cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ardi Susanto
Contact Email
ardisusanto@poltektegal.ac.id
Phone
-
Journal Mail Official
informatika.ejournal@poltektegal.ac.id
Editorial Address
Gedung B, Politeknik Harapan Bersama, Jl Mataram No 9 Pesurungan Lor Kota Tegal
Location
Kota tegal,
Jawa tengah
INDONESIA
Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 Documents
 1   2 
Search results for , issue "Vol 8, No 2 (2023): JPIT, Mei 2023" : 16 Documents clear
Rancang Bangun Aplikasi Bon Permintaan Dan Pengeluaran Barang Menggunakan Metode Prototype Berbasis Website Aielsa Naomi Athaya; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5220

Abstract

Goods purchase requisitions and goods issue documents are receipts for purchase requisitions and goods issues for distribution goods from the unit of work to the warehouse. pt. Perkebunan Nusantara V still uses the manual method of registering and approving the Goods Request Form using a form filled out by a factory assistant and signed by multiple parties. Therefore, it takes 5-30 business days to collect all signatures. If all parties are present, the product request notification can be signed and approved immediately. However, if this is not the case, the bill of goods approval process will be delayed. For this reason, urgent needs often result in goods being released from the warehouse before the invoice has been fully approved. Therefore, there is a need for an application that helps companies manage good purchase requisitions from warehouses. The application is implemented as a website that allows users to approve notes step-by-step online. The prototyping method allows developers to design and build systems more efficiently because discussions take place between users and developers during the system development process. PHP Laravel is used as programming language and MySQL as database. The tests for this application are based on the ISO 9126 test standard and give the following results: According to the USE survey, functionality scored 100%, reliability scored A, usability scored 90.07 across the four factors, efficiency scored B, performance score 88%, The structural score was 87%. Maintainability was evaluated as A grade with a debt ratio of 2.6%, and portability was evaluated as 100%. This application reduced the approval time to less than 5 hours and test results showed that the application works well and is suitable for enterprise use
Perangkingan Pegawai Untuk Menentukan Penerima Bonus Akhir Tahun Menggunakan Teknik ELECTRE Pada BPR Rasyid Tb Ai Munandar
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4882

Abstract

Employee rankings can be done for various needs, one of which relates to year-end bonuses. So far, the awarding of year-end bonuses to research objects has only relied on one criterion, namely targets and achievements. In fact, there are many other indicators that can be assessed, such as attendance, discipline, communication style, cooperation, and initiative. This study aims to provide an alternative computation-based employee ranking method with multi-attribute decision making (MADM). The Elimination and Choice Translation of Reality (ELECTRE) technique is used in research to rank employee data based on their attribute values. The results of the study show that, of the ten employees assessed, four alternatives (employees) are recommended to be selected based on the results of a comparison of the dominant aggregate values. In this study, it can also be seen that alternative 6 (Alt-6) is the strongest alternative to be recommended for selection. Because alternative 6 (Alt-6) is not only better than alternatives 1, 4, 8, 9, and 10, but also better than alternative 3 (Alt-3) and alternative 4 (Alt-4). The order of the second, third, and fourth alternatives, respectively, are alternatives 5, 7, and 8. The recommendations of these four employees can be used as decision-making material for policymakers, given the need to award year-end bonuses.
Implementasi Aplikasi Berbasis Mobile Untuk Pelayanan Jasa Kesehatan Bella Primin; Adityo Permana Wibowo
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5076

Abstract

Health is one of the important aspects of society's life. Karangampel Health Center is one of the health centers that provide public health services in general. There are several health services provided by the puskesmas including regular medical practices, specialist doctors practices, KIA service practices (Mother and Child Health), and KB service practices. Currently, the queue system at the puskesmas does not yet use the computerization so it is less effective. Many patients who have signed up complain because they don't know for sure the operational schedule at the health center. The purpose of this study was to build a mobile-based computerization application that contains information about the doctor's practice schedule, registering online, the patient's examination history, as well as submitting an online reference letter. The app also had a feature to get an ambulance call quickly. This research method includes observations and System Designers using Systems development of life cycle (SDLC) by the prototype method, and the creation of the system so that it produces a Mobile-based puskesmas information system. Applications are made, then the test is carried out using a black box. The test results result in a value of 80% so it shows that the application is worth using
Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin Cinantya Paramita; Fauzi Adi Rafrastara; catur Supriyanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5216

Abstract

This research aims to demonstrate the implementation of the Anti-Corruption Law in the Banjarmasin District Court by utilizing the K-Means algorithm. Corruption, which persists in Indonesia over a prolonged period, has reached a critical level, making it crucial to enforce the law fairly and firmly. In this study, the panel of judges in the Banjarmasin District Court was analyzed using the K-Means Clustering method and silhouette coefficient to decide corruption cases that result in state losses. The research findings indicate that the optimal number of clusters is 3, with a value of 0.686. However, there is also a lowest value among the 4 clusters, which is 0.454. These clusters are then divided into three categories of enforcement, namely cases that have been executed (108 cases), cases that will be executed (26 cases), and cases that have not been executed (2 cases). All clusters have a silhouette score of 0.742, indicating successful enforcement. This research provides concrete evidence that the panel of judges in the Banjarmasin District Court has implemented the Anti-Corruption Law while considering state losses. By utilizing the K-Means algorithm, this study also contributes to a better understanding of enforcement practices in the court. It is expected that the results of this research will support efforts to enhance the implementation of the Anti-Corruption Law in Indonesia, particularly in the Banjarmasin District Court
Desain dan Analisis Sistem CyberShare Menggunakan Four Node Interplanetary File System (IPFS) Tony Haryanto; Kalamullah Ramli
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5141

Abstract

Cybersecurity information sharing is a proactive and collaborative measure in enhancing organizational security by exchanging cybersecurity information using a centralized repository service. However, in practice, the use of centralized services poses a threat to distributed denial-of-service (DDoS) attacks which can result in system failure and cause single point of failure as well as man-in-the-middle (MITM) attacks which can result in modification of information and theft of exchanged information. This threat results in a lack of user confidence in the confidentiality, integrity, and availability of information. This study proposes the design of a secure cybersecurity information sharing (CyberShare) system using a private interplanetary file system (IPFS) network as a decentralized information storage. Unlike centralized storage which only has a single-node, CyberShare systems use four-node IPFS interconnected with swarm keys as authentication keys. This system allows users to store and share information from the sender to the recipient of information, avoiding dependence on a central server and reducing server load. The results of the analysis show that the proposed CyberShare system can guarantee the confidentiality, integrity, and availability of cyber security information. CyberShare systems can enhance the security of the information exchanged so that organizations can safely share and utilize cybersecurity information.
Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal Sunarti Passura Backar; Purnawansyah Purnawansyah; Herdianti Darwis; Wistiani Astuti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5186

Abstract

Plants are vital to human life on earth, and the leaves and their whole parts have many benefits. These parts of the plant can help distinguish between different species. The leaf identification can be performed at any time, while the other parts of the plants can only be identified at a certain time. The study aims to classify two types of herbs i.e. saur-opus androgynous and moringa oleifera, implementing the Fourier Descriptor method to extract the shape and texture features. In the process of classification using the Naïve Bayes method with three types of nuclei (Gaussian, Bernoulli, and Multinomial) and a Convolutional Neural Network. The testing process was carried out using two scenarios, dark and light, where each scenario consisted of 240 images for a total of 480 images divided into 20% of the data testing and 80% of the training data. The Fourier Descriptor-Bernoulli Naive Bayes method gives the lowest accuracy in both light and dark scenarios, at 46% and 52%, respectively. As for the classification of herbal leaves using a combination of the Fourier Descriptor-Convolutional Neural Network method, it is recommended to be used in light image scenarios and Fourier Descriptor-Gaussian Naive Bayes in the dark scenarios because it is able to detect herbal leaf types with 100% accuracy.
Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan Neni Purwati; Yogi Pedliyansah; Hendra Kurniawan; Sri Karnila; Riko Herwanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4876

Abstract

 Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.
Klasifikasi Citra Virus SARS-COV Menggunakan Deep Learning Indah Susilawati; supatman supatman; Arita Witanti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4587

Abstract

Various variants of the SARS-COV virus emerged from 2003 to early 2022. This resulted in a heavy burden on the health sector in carrying out its duties and public services. It would be very helpful if a computer-assisted application was available that could distinguish between the variants of the SARS-CoV virus. From a scientific point of view, this is an opportunity for information technology to play its role to classify SARS-COV variants using supporting algorithms, including the use of artificial intelligence. Artificial intelligence-based and computer-assisted processes are certainly more immune to negative effects due to repetitive works and fatigue. In this study, Classification of the SARS-COV Virus Image Using Deep Learning (CNN) was carried out based on microscopic data called Transmission Electron Microscopy (TEM) images. The aim of the research is to produce a neural network (CNN/Deep Learning) that has been trained to classify two types of variants of the SARS virus, namely SARS-COV and SARS-COV2. The research phase includes data collection, data pre-processing (consists of the image format conversion and enhancing process), and the classification stage. The classification is carried out using both of the original and enhanced image data. The highest classification accuracy was obtained when the original image data was used, namely 77.66%. It was also found that the classification accuracy increased with an increase in the input image size, but the image data enhancing process used was not able to increase the classification accuracy beyond the classification accuracy achieved when using the original image.
Sistem Pakar Diagnosis Penyakit Pada Ikan Bawal Bintang dengan Pendekatan Naive bayes Dasril Aldo; Yohani Setiya Rafika Nur; M. Yoka Fathoni
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4750

Abstract

 The star pomfret is a type of cultivated fish that has high economic prospects. The focus of the main problem in this study is the disease that attacks the star pomfret fish commodity. If this is allowed to continue, it will cause crop failure and cause the fishermen to lose money. Through this research, an expert system is one solution that can overcome these problems. The expert system built will apply the Naive Bayes method with the stages of entering the dataset into the database which will be used as training data, then the user inputs testing data to be processed into the Bayes method, in the final result the probability value of each disease will be displayed which will then be given recommendations on how to control it disease. From the symptoms selected by the user, namely: white or pale spots on the surface of the body, bleeding on the surface of the body, protruding eyes, the fish looks difficult to breathe, mucus production increases until the body runs out of mucus / roughness, fish lose their appetite, slow movement and slow growth get disease results Cryptocaryon with a value of 93.4. The results of tests carried out on 17 data obtained an accuracy value of 94% so that the expert system is suitable for use as a tool for diagnosing disease in pomfret
Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara Yohanes Setiawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023): JPIT, Mei 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4994

Abstract

Detecting breast cancer in early stage is not straightforward. This happens because biopsy test requires time to determine whether the type is benign or malignant. Data mining algorithm has been widely used to automate diagnosis of a disease. One of popular algorithms is nearest neighbor based because of its simplicity and low computation. However, too many features can cause low accuracy in nearest neighbor based models. In this research, nearest neighbor based with feature selection is developed to detect breast cancer.  Conventional k-Nearest Neighbor (KNN) and Multi Local Means k-Harmonic Nearest Neighbor have been chosen as nearest neighbor based models to experiment. The feature selection method used in this study is filter based, namely Correlation based, Information Gain, and ReliefF. The experimental result shows that the highest recall metric of MLM-KHNN and Information Gain is 94% with 5 features. In brief, MLM-KHNN algorithm with Information Gain can increase the recall of the prediction of breast cancer compared with the conventional K-NN algorithm and have been deployed into website using Streamlit such that the model can be used to detect breast cancer from chosen Wisconsin dataset features.

Page 1 of 2 | Total Record : 16

 1   2 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 8, No 3 (2023): JPIT, September 2023 Vol 8, No 2 (2023): JPIT, Mei 2023 Vol 8, No 1 (2023): JPIT, Januari 2023 Vol 7, No 3 (2022): JPIT, September 2022 Vol 7, No 2 (2022): JPIT, Mei 2022 Vol 7, No 1 (2022): JPIT, Januari 2022 Vol 6, No 3 (2021): JPIT, September 2021 Vol 6, No 2 (2021): JPIT, Mei 2021 Vol 6, No 1 (2021): JPIT, Januari 2021 Vol 5, No 3 (2020): JPIT, September 2020 Vol 5, No 2 (2020): JPIT, Mei 2020 Vol 5, No 1 (2020): JPIT, Januari 2020 Vol 4, No 3 (2019): JPIT, September 2019 Vol 4, No 2 (2019): JPIT, Mei 2019 Vol 4, No 1 (2019): JPIT, Januari 2019 Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol Vol 3, No 3 (2018): JPIT, September 2018 Vol 3, No 2 (2018): JPIT, Mei 2018 Vol 3, No 1 (2018): JPIT, Januari 2018 Vol 2, No 2 (2017): JPIT, Juli 2017 Vol 2, No 1 (2017): JPIT, Januari 2017 Vol 1, No 2 (2016): JURNAL INFORMATIKA Vol 1, No 1 (2016): JURNAL INFORMATIKA More Issue