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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 10 Documents
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Search results for , issue "Vol 9, No 2 (2024)" : 10 Documents clear
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.
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.
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)".
Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review Mujahidin, Irfan; Rakhman, Fikri Arif
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.v9i2.7051

Abstract

Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machine learning models, some are single models and others are hybrid models. Optimization algorithms are used to optimize parameters and improve the prediction accuracy of machine learning models. This research reviews fifteen journals that have been filtered to obtain those discussing optimization algorithms in the predictive models of solar panel output power. This journal will examine the optimization algorithms used in machine learning models for predicting solar panel output power, discussing various types of optimization algorithms, their application in machine learning models, the prediction results from these models, the input data used, and the data collection locations that significantly influence the prediction outcomes. From the results of this research, it does not conclude which machine learning model is the best, due to the many factors that influence it. However, this research is expected to provide references on the application of machine learning models in predicting the output power of solar panels, thereby encouraging the use of renewable energy sources.
Campus Market Segmentation Through the Binary Logistic Regression and GIS Technology Nengsih, Titin Agustin; Huda, Imam Arifa’illah Syaiful; Ladin, Urwawuska
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.v9i2.6521

Abstract

Understanding and targeting specific consumer segments has become paramount in the evolving marketing landscape. Within the confines of a university campus, a unique characteristic of potential consumers with distinct preferences and behaviors exists. The aim of this research is to model of interest in choosing UIN Sulthan Thaha Saifuddin Jambi. This research uses primary data, data from high school students of XII students in Jambi Province. The sample used 1205 students from six districts/cities in Jambi Province. Binary Logistic Regression analysis is employed for the analysis. The findings indicate that the variables of gender, region of origin, and majors of high school students have a significant influence on the interest in choosing UIN Sulthan Thaha Saifuddin Jambi. The regional origin variables, Merangin Regency and East Tanjung Jabung Regency did not have a significant effect on the interest in choosing UIN Sulthan Thaha Saifuddin Jambi. Meanwhile, Jambi City, Kerinci Regency, Tebo Regency, and Bungo Regency influenced the interest in choosing UIN Sulthan Thaha Saifuddin Jambi. The variables from school majoring in science and social studies have a significant influence on the intention to choose UIN Sulthan Thaha Saifuddin Jambi.
Monitoring System for Website-Based Micro Hydro Power Plant using Firebase Hidayat, Sidiq Syamsul; Mulyaman, Heri Bertus; Basuki, Budi; Mujahidin, Irfan; Prabowo, M. Cahyo Adi; Lestari, Melisa Yufit; Rakhman, Fikri Arif
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.v9i2.6542

Abstract

The use of electrical energy is a basic need for everyone. Micro Hydro Power Plant is one of the technologies that has developed recently. This technology has little adverse impact on the environment. This plant utilizes flowing water, discharge from water, and water pressure. The highlands or mountainous areas where there is flowing water. This water flow can be used as a driving force to drive a turbine, which is the driving force for this power plant because the generator uses a generator that requires motion power to generate electricity. Because this plant utilizes flowing water as a power source to drive a turbine and turn a generator. So basically, where there is running water, there is electricity. Moreover, micro Hydro does not need to build large reservoirs like hydropower. The purpose of making this system is to make it easier to check the condition of the MHP equipment and record the data obtained from the sensors that have been installed. This Website was successfully implemented using HTML, PHP, Firebase Database, CSS, JavaScript, JSON, etc. This Website will use the waterfall method, which consists of observation and needs analysis, system design, modeling, implementation and coding, testing, and maintenance
Analisis Opini Publik Tentang Boikot Produk Pro-Israel di Twitter Berbahasa Indonesia Menggunakan Metode SVM alifa, Chairunnisa fadia; Alita, Debby
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.v9i2.6559

Abstract

The century-long Israeli-Palestinian conflict has created diverse opinions in Indonesian society. The escalation of tensions in Gaza triggered calls for boycotts of products suspected of supporting Israel. In this study, a Support Vector Machine (SVM) method is used to analyze sentiment on Twitter related to pro-Israel boycotts. By understanding public opinion, this study evaluates the performance of SVM with linear kernel and RBF. Data collection was done through crawling Twitter with the keyword "Pro-Israel boycott", resulting in 2600 data. Data preprocessing involved case folding, cleaning, stopwords, stemming, and TF-IDF weighting. Manual labeling was done for 1560 support data and 1040 non-support data. Implementation of the SVM model resulted in 92.5% accuracy for the linear kernel and 91.92% for the RBF kernel. Word cloud analysis provided visualization of key words and sentiments related to the boycott. This research shows the dominance of positive sentiment with 1560 positive tweets and 1040 negative tweets. For development, it is recommended to add sentiment analysis methods, use a wider dataset, and consider supporting variables to improve accuracy and understanding of public sentiment on the issue.
Implementasi Aplikasi Internal Service Order (ISO) Berbasis Web pada Perusahaan Manufaktur Furniture Simangunsong, Jumadi; Hutagaol, Nindhia; Pasaribu, Faizal Asrul
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.v9i2.6813

Abstract

Technological advancements and competitive challenges in the industrial sector have driven an increasing need for information systems. The implementation of information systems is believed to significantly enhance a company’s operational effectiveness and efficiency. In the furniture manufacturing industry, production machines are critical assets that require regular maintenance. PT Ebako Nusantara operates more than 200 production machines. Overall, the demand for machine repairs at PT Ebako Nusantara is managed manually, leading to frequent delays and difficulties in tracking repair history. To address these issues, this study was conducted with the aim of utilizing information systems and data analytics, enabling the company to predict machine maintenance and minimize production downtime. The study results show that furniture manufacturing companies that adopt information systems experience a 22% increase in production efficiency and a 50% reduction in production errors. The development process of the ISO application adopts the Agile methodology with six stages: planning, implementation, software testing, documentation, application deployment, and maintenance. The features available in the application include submitting repair requests and tracking machine repairs, which are designed to streamline and expedite the machine repair workflow. This research has facilitated interdepartmental integration in terms of machine repair requests, significantly improving operational efficiency. The use of the ISO application also simplifies the maintenance department’s scheduling of technicians for machine repairs and routine maintenance.
Implementasi Metode SVM Pada Sentimen Analisis Terhadap Pemilihan Presiden (Pilpres) 2024 Di Twitter anggraini, jenny; Alita, Debby
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.v9i2.6560

Abstract

The focus of the research is the use of Twitter as a platform to express the political opinions of the Indonesian people regarding the 2024 Presidential Election. By utilizing sentiment analysis using the Support Vector Machine (SVM) method, this research aims to evaluate the accuracy of SVM in classifying tweets and compare the performance of four types of SVM kernels. Visualizations of positive and negative sentiments are also generated to provide a clearer picture. The stages of the research involve Twitter data collection, and pre-processing with steps such as data cleansing, case folding, tokenizing, stemming, and filtering. Labeling is done to identify sentiment, then feature extraction using TF-IDF. SVM implementation with linear, polynomial, RBF, and sigmoid kernels is performed, followed by model evaluation using precision, recall, F-measure, and accuracy metrics. The study used SVM to analyze the sentiment of the 2024 presidential election on Twitter data. As a result, out of 3938 tweets, 1575 were positive and 2363 were negative. The SVM model achieved 95.05% accuracy, superior in predicting negative sentiment. Comparison of SVM kernels shows the highest accuracy in the linear kernel 95.43%. Sentiment analysis on tweets shows a majority of positive support for Ganjar 54.9%, while Anies and Prabowo have support levels of 15.8% and 29.3% respectively.
Identifikasi Pola Kepuasan Mahasiswa Terhadap Proses Pembelajaran Menggunakan Algoritma K-Means Clustering. Kurniawan, Heru Purnomo; Farhatuaini, Lia
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.v9i2.6740

Abstract

Student satisfaction levels with the learning experience at higher education institutions often exhibit variability. This study aims to comprehend the varying degrees of student satisfaction at Institut Agama Islam Negeri (IAIN) Syekh Nurjati Cirebon. Employing the K-Means clustering method, this research categorizes students based on their satisfaction levels. The survey data analyzed includes 20 dimensions of Service Quality criteria evaluated by students, with these 20 dimensions grouped into five key aspects of Service Quality assessment: tangible, reliability, responsiveness, assurance, and empathy. The analysis reveals three distinct groups of students with differing satisfaction levels: neutral/fair (class 1), agree/good (class 2), and strongly agree/excellent (class 3). Comparisons among these groups highlight the diversity of student perceptions. Furthermore, an examination of the distribution of evaluations within each class uncovers differing priorities in assessment criteria. These research findings offer insights into the spectrum of student satisfaction levels and pinpoint areas warranting further attention in each class. Such insights can inform the development of policies and strategies aimed at enhancing the quality of learning experiences at IAIN Syekh Nurjati Cirebon.

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