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
Penerapan Metode Naïve Bayes Dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen Putri Ramadani; Gunadi Widi Nurcahyo; 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.v5i2.361

Abstract

Student satisfaction in higher education is the main focus in improving the quality of education. In the Tridharma paradigm, satisfaction is measured through a comparison of expectations and teaching realization as the main indicator of learning effectiveness. This research method uses Naïve Bayes classification, through the steps of reading training data, calculating prior probabilities, training data probabilities for each category, reading testing data, and calculating final probabilities. This research aims to evaluate student satisfaction with lecturers' teaching at the LP3I Polytechnic, Padang Campus. The data used in this research was 574. The results of research with 574 data (516 training and 58 testing) showed that 52 data (89.648%) stated "Very Satisfied", while 6 data (10.344%) stated "Satisfied". Prediction accuracy reached 98.28%. However, when using the Naïve Bayes method with 574 data (574 training and 574 testing), 397 data (69.078%) stated "Very Satisfied" and 177 data (30.798%) stated "Satisfied". Without the Naïve Bayes method, 402 data (69.948%) stated "Very Satisfied" and 172 data (29.928%) stated "Satisfied". An improvement of 0.87% occurred for the "Very Satisfied" category and -0.87% for "Satisfied". There are no differences in percentages for other categories. From the comparison of results, it can be seen that the Naïve Bayes method is superior in predicting student satisfaction levels compared to calculations without this method. Therefore, it can be concluded that the Naïve Bayes process model is suitable for use as a method for determining good decisions in predictions
Enterprise Architecture Integrated Management Information System Untuk Optimalisasi Layanan Manajemen Pendidikan Tinggi Yasinta Dewi Umi Latifah; Febriliyan Samopa
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.v5i2.352

Abstract

This study focuses on implementing Enterprise Architecture (EA) to address the challenges of integrating the higher education management systems at University X. Currently, the university faces issues of non-integration among its 4 service areas, each utilizing different platforms and manual business processes. As a solution, this research designs an integrated EA using the TOGAF ADM framework. The research process involves interviews with education stakeholders and gap measurements between the current and planned systems through questionnaires and focus group discussions. The results indicate that the EA design aligns with a validation score of 4.3, with a note on the need for attention to the details of data and technology architecture, as well as further analysis regarding cost priorities and human resources in the implementation roadmap. This study provides an EA blueprint to create an integrated information system, optimize business processes, and enhance services through the integration of data, applications, and technological development. The implementation of EA is expected to improve efficiency in 29 business activities currently carried out manually at University X
Comparative Analysis of Deep Learning Architectures for Emotion Recognition in Text Gregorius Airlangga
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.v5i2.384

Abstract

This study delves into the intricacies of emotion recognition within textual data, presenting a comprehensive analysis of three prominent deep learning models: Long Short-Term Memory networks (LSTMs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Employing a 5-fold cross-validation methodology, the research meticulously evaluates each model's performance in accurately classifying a spectrum of emotions, using metrics such as accuracy, precision, recall, and F1 score. Results indicate that LSTMs outperform their counterparts with an accuracy of 93.48%, closely followed by CNNs at 91.78%, while RNNs lag, showcasing the importance of sophisticated architectural features in handling complex emotional nuances. The study not only highlights the strengths and limitations of each model but also sheds light on the significant role of temporal and contextual understanding in emotion recognition tasks. Through this investigation, we provide insights into the evolving landscape of natural language processing and its capability to decode human emotions, proposing directions for future research in enhancing model performance. This work has broader implications for applications in mental health, customer service, and social media analysis, aiming to refine the interaction between humans and machines in understanding and processing emotional content
Vehicle Classification in Electronic Toll Collection System Using YOLOv8 Mochammad Idham Triyunanto; Amalia Zahra
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.v5i2.375

Abstract

This research aims to initiate an automatization process in the method of classifying vehicle types in the Jasa Marga transaction service system, which is the largest toll road operator company in Indonesia. The method used is YOLOv8 which is the latest version of the YOLO algorithm which is state-of-the-art performance in image processing. The dataset used in this study consists of vehicle images obtained from transactional data in an electronic toll collection system operating on toll roads, comprising five vehicle classification classes. In the initial stage, the images are examined and processed using pre-processing techniques such as data cleaning, image masking and data annotation. Next, the YOLOv8 model is trained using the data and tested on a separate validation dataset to measure the model's performance. Based on the results of experiments that have been carried out in this research, the performance of the YOLOv8 model without handling imbalance data resulted in an accuracy of classification of vehicle class types of 91.4%, while the performance of the model that handled imbalance data using under-sampling resulted in an increase in classification accuracy of vehicle class types to 94.4 %.
Framework LTSA untuk Analisis dan Pengembangan Learning Management System Dalam Mendukung Peningkatan Proses Pembelajaran Nur Aini; Sarjon Defit; S Sumijan
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.v5i2.366

Abstract

Learning Management System is a software for the need to manage learning activities such as searching for materials, reporting learning matters, providing materials for learning matters carried out online and connected to an internet connection. The benefits that can be obtained Form the use of e-learning are the existence of facilities for e-moderating where teachers can carry out learning activities without being constrained by distance, teachers and students can also use teaching materials via the internet, students can review learning materials online, if students require additional materials for learning so students can access the internet, changes in the role of students and teachers become more active and learning is relatively more efficient and effective. This research aims to apply the LTSA framework to the design of a Learning Management System. The method used in this research is the LTSA framework. This method explains that the LTSA framework consists of five architectural layers, each layer describes a system at a different level. The dataset processed in this research comes Form SMK N 1 Ranah Batahan. The dataset consists of students majoring in TKJ class XI in Indonesian, English, mathematics and vocational subjects. The results of research using the LTSA framework make learning data more structured in managing learning activities. This research can be a reference in developing a Learning Management System using other methods
Perbandingan Tingkat Optimalisasi Metode K-Nearest Neighbor Dan Naïve Bayes Dalam Klasifikasi Kelayakan Alat Laboratorium Kimia Sri Mulya; Gunadi Widi Nurcahyo; 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.v5i2.357

Abstract

Classification of the appropriateness of equipment in the laboratory is needed by university management to determine future laboratory development steps. The suitability of laboratory equipment can be influenced by various factors, so it is necessary to know which variables are crucial in influencing the condition of the laboratory equipment's suitability. Data mining techniques can be used to explore new knowledge so that it can produce appropriate laboratory equipment. Some algorithms that can be used are K-Nearest Neighbord and Naive Bayes. The aim of this research is to compare the level of optimization of two methods in classifying the suitability of Chemistry laboratory equipment at FMIPA Unand using the K-Nearest Neighbor and Naive Bayes methods. The attributes used are year of procurement, level of use, level of damage, length of use of the tool, and condition of tool accessories. The data used is Materials Chemistry laboratory equipment, FMIPA, Andalas University from 2010-2023 with a total of 105 data. The research results show that the accuracy level of the Naive Bayes Method is better than the K-Nearest Neighbor Method. This is proven by the results of the Rapidminer test, which obtained the highest accuracy of 94.74% at a total testing data of 30% of the total data, while for the K-Nearest Neighbor method, the highest accuracy was obtained at 79.03% at a total testing data of 50% of the total data. It is hoped that the results of the tool classification can serve as guidance and evaluation to support the development of the FMIPA Chemistry laboratory at Andalas University
Data Communications and Computer Networks: Research and Impact Sinek Mehuli Br Perangin-angin
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.v5i2.380

Abstract

Science and technology have brought society to an advanced level. The use of human labor, which is becoming increasingly scarce, often results in people losing their jobs because their tasks have been replaced by equipment or machines. As a means of providing information and communication, computers can be used as a means of the Internet. Through the Internet, people can search for various information and communicate. Obtaining information for personal life, such as information about health, hobbies, recreation, and spirituality, is the role that this application of information technology can play. In addition to the benefits, it turns out that information and communication technology devices also have negative effects on their users. As a result of inappropriate or irresponsible use by users, these negative effects occur. Some of these negative effects are 1). Kids spend more time watching TV than doing other things (such as studying and playing sports), 2). Children lose the ability to mingle with society and tend to be comfortable with online life, 3) Copyright infringement, 4). Cybercrime, 5). Spread of computer viruses, and 6). Pornography, gambling, fraud, violence. The ways to overcome these negative effects are: 1). Build relationships with people you already know, 2). Find a positive community that often meets in the real world, 3). The need for law enforcement, which involves the establishment of Internet police, 4). Avoid the use of cell phones with sophisticated features by minors and supervise the use of cell phones, 5). Reading more books that are educational and faith-based as well as computer applications that are educational in nature, and 6). The need for time management in front of the computer or television.
Klasifikasi Varietas Benih Padi Berdasarkan Morfologi dengan Algoritma Random Forest Muhamad Hafidz Ghifary; Enny Itje Sela
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.v5i2.371

Abstract

Rice seeds are one of the main elements in agricultural businesses. The choice of type of rice seed planted can influence the quality of the harvest obtained. The large number of varieties of rice seeds with similar shapes makes identifying the type of rice seed an activity that is not easy and requires experts to do. One fairly fast way to identify rice seed varieties is to use machine learning technology. This research will implement machine learning classification algorithms, namely KNN, Naïve Bayes, and Random Forest. Identification of rice seed varieties is carried out based on the morphological features of the seeds. The dataset used is in the form of seed morphological feature values, namely aspect ratio, solidity, circumference, area, area, roundness, circularity and equivalent diameter. Research stages starting from preprocessing, feature extraction, and experimental parameter values were carried out to find the model with the best performance. Feature selection can increase the testing accuracy on KNN and Random Forest models. The test results obtained an accuracy of 78.3% with KNN, 61.7% using Naïve Bayes, and 90% using Random Forest.
Metode Tracking 3D Image dalam Teknologi Augmanted Reality untuk Pembelajaran Animasi Sekolah Lanjutan Tingkat Atas Hadrila P A; Y Yuhandri; S Sumijan
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.v5i2.362

Abstract

Augmented reality is a technique that combines two-dimensional and three-dimensional virtual objects into a real three-dimensional scope and then projects these virtual objects in real time. The use of markers in this application is chosen apart from being suitable for implementation as learning and also tends to be fast in terms of reading on the process of the emergence of 3-dimensional objects in visual Form. This research aims to be a learning medium for the 12 Principles of Animation at the high school level. The method used in this research uses the 3D Image tracking method, which has four stages, namely 3 Dimensional Model Development, Marker development, Model implementation into development tools, Augmanted Reality Application Implementation, Dataset consists of 12, 3 Dimensional images representing each of the 12 Principles of Animation. This research produces animated Augmanted Reality digital learning media that can attract student interest and make students understand more quickly. Reference: The use of animated Augmanted Reality learning media makes learning active, creative, effective and fun.
Penerapan Algoritma Regresi Linier Berganda Untuk Memprediksi Hasil panen Padi Di Kota Pagar Alam Dedi Setiadi; S Sasmita; Melza Yolanda
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.v5i2.353

Abstract

Many farmers experience crop failure. Apart from that, farmers do not know what factors most influence the crop yield, so farmers cannot anticipate crop failure. Based on these conditions, a prediction model is needed that is able to predict rice crop yields so that farmers can find out the causes of crop failure, the factors that most influence crop yields and make better estimates of what will happen in the future, so that farmers can make policies and measures to anticipate crop failure in the next planting. This research aims to predict rice harvest yields in Pagar Alam City so as to help farmers predict rice harvest more easily, farmers can take policies and actions to predict crop failure using a multiple linear regression algorithm. Development method. The system used is the waterfall method and testing using black box testing. The rice harvest prediction system is an innovative solution that aims to accurately estimate rice harvest yields in Pagar Alam City.

Page 1 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