cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Miftahul Huda
Contact Email
hudablue11@gmail.com
Phone
+6282273233495
Journal Mail Official
aguspw.amcs@gmail.com
Editorial Address
Sekretariat KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) Jln. Jendral Sudirman Blok A No. 1/2/3 Kota Pematang Siantar, Sumatera Utara 21127
Location
Kota pematangsiantar,
Sumatera utara
INDONESIA
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
 1   2   3   4 
Search results for , issue "Vol 5, No 2 (2024): Edisi April" : 40 Documents clear
Integrasi Knowledge Management System Dan Teknik Knowledge Discovery In Database Dalam Sharing Culture Pada Proses Pembelajaran Berbasis Blended Learning Iswandi Saputra; 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.v5i2.385

Abstract

Education is rapidly changing in the digital age, especially with blended learning, which mixes online and in-person classes. This approach is popular because it offers a well-rounded learning experience. However, getting students and teachers to share knowledge remains a challenge. This study looks at how combining Knowledge Management Systems (KMS) and Knowledge Discovery in Databases (KDD) can help improve knowledge sharing in blended learning at universities. By analyzing data from the E-Learning section of UPI YPTK Padang, involving 120 students, the research aims to create more effective learning systems that encourage sharing. It's a step towards better education in the digital era, promoting collaboration and knowledge exchange among students and educators.
Implementasi Python API dengan Framework Flask sebagai Cloud Run Service Untuk Proses Update di PT. XYZ Rizky Nandang Pratama; Yeremia Alfa Susetyo
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.v5i2.376

Abstract

This study focuses on implementing an API using the Flask framework in the Python programming language, which is then deployed as a Cloud Run service to facilitate data updating processes in XYZ company. By utilizing Flask as a lightweight and user-friendly API development framework, we successfully integrated it into the Google Cloud Platform's (GCP) Cloud Run environment to leverage its scalability and high performance. Our testing indicates that the Cloud Run service provides benefits in terms of automatic scalability, easy infrastructure management, and high reliability in efficiently processing data updates. Thus, the findings of this research affirm that this approach offers an effective solution for enhancing data updating processes in XYZ company, leveraging the advantages of Flask technology and the Cloud Run service.
Implementasi K-Means Clustering Dalam Analisa Soal Ujian CBT Universitas Baiturrahmah Rico Anggara; Sarjon Defit; 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.v5i2.367

Abstract

Computer-based exams (CBT) are a type of exam where participants take the exam using a computer or digital device. CBT has become a common choice in exam administration. Exam question management is important for CBT success. Participants answer digital questions via a computer interface, and the results are processed automatically by the computer system. The results of this test can be used to assess student understanding and as a learning evaluation. This research aims to group exam questions based on participants' answers. The method used in this research is K-Means Clustering. This method has 5 stages, namely cluster center initialization, data grouping, calculation of new cluster centers, convergence and evaluation of results. This process repeats until the cluster center does not change any more or convergence has been achieved. Next, the K-Means Clustering algorithm is applied to group exam questions into appropriate clusters. This grouping process is carried out by considering the similarities between the exam questions based on the number of correct answers and the number of incorrect answers. Dataset source from UPT CBT, Baiturrahmah University. The question dataset consists of 100 exam questions that have been tested on students at the Faculty of Medicine, Baiturrahmah University. The results of this research can group exam questions into groups of difficult questions, medium questions and easy questions. This research can be a reference for academics in evaluating exam questions created by lecturers and can evaluate the level of understanding of students at Baiturrahmah University.
enerapan Metode Weighted Product Untuk Penerima Insentif Karyawan Romzi Rahman; Gunadi Widi Nurcahyo; Y Yuhandri
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.v5i2.358

Abstract

The development of science from time to time has succeeded in bringing humans into an era of information technology. Organizations need to change the way they manage and develop human resources in the face of technological change. Stakeholders need to evaluate employee performance periodically so that it can become a reference for determining employee incentives. Partial incentives will have a positive effect on psychological empowerment which will then also have a positive effect on employee performance. This research aims to build a Decision Support System in determining employee incentive recipients. The method used in this research is the weighted product method. This method has six stages, namely the alternative value of each criterion, the alternative value of each criterion after weighting, determining the preference weight of the criteria, calculating the preference value of Vector S, calculating the value of Vector V, and ranking results. The processed dataset comes from Institut Teknologi dan Bisnis Haji Agus Salim Bukittinggi. The dataset consists of 14 employee data with their respective criteria values. The results of this research can determine employee incentive recipients with an accuracy rate of 86%. Therefore, this research can be a reference for stakeholders to determine recipients of employee incentives in a certain period. 
Unveiling Risks through Machine Learning: Analyzing Indonesian User Feedback Dataset of Capsule Hotel Experiences Yehezkiel Gunawan; Ford Lumban Gaol; Tokuro Matsuo
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.v5i2.349

Abstract

The rise in popularity of capsule hotels as a unique and affordable lodging alternative, especially in Indonesia, has highlighted the necessity of skillfully recognizing and controlling any potential risks connected with such unusual lodgings. This paper introduces the large collection of 700 data examples that includes priority scores, problem areas, and verbatim user comments. Furthermore, we conduct a two-phase experiment using the Random Forest algorithm to classify risks. In the first stage, a custom BERT model for word embedding is integrated, and in the second stage, the pre-trained Indo LEM (BERT) model is used. Our results clearly demonstrate the higher effectiveness of the second step, demonstrating how the addition of Indo LEM as word embedding considerably improves classification accuracy. This demonstrates the enormous potential of utilizing cutting-edge machine learning techniques to improve risk classification processes, providing players in the capsule hotel industry with priceless information to improve safety regulations and better the overall guest experience. At (https://github.com/yehezkielgunawan/thesis-risk-classification), we provide full access to all relevant coding scripts for reference and replication as an addition to the dataset
Python Web Scraping for Threat Intelligence Arya Adhi Nugraha; Domy Kristomo
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.v5i2.381

Abstract

The relentless evolution of cyber threats poses significant challenges to organizations striving to maintain robust cybersecurity defenses. In this context, the effective gathering and analysis of threat intelligence data play a crucial role in enhancing situational awareness and informing proactive security measures. This journal entry explores the utilization of Python web scraping techniques for threat intelligence purposes, with a focus on extracting valuable insights from the Cybersecurity and Infrastructure Security Agency (CISA) website. Through the development and implementation of a Python script for web scraping, the process of systematically gathering threat intelligence data is examined, highlighting the efficacy of automation in streamlining the collection and analysis of real-time threat data. The results demonstrate the effectiveness of the Python script in facilitating the rapid aggregation of threat intelligence from diverse online sources, providing security professionals with actionable insights to strengthen their cybersecurity defenses. Additionally, considerations regarding the ethical and legal implications of web scraping are addressed, emphasizing the importance of responsible data collection practices. Overall, this exploration of Python web scraping for threat intelligence underscores its potential as a valuable tool for enhancing cybersecurity resilience in the face of evolving cyber threats.
Analisis Keamanan Jaringan dan Perlindungan Data Terhadap Serangan Siber di Perusahaan Luar Sekolah Muhammad Mikail Ziyad; Suprih Widodo
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.v5i2.372

Abstract

Threats to vital objects and file security are growing rapidly. Cybercriminals are becoming more cunning and skilled at exploiting security vulnerabilities in digital systems. To improve cybersecurity, a thorough analysis of the threats faced in the digital environment and applicable solutions is required. The aim of this research is to analyze vital objects, identify the main challenges in protecting them from cyber attacks on personal data in Out-of-School companies. The research method uses a qualitative approach using interview and observation techniques. Potential cybercrime threats in Indonesia include hacking, cracking, cyber sabotage and spyware. The risk management process involves identifying, assessing, addressing and controlling risks. To anticipate these threats, technological experts are needed to support the development of advanced national defense systems and establish a cyber security command center.
Internet of Things (IoT) Sistem Monitoring Suhu, Kelembapan dan Insensitas Cahaya Pada Ruang Penyimpanan Obat Novalin Koru; Abdul Zaid Patiran; Lorna Yertas Baisa
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.v5i2.363

Abstract

Medicine storage space is a crucial element in the healthcare industry. The safety and quality of medications heavily rely on the appropriate temperature and humidity conditions. Uncontrolled temperature fluctuations or inadequate humidity levels can lead to the loss of effectiveness of medications or even pose risks to patients. This research aims to monitor temperature, humidity, and light intensity at Nabila Farma pharmacy in real-time using hardware devices such as ESP8266 and sensors like DHT11, BH1750. The collected data will be sent to Google Sheets for easy access and monitoring. This presents a practical solution to maintain the quality and safety of drugs by ensuring optimal storage conditions
Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Backpropagation Untuk Memprediksi Kunjungan Poliklinik (Studi Kasus Di Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi) Eka Ramadhani Putra; Gunadi Widi Nurcahyo; Y Yuhandri
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.v5i2.354

Abstract

Artificial Neural Networks (ANN) are computational models inspired by the structure and function of biological neural networks. ANN can model and learn complex patterns in data. The Backpropagation algorithm is a training algorithm used to optimize weights and biases in ANN.. Use of Python Applications is a popular form of computing used in the fields of science and engineering, including in the development and implementation of ANN. Python provides powerful library for building, training, and deploying ANNs. This research aims to have the ANN Backpropagation Algorithm train data using previously collected polyclinic visit data so that the ANN can learn to predict the burden of polyclinic visits in the future. The method in this research uses the Backpropagation Algorithm. This method has six stages, namely data input, normalization, training, testing, calculating test accuracy, and prediction. The dataset processed in this research comes from the annual report of Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi from 2020 to 2022. The dataset consists of 36 months of visits to the polyclinic. The results of this research use the 3-10-1 pattern and can identify or calculate predictions for the next 5 months, 2547 people, 2506 people, 2463 people, 2482 people, and 2495 people. The percentage of predictions for polyclinic patient visits with an accuracy level of computing time requiring 0.001 seconds, an average error of 8.794%, and an average accuracy of 91.706%. Therefore, this research can be a reference in predicting polyclinic patient visits in the future so that it can be a consideration for hospital management.
Analisa Hasil Performansi Algoritma Apriori dan FP-Growth dalam Rekomendasi Kombinasi Menu Maulana Hassan Sechuti; Yisti Vita Via; Hendra Maulana
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.v5i2.386

Abstract

Currently, technological developments are increasingly rapid with the emergence of various technologies that make it easier for humans to do their activities, for example in the food business sector. However, the development of technology has not been maximally utilized by several food business, one of which is the Sidoarjo area ropi bakery which has problems managing its stock and has difficulty in determining menu combinations for promotional activities. This can be overcome by analyzing all transaction data at the Sidoarjo area ropi bakery. Analysis of transaction patterns is carried out to obtain menu combinations. This analysis can be done using data mining association algorithms. This research focuses more on comparing the Apriori and FP-Growth data mining association algorithms when the two algorithms are implemented into a web-based information system. In this study, a comparison was made by analyzing 637 transaction data. In analyzing 637 transaction data, the minimum support value variation used is less then equal 10% with a minimum confidence value of 60%. The result of the analysis of the two algorithms when implemented into the information system are superior to the FP-Growth algorithm.

Page 2 of 4 | Total Record : 40

 1   2   3   4 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 6, No 1 (2025): Edisi Januari Vol 5, No 4 (2024): Edisi Oktober Vol 5, No 3 (2024): Edisi Juli Vol 5, No 2 (2024): Edisi April Vol 5, No 1 (2024): Edisi Januari Vol 4, No 4 (2023): Edisi Oktober Vol 4, No 3 (2023): Edisi Juli Vol 4, No 2 (2023): Edisi April Vol 4, No 1 (2023): Edisi Januari Vol 3, No 1 (2022): Edisi Januari Vol 2, No 4 (2021): Edisi Oktober Vol 2, No 3 (2021): Edisi Juli Vol 2, No 2 (2021): Edisi April Vol 2, No 1 (2021): Edisi Januari Vol 1, No 4 (2020): Edisi Oktober Vol 1, No 3 (2020): Edisi Juli Vol 1, No 2 (2020): Edisi April Vol 1, No 1 (2020): Edisi Januari More Issue