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INDONESIA
JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Published by STMIK Global Informatika MDP
ISSN : 24074322     EISSN : 25032933     DOI : -
Core Subject : Science,
Computer Science & IT
JATISI bekerja sama dengan IndoCEISS dalam pengelolaannya. IndoCEISS merupakan wadah bagi para ilmuwan, praktisi, pendidik, dan penggemar dalam bidang komputer, elektronika, dan instrumentasi yang menaruh minat untuk memajukan bidang tersebut di Indonesia. JATISI diterbitkan 2 kali dalam setahun (September dan Maret), makalah yang diterbitkan JATISI minimal terdiri dari 60% dari luar Sumatera Selatan, dan 40% dari Sumatera Selatan. Makalah yang diterbitkan melalui tahap review oleh reviewer yang berpengalaman dan sudah memiliki makalah yang diterbitkan di jurnal internasional yang terindeks SCOPUS.
Arjuna Subject : -
Articles 1,216 Documents
 105   106   107   108   109 
S SYSTEM FOR DETERMINING RECOMMENDATIONS FOR BIRD'S NEST CONSTRUCTION LOCATION IN RIAU USING THE WEIGHTED PRODUCT METHOD wahyuri, hafiz
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8949

Abstract

A very important activity in the bird industry is the construction of bird nests. The right location to build a whalet nest affects the productivity and success of the company. Therefore, in order to help the entrepreneur in making the right decision, a location recommendation for a whalet nest should be developed. The Weighted Product (WP) method is used to calculate and evaluate criteria relevant to the location of a whalet nest. These criteria include economic factors such as land costs and potential benefits, as well as the availability of natural resources and supporting infrastructure.
Metode Convolutional Neural Network(CNN) Untuk Klasifikasi Tingkat Kesehatan Tanaman Lidah Buaya Berbasis Web Nurlatifa, Nur Latifa
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9003

Abstract

Deep Learning is part of the field of digital image processing. Convolutional Neural Network (CNN) is an advancement of the Multilayer Perceptron (MLP) designed to process two-dimensional data. CNN is included in the category of Deep Neural Networks due to its deep architecture and is widely applied to image data. The aim of this research is to determine the accuracy level of the CNN method in classifying the health level of aloe vera plants integrated into a web platform. This study builds a model using the Convolutional Neural Network (CNN) algorithm. Convolutional Neural Network is one of the effective algorithms in image processing. Images will go through processes of resizing, normalization, and data augmentation. The dataset used consists of aloe vera plants with a total of 3,495 image samples: 1,514 images for training data, 630 images for validation data, and 351 images for testing data. In the modeling process, there are four convolutional layers and four max pooling layers followed by two fully connected layers. The training results of the built model have an average accuracy of 92% and validation accuracy of 85%, while the model testing results achieved an average accuracy of 89%. The CNN method's results for classifying the health level of aloe vera plants are effective in predicting the health level of aloe vera plants using the website.
SISTEM POINT OF SALE BERBASIS WEB PADA BUTIK TUFFAHYZ Haryanto, Teguh
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9068

Abstract

This research developed a web-based point of sale (POS) system for Tuffahyz stores. The main goal of this system is to improve the efficiency and accuracy of managing in-store sales transactions that were previously done manually. This POS system is designed using the Waterfall system development methodology which includes planning, designing, implementation, verification, and maintenance stages. This study uses observation, interviews, and document analysis as data collection methods. The system built allows store owners, administrators and cashiers to manage sales transactions, track reports and manage product inventory more easily. The test results showed that this point of sale system worked as expected, helping to improve the performance and efficiency of the store. It is hoped that this system can improve the quality of service to customers and support the business development of Tuffahyz stores.
Sistem Pakar Diagnosis Diabetes Mellitus Tipe 1 - 2: Forward Chaining - Bayes Chiuloto, Kalvin; Khairani, Sumi; Hutahuruk, Annisa; Dafitri, Haida
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9099

Abstract

Diabetes mellitus (DM) is a chronic disease characterized by hyperglycaemia and can lead to serious complications. In North Sumatra, the prevalence of DM reached 5.3%, with 74% of the patients unaware of the condition. The lack of knowledge and restricted access to health care is a challenge in the treatment of DM. The research aims to develop an expert system for early diagnosis of DM types 1 and 2 using the Forward Chaining and Bayes Theorem methods. The system is designed to provide accurate diagnosis and timely information, helping the field community in detecting DM risks early. The implementation of this expert system is expected to raise public awareness of DM symptoms, provide easy access to information, and help in early diagnosis without having to visit a health facility. The use of a combination of Forward Chaining and Bayes methods aims to improve diagnostic accuracy. The system is expected to be an effective tool in the prevention and early treatment of DM in North Sumatra
Evaluasi Sistem Informasi Pendaftaran Rumah Sakit Di Rumah Sakit Nasional Diponegoro Dengan Menggunakan Metode COBIT 5 Domain MEA sherlina, maria dominika mazzarella; Chotimah, Siti Noor
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9102

Abstract

The use of technology is currently very necessary, in particular the use of the Hospital Information Management System (HIMS). Proper implementation of HIMS will produce accurate, integrated information. HIMS can be implemented within the framework of the COBIT 5 (Control, Evaluation and Assessment) MEA domain. The research carried out is quantitative. The source of data for this study is primary, via questionnaires. The objective of this study was to evaluate the hospital registration information system at Diponegoro National Hospital. The study population consisted of IT staff and medical records unit staff, particularly registration staff. The analysis was carried out using the MEA's capability level assessment process for each domain. The results showed that Diponegoro National Hospital's hospital registration information system achieved a capability level of 2,75 or at level 3 (established process). The MEA01 domain measurement has a capability of 3.81 or at level 4, the MEA02 domain has a capability level of 0.31 or at level 0, and the MEA03 domain has a capability level of 4.12 or at level 4. There are a number of influencing factors, such as the control system not having been optimally implemented, and the lack of human resources affecting the operation of the systems.
Klasifikasi Penyakit Mata pada Citra Fundus Menggunakan VGG-16 Sutanto, Steven Yesua; Udjulawa, Daniel
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9165

Abstract

The eye is a vital sensory organ crucial for vision and various aspects of daily life. Eye diseases such as diabetic retinopathy, glaucoma, cataracts, macular degeneration, hypertension, pathological myopia, and other diseases are global health issues that significantly impact quality of life. The 2022 RAAB survey by Perdami revealed that 8 million people in Indonesia suffer from visual impairments, with 1.6 million of them being blind. Diagnosing eye diseases often requires considerable time and depends on the accuracy and subjectivity of doctors analyzing fundus images. Convolutional Neural Network (CNN) methods can process images and recognize complex patterns and features, assisting in the classification of eye diseases with high accuracy and efficiency. This research aims to classify various eye diseases automatically using the CNN method, speeding up the diagnosis process, enabling faster treatment, and improving effectiveness in the medical field. The implementation of the CNN method with the VGG-16 architecture was successful, capable of classifying 8 types of eye diseases, with the best result obtained in the 10th trial, achieving an accuracy of 54.17%
Perancangan Sistem Informasi Kepegawaian Berbasis Web pada PT Bintang Selatan Agung Lie, Valentino
JATISI Vol 11 No 3 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i3.9283

Abstract

Information systems are essential elements within organizations, supporting daily transactions and managerial functions to meet both internal and external information needs. PT Bintang Selatan Agung, a civil construction company, faces challenges such as the risk of damage and loss of physical files during recruitment, inefficiency in file retrieval, and delays in the administration of leave, promotion, demotion, termination, and punishment due to the use of manual systems. This study aims to design and implement a web-based personnel information system for PT Bintang Selatan Agung to improve the efficiency and security of employee data management. The objectives are to prevent the accumulation and damage of physical files and to expedite personnel administrative processes. The developed information system is expected to facilitate employee data management, maintain information accuracy, speed up decision-making, and enhance overall productivity and company reputation. The methodology used is the SDLC (System Development Life Cycle) with the Waterfall model. The result is a web-based personnel information system that manages recruitment, attendance, leave, promotion, demotion, transfer, reward and punishment, and termination processes. This system prevents file accumulation, reduces the risk of loss and damage, and accelerates administrative processes, thereby increasing the efficiency and effectiveness of human resource management at PT Bintang Selatan Agung.
Penerapan Algoritma Naive Bayes Untuk Prediksi Penyakit Paru-Paru Pada Sumber Kaggle Menggunakan Aplikasi Rapid Miner Saputra, Suwanda Aditya
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9365

Abstract

The development of information technology is very rapid and has been used in many fields, one of which is the health sector. The development of information technology has a very significant role in treating diseases, one of which is lung disease. In this research, the researcher took the data source from Kaggle. The dataset used can be accessed via the link https://www.kaggle.com/datasets/andot03bsrc/dataset-predic-terkena-penyakit-paruparu and data processing uses the Naive Bayes method with the Rapid Miner supporting application. The amount of training data is 80% and the amount of test data is 20% of the prediction results for each class of accuracy, recall and precision in each target class. Performance Vector also informs the number of true positive values, 2499 data, true negative 403 data, false positive 371 data, false negative 2727 data. In the Vector performance we can see that the resulting accuracy is 87.10%, the resulting Class Recall is 87.09% and the resulting class precision is 87.06. The accuracy prediction results 87.10% show good performance in predicting a number of positive cases of lung disease.
Paradigma Smart City Dalam Memanfaatkan Potensi Big Data Sawitri, Dara
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9542

Abstract

Smart City is a city where the existing command center makes it easier to access information in order to provide services to the community. The services that can be provided are varied and usually involve big data technology in order to improve people's quality of life and sustainable city efficiency. Where the increase in population in smart cities will be directly proportional to the increase in demand for city services such as smart transportation systems, smart electricity networks and so on. The right big data paradigm plays an important role in encouraging infrastructure growth and encouraging innovation that makes smart cities responsive to the needs of their residents and able to optimize the standard of living of their citizens. This is due to the ability of big data which is able to analyze large amounts of data so that it can manage resources efficiently for smart cities by utilizing advanced technology to improve services, efficiency, continuity and overall quality of life. Big Data in the development of smart city infrastructure will be examined for its benefits in its implementation. By utilizing the wealth of data in big data, smart cities can gain knowledge about patterns, trends and dynamics formed from daily activities in city development. By analyzing huge data sets, governments can make decisions about which areas need improvement to address critical issues and allocate resources efficiently for smart cities.
Klasifikasi Penumpang Kereta Api DAOP 6 Yogyakarta Berdasarkan Kelas Stasiun Menggunakan KNN ichsanudin, Ichsanudin; supatman, supatman
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.9569

Abstract

Transportation companies continue to adapt to technological developments to improve services to service users. The train is the most crossed mass transportation for service users today. Because of the level of timeliness, comfort and traffic-free so that the train becomes the mainstay mode of transportation for service users. The more service users, of course, the train must improve services to improve services. Therefor The author wants to conduct research on the classification of train passengers, the classification algorithm is used to analyze the number of passengers at the station. This research was conducted using the K-Nearest Neighbor method in determining the number of passengers based on the station class. The K-Nearest Neighbor method is a technique for finding the k target members in the data (training) that are closest to the test data. The dataset in this study uses data sourced from the Passenger Transport Unit DAOP 6 Yogyakarta PT Kereta Api Indonesia from the volume of up and down passengers from 2016 to 2023. The classification results with the K-Nearest neighbor method obtained very good results with an accuracy rate of 93%.

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