggi Terbaik Yani, Ahmad; Sumijan, S; Ramadhanu, Agung
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.v5i4.498

Abstract

Information technology is a means and object of a system method to organize, transmit, interpret, use, process, obtain, and store data in a meaningful and useful way. Higher education is the form of post-secondary education that includes diploma, bachelor, master, doctoral and vocational programmes, as well as specialized programmes organized by the College based on the culture of the Indonesian nation. The Institute of Higher Education Services (LLDIKTI) is a working unit surrounded by the Ministry of Research Technology, and the Government of higher education that helps to improve the quality of the higher education. Decision support systems combine a variety of techniques and methods aimed at collecting, analysing, and presenting relevant information to support better and more effective decision-making processes. In research using PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) is one of the methods Multi Criteria Decision Making (MCDM) means performing determination or sequencing in a multicriteria analysis. The data used in this study are data of private colleges in LLDIKTI region X of the province of Western Sumatra with a total of 84 Colleges. Private colleges based on calculations using the PROMETHEE method, Private Colleges with the name of Colleges UNIVERSITAS PUTRA INDONESIA YPTK PADANG showed the largest value compared to other Colleges, thus obtaining the best position 1 with leaving flow value 0,58333 entering flow value 0,05556 and net flow value 0.522778. Based on the values obtained, it was concluded that the PROMETHEE method was very effective for use in mating. These results show that the method is capable of producing.
Analisis Pengalaman Belajar Menggunakan AI Dalam Dunia Pendidikan Pada Mahasiswa Baru PBSI FKIP UNSIKA Amal, Bahar; Rakhmatusolikhah, Aulina; Peramita, Cicih; Jitmau, Mildret Sarina; Putri, Nur Anindya Dwi; Maharani, Syahrazade
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.v6i1.544

Abstract

The purpose of this study is to analyze the utilization of artificial intelligence (AI) to optimize the learning experience of first-year students at PBSI FKIP UNSIKA. As a rapidly advancing technology, AI can adapt to individual learning needs, enhance interactivity, and provide relevant learning recommendations. This research uses a questionnaire to assess the effectiveness of AI in facilitating the education of first-year students. The study examines the impact of AI on student engagement, academic performance, and overall learning effectiveness. The results are expected to offer insights into how AI can assist new students in accessing, understanding, and interacting with educational content. Additionally, this research aims to provide practical guidance for educators in integrating AI into higher education to enrich the learning experience and improve educational outcomes in the digital era
Pemanfaatan K-Means Clustering Untuk Pengelompokan Dan Pemetaan Bencana Alam Di Indonesia Otniel, Marcelinus Vito; Prasetyo, Sri Yulianto Joko
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.v5i4.489

Abstract

Indonesia's geographical and geological conditions, which are prone to natural disasters, necessitate the country to mitigate their impact by identifying causes and studying previous disaster events through existing disaster data analysis. This study aims to map cities or regencies in Indonesia based on the clustering results using the K-Means clustering algorithm in the R programming language. Disaster data management, from collection to dissemination, plays a crucial role in disaster management. The research findings reveal that natural disaster data from 2019-2021 divided cities or regencies in Indonesia into five clusters, with Java Island identified as the most vulnerable region to natural disasters compared to other regions. Cluster visualization is presented in the form of a map to facilitate quick reading and understanding of information
Rancang Bangun Web Profile Khayangan Bakery Untuk Memperluas Wawasan Kuliner Wilayah Kendal Pradapa, Sri Yulianto Fajar; Maskur, Ali; Listiyono, Hersatoto; Daniswara, Alfreda Khansa
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.v5i4.521

Abstract

This article discusses the design and development of a profile website for Khayangan Bakery, a bakery located in the Kendal region of Central Java. The purpose of developing this website is to expand the culinary awareness of the people in Kendal and its surroundings regarding the bread and cake products offered by Khayangan Bakery. The website is designed with consideration for users' needs for easy and quick access to information, as well as to strengthen Khayangan Bakery's brand image in the digital world. The methods used in the development include requirements analysis, user interface (UI) design, front-end and back-end development, and functionality testing. The result of this development is a responsive, informative, and user-friendly website, which is expected to enhance Khayangan Bakery's competitiveness and attract more customers from both Kendal and its surrounding areas.
Penerapan Data Mining Clustering Algoritma K-Means Untuk Menganalisa Pola Kejadian Tindak Kejahatan (Studi Kasus Polrestabes Semarang) Kharisma, Ema Titania; Jananto, Arief
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.v5i4.480

Abstract

Crime is a complex social problem that affects the security of the community, especially in the city of Semarang. Therefore, in an effort to deal with the increasing crime, the use of information technology and data analysis becomes very relevant. This research aims to implement data mining algorithm K-Means Clustering in analyzing crime patterns in Semarang City. This research method involves the use of historical crime data from January to December 2022 with a total data of 305 incidents in Semarang City. The K-Means Clustering algorithm in data mining was chosen because of its ability to group data based on similar characteristics effectively. The data was analyzed using RapidMiner software, which facilitated the clustering of crime patterns into seven clusters cluster 1 with 60, cluster 2 with 31, cluster 3 with 36, cluster 4 with 35, cluster 5 with 51, cluster 6 with 55, and cluster 7 with 37. These findings provide a strong basis for the police to design more targeted and efficient crime handling strategies. The implementation of the K-Means Clustering algorithm in this study proved effective in identifying crime patterns and providing useful insights for security policy decision-making. This research also opens up opportunities for the development of more sophisticated information systems in city security management in the future

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