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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 431 Documents
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Pengembangan Aplikasi Warung Kelontong Berbasis Android Mengunakan Framework Apache Cordova Suwarjono Suwarjono; Helmia Tasti Adri; Fauziatul Hamamy; Sobrul Laeli
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 2 (2021): JPIT, Mei 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i2.2428

Abstract

Warung kelontong yaitu warung yang menyediakan kebutuhan rumah tangga seperti sembilan bahan pokok (sembako), makanan dan barang rumah tangga. Warung ini ditemukan berdampingan dengan pemilik rumah yang tidak jauh dengan masayarakat seperti perkamapungan, perumahan dan yang sering ditemui didalam gang. sangat penting untuk memberikan sentuhan teknologi pada pembukuan kegiatan jualbeli melalui aplikasi berbasis android. penelitian ini merupakan pengembangan aplikasi warung kelotong mengunakan tahapan metode waterfall. hasil dari penelitian ini adalah aplikasi mobile warung kelontong berbasis android yang dapat digunakan untuk mengelolah data penjualan secara offline dalam arti tidak perlu terhubung internet yang dibuat dan dikembangkan mengunakan framework apache cordova
Anlisis QoS Open IMS Core berbasis Network Function Virtualization pada Protokol TCP Khoirunnidzom, Nizam; Pranindito, Dadiek; Ikhwan, Syariful
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.1254

Abstract

IP Multimedia Subsystem (IMS) is an NGN technology to convergence support of wireline and wireless networks with QoS guarantees. Therefore IMS is widely used by telecommunications operators. Increasing customers make operators overwhelmed by having to replace or add new devices to increase IMS capacity, which is less profitable for operators on the operational costs. The concept of Network Function Virtualization (NFV) is solve the problem. The concept changed of hardware-dedicated to software-dedicated in a virtual environment allows the NFV to be more flexible in increasing device capacity and can reduce dependence on hardware purchases. In this research IMS implemented using Open IMS Core and NFV infrastructure software using OpenStack. analysis based on QoS file transfer services and web servers by measuring parameters throughput, delay, jitter, and packet loss. Based on the measurement of QoS parameters obtained by traffic load of 0 Mbps, 10 Mbps, 20 Mbps, 30Mbps. The throughput average values on file transfer service are 0.2906 Mbit / s and 16.4366 Mbit / s on web server services. The delay average value on file transfer service is 25.5077 ms and 0.7708 ms on the web server service. The resulting jitter value is less than 1 ms. Percentage of packet loss is less than 1%.
Developing Fishpond Control System for School Natural Laboratory Automation Sebastian, Danny; Chandra, Dian Widiyanto; Wijono, Sutarto; Prasetyo, Sri Yulianto Joko; Trihandaru, Suryasatriya; Saputra, Laurentius Kuncoro Probo
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5640

Abstract

Pandemi Covid-19 memaksa kegiatan belajar dilakukan secara daring. Sekolah berusaha melakukan kegiatan secara luring dengan membatasi jumlah siswa atau dengan melaksanakan kegiatan di laboratorium alam. Mengelola laboratorium alam membutuhkan banyak biaya terutama pada kondisi pasca covid-19. Internet of Things adalah teknologi yang memungkinkan kendali jarak jauh dan otomatisasi. Hal ini memungkinkan pengelolaan laboratorium alam dilakukan dari jarak jauh atau secara otomatis. Penelitian ini bertujuan untuk membuat desain dan sistem IoT yang meliputi penentuan modul dasar dan fungsinya, penentuan perangkat sensor dan aktuator yang dibutuhkan. Sistem dibangun menggunakan arsitektur MQTT. Aplikasi Android dibuat untuk mengontrol periferal IoT. Sistem yang telah berhasil dibangun diuji dengan metode blackbox testing. Berdasarkan hasil blackbox testing, aplikasi Android dan periferal IoT dapat berkomunikasi dan berfungsi dengan baik. Penelitian ini masih memiliki keterbatasan yaitu perlu dilakukannya kalibrasi perangkat IoT dan pengujian perangkat keras IoT dalam jangka waktu yang lama.
Perbandingan Hasil Nilai Baca Konsumsi Air Antara Sensor Water flow YF-B6 dan YF-S201 dalam Penggunaan Internet of Things Kurnia Bakti, Very; Rais, Rais; Basit, Abdul; Nugroho, Wildani Eko; Nishom, M.
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6530

Abstract

The use of clean water is very necessary for human life, the existence of clean water requires adequate infrastructure, especially in the distribution and quality of the water. Water usage measurements at PDAM and Pamsimas still use analog water meters, although the reading values are relatively accurate, the entire calculation process is less efficient because it has to be done manually by humans which requires energy and time and there is the possibility of recording errors. Water flow sensors are able to provide an alternative as a projection for the future which makes it possible to build smart water meters by applying IoT. However, the level of accuracy of water flow sensor readings needs to be studied more deeply for its use considering the many types of shapes, materials and sizes of water flow sensors. This research presents a comparison of sensor reading values by changing several constant parameters in each test, which is the most precise and accurate in each measurement test. These results are up to the accumulated amount of water in milli liters, then measured to obtain the accuracy value of each sensor reading value, thus the comparison of two water flow sensors can be used as a reference for the use of which sensor is most suitable for use as a water meter for each different uses.
Aplikasi Prediksi IHSG Berbasis Web Dengan Integrasi Multi-Algoritma Waluyo, Dwi Eko; Paramita, Cinantya; Kinasih, Hayu Wikan; Pergiwati, Dewi; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6193

Abstract

The four regression algorithms used in predicting the Composite Stock Price Index (IHSG) have contributed significantly, as the test results show that the Decision Tree algorithm outperforms k-Nearest Neighbor, Linear Regression, and Random Forest, especially in terms of Mean Squared Error (MSE) and R2 score. The stages of data collection, pre-processing, and modeling, followed by model performance measurement, have provided valuable insights into the effectiveness of each algorithm. The success of the Decision Tree in this testing has further propelled its development into a web-based application. This conversion process, following the outlined flowchart, integrates various essential aspects of the model, including user interface and back-end integration, ensuring that the application can be accessed and used efficiently and effectively. Furthermore, the black box testing and User Acceptance Testing (UAT) results, using the Mean Opinion Score (MOS), enhance the validity and reliability of the application. Black box testing involving 2 features with 37 steps demonstrates the system's effectiveness in producing valid outputs, from the initial menu display to the prediction results. Additionally, UAT involving students and entrepreneurs as respondents provides in-depth insights into user acceptance. With a focus on functionality at 97.08%, reliability at 96.09%, and usability at 98.09%, UAT yields high scores in all three aspects, with usability achieving the highest score. These results not only confirm the efficiency of the system in performing its functions but also indicate a high level of user satisfaction, strongly suggesting the potential for widespread adoption of this application in the future.
Teknologi Deteksi Dini Banjir Daerah Aliran Sungai menggunakan Heltec Wifi LoRa 32 V2 Amanda, Feby; Samsugi, Selamet; Styawati, Styawati; Alim, Syahirul
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5892

Abstract

In Indonesia there are often natural disasters, one of which is flooding. Flooding is a natural disaster that is marked by the overflowing of river water irrigation channels in urban areas, one is the river Irrigation that exists at the Technokrat University of Indonesia. Therefore, the study aims to develop a flood early detection tool using LoRa (Long Range) technology to monitor potential flooding in Kalibalau, Indonesian Technocratic University, Bandar Lampung. The research method involves installing an ultrasonic sensor in the Kalibalau River and connecting it to the Heltec Wifi LoRa 32 V2 microcontroller. Test results show that the LoRa transmitter and receiver operate as planned. This tool does not require an internet connection because it uses the Heltec Wifi LoRa 32 V2. The status of the river is categorized into four: Safe, Alert 1, Alert 2, and Danger, with appropriate warnings. The test showed a delay of 5 seconds on the water height reading. At safe (water height 44 cm), the buzzer does not sound. At morning 1 (water altitude 82 cm), it sounds once with a 1 minute delay. The device has a communication capacity of up to 400 meters. Thus, the tool is effective in monitoring the Kalibalau river and giving early warning of potential floods. This research has contributed to the development of flood monitoring technology to increase public alertness and safety in flood-prone areas
Efficient Weather Classification Using DenseNet and EfficientNet Mutasodirin, Mirza Alim; Falakh, Faiq Miftakhul
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.7539

Abstract

Classifying images of weather conditions using deep learning models is a challenging task due to the computational intensity and resource requirements. To deploy AI models on resource-constrained devices like smartphones and IoT devices, compact and computationally lightweight models are necessary. Efficient deep learning models for weather classification are essential to reduce energy consumption and costs, making AI more accessible and sustainable. To the best of our knowledge, there are limited studies comparing MobileNet, DenseNet, and EfficientNet as efficient models and did not report any hyperparameter optimization. Our study contributes by investigating efficient models with hyperparameter optimization. Firstly, we measured the inference speed of 14 models, namely MobileNet, MobileNetV2, MobileNetV3, EfficientNetB0, EfficientNetV2B0, NASNetMobile, DenseNet121, VGG16, Xception, InceptionV3, ResNet50, ResNet50V2, ConvNeXtTiny, and InceptionResNetV2. Then, the top-7 fast models, which are MobileNet, MobileNetV2, MobileNetV3, EfficientNetB0, EfficientNetV2B0, NASNetMobile, and DenseNet121, were benchmarked for their accuracy. The models were compared by a small dataset having four classes: cloudy, rain, shine, and sunrise. Batch size and learning rate for each model were optimized by grid search method. It turns out that DenseNet121 achieved the best and the most balanced validation and testing accuracy, 0.9821 and 0.9837, followed by EfficientNetB0 with 0.9821 and 0.9740 respectively. This study is important to find efficient models with optimal comparison.
Perbandingan Kinerja Algoritma K-means dan Agglomerative Clustering Untuk Segmentasi Penjualan Online Pada Customer Retail Ramadhan, Ghanim; Astuti, Yuli
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5735

Abstract

This research focuses on the comparison between two popular algorithms in data science, namely K-means and Agglomerative Clustering algorithms. The main context of this research is customer data segmentation, a very important process in the business world to understand and serve customers better. The main objective of this research is to evaluate and compare the performance of the two algorithms in generating effective and efficient customer segments. In this research, the dataset used is a retail customer dataset. This dataset includes various attributes that reflect customer characteristics and behavior. To measure the performance of both algorithms, this research uses the RFM (Recency, Frequency, Monetary) weighting method. This method is a commonly used method in customer analysis to identify the most valuable customers based on how recently they transacted (Recency), how often they transact (Frequency), and how much their transactions are worth (Monetary). In addition, this research also uses an evaluation metric known as silhouette score. This metric is used to measure how well an object fits into its own cluster compared to other clusters. The results of this study provide valuable insights into the quality of both algorithms in segmenting customer data. It was found that the K-Means algorithm produced a silhouette score value of 0.5087, while Agglomerative Clustering produced a higher value of 0.6363. This suggests that, in the context of this dataset, Agglomerative Clustering may be more effective compared to K-Means. However, further research is certainly needed to validate these findings and to further explore how these two algorithms can be optimized for customer data segmentation
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan Waluyo, Dwi Eko; Kinasih, Hayu Wikan; Paramita, Cinantya; Pergiwati, Dewi; Nohan, Rajendra; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6105

Abstract

Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
Usability Sentiment Analysis Menggunakan Metode SUMI, NLP Scikit-Learn pada Aplikasi New Sakpole Aminudin, Agus; Hadiono, Kristophorus; Nugroho, Kristiawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.5451

Abstract

This research will discuss issues related to how to evaluate the usability and Sentiment Analysis aspects of the New Sakpole application system, how to determine the level of user satisfaction in using the New Sakpole mobile application and to determine sentiment analysis based on the results of analysis using the SUMI and NLP tools. The research objective is based on the formulation of existing problems to provide usability aspect values for the development of the New Sakpole mobile application and generate recommendations for improvement and determine the level of positive and negative sentiment analysis by using the New Sakpole Application as a medium for paying Motor Vehicle Tax. The test uses the Software Usability Measurement Inventory (SUMI) tool, the New Sakpole mobile application system, which is very helpful and can provide value to the community in the online vehicle tax payment process. This can be seen and obtained from a scale of helpfulness and efficiency resulting from a maximum score of 100 with an average score of 101 and 86.2. The results of the test using the SUMI tool, all average aspects get above average results, so the level of usability that occurs is that the use of New Sakpole has worked and is running well. The test uses Scikit-Learn Natural Language Processing (NLP) that the results of processing the review dataset on the New Sakpole Application from the Google Play Store with a total of 4704 reviews and a sampling of 500 reviews, that the response or reviews of the community using the New Sakpole application are negative even though for Acuracy word (words) that conveyed a review of 80.90%. From the results of the sample data test that index 0 is negative so that the words "good, very enlightening" can be concluded with Sentiment is 1 (POSITIVE)".

Page 28 of 44 | Total Record : 431

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