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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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Penggunaan Algoritma random forest dan Agile scrum dalam Pengembangan Aplikasi Kesehatan mental Berbasis Web 'MentalWell' Masbahah, M; Putri, Azzahra Kareena Rendri; Lathifa, Diana; Nawang, Diah Munica; Anshori, Fauzi Ihsan; Kusnadi, Bimo Adji
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.v5i3.455

Abstract

Teenage mental health is a major concern today. Teenagers often suffer from various mental health problems such as depression, anxiety, and stress, which can have a negative impact on their development and lives. The aim of this research was to develop a mental health website that helps adolescents identify and overcome mental health problems. The "MentalWell" website uses a random forest algorithm to classify youth mental health questionnaires and provide appropriate intervention recommendations. This website was created using the System Development Life Cycle (SDLC) method with an Agile Scrum approach, based on a design that was carried out using the React.js framework for the front-end and CodeIgniter 4 (CI4) for the back-end. In the Agile scrum model, software development is divided into short iterations called sprints. Each sprint involves sprint planning, daily scrum, sprint review, and sprint retrospective. The main steps in the process of classifying mental health disorders using the random forest algorithm, starting from data collection to testing and final results. Testing uses the black box method, with valid results for all features developed.
Predictive Maintenance Air Conditioner Using Machine Learning Fambudi, Ranggi Tino; Isa, Sani Muhamad
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.v5i3.414

Abstract

Predictive maintenance will take care of the machine's needs in terms of power loss from damage that lowers performance, operational costs from severe damage, business interruptions from damage that renders the machine unusable, and much more. Almost every home has an air conditioner, the machine that requires constant maintenance of temperature and humidity, especially in offices with servers or control rooms. Preventive and predictive maintenance is necessary to identify the necessary steps for technicians to take when handling an AC before the damage worsens. In this research we implemented and proposed an Air Conditioner detection system using machine learning with three methods, namely K-Nearest Neighbor, Decision Tree, and Random Forest. In order to understand the actual conditions of each AC, we use data sheets that we gathered through surveys with engineering teams at multiple hotels as well as technical teams that handle servers and control rooms. There are 20 features in the gathered data set; however, since only 14 of the features affect the value, extraneous data will be removed. Then the data was divided into two groups, namely 23 AC Failures yes, which means the AC condition is not normal and 110 AC Failures No, which means the AC condition is not damaged. Using the stratified random sample method, 25% of the data will be oversampled. In this study, Kbest and backward elimination were employed for feature selection. The SMOTE approach was then applied for oversampling due to the unbalanced groups. With accuracy values of 91.18%, precision 91.18%, recall 90.90%, and f1-score 90.92%, the Random Forest model with the suggested model outperformed the Decision Tree and KNN models, according to the experimental findings.
Pendeteksian Level Kualitas Modifikasi Citra Manusia Dalam Eksperimen Metode Error Level Analysis (ELA) Rantiasi, R; Himamunanto, AR.; Sumihar, Yo’el Pieter
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.v5i3.446

Abstract

Research on image processing methods has become increasingly diverse in modifying images with more attractive visuals. The results of visual modification of this image are often used to convey certain information that will often be found in various media. The method used to identify images that have been modified is the Error Level Analysis (ELA) method, which detects the quality level of a visual image compared to other images. So the method approach proposed in this research involves computing the visual components of images with shape, color and texture features. The method used in shape computing is Prewitt edge detection, while for color features using HSV color transformation and Grayscalling. The method used to identify texture is using the Gray Level Co-occurrence Matrix (GLCM). The urgency of the method proposed in this research is very important to keep up with the various image processing methods that are developing increasingly rapidly. The results of the research are the Error Level Analysis (ELA) method with an analysis approach to shape components using the edge detection method, analysis of color components using the HSV and Grayscalling color transformation methods, and analysis of texture components using the Gray-Level Co-Occurrence Matrix (GLCM) method. ) can be used to detect image authenticity based on the statistical output of processing data. The Error Level Analysis (ELA) method with identification of shape, color and texture shows the differences between the original image and the manipulated image, so that the method used in the research can be a recommendation in completing the system. It is hoped that the approach method in this research will become an instrument for identifying images that have been modified to avoid misuse of visual image information.
Aplikasi Mobile Deteksi Gangguan Mental dengan Integrasi Metode Forward Chaining dan Certainty Factor Menggunakan Rapid Application Development RAD Fergina, Anggun; Pebrian, Riko; Adela, Dhea
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.v5i3.405

Abstract

Mental disorders represent a serious public health challenge, requiring effective approaches to prevention and early recognition. Limitations in access to mental health services, such as distance, cost, and social stigma, prevent individuals from getting professional help. The system developed in this research provides an early detection solution through collecting data from questionnaires and other health information. Implementation of the Certainty Factor method measures the level of belief in symptoms of mental disorders, while Forward Chaining produces diagnoses and recommendations for action. The system integrates data from various sources, including user activity on digital platforms, making a significant contribution to technology supporting the management of mental disorders. This research is supported by literature studies and uses Unified Modeling Language (UML) for software design, strengthening the theoretical foundation in overcoming mental disorders..
Pendekatan Data Science terhadap Pemilu 2024: Memahami Persepsi Publik dan Tren Opini Politik Madika, Fiwi Fishinsky; Mailoa, Evangs
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.v5i3.437

Abstract

In this study, Twitter is used as a data source to analyze sentiment and public opinion related to the 2024 General Election. The sentiment analysis method is employed to understand how public views are reflected in tweets containing relevant hashtags. The research aims to identify trends in political opinion and public perception that emerge on Twitter during a specific period before, during, and after the election. By analyzing relevant tweet data, this study will provide in-depth understanding of how public opinion evolves and changes over time, as well as identifying the most influential and popular accounts in political discussions on Twitter. The results show that the majority of public opinion about the 2024 General Election on Twitter is positive, with dominant support and sympathy for presidential candidates. Social network analysis reveals a well-structured network with @eternaciumentaa being the most influential and @geloraco being the most popular
Sistem Pendukung Keputusan Metode ANP Dalam Menentukan Stasiun Televisi Terbaik Destari, Ratih Adinda; Azhar, Asbon Hendra
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.v5i3.428

Abstract

Television media is an information technology that displays images and sound, where television is a communication medium that provides various updated information and disseminates it to the public. At this time there are so many television stations that there is increasingly competition among television stations to provide interesting programs, these television stations are competing in terms of providing innovation and information and there are those who compete only to attract the public's attention so that the television station's programs get attention. high rating to seek its own profit. To overcome the above problems, a decision support system is needed where the method uses the Analytical Network Process (ANP) method, which this method is used because it can provide the best decision based on the highest value to the lowest value and will provide recommendations to the public which television stations can be recommended as worthy. watched by the public according to the needs of the community. From the calculation results, it was found that RCTI  had a value of 0.3775, SCTV had a value of 0.1954, Indosiar had a value of  0.1770, TV One had a value of 0.1363 and Kompas TV had  a value 0,0325
Implementasi Algoritma Komputasi Linear Regression untuk Optimasi Prediksi Hasil Pertanian Hakim, Irma; Asdi, A; Afriliansyah, Teuku
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.v5i3.460

Abstract

The main objective of this research is to implement the Linear Regression computational algorithm to predict crop yields more accurately. The research method includes collecting and analyzing historical data from 10 agricultural samples that include these variables. This data is then used to train a prediction model. The model evaluation used the Mean Squared Error (MSE) and R² score metrics to assess prediction accuracy. The research results show that the Linear Regression model can provide accurate predictions, with prediction results on new data reaching 479.5 kg/ha. Data visualization revealed a significant relationship between environmental variables and crop yields, which supports the validity of the model constructed. The conclusions of this research confirm that implementing computational algorithms can be an effective tool to help farmers make more informed decisions regarding planting times and land management strategies. This not only increases agricultural efficiency and productivity but also helps in reducing uncertainty in crop yields. The implementation of technology using the linear regression algorithm is expected to make a significant contribution to more sustainable and efficient agricultural practices, as well as support increased crop yields in the future.
Sistem Pendukung Keputusan Menentukan E-Commerce Terbaik Menggunakan Metode Topsis Siregar, Farid Akbar; Siregar, Annisa Fadillah; Setiadi, Eka Widya Ningsih
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.v5i3.419

Abstract

One of the trading activities that has become increasingly popular recently is online buying and selling transactions. The development of the digital world indirectly influences the growth of online buying and selling transactions or e-commerce. This rapid growth has led to a variety of service and product offerings from various e-commerce platforms, often leaving users confused about choosing the platform that best fits their needs and availability. Users frequently face complex questions such as which platform offers the best service quality, which platform provides the most optimal transaction security, and which platform is the most reliable for transactions. Therefore, an evaluation is needed to help users assess which platform best meets their needs. This study utilizes the TOPSIS method, as this method is considered to have a simple concept in producing alternative decisions in an accurate mathematical form. The results of this study, using 5 criteria and 8 alternative e-commerce platforms, indicate that Shopee (A1) is the best alternative with a score of 0.9564.
Deep Learning Techniques For Skin Cancer Detection And Diagnosis Aryono, Gagah Dwiki Putra; Audina, Alisa; Auliana, Sigit
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.v5i3.451

Abstract

Skin cancer is the most common type of cancer globally, and early detection is crucial for effective treatment. This research reviews the use of deep learning techniques in detecting and diagnosing skin cancer. A review of current methodologies was conducted to propose new strategies for improving the accuracy and reliability of the detection and diagnosis processes. Various deep learning models, including convolutional neural networks, were evaluated using three publicly available datasets. The PSO algorithm was utilized for segmentation and feature extraction, while also exploring the impact of transfer learning, data augmentation, and model ensemble on model accuracy. The findings of this study indicate that deep learning techniques can significantly enhance the detection and diagnosis of skin cancer
Analisis Keamanan Dan Eksploitasi Kernel Android 13 Menggunakan Metasploit Reverse_Tcp Alhidamkara, Salman; Somantri, S; Kharisma, Ivana Lucia
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.v5i3.410

Abstract

The development of information technology, especially in the mobile field, has changed the way we interact with devices substantially. Android, as the most dominant mobile operating system used worldwide, attracts significant attention to its security aspects. Despite improvements in the security of Android devices, exploitation attempts continue to be made by security researchers and hackers using various methods, including exploitation via Reverse_TCP with tools such as Metasploit. This research aims to analyze the security of Android 13 devices using the Reverse_TCP method via Metasploit. The methods used involve exploitation by sending backdoor applications, opening Meterpreter sessions, and stealing data such as SMS and call logs. The results showed that Google Play Protect detected malicious applications, but the applications could still be installed and run, indicating a weakness in the security detection system. Reverse_TCP exploits can lead to unauthorized access to personal data and full control of the device, posing significant risks to users. Proposed preventive measures include using the Mobile Security Framework (MobSF), enabling Google Play Protect, and disabling unnecessary app permissions. This study suggests further research to overcome limitations and explore further the security aspects of Android

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