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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 13 Documents
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Search results for , issue "Vol 9, No 3 (2024)" : 13 Documents clear
Sistem Monitoring Pertumbuhan Tanaman Sawi Menggunakan Artificial Intelligence Pada Aquaponik deviana, lyla putri; Styawati, Styawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.5897

Abstract

Modern agriculture increasingly relies on technology to increase efficiency and productivity. Aquaponics, a sustainable farming method that combines fish and plant farming, has emerged as one promising approach. To maximize yield in an aquaponics system, monitoring plant growth becomes very important. In this context, Artificial Intelligence (AI) offers innovative solutions to monitor and optimize plant growth in realtime. AI-based aquaponics technology is designed portably so that it allows people to grow crops inside and outside the home. AIbased aquaponics technology uses a camera that functions to monitor plants in real-time. The data on the camera will be processed and analyzed by the AI system so that automatic monitoring of the plant growth environment in the system can be carried out. Where will output results that show whether the leaves are still fresh, immediately wither Using CNN's deep learning method, this technology contributes to sustainable food production with higher efficiency in managing resources.  This system can increase productivity and strengthen food security in the face of future challenges. This aquaponics technology can make a significant contribution to the development of sustainable agriculture and can provide guidance and inspiration for agricultural and food industry players. By optimizing food production through AI-based aquaponics systems, communities can face global food security challenges and move towards more environmentally friendly, efficient, and sustainable solutions for the future.
Segmentasi Pembelian Produk Menggunakan Algoritma K-Means Berdasarkan Clusterisasi pada pemilihan menu yang ada diUMKM Kuliner Annisa, Lolanda Hamim; Rusvinasari, Dian
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6556

Abstract

Marketing strategy can be seen as one of the bases used in preparing comprehensive SME planning. One of the SMEs that will be highlighted in this research is restaurants. Customer loyalty is an important thing that must be maintained by companies for the sustainability of the company and can improve good relationships between service provider companies and their customers. K-Means Cluster Analysis is a non-hierarchical cluster analysis method that attempts to partition existing objects into one or more clusters or groups of objects based on their characteristics, so that objects that have the same characteristics are grouped in the same cluster and objects that have similar characteristics. different groups are grouped into other clusters. The purpose of this research is to find out how to group menus that have high selling power and also the relationship between one menu variable and another menu when a transaction or purchase occurs by a customer. The results obtained in this research were to create a segmentation of products purchased by customers in the period May-November 2023 in SMEs operating in the culinary sector in the Central Java area. The results showed that the types of products most frequently purchased were Chicken Rice, Tea, Chicken, White Rice which is at the highest purchase order. Where Chicken Rice was purchased 5409 times, Tea 1867 times, White Rice 1452 times, Chicken 1110 times. In the K-Means Algorithm to determine which products sell frequently and require more inventory and which do not .
Performance and Security Analysis of Lightweight Hash Functions in IoT Mufidah, Nada Fajri; Nuha, Hilal Hudan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.7633

Abstract

The rapid proliferation of Internet of Things (IoT) devices across various sectors, including healthcare, automotive, smart homes, and agriculture, has created a need for robust security measures that do not compromise the limited resources of these devices. This study analyses the performance and security of several lightweight cryptographic hash functions, specifically SHAKE128, BLAKE2s, SHA-256, SHA3-256, SipHash and xxHash, within the context of the Internet of Things (IoT). A series of experiments conducted on the Arduino Uno platform allows for an evaluation of these functions in terms of throughput, memory usage, and avalanche effect. The findings indicate that while SHAKE128 and SHAKE256 demonstrate superior throughput, they require greater memory, particularly with larger input sizes. BLAKE2s exhibits a robust equilibrium between throughput, memory efficiency, and consistent avalanche effects, rendering it a dependable option for 256-bit outputs. Conversely, xxHash and SipHash provide high throughput and minimal memory usage, yet exhibit reduced avalanche effects. The findings of this research provide critical insights for developers and researchers on the selection of appropriate cryptographic solutions, which must be tailored to the constraints and security requirements of IoT devices.
Pengembangan Motor IoT untuk Pemantauan Kecepatan dan Pemeliharaan Melalui Telegram Pranata, Rangga; Styawati, Styawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6126

Abstract

This research focuses on developing Motor IoT as an advanced solution for real-time motor speed monitoring and maintenance notifications via Telegram. The research method includes installing a magnetic sensor on the motor to measure wheel rotation and produce accurate speed data. The data is sent to the IoT platform and integrated with Telegram, providing users with speed monitoring information as well as providing timely maintenance notifications. In addition, this system monitors the overall condition of the motorbike. The results of research and testing show that Motor IoT can be used and applied to motorized vehicles with the ability to provide accurate information and efficient maintenance notifications. The use of IoT and Telegram technology provides an effective solution for monitoring motor performance, optimizing maintenance and reducing potential damage. IoT motorbikes not only increase the efficiency of using motorized vehicles, but also contribute to minimizing the risk of damage and increasing the overall service life of motorbikes. In addition, the magnetic sensor was successfully integrated with the motor monitoring and maintenance system via Telegram, providing appropriate responses to predetermined conditions. This system is also able to send notifications to Telegram when the motorbike is started, providing information on the distance traveled by the user, with reminders to change the oil every 1000 kilometers. This research produces innovations that can have a positive impact on the automotive industry and user welfare.
Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024 septiana, ika; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6640

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.
Analisis Sentimen Inses di Social Media menggunakan Algoritma Naïve Bayes Salsabilla, Tasya; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6611

Abstract

Sexual violence, especially against women and children, is a serious problem in Indonesia. Cases are increasing every year, including incest, which involves sexual relations between close family members. Girls, who are often considered weak and vulnerable, are the main victims. The latest data from the National Commission on Violence Against Women records a decrease in incest cases from 1,210 in 2017 to 215 in 2020. However, attention is still needed, especially because biological fathers are the largest perpetrators. This research uses the Naïve Bayes algorithm for sentiment analysis. This algorithm is an effective classification method based on Bayes' theorem with simple assumptions but is quite effective. Assuming that each feature in the data is independent, Naïve Bayes can work well in text analysis. The research results showed an accuracy rate of 94%. Continued attention to sexual violence, especially incest, is needed to protect vulnerable girls. Protection efforts must continue to be improved, including the application of sentiment analysis methods such as Naïve Bayes for monitoring and early detection. Public awareness and cross-sector cooperation are also key in overcoming this phenomenon.
Analisis Sentimen Terhadap Calon Presiden Indonesia 2024 dengan Metode Extreme Gradient Boosting (XGBOOST) Yulistiani, Yulistiani; Styawati, Styawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6127

Abstract

In 2024, Indonesia will implement democracy in the election of the Indonesian head of state. Any political figure who runs for head of state and calculates his popularity based on public opinion. After the General Election Commission (KPU) released the names of the 2024 Indonesian presidential candidates, these names were widely discussed, especially on social networks, one of which was Twitter. Twitter or what is often called X is a platform that provides short, concise and clear information. Twitter users responding to the 2024 presidential candidate have different opinions on Twitter. The sentiments used are positive, negative and neutral. The method used to analyze public opinion with data processed on Twitter social media uses Extreme Gradient Boosting (XGBOOST), classifying tweet test data in the form of classification with prediction output with accurate values. This research takes Twitter data to see public opinion on presidential candidates. The aim of this research is to determine the process of digital text analysis and the application of the XGBOOST method to Twitter user sentiment in two categories (positive and negative) and three categories (positive, negative and neutral) for each candidate, namely Ganjar Pranowo, Anies Baswedan and Prabowo Subianto. The results show an accuracy of 0.96%, precision of 0.96% and recall of 0.97%.
Identifikasi Hukum Tajwid pada Citra Teks Al Quran menggunakan SSD MobileNet v2 Kurniawardhani, Arrie; Fathurrahman, Ihya
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.7713

Abstract

Tajweed contains a set of rules for reciting the Qur'an correctly. These rules must be complied with to ensure each letter is pronounced accurately. Arabic script and language compose the Qur'an, yet not all readers are fluent in Arabic. Tajweed serves as a guide to prevent readers from making mistakes when reciting the Qur'an that could alter the meaning. However, Tajweed rules are quite numerous and diverse, causing readers to struggle in memorizing these rules. To address this issue, a preliminary development of a Quran reading assistance system will be established, focusing on detecting Tajweed rules in images of Quranic text. SSD MobileNet v2, a Deep Learning technique for object detection, will be utilized for detecting Tajweed rules. The development of the Tajweed rule identification model begins with the data collection stage by capturing screens of the Al-Quran text pages from the Kemenag Qur'an Application. A total of 520 collected data were divided into 80:10:10 for training, validation, and test data, respectively. All data were subsequently annotated and enclosed in bounding boxes using the tool labelImg. The pre-trained model, SSD MobileNet V2 FPNLite 320x320, was used as the initial weight configuration of the model. Then the identification model was constructed during the training stage using training and validation data. The reliability of the constructed model was tested using test data. The test results indicated that the model could successfully recognize two Tajwid rules, Mad Aridlisukun and Mad Layyin, achieving the minimum loss around 0.15 and the maximum precision around 0.96.
Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree Ermillian, Ade; Nugroho, Kristiawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.8007

Abstract

The phenomenon of student dropouts is one of the main challenges in education, influenced by various factors such as absenteeism, economic pressures on families, low academic performance, and lack of motivation. This issue not only affects the personal development of students but also tarnishes the reputation of educational institutions. Therefore, an innovative technology-based approach, such as data mining, is needed to detect students at risk of dropping out early. This study aims to design a model for detecting the potential of school dropout students using Logistic Regression and Decision Tree methods based on student data from SMA N 4 Tegal. The variables used in the analysis include demographic, academic, and social information such as absenteeism, average semester grades, parental income, and transportation type. The dataset is processed using one-hot encoding and label encoding techniques to convert categorical data into numeric values. The results indicate that both methods have their respective advantages. The Decision Tree model achieves high precision, especially in predicting students who continue their education, with a precision of 0.99 for the "Continue School" class. However, recall for the "Dropout" class remains low (0.60), indicating the need for improvements in detecting students at risk of dropping out. On the other hand, the Logistic Regression model shows better balance in detecting both classes, with more balanced accuracy and recall. This study concludes that both models can be used to monitor the potential of school dropouts and provide data-driven recommendations for more accurate educational decision-making.
Analisis Spam Komentar Instagram menggunakan Support Vector Machine dengan Variasi Hyperparameter Haqimi, Nur Azizul; Roshinta, Trisna Ari
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Instagram (IG) is a web and mobile-based social media application where users can share photos or videos with the available features. These features include captions, tagging, adding locations where photos or videos were taken, editing and filtering photos or videos before they are uploaded from the smartphone application and certain tags so that the photos can be seen by many people. Instagram as social media is not only a medium for communication but also for developing brands and selling products. Spam that often appears in spam comments is a barrier to getting appropriate information. When identifying spam and non-spam comments, a challenging problem is that the number of spam comments is less than non-spam comments, thus causing an imbalanced dataset problem. Imbalanced data sets can affect the performance of classification algorithms. Support Vector Machine (SVM) to classify comments between two classes (spam or nonspam) which is the maximum distance between the hyperplane and the closest item from both classes. Analysis of related research that has been carried out with feature variations states that the addition of 90 different features to the data used to increase classification accuracy on imbalanced data.  Other related research discusses Complementary Naïve Bayes which can be used to balance dataset classes. This research describes the selection of Support Vector Machine hyperparameters, especially for unbalanced data where the level of similarity is almost the same, so hyperparameter experiments are needed for the best accuracy

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