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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 795 Documents
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Rancang Bangun Prototipe Sistem Keamanan Gerbang Rumah Otomatis Menggunakan Nodemcu ESP8266 Dengan Kendali Telegram Nishfahuddin, Ananda; Febriawan, Dimas
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6025

Abstract

In an era of advancing technology, the need for smart and efficient home security systems is increasing. The problem addressed in this research is the lack of flexibility and effectiveness in conventional security systems, which still rely on physical involvement of homeowners and have limitations in providing diverse authentication methods. This research aims to design and develop a prototype of an automatic gate security system based on NodeMCU ESP8266, controlled via the Telegram application, as a solution to this problem. The system utilizes the AS608 fingerprint sensor for biometric authentication and a 4x4 keypad as an alternative input method. Users can remotely control the gate through Telegram, which also functions as a monitoring medium. The method used in this study involves the development of IoT-based hardware and software, along with the integration of multiple layers of authentication (biometric and PIN) to enhance security. The results of the study show that the system improves security by providing several reliable authentication methods. The system successfully integrates automatic gate control through text messages with fast response times. Evaluation indicates that the system functions as intended, with an average fingerprint sensor reading time of 102 seconds and a gate response time of 912 seconds. This system offers an effective solution for enhancing home security automatically and remotely.
Klasifikasi Multi Label untuk Deteksi Keseimbangan Emosi Pengguna Media Sosial Menggunakan K-Fold Cross Validation Misriati, Titik; Aryanti, Riska; Sagiyanto, Asriyani; Fachri, Muhamad; Ramadhani, Arya
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6033

Abstract

Social media has grown in popularity, with millions of people using it to engage with and share information worldwide. Social media, in addition to serving as a communication tool, are crucial for expressing the emotions and feelings of users. The widespread use of social media has had a significant impact on people's emotions. In particular, negative emotions are frequently experienced and can have a significant impact on mental health. This study aimed to analyze multiple classification models to discover the optimal model for detecting emotional balance among social media users. The classification models utilized in this study include the K-Nearest Neighbor, Random Forest, Support Vector Machine, Decision Tree, and AdaBoost to identify the best classification model capable of detecting the emotional balance of social media users. Several classification models are applied and compared with the aim of evaluating model performance. This research project employed K-fold cross-validation to evaluate the categorization model by comparing various k values. The Random Forest algorithm achieved the greatest accuracy of 99.90% at a K-Fold cross validation value of 10 and an Area Under the Curve (AUC) value of 100%. Thus, this study successfully found a reliable model for accurately detecting emotions of social media users, which is expected to contribute to the development of mental well-being monitoring systems on social media platforms.
Penerapan Kriptografi Md5 Pada Sistem Informasi Penjualan Online Produk Cat Berbasis Web Darmawan, Muhammad Albani; Karman, Joni; Intan, Bunga
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6034

Abstract

PT Warna Agung in Palembang is a company engaged in selling paint products. Currently, the sales and marketing process at the company still uses conventional methods. In the digital era that continues to develop, this method is considered less efficient and risks leaving companies behind competitors who are already utilizing technology. In addition, managing sales data manually is often time-consuming and error-prone. Therefore, there is a need for innovation to increase efficiency and security in managing sales data. The main problem faced by PT Warna Agung is limitations in managing sales and marketing effectively in the digital era. In facing increasingly fierce competition, companies must be able to utilize technology to simplify business processes and reach more customers. In addition, data security in digital transactions is very important to protect sensitive company and customer information. The solution offered in this research is the development of a web-based online sales information system equipped with MD5 cryptography to secure data. This system is designed to make it easier to manage sales data digitally, expand the reach of online marketing, and increase the company's operational efficiency. The aim of this research is to provide technology-based solutions that are able to increase efficiency and safety in the product sales and marketing process. The result of this research is an online sales website that can manage data digitally and facilitate online marketing with enhanced security using MD5 cryptography, thereby supporting the sustainability of PT Warna Agung's business in the digital era.
Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Klasifikasi Resiko Penyakit Jantung Dari, Aprillia Wulan Nanda; Fajri, Ika Nur
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6038

Abstract

Heart disease is one of the deadliest diseases in the world, where there is a disruption in the function of the heart and blood vessels that causes chest pain, irregular heartbeat, and difficulty breathing. According to data from the World Health Organization (WHO), there are 17.9 million deaths each year due to heart disease. The difficulty in classifying heart disease accurately and quickly is a significant problem. From this problem, researchers conducted data mining research using the KNN algorithm to classify the risk of heart disease by taking data from the official Kaggle website. In this study, there are 4 stages, namely data collection, model formation, mode evaluation, and prediction interface. By using the KNN algorithm, the analysis results obtained an accuracy of 83%, precision 0.88, recall 0.77 and f1-score 0.82. With the results of the model evaluation data, it shows that the classification of heart disease risk using the KNN algorithm has quite good performance. The results of the modeling are then presented in the form of a website by deploying the model.
Metode User Centered Design (UCD) dalam Perancangan Aplikasi Manajemen Inventaris Tambang Laterite Berbasis Mobile Saputra, Marwan Adi; Suhirman, Suhirman
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6042

Abstract

The laterite mining business is a red rock mining activity or red deposits formed from the decomposition of various types of rocks. In running a laterite mining business, a lot of equipment is needed, such as transportation equipment and also the need for fuel and food for employees. Inventory recording needs to be done to avoid the risk of losing company assets. However, there is still a lot of damage to company asset data, this is because inventory recording is still manual using an inventory book. Data damage generally occurs due to human error, such as books that are accidentally torn or exposed to water while in the mining area. To overcome this, it is necessary to analyze and design an application technology that is able to store data safely and can also record inventory data efficiently. The research was conducted based on the problems that exist at CV. Anugrah Seruyan Raya which is located in Central Kalimantan. Designing an android-based laterite mine inventory management application that can record inventory and stock efficiently and can store data more securely so as to minimize the risk of loss or damage to company data. The application design was carried out using the user centered design (UCD) method and developed using the React Native programming language and MongoDB as the database. The resulting application is able to handle existing problems because the new system with the help of technology can make recording in the company more effective and efficient.
Peramalan Jumlah Permintaan Container Dengan Algoritma Regresi Linear Hsb, Khoiri Sutan; Kurniawan R, Rakhmat
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6047

Abstract

The rapid growth of the logistics industry demands effective management of containers as essential transportation tools. Unpredictable container demand can lead to either overstocking or understocking, which impacts operational efficiency. This study aims to forecast container demand using the simple linear regression algorithm. The data used is historical data from PT. Bintika Bangunnusa (BBN) from January 2022 to August 2024. The independent variable used in the model is the amount of goods exported from Indonesia. The results of the study indicate that the simple linear regression algorithm is capable of predicting container demand with a reasonable level of accuracy. The model evaluation, using Root Mean Square Error (RMSE), shows that this model can serve as a decision support tool in container stock planning. However, the study also finds that the forecasting accuracy could be improved by incorporating additional external variables into the model. This research provides significant contributions to logistics management, particularly in container demand forecasting, which can help optimize the company's operational capacity.
Analisis Perbandingan Klasifikasi Intent Chatbot Menggunakan Deep Learning BERT, RoBERTa, dan IndoBERT Dwiyono, Aswin; Abdiansah, Abdiansah; Fachrurrozi, Muhammad
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6051

Abstract

A chatbot is a software application to designed handle user inputs and generate appropriate replies based on those inputs, which are then communicated back to the user. In able to provide accurate responses, the chatbot must be able to understand the intent of the user accurately. An issue in the development of chatbots is how to accurate classify user intent. Incorrectly understanding user intent can result in irrelevant responses. In order to have a conversation with the user, the intent of the user needs to be classified correctly. This paper compares three state-of-the-art transformer-based models BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and IndoBERT (Indonesia Bidirectional Encoder Representations from Transformer) for the task of intent classification in chatbot systems. Various performance metrics, including accuracy, F1-score, precision, and recall, were analyzed to determine which model performs more effectively in the same parameter conditions. Performance metrics like accuracy and F1-score were compared to assess model BERT, RoBERTa and IndoBERT performs better in a University Chatbot Dataset in Indonesian language. The BERT model achieved an accuracy of 0.89, RoBERTa model achieved 0.84 and IndoBERT model achieved an accuracy of 0.94. The better performance of IndoBERT compared to BERT and RoBERTa is caused by more language-specific training, more relevant pretraining, and more effective adaptation to Indonesian context and structure.
Penerapan Metode Waterfall pada Sistem E-Order Makanan dan Minuman Berbasis Web dan Mobile Nugroho, Bagus Candra; Wibowo, Adityo Permana
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6052

Abstract

Warung Sate Ojolali is a restaurant that sells various food menus, especially satay, located in Kebasen District, Banyumas Regency, Central Java. In the era of increasingly fierce culinary business competition, many restaurants and cafes have sprung up which requires companies to implement effective strategies to attract more visitors. In this study, a problem was found in the management of a culinary business where a business that has many transactions with customers, but these activities are hampered because the details of the food and beverage menu offered by the restaurant are less informative such as offers that are less detailed regarding the available menus and the delivery of menu books as ordering media that are less complete or updated. Therefore, researchers designed the development of a Web and Mobile Based Food and Beverage Ordering System. This system is designed using the Waterfall method which aims to simplify the ordering process for customers. This Android-based ordering application allows customers to order the desired menu in detail through an intuitive interface. Integration with the internet network makes the ordering experience more efficient, without the need to come directly to the location. The results of the research obtained while in the form of an online food and beverage ordering system that can run according to its function where the admin can add, edit and can delete the menu data provided. In addition, the process of recording orders that were previously done manually can be done automatically by the system so that an admin only needs to recap order data to carry out the transaction process. The implementation of this system not only improves operational efficiency, but also simplifies management, expands market reach, and improves user experience. Thus, this innovation is expected to support business growth and significantly increase customer satisfaction.
The Improvement of Android Malware Family Detection through System Call Feature Analysis and Machine Learning Yunmar, Rajif Agung
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6053

Abstract

Malware poses a significant threat to cybersecurity, particularly for Android users. Each type of malware is categorized into distinct categories and families, each exhibiting unique malicious capabilities. Accurately identifying these categories and families is crucial for developing effective prevention and mitigation strategies, allowing for the control of threats before they worsen. Throughout the years, numerous techniques have been proposed for detecting malware families, with system calls emerging as a vital feature. Collected through dynamic analysis, system calls offer in-depth insights into the activities executed by malware, making them a powerful classification tool. This study aims to enhance the detection of Android malware families and categories by analyzing system calls with feature selection method. Using the Gain Ratio algorithm, significant system calls are identified to improve detection accuracy and reduce the complexity of the feature set. The study assesses machine learning algorithms, particularly Random Forest, J48, Naïve Bayes, and Decision Table. The findings show that Random Forest consistently outperforms other algorithms, achieving an accuracy of 88.01% for malware family detection and 89.65% for category detection, with high precision and recall across most metrics. The application of the Gain Ratio feature selection method led to a 68.83% feature reduction and improved model-building speed by 50.26%. This integration of feature selection and machine learning provides a more effective approach to detecting malware families and categories, thus contributing to enhanced Android security.
Pembangunan Aplikasi Asesmen Kompetensi Untuk Meningkatkan Kinerja LSP Sabella, Billy; Fathurrahmani, Fathurrahmani; Aprianti, Winda; Achmad, Ferdiyansyah; Tina, Ridha Rahma
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6054

Abstract

Professional Certification Institute (PCI) of Politeknik Negeri Tanah Laut has the task of carrying out competency assessments for prospective graduates. Competency assessment management starting from registration, the certification process, and reporting certification results is still done manually, so it requires a long management time and is prone to errors. Ineffective management of the 2 existing schemes will further impact the decline in the performance of the PCI of Politeknik Negeri Tanah Laut which is currently in the process of increasing the scope of 16 schemes. This is the rationale for the need for an application that can help manage competency certification data so as to improve Professional Certification Institute performance. The method used in this research starts from the stages of needs identification, design, implementation, testing and evaluation, implementation and training, and system development. The research that has been carried out provides the results of the PCI of Politeknik Negeri Tanah Laut information system which was built and has features for managing assessment data, assessor data, certification scheduling, competency test material components, certification reporting and activity documentation. The system built has been tested using black box testing and shows 100% successful feature functionality. System evaluation using User Acceptance Testing on 12 respondents showed acceptance results of 91.50%. Based on the test results, the assessment application that has been built helps improve Professional Certification Institute performance.

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