cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
jurnal.josh@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josh@gmail.com
Editorial Address
Sekretariat Forum Kerjasama Pendidikan Tinggi (FKPT) Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 754 Documents
 31   32   33   34   35 
Analisa Sentimen Pelanggan pada Review Belanja Online Berbasis Text Mining Menggunakan Metode K-Means Nurul Amalia; Nur Ika Royanti; Indrayanti Indrayanti; Bambang Ismanto
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3781

Abstract

Technological developments have changed conventional sales to online sales. In online sales, a review from a customer is a very important thing that needs attention, because customer reviews will show the quality and credibility of a sale. Customer reviews can increase or attract new customers or vice versa. Therefore reviews from customers need to be carried out sentiment analysis to know and understand, preferences and feelings of customers towards a product or service in online business. One of the analyzes that can be done is by clustering reviews from customers so that it can be seen from what side the customer dissatisfaction arose. In this study an analysis will be carried out by utilizing text mining from customer reviews by conducting clustering reviews using the K-means method. By grouping customer reviews, a model can be formed to classify the types of reviews according to their class. From the research conducted, it can be concluded that the k-means clustering method can be used to analyze customer sentiment grouping with the number of clusters produced there are 3 groups, namely in cluster 1 complaining about slow delivery, in cluster 2 it leads to a mismatch of goods ordered with goods received by customers, while the results of cluster 3 customer sentiment lead to service and packing. The results of this modeling can be used as a basis for making improvements in sales services at online stores.
Sistem Pendukung Keputusan Pemilihan Aplikasi Belajar Online Menggunakan Metode Additive Ratio Assessment Muhammad Bagir; Jefri Rahmadian; Ahmad Fatih Zahir; Irsyad Purbha Irwansyah
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3782

Abstract

In the digital era, there are many educational platforms or learning applications that offer easy learning. However, with so many existing online learning applications, care is needed to choose one that suits your needs. To select an online learning application, users must look for information one by one on the profiles and features of existing online learning applications. This results in the difficulty and duration of making choices. This study aims to build a decision support system through the application of the Additive Ratio Assessment (ARAS) method for selecting online learning applications so that it can facilitate the selection and does not require a long time. The ARAS method looks for the best solution by comparing the utility function of each option with the optimum utility function value. Based on the existing case studies through the implementation of the ARAS method, the best alternative is Ruangguru (A4) with a score of 0.8819, followed by Kelaskita (A2) with a score of 0.8469, Zenius (A3) with a score of 0.8397, and Udemy (A1) with a value of 0.6282. The built-in decision support system produces valid calculations because the calculation results obtain the same value as the manual calculation results. Then, the usability test results produced an average value of 88.33%. This means that the system that is built is able to facilitate users in every functional area.
Analisis Sentimen Komentar Netizen Terhadap Pembubaran Konser NCT 127 Menggunakan Metode Naive Bayes Nisa Qonita Rizkina; Firman Noor Hasan
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3803

Abstract

The present rate of technological advancement has resulted in the rapid spread of information, which is easily available through social media platforms such as Twitter. Users of Twitter can send and read content in the form of text or videos using the facilities that Twitter itself offers. Numerous Twitter users have commented on the NCT 127 concert's recent dissolution, which has drawn both supportive and critical remarks. A dataset of 2451 tweets was created by gathering information from Twitter using the keyword "nct" between November 4 and November 6, 2022. The data was subsequently cleaned, yielding a total of 2451 useable data points. Labeling and the Naive Bayes algorithm were then applied to the data. The goal of this study was to count the number of favorable and unfavorable tweets and evaluate how well the Naive Bayes algorithm was applied. According to the trials done, there were 559 favorable remarks and 1,892 negative ones. The accuracy of the evaluation tests was 82.01%. Additionally, the analysis of negative sentiment produced a f1-score of 79.21%, a recall of 68.52%, and precision of 93.84%. Contrarily, the evaluation of positive attitude produced a f1-score of 84.15%, a recall of 95.50%, and a precision of 75.21%. The Naive Bayes method, it may be inferred, can categorize and process with a very consistent accuracy that approaches near-perfect outcomes.
Glove Detection System on Laboratory Members Using Yolov4 Abdul Khaliq Al Bari; Ema Rachmawati; Gamma Kosala
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3806

Abstract

The use of gloves by laboratory workers has become mandatory in laboratory work intending to maintain the safety of workers from the spread or side effects carried out in the laboratory, but there are still workers who violate the rules by not using gloves when workers are in the laboratory room. This study aims to detect the use of gloves by laboratory workers. The method used in this research is You Only Look Once (YOLO) version 4. YOLOv4 has a system that can complete computer visual tasks in detecting and detecting objects quickly in real time. Based on the results of experiments and testing conducted, the model obtain an Average IoU of 55.56%.
Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine Lita Astri Pramesti; Nunik Pratiwi
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3807

Abstract

The purpose of this research is to analyze Twitter users' opinions on the MBKM program using Decision Tree and Support Vector Machine. The study utilized 849 data with a dataset ratio of 80% for training and 20% for testing. In the dataset, there were 524 instances of positive sentiment and 320 instances of negative sentiment. This indicates that Twitter users' opinions towards the MBKM program tend to be positive. The research evaluation results showed that the Support Vector Machine achieved an accuracy of 84.76%, which is higher than the accuracy of 72.86% obtained by the Decision Tree. Based on these results, it can be concluded that the Support Vector Machine algorithm outperforms the Decision Tree in sentiment analysis of the MBKM program. The findings of this research are expected to provide input for the development of the program.
Perancangan dan Pengembangan Sistem Informasi Monitoring Sewa ATM Berbasis Web Menggunakan Metode SDLC Rizky Darmawan; Bias Yulisa Geni
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3808

Abstract

Several bank institutions in Indonesia often experience obstacles in the process of processing data and information monitoring ATM rentals. The purpose of this research is to facilitate the process of processing data and monitoring related to ATM rentals to be more practical, efficient, and accurate in order to reduce errors that often occur when using the previous system. The data collection method used in this study is by observing and interviewing with parties involved in the administration of the ATM rental process. Based on the problems in the bank that is still doing manual data collection for ATM rental activities, namely by processing data in Microsoft Excel. In designing the system, the method used in this study is the Software Development Life Cycle (SDLC) method and uses the Codeigniter PHP Framework. Web-based ATM rental monitoring information system using the SDLC method is able to provide solutions to problems that often occur in the ATM rental process, and with this web-based monitoring system, it can maximize the monitoring process and improve the data collection process for the better. ATM rental activities can also run effectively and efficiently and are able to solve problems that often occur in the bank. This web-based monitoring information system using the SDLC method can also be developed again according to the needs desired by the user.
Aplikasi Penjadwalan Dan Monitoring Terapi Anak Dengan Autisme Berbasis Android Menggunakan Metode RUP (Rational Unified Proces) I Putu Gede Abdi Sudiatmika; A A Raka Jayaningsih; Rifky Lana Rahardian; Komang Hari Santhi Dewi
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3815

Abstract

Currently, the trend is that the number of children with autism spectrum disorders (autism) is growing in Indonesia is showing an increasing number and a wider spread, not only in urban areas but also in villages and remote areas. Early and intensive treatment and choosing the right method will make a difference for children with autism to achieve behavior that is appropriate to the child's age. The method of handling autistic children is often called an intervention or a treatment that is repeated. Intervention or therapy is carried out at a child autism center or service. Based on the research team's search for autism service centers (PLA), there are approximately 20 autism and disability service centers in Denpasar. Autism service centers usually have therapy services including speech therapy, occupational therapy, physiotherapy, and aquatic therapy. At this time, the use of smartphones has become a way of life for almost all levels of society. People are used to using Android smartphones to communicate through social media. In general, these smartphones have not been utilized optimally for productive activities. This research will discuss an Android-based application that was developed to assist administration services to PLA in the form of data collection on children with autism. The system development method used is the RUP (Rational Unified Process) method. RUP is a software engineering method developed by collecting various best practices in the software development industry. The system to be built has 2 actors, namely the PLA admin and parents as users. The PLA admin is in charge of managing administration such as child data, therapy data, therapy schedules and history of therapy results. Based on the calculation results from the Likert scale, the authors conclude that the level of user satisfaction in terms of benefits ranges from 88.5% which is in the satisfied category, in terms of effectiveness it is obtained 91.1% which is in the very satisfied category, in terms of interface it is obtained 86.5% which is in the very category satisfied and in terms of content, it was found that 89% were in the very satisfied category.
Book Recommender System Using Matrix Factorization with Alternating Least Square Method Hafid Ahmad Adyatma; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3816

Abstract

In this digital age, we are faced with countless choices of books. Finding books that match our interests and desires becomes a complex challenge. However, the existence of a book recommender system is useful to help provide the best decision-making experience that users can have. This research develops a book recommender system using Collaborative Filtering (CF) Matrix Factorization with Alternating Least Squares method which is compared with Singular Value Decomposition method to see an accurate recommender system. This research uses datasets from Goodreads in the form of book data and rating data. This research uses several evaluation metrics, namely RMSE and MAE for regression metrics and F1-Score and Precision for classification metrics. Based on the research that has been done, SVD gets a better accuracy value with an RMSE value of around 0.86822, for MAE values around 0.6903, for F1-Score values around 0.827923 and for Precision values around 0.568347. Meanwhile, the ALS algorithm gets an RMSE value of around 1.09320, for MAE value of around 0.86479, for F1-Score value of around 0.000304 and for Precision value of around 0.000596.
Chatbot-Based Book Recommender System Using Singular Value Decomposition Muhammad Attalariq; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3817

Abstract

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.
Analisis Dempster Shafer Dalam Mendiagnosa Penyakit Coffea Canephora (Kopi Robusta) Khairu Fahriz Ramadhan; Afif Badawi
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3818

Abstract

Indonesia is one of the world's largest coffee producers and has a reputation for producing high quality coffee. As a country located in the tropics with fertile soil, climate, and suitable topography, Indonesia has ideal conditions for growing coffee. Various varieties of coffee in Indonesia, including Arabica and Robusta, grow well in various regions such as Java, Sumatra, Sulawesi, Bali, Nusa Tenggara, Papua and Kalimantan. Some of the typical Indonesian coffee varieties include Gayo Coffee from Aceh, Mandailing Coffee from North Sumatra, Toraja Coffee from South Sulawesi, and Bali Coffee from Bali. Coffee plants can be attacked by various diseases, which can affect growth, quality and yield. The Dempster-Shafer analysis approach for diagnosing disease in Robusta coffee plants is based on the theory of trust (evidence theory) and provides a strong framework for modeling uncertainty in disease diagnosis. The first step is to collect observational data on various disease indicators such as leaf discoloration, decay symptoms, and stunted growth. By calculating the degree of confidence and uncertainty, we can produce reliable conclusions about the presence of disease in Robusta coffee plants. In addition, we also developed the Dempster-Shafer model to identify the stage or severity of the disease, which can assist farmers in taking appropriate action. The results show that the Dempster-Shafer analysis approach provides accurate and reliable results in diagnosing Robusta coffee disease. This model can also provide valuable information in identifying disease stages, which can support effective disease management and control.

Page 33 of 76 | Total Record : 754

 31   32   33   34   35 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol 7 No 1 (2025): Oktober 2025 Vol 6 No 4 (2025): Juli 2025 Vol 6 No 3 (2025): April 2025 Vol 6 No 2 (2025): Januari 2025 Vol 6 No 1 (2024): Oktober 2024 Vol 5 No 4 (2024): Juli 2024 Vol 5 No 3 (2024): April 2024 Vol 5 No 2 (2024): Januari 2024 Vol 5 No 1 (2023): Oktober 2023 Vol 4 No 4 (2023): Juli 2023 Vol 4 No 3 (2023): April 2023 Vol 4 No 2 (2023): January 2023 Vol 4 No 1 (2022): October 2022 Vol 3 No 4 (2022): Juli 2022 Vol 3 No 3 (2022): April 2022 Vol 3 No 2 (2022): Januari 2022 Vol 3 No 1 (2021): Oktober 2021 Vol 2 No 4 (2021): Juli 2021 Vol 2 No 3 (2021): April 2021 Vol 2 No 2 (2021): Januari 2021 Vol 2 No 1 (2020): Oktober 2020 Vol 1 No 4 (2020): JULI 2020 Vol 1 No 3 (2020): April 2020 Vol 1 No 2 (2020): Januari 2020 Vol 1 No 1 (2019): Oktober 2019 More Issue