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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.
Arjuna Subject : -
Articles 471 Documents
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Rancang Bangun Interkoneksi Jaringan Berbasis VPN Menggunakan Metode EOIP Tunnel Verian Nugroho, Dimas; Noprisson, Handrie
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.v7i3.7549

Abstract

This research aims to solve the problem of automatic data backup between the server at the Cyber ​​Data Center and the server at the XYZ Apartment using Ethernet over IP (EoIP) Tunnel technology on the MikroTik network. The main problem faced is that there is no direct path to connect the two servers, so the data synchronization and backup process must be done manually. Experimental research methods using the Network Development Life Cycle (NDLC) approach are used to analyze needs, design new networks, implement and evaluate the designed solutions. EoIP Tunnel implementation is carried out to create an exclusive and secure communication path, equipped with static route configuration and automatic robocopy-based scripts to support regular data backup. Connectivity testing shows optimal results with a stable transfer speed of 49 Mbps, without problems in sending files from the AST server to the QNAP server.
Optimasi Strategi Pemasaran E-Commerce Melalui Prediksi Konversi Berbasis Machine Learning Agustina Heryati; Terttiaavini, Terttiaavini; Septa Cahyani; K.Ghazali; Harsi Romli; Iski Zaliman
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7553

Abstract

The research identifies the problem of enhancing e-commerce sales conversion through TikTok amidst intense content competition. The objective of the study is to develop a machine learning-based marketing strategy to analyze user behavior and categorize them into Non-Purchasers and Purchasers.The method employed includes clustering using K-Means, K-Medoids, and Fuzzy C-Means algorithms, with K-Means demonstrating the best performance, achieving the highest Silhouette Coefficient (0.1857) and the lowest Davies-Bouldin Index (1.9991). Following clustering, classification is performed using Naïve Bayes, Decision Tree, and Random Forest algorithms. The Random Forest model yields the best results with an accuracy of 0.9945, showcasing its effectiveness in predicting sales conversions.The conclusion of this study indicates that K-Means and Random Forest are the optimal methods for clustering and classification, respectively, in understanding user behavior on TikTok. These findings can assist e-commerce players in tailoring their marketing strategies, improving sales conversion rates, and enhancing advertising efficiency
Klasifikasi Penyakit Tanaman Berdasarkan Analisis Citra Daun Padi Menggunakan Metode SVM Dan CLAHE Ayumi, Vina
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.v7i3.7559

Abstract

Rice disease has been found in Indonesia and needs to be identified early for the prevention process. Blast and brown spot disease in rice is considered the most prominent and dangerous disease. This study will focus on identifying four rice leaf disease detections, including Bacterial Blight, Blast, Brown Spot and Tungro. This study aims to determine the effect of contrast enhancement method on SVM method for plant disease classification based on rice leaf image analysis using SVM and CLAHE methods. The dataset consists of four classes namely: Bacterial Blight, Blast, Brown Spot and Tungro with .jpg format. The dataset consists of 480 data for each class. Based on the experimental results, the accuracy of the SVM model reached 100% for the training stage and 94.81% for the testing stage. The accuracy of the CLAHE-SVM model reaches 100% for the training stage and 95.95% for the testing stage. Based on the accuracy value, the CLAHE-SVM model has better performance than the SVM model
Pengembangan Aplikasi Taskify Untuk Manajemen Tugas Menggunakan Framework Laravel Zamzami, Muhammad Aryaka; Kurnia Kito, Ramadani; Suratno, Igo Prayoga; Maritza, Kaka Irsyad; Salamah, Umniy
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.v7i3.7560

Abstract

Task management is one of the important aspects that support success in learning and working activities. The number of tasks and responsibilities often creates challenges, such as forgetting schedules or errors in recording tasks manually. To overcome these problems, a technology-based solution is needed, one of which is a web-based task management application that can help users organize and monitor tasks in a more structured manner. This research adopts a software development approach based on the Agile method, which allows the process of iteration and development that is adaptive to user needs. The “Taskify” application was developed using the Laravel framework to support backend functionality and MySQL as a database. Application testing is carried out using the Blackbox Testing method to ensure all features run according to user needs. The app also features a user-friendly and responsive interface, providing an optimal user experience..
Pengembangan Sistem Lelang Online Menggunakan Metode Algoritma Greedy Berbasis Model-View-Controller (MVC) Prasha, Achmad Ardani; Sambada, Arga; Athillah, Naufal; Salamah, Umniy
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.v7i3.7561

Abstract

This research develops an online auction system utilizing the Django framework and Greedy algorithm to optimize the bidding process, addressing challenges in traditional auction systems such as geographical limitations, lack of transparency, and manual processes prone to errors. The study employs an Agile methodology with a Scrum approach, implementing a Model-View-Controller (MVC) architecture to separate business logic, presentation, and data management. The development of the auction application involves planning to identify system requirements, designing the architecture, implementing the backend/frontend, testing features and algorithms, deploying to the server, and conducting evaluation and adjustments based on feedback. Results demonstrate that the use of Django accelerates development and enhances system security, while the implementation of the Greedy algorithm successfully optimizes real-time bidding and winner selection processes. Key features developed include auction management, an automated bidding system, and secure payment integration.
Analisis Deret Waktu untuk Forecasting Populasi Ternak di Indonesia dengan Model LSTM Prabowo, Tito; Lestariningsih; Fauzan, Abd. Charis; Mafula, Veradella Yuelisa
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7566

Abstract

Livestock population in Indonesia is one of the key indicators supporting national food security, particularly in meeting the demand for animal-based protein. However, the suboptimal utilization of livestock population data for strategic planning remains a challenge in the livestock sector. This study aims to predict livestock population in Indonesia using the Long Short-Term Memory (LSTM) method, a variant of Recurrent Neural Network (RNN) designed for time series data analysis. The livestock population data used in this research was obtained from the Central Statistics Agency (BPS) for the period of 2006 to 2022. The LSTM model was trained using 80% of the data for training and 20% for testing, with evaluation conducted using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the LSTM model can forecast the national livestock population up to 2033 with good accuracy, particularly for livestock such as goats (MAPE 5.47%) and beef cattle (MAPE 5.64%). However, a higher error rate was observed for buffalo (MAPE 16.57%). The predictions indicate a significant growth trend in poultry populations, such as broiler chickens and laying hens. In conclusion, this model can support data-driven decision-making to ensure stable and sustainable animal protein availability, thereby strengthening national food security.
Implementasi Data Warehouse dan Bussiness Intelligence Kasus AIDS di Jawa Barat Budy Santoso, Cahyono; Muhammad Mujiburochman; Reyner Shaquille Rachim; Raihan Cikal Herlambang
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7567

Abstract

This study discusses the design of a data warehouse for analyzing AIDS cases in West Java using the Nine Step Methodology. The background of this research is the high prevalence of AIDS cases in West Java during 2018–2019 and the need for an integrated data management system to support data-driven health policies. The objective of this study is to design and implement a data warehouse capable of integrating data from various dimensions, such as region, age group, gender, and year, to support epidemiological analysis of AIDS. The methodology employed includes stages such as data extraction from various sources, data transformation to enhance quality, and data loading into a PostgreSQL-based data warehouse system. The study also utilizes the ETL (Extract, Transform, Load) process to ensure the integrity of the processed data. The results indicate that the designed data warehouse successfully maps the distribution of AIDS cases based on relevant dimensions. Key findings reveal that the productive age group (25–49 years) and males have the highest number of cases, with Bandung City being the region with the most cases. The contribution of this study is the provision of a data platform that supports evidence-based decision-making while identifying high-risk regions and groups for more effective health interventions. Limitations include the scope of data limited to two years and the absence of predictive analytics features. Future research is recommended to expand the time coverage and integrate predictive analysis to enhance the effectiveness of health policy
Model Platform Berbasis Mobile Untuk Manajemen Data Rekam Medis di Pos Pelayanan Terpadu Menggunakan Feature Modeling dan Unified Modeling Language Ardianto, Darmawan; Noprisson, Handrie
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.v7i3.7572

Abstract

As one of the Posyandu, Chica II also provides medical record data for the community. However, the management of this data often faces challenges, such as manual recording, unorganized data, and difficulty in accessing information quickly. This study aims to develop a mobile-based platform model for managing medical record data in Posyandu. The system development uses the waterfall methodology, which includes the stages of requirement analysis, design, implementation, and testing. The system is implemented using Java programming with MySQL as the database, developed through Android Studio using a native approach. The results of the study indicate that the developed system includes key features such as registration management, user management, health monitoring, mapping, reporting, and education. The system is also integrated with a previously developed web-based system, enabling comprehensive data management, including managing child data, immunizations, weighing records, and user management, designed to meet the operational needs of Posyandu.
E-Arusun: Pengembangan Aplikasi Untuk Manajemen Data Administrasi Rumah Susun Berbasis Web Menggunakan Metodologi Prototype Wina Firly, Tiara; Ayumi, Vina
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.v7i3.7573

Abstract

Managing apartment buildings is a critical aspect of property management, encompassing leasing, resident administration, facility maintenance, and complaint handling. Manual management often faces various challenges, such as inefficient data organization, slow responses to complaints, and a lack of operational transparency. This study aims to develop E-Arusun with a focus on Rumah Susun Sewa (Rusunawa) KS. Tubun Jakarta as the research object. The application utilizes the prototype methodology, starting from the requirements gathering phase, prototype development, evaluation, coding, and system testing. E-Arusun was developed using PHP as the server-side programming language and HTML and CSS for the user interface. The system is designed to address various challenges in managing apartment buildings by providing key features such as register, login, user management, apartment management, kiosk management, rental transaction management, and complaint management. Additionally, tenants can utilize features such as viewing bills, submitting complaints, and monitoring payment status
Perancangan Prototipe Aplikasi Prediksi Kematian Akibat Gagal Jantung Menggunakan Metode Machine Learning Berdasarkan Data Heart Failure Clinical Records Jumardin, Jumardin; Noprisson, Handrie
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.v7i3.7574

Abstract

This research aims to develop a prototype of the Heart Failure Death Prediction Application using machine learning methods based on clinical data from the Heart Failure Clinical Records. The application utilizes clinical patient data, such as age, blood pressure, ejection fraction, creatinine levels, and other attributes, to build a predictive model for mortality risk. Several machines learning algorithms, including Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN), were employed to model and analyze the data. The dataset used in this study consists of 299 clinical records with 13 attribute columns. The target attribute is Death Event, while other attributes, such as age, gender, medical history (anemia, diabetes, high blood pressure), and laboratory test results (creatinine, sodium, and ejection fraction), were used as predictors. The application is equipped with several main menus to support its functionality, such as the Dashboard, which provides a summary of statistical prediction information and related reports, and Blog/News, which offers heart health education. The Data Master menu allows for the management of supporting data, while the Diagnosis menu is used to perform predictions based on patient input data. The Diagnosis History menu stores previous prediction results, while the Patient Data menu facilitates the management of patient information.

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