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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 28 Documents
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Search results for , issue "Vol 4, No 3 (2023): Edisi Juli" : 28 Documents clear
Sistem Pakar Berbasis Dekstop Untuk Mendeteksi Penyakit Pada Ikan Mas Menggunakan Metode Certainty Factor (Kasus Di Pusat Pelatihan Mandiri Kelautan Dan Perikanan SINI SUKA Biru-biru Sumatera Utara) Siti Agus Kartini; Puji Chairu Sabila; Richard Parlindungan Simanjuntak
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.v4i3.218

Abstract

Goldfish (Cyprinus carpio) is a freshwater fish that has important economic value and has spread widely in Indonesia. Goldfish are one of the most recognizable fish species in the world. It is found worldwide as a pet. Where the level of consumption of this fish is very high in all circles of society, because in general all humans eat fish every day to add nutrition, but problems that arise if you are unable to master the technique of controlling it in terms of disease cause enormous losses for fish farmers. Disease is one of the causes of many fish deaths. One of the fish is infected with the disease, then the disease will spread throughout the pond. This will result in huge losses. This study aims to detect fish diseases more precisely and quickly with the help of a computer. The method used is the Certainty Factor by taking data from the Maritime and Fisheries Independent Training Center SINI SUKA Biru-biru, North Sumatra. By using data on symptoms and diseases in goldfish and then assigning a weight value to each symptom and calculating according to the steps of the Certainty Factor method with an accuracy close to 100%, this method can be applied to detect goldfish disease so that this research is very very helpful in identifying goldfish disease.
Model Sistem Rekomendasi Guna Peningkatan Kesesuaian Kebutuhan Program Kampus Merdeka Belajar Dwi Agus Diartono; R. Soelistijadi; Herny Februariyanti; Eri Zuliarso
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.v4i3.209

Abstract

This paper discusses the automatic matching of students applying for the Internship and Independent Study (MSIB) program with partner companies. Many companies offer MSIB programs. This makes it difficult for students to register according to their competence. For this reason, a system is needed that will help students recommend vacancies according to their competence. This research will build a MSIB recruitment recommendation system. The research will begin by building a database of activity details for apprentice and independent study program partners. Details of partner activities contain program descriptions, Developed Competencies and Participant Criteria. This data will be used to build a MSIB vacancy recommendation system. Matching is done by comparing the Company's needs with the portfolio. At the beginning of the research, notes will be made based on students who are accepted into the MSIB program. Next, build a matching system between the MSIB vacancies offered and the work portfolios made by students. This system is expected to help students to be accepted into the MSIB program. The model used is content-based recommendation system. A content-based recommendation system will provide a list of vacancies that best match the student portfolio. The content-based recommendation system will use the cosine similarity algorithm and K-Nearest Neighbor (K-NN).The output of this study is a recommendation model for recruiting apprenticeship programs and independent studies. It is hoped that this system will help students determine internship programs and independent studies that suit their portfolios
Sistem Pendukung Keputusan Penilaian Kualitas Jasa Hotel dengan Mengimplementasikan Metode Analytical Hierarchy Process (AHP) Hersatoto Listiyono; Muhammad A.R. Hidayat; P Purwatiningtyas; Eko Nur Wahyudi
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.v4i3.200

Abstract

It is time for hotels to use advances in computer technology, both software and hardware, in an effort to handle management, which was previously done manually. One of them is the assessment of the quality of services at hotels because later the ratings from visitors can become a reference for improving the quality ofservices at hotels. This study aims to create an application program for assessing the quality of hotel services, with anapplication program for assessing the quality of hotel services, it will help hotel management in improving hotel services to the fullest. In this service quality assessment application program, theAanalytical Hierarchy Process method is used to determine the valueofthe weightof the criteria. The endresultofthis research is a service quality assessment application program where the result of the program is a recapitulation of hote lservice quality assessment
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 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.
Klasifikasi Jenis Buah Kelengkeng Dengan Metode K-Nearest Neighbor (KNN) Berdasarkan Citra Warna Buah Muhammad Akbar Anugrah Illahi; Widiyanto Tri Handoko
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.v4i3.205

Abstract

In the study titled "Classification of Longan Fruit Types Using KNN Method Based on Fruit Color Images" with the use of the TensorFlow Framework, a series of system testing was conducted using various variations of longan fruit images, totaling 360 samples. The aim of this research was to classify longan fruit types based on the extraction of color features from fruit images. The test results showed the highest accuracy rate reached 98.7% and an average accuracy of 89.6% on the train and test data with an 80%:20% ratio. The developed application successfully distinguished five categories of longan fruit, namely diamond river longan, itoh longan, mata lada longan, red longan, and pingpong longan. This study used a multi-class dataset as the data source. By using the KNN method with a parameter k=5, the system was able to classify longan fruit images with 78% accuracy in the 80%:20% train-validation data split scenario. These findings provide a positive perspective on the potential application of the KNN method in classifying longan fruit types based on the extraction of color features from fruit images. This research makes a significant contribution to the development of automatic recognition and classification systems for longan fruit using image processing techniques
Analisis Data Kepuasan Pengguna Layanan E-Wallet Gopay Menggunakan Metode Naïve Bayes Classifier Algorithm I Gede Iwan Sudipa; I Made Dwi Putra Asana; Ketut Jaya Atmaja; Putu Praba Santika; Dwiki Setiawan
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.v4i3.219

Abstract

E-Wallet or digital wallet is a digital payment instrument using electronic media as a means of payment, in this case, GoPay is one of them. To determine the satisfaction of GoPay digital wallet users in East Denpasar, this research was conducted using the Naïve Bayes Classifier Algorithm method to see how much satisfaction GoPay service users have with GoPay service itself. In collecting data, the researcher used a questionnaire as a data collection method. The data obtained is 100 data, which is divided into two types of datasets, namely training datasets, and testing datasets. The variables used for classification are 2 self-data variables, and 11 question variables based on the attributes that have been used. In determining user satisfaction in this study, researchers used the "Satisfied" class for the satisfied category, and the "Unsatisfied" class for the dissatisfied category. The results obtained from this study are 79 data predictions categorized as satisfied which have the same class as the actual data, 9 prediction data categorized as dissatisfied with the actual data, and test and score which have results AUC 0.995, CA 0.880, F1 0.900, Precision 0.949, Recall 0.880
Penggunaan Datamining Untuk Memprediksi Masa Studi Mahasiswa di Universitas Muhammadiyah Sidoarjo Dengan Algoritma Naive Bayes Muhammad Mursidil Arif; Hamzah Setiawan; Arif Senja Fitrani
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.v4i3.210

Abstract

In the higher education, improving student performance and improving the quality of education is very important. The education system requires innovative ways to improve the quality of education, achieve the best results and minimize student failure rates. One of the innovative ways is to apply data mining to predict students' study period. The results of these predictions will help students or academic adviser to provide early warning or give more precise directions to each student, so that they can do the best things to increase the chances of graduating on time. In this study, 9 academic and non-academic variables were used, consisting of semester grade point index, Semesters 1, 2, 3 and 4, GPA, school origin (public/private), finance (constrained by financial problems or not), scholarship (whether get a scholarship or not), Student Affairs (active or not in the student program). The use of academic and non-academic data variables in this study aims to broaden the predictions of student graduation which are not only assessed from an academic point of view, but also look at non-academic factors. The data used is student’s data for the 2017-2018 Informatics study program at the Muhammadiyah University of Sidoarjo. This data is obtained from the Directorate of Information Systems Technology (DSTI) Muhammadiyah University of Sidoarjo as many as 200 data. Modelling using the naïve Bayes algorithm using Anaconda Navigator software with IDLE Jupyter Notebook and the Python programming language, after evaluation using the confusion matrix and accuracy score, the results obtained were 68% accuracy, precision value 0.67, recall 0.77 and f1-score 0.72. while the accuracy score evaluation value gets 67.35%
Smart Solar Panel Tracking Dual Axis Menggunakan Sensor LDR Berbasis Arduino Joni Eka Candra; Ririt Dwiputri Permatasari; Zainul Munir; M. Ansyar Bora
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.v4i3.201

Abstract

Renewable energy is the choice for several reasons, including: it is relatively cheap, carbon neutral, mostly non-polluting and has support from various NGOs to replace energy solutions based on non-renewable fuels. In addition, the application of this technology in rural communities can provide opportunities for rural communities to independently manage and meet their own energy needs and solutions. One form of renewable energy is solar energy. To be able to convert solar energy into electricity, solar panels are needed that can convert solar energy directly into electricity and store it in batteries. The use of solar panels has been widely used in Indonesia, but not optimal. So far, the solar panels used by the community are static, resulting in less than optimal use of solar energy. A statically placed solar panel can only receive a constant maximum of 3 hours of insolation. Therefore, we need a tool that can make dynamic solar panels that can follow the movement of sunlight so that the solar panels receive a constant maximum light for a longer period of time, thereby reducing the cost of purchasing the number of solar panels that can be used. To generate solar energy, dynamic panel movement when the sun moves requires an electronic device as a controller, an example of a controller that can be used is Arduino. From the test results, the design with a dynamic angle of 60˚ solar tracker shows the highest average power of 34.59 W/hour and an average efficiency value of 3.58%. This is compared to testing at an angle of 300 which averages 32.64 watts/hour with an efficiency of 3.33%, and at an angle of 90° which averages 13.03 watts/hour with an efficiency of 1.30%.
Implementasi Business Intelligence Untuk Menganalisis dan Memvisualisasikan Data Penumpang Bus Transjakarta Menggunakan Tableau Anwar Hidayat; Zuhri Halim; Firman Noor Hasan
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.v4i3.224

Abstract

The existence of transportation facilities such as the Transjakarta bus will make it easier for human activities to move from one place to another. This Transjakarta bus is used as an alternative choice of means of transportation by many people in Jakarta. Because Transjakarta buses use special lanes that allow faster travel. The purpose of this study is to analyze Transjakarta passenger data by implementing a Business Intelligence system to display the number of passengers by type of bus, the number of passengers by route, the most favorite type of bus, the total number of passengers, the average passenger and the top five bus routes. The research method used is dataset processing, namely data for Transjakarta bus passengers in 2021 sourced from data.jakarta.go.id and the data is processed using Tableau tools. The results of this research report are in the form of a dashboard, such as the number of passengers as many as 120,308,547 people and the average passenger is 81,676 people. It is hoped that reports made in the form of data visualization and dashboards can be used in decision making. 

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