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 20 Documents
 1   2 
Search results for , issue "Vol 8, No 3 (2023)" : 20 Documents clear
Pemanfaatan Narrowband IoT (NB-IoT) dalam Peningkatan Produktivitas Peternakan melalui Monitoring Otomatis Rakhman, Arif; Sutanto, Achmad; Hernowo, Rudi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5824

Abstract

The rapid advancements in Narrowband IoT (NB-IoT) technology present significant opportunities for creating innovative products that can be implemented in daily life. One of these innovative products is the utilization of NB-IoT for monitoring cage conditions, maintenance, and boosting livestock productivity under challenging conditions that are difficult to manually control. This study aims to design an automated system capable of maintaining ideal cage conditions, including temperature, humidity, levels of ammonia (NH3 and CO2), as well as providing feed/water to livestock automatically and periodically. The research methodology involves the integration of various sensors mounted on a microcontroller, such as temperature sensors, humidity sensors, ammonia sensors, water level sensors, and pH sensors. The program executed by this microcontroller is connected to a control panel, and through the internet network, control and monitoring can be carried out using mobile and desktop devices. The test results indicate that this system is capable of providing ease in controlling the chicken coop for owners and workers, maintaining poultry health, and increasing livestock product yields from 97.17% of harvested poultry to 98.263%, with a decrease in the mortality rate from 2.830% to 1.737%. Overall, the utilization of NB-IoT technology in this research provides a positive impact on livestock management, offering an automated solution that enhances efficiency and productivity in the agricultural sector.
Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network Nafiiyah, Nur
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4985

Abstract

The science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods have good accuracy. We present five Convolutional Neural Network (CNN) architectures for identifying brain tumors (glioma, meningioma, no tumor, and pituitary) on MRI images. This study aims to develop an optimal CNN architecture for identifying tumors. We use the dataset from Kaggle with a total training data of 5712 and testing of 1311. Of the five proposed CNN architectures, architecture c has the highest accuracy of 82.2% with an unlimited number of parameters of 29605060. A good CNN architecture has many convolution layers. We also compare the proposed architecture with CNN transfer learning (Inception, ResNet-50, and VGG16), and with CNN transfer learning architecture, the accuracy is higher than our proposed architecture.
Deteksi Penyakit Tanaman Cabai Menggunakan Algoritma YOLOv5 Dengan Variasi Pembagian Data Riva, Laurenza Setiana; Jayanta, Jayanta
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5679

Abstract

Rapid technological developments have resulted in various innovative techniques that help humans, including object detection which functions to identify each element in an image. Object detection is often used to overcome problems that occur because of its ability to identify each element in the image. One of the problems that is often encountered is a decrease in agricultural income due to disease in chili plants. The maintenance of chili plants has various obstacles including the impact of weather which causes the development of diseases and pests so that chili production has decreased. By implementing the object detection, farmers can easily identify diseases that attack chili plants through pictures so that chili disease can be treated more quickly. This study uses the YOLOv5 algorithm to test the performance of the model in identifying diseases in chili plants. Pictures were taken using a cellphone camera with dimensions of 3472x3472 pixels. The amount of image data used is 430 data. Image data is divided into 3 parts, namely train data, validation data, and test data. To get the best model, this study also conducted three experiments with different distribution of data. Experiment 1 with a division of 70:20:10, experiment 2 with a division of 75:15:10, and experiment 3 with a division of 80:10:10. From the experiments carried out, the best results were obtained, namely in experiment 3 with an average value obtained in the test of 0.947 with a translation of the precision, recall, and mAP values, namely 0.946, 0.936, and 0.959 respectively.
Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV Saluky, Saluky; Marine, Yoni; Bahiyah, Nurul
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4929

Abstract

Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.
Rancang Bangun Aplikasi Diet untuk Ibu Menyusui Pasca Persalinan dengan Algoritma Mifflin-St Jeor Wiryonoputro, Tinara Nathania; Saputri, Theresia Ratih Dewi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5733

Abstract

Pregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platform
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Tanah Berbasis Android Astuti, Yani Parti; Subhiyakto, Egia Rosi; Wardatunizza, Indah; Kartikadarma, Etika
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5026

Abstract

Bawen District is one of the sub-districts in Semarang Regency, Central Java. This region has an area of land used for agriculture around 63.29%. In this area the population still uses soil as a planting medium. Soil is one of the planting media which plays an important role for the survival of plants. With so many types of soil that have different properties and characteristics, the treatment of these soils is also different. So it is necessary to have a soil classification to know how to manage the soil properly. To facilitate the classification of soil types, Deep Learning technology can be utilized with images as input which are then processed using the Convolutional Neural Network (CNN) algorithm. In order to get a model that has a high level of accuracy, an experiment was carried out on several influential parameters and an evaluation of the model was carried out using a confusion matrix. The confusion matrix has several values such as accuracy, precision, recall, and f1-score. Tests have been carried out and the results of this study are models that have a training accuracy of 97% with a loss value of 0.0880 and a testing accuracy of 95% with a loss value of 0.1513.
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN Zalvadila, Alya
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5341

Abstract

Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods
Analisis Perbandingan Metode Fuzzy Logic Dan Metode SAW Dalam Pemilihan Keluarga Penerima Bantuan Sosial Latifah, Siti Ma'rifatul; Diartono, Dwi Agus
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5374

Abstract

Ensuring and fulfilling the needs of the community isa form of government responsibility to reduce existing socialinequalities. One of the efforts that the government has made isto provide social assistance through the Non-Cash FoodAssistance program. However, the process of selecting recipientsof social assistance is often not on target. For this reason, it isnecessary to build a system that is able to support in determiningdecisions for the selection of families receiving social assistance.To help the selection process of social assistance recipients, ofcourse, it must use the right and appropriate method so that theselection process produces social assistance recipients who reallydeserve assistance. The selection process in this study uses twodecision support methods, namely Fuzzy Logic and SimpleAdditive Weighting (SAW) and has conducted accuracy tests onboth methods against the suitability of recipient eligibility data,so that it can be seen which method has the highest level ofaccuracy in the selection of social assistance recipients. Theresults of the accuracy test carried out in this study are that bothmethods produce the same high level of accuracy in thesuitability of prospective recipient eligibility results, namely100%, this means that both methods can be used in determiningrecipients of social assistanc.
Penyesuaian Model Ketahanan Siber Umkm Di Indonesia Dengan Nist Cybersecurity Framework balafif, sabri
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5662

Abstract

Artikel ini menyelaraskan dengan penyesuaian kerangka kerja model ketahanan siber dari NIST Cybersecurity Framework (NIST-CSF) dengan penambahan aspek kesadaran dan kewaspadaan yang terhubung melalui aspek resistensi untuk mencapai ketahanan siber bagi UMKM yang saat ini rentan terhadap berbagai serangan siber. Khususnya, dalam proses tranformasi digital bisnis proses usahanya. Metodologi penelitian ini bersifat analisis deskriptif berbasis kualitatif, guna mengeksplorasi hasil secara intuitif dengan struktur sistematik dalam merekonstruksi pandangan inovatif guna menjawab tantangan pengembangannya. Hasil dalam pembahasan kajian ini adalah rekonstruksi model keamanan siber yang merupakan sebuah tema besar dengan prinsip-prinsip strategis dalam upaya harmonisasi resistensi serangan dengan ketahan Siber. Hal ini dapat membantu organisasi seperti UMKM untuk mengidentifikasi, menilai, dan mengurangi ancaman dalam dunia siber secara komprehensif dan berkelanjutan.
Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine Saddam, Mohd Amiruddin; Kurniawan D, Erno; Indra, Indra
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4884

Abstract

Termination of employment (PHK) on a large scale has a very significant impact on society and the economy. Mass layoffs have led to an increase in the number of unemployed people. Many people who have lost their jobs without a stable source of income struggle to find new jobs. This exacerbated the situation on the labor market and increased the number of unemployed people. Mass layoffs can also reduce economic activity and consumption. The sentiment analysis carried out aims to determine public sentiment regarding the phenomenon of mass layoffs that are currently happening in Indonesia based on positive and negative categories. In this study, the classification method used is the SVM method, which is one of the supervised learning methods in machine learning and also uses Nave Bayes as a comparison method. After classification, the next stage is the testing process using the K-fold cross-validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 108 positive sentiments and 333 negative sentiments related to mass layoffs, while the results obtained from the test results using the SVM method show an accuracy of up to 84% while using the Nave Bayes method shows an accuracy of up to 74.1 percent

Page 2 of 2 | Total Record : 20

 1   2 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 11, No 1 (2026) Vol 10, No 4 (2025) Vol 10, No 3 (2025) Vol 10, No 2 (2025) Vol 10, No 1 (2025) Vol 9, No 3 (2024) Vol 9, No 2 (2024) Vol 9, No 1 (2024) Vol 8, No 3 (2023) Vol 8, No 2 (2023) Vol 8, No 1 (2023) Vol 7, No 1 (2022): JPIT, Januari 2022 Vol 7, No 3 (2022) Vol 7, No 2 (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