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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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Perbandingan Algoritma Apriori Dan Fp Growth Terhadap Market Basket Analysis Pada Data Penjualan Bakery Muhammad Fathurrahman; Adi Rizky Pratama; Tohirin Al-Mudzakir
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.v4i2.161

Abstract

Tight competition in the business world makes bakery business people have to think harder in developing strategies to face this competition. In addition, the problems experienced by business people are having difficulty knowing consumer purchasing patterns due to limitations in analyzing bakery transaction data. So in this problem it is necessary to apply the Market Basket analysis method to find out consumer buying patterns. Market basket analysis is a methodology for analyzing consumer buying habits by finding associations between several different items that consumers place in a shopping basket that they buy in a particular transaction. The results in this study on the combination pattern obtained from the association method with the a priori algorithm, namely having the highest confidence combination pattern is Alfajores, then you also buy coffee with a confidence value of 54.06%, and if you buy cake, you also buy coffee with a confidence value of 52.69%. . While the results of the combination pattern obtained from the association method with the fp-growth algorithm are if you buy Pastry then you also buy Coffee with a confidence value of 55.21%, and if you buy Cookies then you also buy Coffee with a confidence value of 51.84..
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
Persepsi Mahasiswa Ilmu Komunikasi Universitas 17 Agustus 1945 Surabaya Mengenai Komunikasi Gender Irmasanthi Danadharta; Dewi Sri Andika Rusmana
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.v4i3.206

Abstract

The purpose of this research is to understand the perceptions of Universitas 17 Agustus 1945 Surabaya Communication Students on Gender Communication. In this research, the Gender Communication will focus on the voice, bodies and language on men and women. The method of this research is Descriptive Quantitative. This research’s population and samples are students in Communication major that are currently enlisted in Gender Communication class, with the total of 49 students. The data collecting technique used in this research is questionnaire. The technique used for data analysis is Quantitative Descriptive. This research concluded that male and female students have significantly different perceptions in terms of Gender Communication. In gendered voices, the male students’ perception was women are more polite than men. Female students’ perception was men uses cursing and insults to develop relations and tends to be more emotional. Regarding bodies, male student’s perception was women are feminine, focuses on physical appearance. On the other hand, female students’ perception on men were having a masculine appearance, taking care of their own appearance, and doesn’t focus too much on their physical appearances. In language, the male students’ perception was women tend to talk more than men because of their richer vocabularies, while female students’ perception was that there was domination in the languages of men
Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5 Sanni Ucha Putri; Eka Irawan; Fitri Rizky
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 1 (2021): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i1.56

Abstract

Diabetes is a worldwide health problem with an estimated 120 million sufferers. This figure will increase if there is ignorance of the general public about the factors that can trigger diabetes. In this study, the aim of this research is to make a prediction model using Data Mining Algorithm C4.5 which produces a decision tree and tests carried out using Rapidminner so that diabetes prevention can be done as soon as possible. In this study, there are several classification attributes, namely body weight, age, blood pressure, pulse and blood sugar levels. The results of this study will be used as a reference to be able to see whether a person is at risk of diabetes or not based on predetermined attributes.
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.
Prediksi Penyakit Stroke Menggunakan Metode Random Forest Priyo Wahyu Setiyo Aji; S Suprianto; Rohman Dijaya
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.242

Abstract

Stroke is a medical condition classified under cerebrovascular diseases, which involve disruptions in the blood vessels of the brain. In a stroke, there is a blockage or rupture of brain blood vessels, leading to an interruption in the blood supply to specific brain regions. This can result in brain damage and symptoms related to brain functions, such as speech impairments, movements, and other functions. Nowadays, technology is advancing rapidly, greatly benefiting the medical community. One example is the development of artificial intelligence-powered programs for stroke detection. In this research, data was sourced from Kaggle.com, and the researchers utilized the random forest machine learning method. Random Forest combines independent decision trees originating from the same distribution, where the final prediction outcome is determined through a voting process. This research involved several stages, including preprocessing, processing, and evaluation. The research yielded an accuracy of 99%.
Penerapan Data Mining untuk Mengolah Data Penempatan Buku Perpustakaan di SMP Negeri 2 Wewewa Barat dengan Algoritma Apriori Adriana Yulita Bili; Yulius Nahak Tetik; Karolus Wulla Rato
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.266

Abstract

A school library is a unit in educational institutions that plays an important role as a source of knowledge for anyone or can also be said to be a supporting facility in increasing insight and knowledge for students. The many book collections in the library will certainly make it difficult for visitors (students) who are registered as library members to find the books or literature they want to read. Therefore, it is necessary to carry out a good and structured book placement technique according to each category. The technique used by the author in this research consists of observation, and interviews. At the observation stage in this research, the author carried out a direct inspection of the library at SMPN 2 Wewewa Barat to see directly the activities or management and placement of books, while in the interview, the author collected the results of the interview. with the Head of the library, namely Sister Magdalena, CSV. This interview process was carried out to obtain direct information about the book placement patterns that have been implemented in the West Wewewa 2 Middle School library. The results of the interview with the head of the library were used as primary and secondary data. From the data obtained, an analysis of the problems currently occurring in the library is then carried out to become a reference in modeling a structured book placement system. Apart from the interview data collected, transaction data was also collected during observations carried out directly in the library. Based on the results of the research including implementation, discussion, and testing of the system that has been described, it can be concluded that the application of algorithms using data mining techniques is very efficient in speeding up the process of forming tendencies towards combinations of transaction data set items and the results of the algorithm testing process used to produce association rules that are formed. from the combination of items that meet the minimum support, namely 3% and minimum confidence 7% and have the 2 highest item sets with support of 83.33%
Sistem Informasi Pemesanan Pada Ann Printing Berbasis Website Elsa Monika Affiani; Novita Mariana
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.v4i1.102

Abstract

The use of the internet plays an important role in exchanging data and information. Ann printing is a convection engaged in screen printing design services such as t-shirts, polo shirts, hoodies and others. At the time of carrying out sales transactions, it was still carried out using a manual sales system such as the use of social media and did not have a personal website. Currently, the problems faced in making transactions are still done manually using sales notes, sometimes these notes are not immediately recapitulated into a ledger, resulting in lost sales notes, besides that consumers experience time management problems in getting a confirmation of receipt of goods. Based on the research results, it is proposed to create a sales information system website with built-in features that can generate sales data, print invoices and make it easier for consumers to get a confirmation regarding the estimated collection of goods in a timely manner.
Analisis Sentimen Terkait Ulasan Pada Aplikasi PLN Mobile Menggunakan Metode Support Vector Machine Hibatullah Faisal; Arafat Febriandirza; Firman Noor Hasan
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.v5i1.339

Abstract

PLN's mobile applications have become an important part of modern society, providing easy and fast services. However, the user experience of these apps often reflects dynamic changes in the technology environment and user needs. Therefore, sentiment analysis of user reviews becomes very important to find out what users feel and how best to improve the application. This thesis uses the Support Vector Machine (SVM) method to perform sentiment analysis of PLN Mobile app user reviews. SVM is an effective algorithm in text classification based on sentiment. Through this study, it is expected that the analysis results can be used for improvement and enhancement of the PLN Mobile application, thus providing a better user experience.
Model Mekanisme Patahan Gempa Bumi Tarutung 2022 Mw 5.8 Endah P. Sari; Resa Idha; Hendro Nugroho; Syahrul Humaidi; Andrean V. H Simanjuntak; Umar Muksin
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.v4i2.184

Abstract

On October 1, 2022, an earthquake with a magnitude of 5.8 shook the Tarutung area which was generated by an active fault at a shallow depth of 10 km. In this study, relocating the hypocenter and determining the mechanism of the earthquake was carried out to understand the active tectonic structure. The distribution of hypocenter relocation figures a pull-apart pattern at shallow depths. The earthquake mechanism shows a dextral pattern in the Southwest – Southeast direction with a strike of 138º – 158º. The aftershocks are more dominantly distributed in the pull-apart system in the southeastern part and show the greater part of the transfer of seismic static stress to the southeastern side of the Toru fault. The pull-apart tectonic system scheme in the Tarutung basin with secondary faults as extensional faults is proposed to be a fault source model that forms a negative-flower structure geological pattern. The results of this study can be used as a reference for studying the Tarutung tectonic system and applied as a mitigation study.

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