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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 43 Documents
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Search results for , issue "Vol 7 No 3 (2024): November" : 43 Documents clear
IMPLEMENTASI METODE K-NEAREST NEIGHBOR UNTUK PREDIKSI PENJUALAN PRODUK RUMAH TANGGA TERLARIS Sonita, Anisya; Ayu Lestari
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.6350

Abstract

Household products in the Idola 2 Store certainly have a large number, making it difficult for the store to determine which products have the best selling sales. This research aims to determine the best-selling household products that are most likely to sell at Idola 2 Stores using the K-Nearest Neighbor (K-NN) method and to apply the K-Nearest Neighbor (K-NN) method to predict best-selling products by utilizing sales data. This research uses the RAD (Rapid Application Development) system development method which consists of several stages. The results of this study have created a system using the K-NN method in predicting the determination of best-selling household products. Based on the results of the K-NN method process in predicting the best-selling household products, it is obtained from 20 products, there are 5 products that are included in the best-selling household products, namely Motif Glass Jars, Plastic Jars, Foot Mats, Hanger and Multipurpose Shelves. From this data, the Idol 2 Store can predict what products are likely to have good sales or sell well in the next month.
Implementasi Metode Pattern-Based Natural Language Processing (NLP) Pada Chatbot Rahayu, Tri; Agung Kharisma Hidayah; Hary Witriyono; Khairullah; RG Guntur Alam
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.6739

Abstract

Natural Language Processing (NLP) based chatbots are increasingly used in various applications, ranging from customer service to education. One simple yet effective approach to implementing NLP on a chatbot is the pattern-based method, which uses language patterns to understand and respond to user input. This research aims to implement the pattern-based NLP method on a chatbot and evaluate its performance using the BLEU metric. Conversation data is collected and analyzed to identify common patterns, which are then mapped into appropriate responses. Testing was done by measuring the level of bigram match between the chatbot response and the reference response, where the BLEU-2 precision reached a value of 0.75 or 75%. These results show that the pattern-based method is capable of generating relevant responses but still requires refinement to achieve higher accuracy.
Evaluasi Kualitas Usability Sistem Informasi Manajemen Rumah Sakit dengan Metode Heuristic Evaluation dan End User Computing Satisfaction Fadhil, Muhammad; Irman Effendy
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7082

Abstract

This study seeks to assess the usability of the Hospital Management Information System (SIMRS) in OKU RSUD Timur through the application of Heuristic Evaluation and End-User Computing Satisfaction (EUCS) methodologies. The implementation of SIMRS began in 2018 with the aim of improving the operational efficacy of the healthcare facility. However, various technical challenges, including system malfunctions and inaccuracies in data entry, continue to hinder the effectiveness of its utilization. The research methodology used is quantitative descriptive, utilizing data collected through a questionnaire distributed to 78 personnel in OKU RSUD Timur. Data analysis revealed that the average user satisfaction score on EUCS was 81.74%, while the Heuristic Evaluation produced an average score of 82.58%. Both evaluative methodologies indicate that the usability of SIMRS can be categorized as commendable, although the Heuristic Evaluation showed slightly superior results. Given these findings, usability improvements can be directed at refining aesthetic and minimalist features, as well as strengthening system controls and security measures. This investigation contributes to the body of empirical evaluations related to the usability of MIS in health care institutions, thereby encouraging improvements in the quality of health information systems.
Analisis Data Peserta Didik Sekolah Menengah Atas (SMA) Menggunakan Visualiasasi Google Looker Studio Oktaviani, Fenny; Nita Rosa Damayanti
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7084

Abstract

Google Looker Studio, previously known as Google Data Studio, is one of the data visualization tools that can make it easier to manage such data. Looker Studio allows users to integrate data from various sources, analyze it, and present it in an interactive and easy-to-understand visual form. Using Google Looker Studio, schools can create reports and dashboards depicting learner data in real-time, facilitating effective analysis and reporting. This study concludes that Google Looker Studio is an effective and efficient tool for learner data visualization in high schools. It helps in data processing and analysis and supports faster and evidence-based decision-making. Users perceived significant benefits in terms of efficiency with a result of 85%, data understanding of 78%, and reporting quality of 82%, so Looker Studio has great potential to be applied more widely in educational settings.
Optimasi Metode Naïve Bayes Classifier Menggunakan Pendekatan Term Frequency-Inverse Document Frequency (TF-IDF) Pada Analisis Sentimen Ardi, Ardiansyah; Kurniawan
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7153

Abstract

The main objective of this research is to conduct an analysis of public sentiment directed toward RSUD Siti Fatimah, using the Naïve Bayes Classifier methodology. This analytical approach was used to systematically categorize reviews into positive and negative sentiments. Data relating to the reviews was obtained through web scraping techniques from Google Maps, followed by a series of text preprocessing procedures, which included text sanitization, tokenization, and the application of TF-IDF for weighting. Based on the positive Classification values Precision shows 83%, Recal 1.00, and F-1 Score 0.91 which means the Model shows excellent performance in identifying positive sentiments. However, the model is less effective in identifying negative sentiments, with very low recall.
Optimasi Pengambilan Keputusan Akademik Perguruan Tinggi Menggunakan Visualisasi Data dan Analisis Performa dengan Implementasi Dashboard Grafana Giri Purnama; Boy Yuliadi; Rani Laple Satria Putra; Asep Supriyadi; Muhammad Julius Saputra
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7185

Abstract

This research aims to improve the efficiency of academic decision-making at Dian Nusantara University by applying data visualization technology using Grafana. Although the Academic Information System (SISKA) has facilitated data collection related to students, lecturers, and the Teaching and Learning Process (TLP), the utilization of these data for analysis and evaluation is still not optimal. The main challenge lies in processing the data comprehensively to produce relevant information for academic decision-making. This research was conducted through several stages, from identifying specific academic data needs through discussions with stakeholders, access and extraction of data from the SISKA database, and data integration into Grafana. Furthermore, the collected data is analyzed and visualized using an interactive dashboard that displays trends, patterns, and anomalies related to student and lecturer performance. The results showed that using Grafana could facilitate understanding of academic data, enabling faster, more precise, and data-based decision-making. Clear and intuitive visualizations also help the university monitor PBM effectively and identify areas that need improvement.
Optimasi Metode Naïve Bayes Menggunakan Smoothing dan Feature Selection Untuk Penyakit Demam Berdarah Dengue Lemi; Ilma Hasana Kunio, Nurul; Sukma Wati, Ade
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7208

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease caused by the Dengue virus and has emerged as a significant health issue in many tropical countries, including Indonesia. Early identification of the disease is crucial to prevent further spread and complications. This study aims to refine the Naïve Bayes methodology to improve the accuracy of early detection of medical data related to patients suffering from DHF. The application of Naïve Bayes is expected to enhance predictive accuracy and facilitate healthcare professionals in diagnostic procedures. The data used in this research consists of clinical patient information, including laboratory findings and experienced symptoms. The results show that the optimization of the Naïve Bayes method successfully increased prediction accuracy to 92%, which could serve as an effective diagnostic alternative for early DHF detection. The conclusion of this study is that Naïve Bayes can be relied upon to identify DHF more quickly and accurately, ultimately contributing to the medical decision-making process.
Penerapan Algoritma K-Means Clustering Pada Segmentasi Tenaga Kerja di Batam Dalam Sektor Manufaktur darmansah; Fitra Kasma Putra; Tomy Nanda Putra
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7209

Abstract

This study aims to apply the K-Means Clustering algorithm in workforce segmentation in Batam in the manufacturing sector, which is one of the important industrial centers in Indonesia. In facing the challenges of increasing productivity and efficiency in this sector, segmenting the workforce based on certain characteristics is important to help companies manage human resources more effectively. K-Means Clustering algorithm is used to group workers based on variables such as Worker ID, Age, Education, Work Experience, Salary and Technical skills. The data used in this study comes from a manufacturing company in Batam with a significant number of workers. After going through the data preprocessing stage, the K-Means algorithm is applied to identify worker segments that have similar characteristics. The results of the research are that the research resulted in the grouping of workers in the manufacturing industry in Batam into 3 groups, namely C_0, C_1 and C_2. Then the number of workers in the manufacturing sector in Batam City is mostly in cluster C_0 in the category of beginner workers with a high school education level, C_1 in the category of middle workers for D3 and S1 education levels and C_2 in the category of senior workers with a S2 education level.
Implementasi Firebase Cloud Messaging (FCM) Pada Aplikasi Agenda Rapat Firaninda Cahya Prista; Sri Wulandari
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7236

Abstract

One type of activity of the HIMATIKA UTY organization is holding meetings. This activity is carried out regularly during the management period. However, the management of meeting activities is still done manually so it is not efficient. The purpose of this research is to build the HIMATIKA UTY Meeting Agenda application which is used to optimize the implementation of meeting activities. The system built in this study will implement Firebase Cloud Computing (FCM) which is used by the secretary and management. This application serves to facilitate the secretary in managing meeting activities, managing attendance and submitting permits, and providing notifications to the management if there is a meeting schedule, minutes, or meeting information. Based on the SUS score of 89.08, this system falls into the “Highly Satisfactory” category, indicating that users overall have a very good experience using the system. This figure shows that the system has met the standards of ease of use and good interaction quality.
Prediction of Customer Creditworthiness with the C4.5 Algorithm at PT Menara Indonesia Company Dhoni Hanif Supriyadi; Siahaan, Fernando B; Anwar, Syaiful; W Handono, Felix
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7237

Abstract

Customer credit assessment is still carried out using traditional methods that are time-consuming and less accurate. This is evidenced by the fact that there are still customers with problematic credit who pass the loan application process. To address this issue, this research aims to develop a method using the C4.5 algorithm. The purpose of this research is to improve the efficiency and effectiveness of the company’s debt collection process. This research uses customer data from PT Menara Indonesia, which has credit loans. The data includes seven independent variables: net income, loan amount, credit score, number of arrears, tenure, assets, and loan age, as well as one dependent variable, namely credit risk. The C4.5 algorithm is applied to build a customer credit repayment prediction model. This model is tested using the k-fold cross-validation method with k = 10. The test results show that the C4.5 model has excellent performance, with an accuracy of 99.70%, precision of 99.25%, recall of 98.52%, and an F1-score of 98.88%. The advantage of this method is its ability to provide highly accurate predictions, thereby helping the company identify high-risk customers and improve the overall debt collection process

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