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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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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
Analisis PIECES untuk Evaluasi Layanan Aplikasi Disney+ Hotstar Dewi Anggraini P Hapsari; Nurul Adhayanti; Romdhoni Susiloatmaja
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 3 (2023): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i3.223

Abstract

Disney+ Hotstar is a popular streaming service provider platform that offers a variety of entertainment content to users in various countries. This study aimed to analyze Disney+ Hotstar user satisfaction using the PIECES (Performance, Information, Economics, Control, Efficiency, and Services) method for evaluating the services provided. This method is used to identify the strengths and weaknesses of aspects that affect user satisfaction on this platform. The results of the identification are then used as a reference in the evaluation as an effort to improve service and customer satisfaction. This study uses a quantitative approach. Data collection uses a questionnaire outlined in the Google form to determine user ratings of PIECES aspects. Respondents were 121 Disney+ Hotstar users in the Jakarta area who were randomly selected. Data analysis used descriptive analysis techniques. The study results show that users are very satisfied with the application's performance, the information presented, the efficiency, and the services provided. The challenges are found in the economic aspects and user control, which are satisfactory but still need to be improved to satisfy the user more so that the user is very satisfied with all aspects of PIECES. The recommendations are improvements to the method of transaction and payment methods, more competitive prices, greater guarantees of transaction security, and customer data security. This recommendation aims to increase Disney+ Hotstar user satisfaction and strengthen the platform's position in the increasingly competitive streaming service market
Implementasi Algoritma Apriori terhadap Kombinasi Pola Penjualan Kuliner Sidamanik Square Fachrul Rodzy; Ihda Innar Ridho
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 3 (2021): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i3.78

Abstract

Data mining is an artificial intelligence that is used to process a lot of data. In data mining, there is one algorithm, namely the Apriori algorithm, which is the most famous algorithm for finding high frequency patterns. For this reason, this algorithm can combine items or items sold at Sidamanik Square Culinary Tour. The resulting apriori algorithm will be implemented and tested with the Tanagra application. This research was conducted to obtain a combination pattern of purchases at Sidamanik Square which is useful for sellers to find out patterns that affect sales and merchandise inventory, namely Penyet Chicken + Rice and Bakso Bakar, namely 60% and Chicken Satay and Bakso Bakar, namely 55.56%.
Komparasi Pergerakan Saham Apple Dan Samsung Menggunakan Algoritma Support Vector Machine (SVM) Falentino Sembiring; Mayang Gunawan; Rosalinda Hakim; Vemi Januarita Putri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 1 (2023): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i1.118

Abstract

The capital market creates opportunities for the public to participate in economic activities, especially in investing. One of the assets for investment is stock. The capital market creates opportunities for the public to participate in economic activities, especially in investing. One of the assets for investment is stock. The purpose of this research is to compare stock price movements between Apple companies and Samsung companies after the pandemic. One method that can be used to predict stock price movements is the support vector machine (SVM). This study uses two approaches as input models, the first approach for data input is obtained from the calculation of ten technical parameter indicators using trading data (open, high, low, close, price) while the second approach focuses on stating the results of calculations using several indicators Technical parameters become trend deterministic data preparation. Even this research uses historical data from each company from 2017 to 2022. This data is used to study patterns that can ultimately predict stock price movements of each company. From the results of this study with the help of Orange software, it can be concluded that the application states that in terms of data, Samsung's ROC analysis is 0.435% superior to Apple, only 0.359%.
Model Prediksi Kunjungan Wisata: Mengoptimalkan Arsitektur Algoritma Backpropagation untuk Prediksi Kunjungan Wisata Mancanegara (ASIA) Mayang Sari; Dian Agustini; Muthia Farida
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.332

Abstract

This research focuses on developing a prediction model for tourist visits for foreign destinations in the Asian region using Backpropagation algorithm architecture optimization. Tourism has become a crucial economic sector in Asia, and accurate tourist arrival predictions have a significant impact on decision making in this industry. The main approach used is the Backpropagation algorithm in the context of artificial neural networks. Although these algorithms have been successful in a variety of applications, optimizing Backpropagation architectures for tourism visit prediction remains a significant challenge. This research aims to improve model accuracy and performance by adjusting the Backpropagation algorithm architecture. Through careful optimization, this research seeks to overcome these complex dynamics and produce a model that can provide more accurate estimates of tourist visits. This research presents predictions of foreign tourist visits to Indonesia by optimizing the artificial neural network architecture using the Backpropagation algorithm. Focusing on visit data from various nationalities in the period 2018-2024, the test results highlight the performance variations between architectures in 2023 and 2024. Prediction results show that the 4-3-7-1 architecture obtains high test accuracy in 2023 (88%) , but will decrease in 2024 to 74%. The 4-5-1 architecture showed good consistency with test accuracy remaining high in both years (92%). These findings provide valuable insights for optimal architectural selection in predicting future tourist visits and identifying changing patterns of trends at the national level. However, it should be noted that these results are projective and may be influenced by external factors that may change, requiring ongoing evaluation to ensure model accuracy and responsiveness.
Analisis Perbandingan Ruang dan Waktu pada Algoritma Sorting Menggunakan Bahasa Pemrograman Python Yayan Heryanto; F Fauziah; Trinugi Wira Harjanti
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 2 (2023): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i2.169

Abstract

The role of algorithms in software or programming is very important, so understanding the basic concepts of these algorithms is essential. A lot of programming logic has been made, and in sorting data, insertion sort, quick sort, bubble sort, selection sort, and merge sort algorithms have been used. If you get random data with values of 200, 300, 400, and 500, using the manual method will take a long time, so the five algorithms are used with the Python programming language to sort random data of integer data type. The time required and the size of memory used in each algorithm will be examined during the sorting process. An effective algorithm is one that has a short processing time and uses little memory, so that in this journal, the results for the time efficiency of the Quick Sort algorithm are superior, namely with inputs of 200, 300, 400, and 500, it takes 0.001 seconds, 0.001 seconds, 0.003 seconds, and for memory usage, the Bubble Sort algorithm is superior because it only requires a small amount of memory.
Penerapan Metode ELECTRE II Dalam Menentukan Faktor Yang Mempengaruhi Prestasi Belajar Mahasiswa Untuk Meningkatkan Nilai IPK Nurinda Utari Damanik; Poningsih P; Irawan I
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 1, No 1 (2020): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v1i1.18

Abstract

One of the goals of the teaching and learning process is changes in behavior both aspects of knowledge (cognitive), attitude (effective),and psychomotor aspects. Learning achievement achieved by students is essentialy a reflection of learning efforts. In general,the better the learning effort, the better the achievement will be,of course this is inseparable from the factor that influnce it, among others, motivation of parents’ level of education, ways of learning taught by lecturers, concentration. Hower most students lack learning achievement so the IPK value decreases. Whereas if the IPK is high the it will be very easy to determine the job. Student learning achievement and to increase the IPK is strongly influenced by a factor for that it requires a study to increase the value of I. Realizedby making a decision support system in determining the factors that influence student achievement to increase the IPK score.
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
Implementasi Data Mining Pada Penjualan Sepatu Menggunakan Algoritma Apriori (Kasus Toko Sepatu 3Stripesid) Danilla Oktaviyana Nurlyta Eka Saputri; Endang Lestariningsih
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 3 (2023): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i3.214

Abstract

Stock of goods is an important thing in the world of shops, stock of goods that are not carried out optimally will result in a vacancy of one of the available items. Likewise, too much stock of goods will cause over stock. This also happens at the 3stripeds.id store where there is often a vacancy in one of the inventory items purchased by customers, due to the lack of information regarding inventory control habits. So it is necessary to extract information on transaction data. The Apriori algorithm can help find out the names of items with the most sales. The a priori algorithm is a type of association rule in data mining, an association can be known by two benchmarks, namely support and confidence. Support (support value) is the percentage combination of these items, while confidence (certainty value) is the relationship between items in the association rules. The results obtained from the a priori algorithm process are combinations of items or rules with association values in the form of support values and confidence values. the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 3% and a minimum confidence of 10%, while the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 30% and minimum confidence 85% and there are 2 highest itemsets with 30% support and 100% confidence.Stock of goods is an important thing in the world of shops, stock of goods that are not carried out optimally will result in a vacancy of one of the available items. Likewise, too much stock of goods will cause over stock. This also happens at the 3stripeds.id store where there is often a vacancy in one of the inventory items purchased by customers, due to the lack of information regarding inventory control habits. So it is necessary to extract information on transaction data. The Apriori algorithm can help find out the names of items with the most sales. The a priori algorithm is a type of association rule in data mining, an association can be known by two benchmarks, namely support and confidence. Support (support value) is the percentage combination of these items, while confidence (certainty value) is the relationship between items in the association rules. The results obtained from the a priori algorithm process are combinations of items or rules with association values in the form of support values and confidence values. the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 3% and a minimum confidence of 10%, while the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 30% and minimum confidence 85% and there are 2 highest itemsets with 30% support and 100% confidence.
Pengelompokan Jumlah Kasus Penyakit Aids Berdasarkan Provinsi Menggunakan Metode K-Means Rut Indra Lita Sinaga; Widodo Saputra; Hendry Qurniawan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 2 (2021): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i2.64

Abstract

Acquired Immune Deficiency Syndrome (AIDS) is a collection of symptoms due to a gradual decline in the immune system caused by infection with the Human Immunodeficiency Virus (HIV). This disease is a dangerous disease and should be watched out for where it spreads very quickly. AIDS is one of the top infectious diseases that can cause death. K-Means is an algorithm in data mining that can be used to group / cluster data. There are many approaches to creating clusters, one of which is to create rules that dictate membership in the same group based on the level of equality among its members. The purpose of this study was to classify the number of AIDS cases by province. To solve the existing problems, the authors will use the K-Means cluster method using 2 clusters to determine the province which has the highest cases of AIDS and the province with the lowest cases of AIDS by calculating the centroid / average of the data in the cluster. It is especially recommended that the government take advantage of the results of this research to pay more attention and make efforts in overcoming AIDS in provinces with high AIDS disease.

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