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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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Pengaruh Penggunaan E-Marketplace terhadap Kemampuan Inovasi dan Kinerja (Kasus UKM Furniture, Home Decor dan Kriya) Susetiyono, Agung; Baihaqi, Imam
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.v6i1.566

Abstract

The use of e-marketplaces in a business will certainly have an impact on innovation capabilities and performance quality. This study aims to gain a better understanding of how e-marketplaces can contribute to innovation capabilities and SME performance. The method used in this study is a quantitative approach with data analysis using Structural Equation Modeling (SEM). Respondents consisted of SME owners or employees who had used e-marketplaces for at least one year and had attended digital marketing training. Data were collected through a questionnaire that measured the variables of e-marketplace usage, innovation capabilities, and performance. The results of the analysis show that e-marketplace usage has a significant positive effect on SME innovation capabilities, and innovation capabilities contribute positively to performance. In addition, this study found that innovation capabilities function as a mediator in the relationship between e-marketplace usage and SME performance. The theoretical implications of this study are to contribute to the development of technology management theory, especially in the context of e-marketplace usage, innovation, and SME performance. Practically, the results of this study can be used as evaluation material and input for SMEs and the government in decision making to improve innovation and performance of furniture, home decor and craft SMEs through the use of e-marketplaces.
Analisis Perbandingan Metode ARIMA Dan LSTM Untuk Prediksi Penjualan Harga Saham BNI Tunggal, Anisa; 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.v6i1.557

Abstract

This study aims to compare the Autoregressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) in predicting the closing stock prices of Bank Negara Indonesia (BNI) from September 2021 to September 2024. Historical stock data was obtained through web scraping from Yahoo Finance and analyzed using evaluation metrics such as MAPE and RMSE. The results show that ARIMA outperforms LSTM in prediction accuracy, with lower MAPE and RMSE values for both training and testing data. Additionally, the 7-day ahead stock price predictions indicate that LSTM experienced a 3.42% decrease compared to ARIMA. Based on this study, ARIMA can be concluded as a more accurate model in predicting BNI stock prices compared to LSTM
Klasterisasi Tingkat Kecanduan Penggunaan Tiktok Terhadap Minat Belajar Menggunakan Algoritma K-Means Clustering Ciptandini, Madyantari Ipmas; 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.v6i1.548

Abstract

The decline in interest in learning among students is one of the significant challenges in the digital era, especially due to the excessive use of social media such as TikTok. TikTok, with its engaging and interactive short video content, often distracts learners from studying. These negative impacts include decreased focus, sleep disturbances and less time allocated to study, which ultimately affects academic achievement. Therefore, this study aims to cluster data related to the level of TikTok addiction and decreased interest in learning using the K-Means Clustering algorithm. The K-Means method was used to cluster a dataset of 137 samples into two groups based on the pattern of TikTok usage frequency and study interest level. The model evaluation process shows good performance, with an accuracy value of 96%, recall 98%, precision 91%, and F1 Score 94%. These results support the effectiveness of K-Means in identifying groups at high risk of declining interest in learning. This research proves the potential of clustering techniques in identifying distractions and offering solutions to deal with them
Implementasi Agile Pengembangan Sistem Informasi Karyawan Berbasis Next.Js dan PostgreSQL: Studi Kasus Yayasan Almadani Sasongko, Agung; Lailiah, Badariatul; Hadikusumah, Prima Surya; Marien, Yunita
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.v6i1.571

Abstract

This research aims to develop a web-based employee information system for Almadani Syarif Abdurrahman Pontianak Foundation to improve the efficiency of employee data management and facilitate access to employee-related documents. The system was developed using Agile methodology, which enables an adaptive development process through sprint cycles. The Next.js framework was used to improving interface speed, while PostgreSQL was chosen as the database to ensure data integrity and security. Testing results through User Feedback at 9 sprints showed a gradual increase in user satisfaction. The average user assessment scores for key modules, such as employee data management, showed a steady improvement with adjustments made after each sprint, resulting in an average score of 4.14 out of 5. The burn-down diagram showed work progress consistent with the target time, indicating that the development team managed to effectively reduce the backlog until the project was completed on schedule in 45 days. User Acceptance Testing (UAT) revealed high satisfaction levels: 90% for design, 92.5% for ease of use, and 89% for efficiency. System page caching reduced access times by 60-80%, especially on pages with large data loads. Overall, the developed system successfully improved administrative efficiency and streamlined employee data search and management processes.
Komparasi Metode SVM dan Logistic Regression untuk Klasifikasi Hipotesa Penyakit Kanker Paru Paru Berdasarkan Gejala Awal Rahmaeda, Shafara; 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.v6i1.562

Abstract

Lung cancer is the uncontrolled growth of cancer cells in lung tissue that occurs due to various carcinogenic substances. Throughout Indonesia, this disease is still the leading cause of death from cancer. The main risk factors include smoking habits, exposure to cigarette smoke, chest pain. Namely, classification is one way of early detection that can reduce the death rate of lung cancer. Various classification techniques have been proposed in various fields such as machine learning and expert systems. In machine learning, there are two methods used in classification, namely SVM and Logistic Regression. The advantage of SVM is to divide data into hyperplanes so that the data space is divided into two classes. SVM theory begins by collecting data that can be separated by a straight line using a hyperplane, then grouped by class. While Logistic Regression is used to describe the relationship between categorical response variables and covariates. Specifically, there is a direct relationship between the independent variable and the logarithm of the probability of an event occurring. This study aims to compare which is the best using the SVM algorithm and the Logistic Regression Algorithm in the classification of lung cancer. The lung cancer disease dataset has a total of 309 data where the data is separated into two parts, namely 70% training data consisting of 216 data, while 30% test data consists of 93 data. The performance used in predicting the model is Accuracy, Precision, Recall, and F1-Score. From the research conducted, the Accuracy value of the Logistic Regression Algorithm was 97.85%. In this case, the Logistic Regression algorithm has better performance in classifying lung cancer than the SVM algorithm.
Pengembangan Dashboard Admin menggunakan React JS dan Ant Design pada Toko Berkat Pradipta, Rafael Deandra; 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.v6i1.553

Abstract

The development of a web-based administrative dashboard for CV Berkat was conducted using the React JS framework and the Ant Design library. The application development process followed the waterfall method, ensuring that each phase, from requirements analysis to system maintenance, was fulfilled and completed systematically. This dashboard is designed to monitor sales data, product management, and employee reports with an attractive and interactive user interface. Based on testing result using Lighthouse, it was shown that the application performs more optimally when hosted compared to running locally, with assesment metrics such as Largest Contentful Paint (LCP) and Total Blocking Time (TBT) showing better results. The combination of React JS and Ant Design has proven to accelerate the development process thanks to the readily available components and support for the Single Page Application (SPA) method. Overall, this dashboard meets CV Berkat’s needs for real-time and efficient data monitoring while also facilitating future feature development.
Deteksi Citra CT Scan Paru-paru untuk Penentuan Luas dan Keliling dengan Metode Active Contour Negoro, Wahyu Saptha; Azhar, Asbon Hendra; Destari, Ratih Adinda
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.v6i1.567

Abstract

Much research has been carried out on medical image processing by developing various methods of image processing. The research was carried out with the aim of improving image quality, so that it is easier to interpret and analyze images objectively. The same is true for CT scan images of the lungs, which are images in DICOM (Digital Imaging and Communications in Medicine) format which were researched using the Active Contour method to be able to segment the borders of the lungs and be able to calculate the size of their area and circumference more appropriate. There were 5 CT scan images of the lungs used in this research as examples of segmentation using the Active Contour method. The results obtained from detecting CT scan images of the lungs based on validation of the suitability of calculating the area and circumference of the lungs by doctors have an accuracy of 80%. Based on this research, it can be used as a medical reference for determining the size of the area on CT scan images of the lungs.
Implementasi Algoritma FIFO Dalam Sistem Pengaduan Online Berbasis Mobile Pada Dinas Lingkungan Hidup Kabupaten Sleman Nurochman, Rizal Sofyan; Widodo, Tri
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.v6i1.558

Abstract

Increasingly complex environmental problems and increasing public awareness of environmental issues require easy facilities for making complaints about environmental problems, so that they can be immediately followed up by the relevant agencies. This research develops an online complaint system for the Environmental Agency using the Rapid Application Development (RAD) method which emphasizes rapid and iterative system development. The mobile application was developed using React Native, the admin web application using React, and Firebase as Backend-as-a-Service (BaaS). The result of this research is a mobile application that makes it easy for people to complain about problems with data in the form of location details, descriptions, and photos or videos as supporting evidence. The web application makes it easy for admins to manage and follow up on complaints by applying the FIFO algorithm for complaint management. The application also helps the community monitor the complaint handling process to see the progress of handling the problem complained about.
Klasifikasi Kualitas Produk Mesin Pertanian Berdasarkan Evaluasi Kinerja Algoritma Random Forest Hakim, Irma; Asdi, Asdi; Lubis, Mhd. Dicky Syahputra; Harahap, Mely Novasari; Bara, Lokot Ridwan Batu
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.v6i1.577

Abstract

This study aims to classify product quality in the agricultural industry using the Random Forest algorithm. The data used includes various inspection result parameters, such as dimensions, weight, product color, quality status, defect image, inspection time, temperature, machine speed, and indicator lights. The model is developed to classify products into "good" and "defective" categories, and is evaluated based on accuracy metrics and confusion matrix analysis. The results show that the Random Forest model is able to achieve an accuracy of 85% in classifying product quality. Based on the confusion matrix, the model has a perfect prediction rate for good quality products (100% precision) and several misclassifications in the defect category. Feature importance analysis shows that the parameters of inspection time, machine temperature, and defect image are the most significant factors in determining product quality. This study proves that the Random Forest algorithm can be a reliable tool to support the product quality inspection process in the agricultural industry, with further integration into IoT-based systems, this approach can improve the efficiency of the inspection process, reduce manual errors, and ensure more consistent product quality standards.

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