Jurnal ELTIKOM
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154 research outputs found
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Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition
Full text linkObesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models that effectively address the complex interplay between lifestyle and physical attributes. This study tackles the absence of an optimal machine learning model for accurately classifying obesity based on these multifaceted factors. To address this gap, the study evaluates the performance of three machine learning algorithms: Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The primary objectives are to identify the most accurate classification approach, analyze the strengths of these algorithms, and highlight the importance of lifestyle and physical attributes in obesity prediction. Experimental findings show that SVM with RBF kernel achieves the highest accuracy at 89%, surpassing the performance of the other models. This study advances the field of obesity classification by offering a detailed comparative analysis of machine learning algorithms and underscoring the critical role of integrating lifestyle and physical factors into predictive modeling
Optimization Of Solar Panel Usage In Grid-Connected Hybrid Energy Systems Using Fuzzy Method
Full text linkThe hybrid grid-connected power generation system combines solar power, wind power, and the PLN grid to meet the electricity demands of facilities such as schools, laboratories, mosques, and kindergartens at MTs Parmiyatu Wassa\u27adah School. Due to insufficient wind speed below the turbine\u27s operational threshold, wind turbines cannot contribute to electricity generation, making solar power the primary energy source. Solar power capacity is crucial for meeting the electricity needs of these facilities. This study applies the Fuzzy method to analyze the optimal utilization of solar panels in a grid-connected hybrid system for electricity demand. Simulation results indicate three levels of solar panel utilization, with the most optimal performance achieved when school electricity usage is low, and additional loads are minimized
ANALISIS DAN PERBANDINGAN STEGANOGRAFI PADA MEDIA AUDIO DAN GAMBAR MENGGUNAKAN LSB DAN RC4
No full textIn the current digital era, it is deemed essential to ensure security and confidentiality of information when exchanging information through communication networks. This is done to allow recipients to receive the information from senders in its entirety without any interference from third parties who are not entitled to the information. Cryptography and Steganography are some useful methods to secure a confidential message, including the RC4 algorithm as one type of applicable method to secure the original message into a random secret message to make it remain unknown to others. One of the methods used in steganography to secure messages, including images, audio, video, and documents, is the least significant bit (LSB) algorithm. This study aims to analyze the comparison of the two-storage media, namely audio and images using LSB and RC4 in order to see the effect of the LSB and RC4 algorithms on the container media based on the aspects of imperceptibility, fidelity, recovery, and capacity. Having tested the imperceptibility aspect as indicated by the histogram of the image and the audio spectrum, it is clear that there is no difference between the image and audio before and after insertion. The fidelity test of the PSNR (Peak Signal to Noise Ration) resulted in an average value of > 30 dB, while the recovery test shows 100% success because there is no difference between the original message and after extraction. The capacity test indicates that the larger the size of the container media, the larger the message that can be inserted.Pada zaman digital saat ini, memberikan keamanan dan kerahasiaan informasi sangat penting ketika melakukan pertukaran informasi melalui jaringan komunikasi. Hal ini bertujuan agar informasi yang dikirimkan oleh pengirim dapat diterima secara utuh oleh penerima tanpa adanya campur tangan pihak ketiga yang tidak berhak atas informasi tersebut. Kriptografi dan Steganografi merupakan teknik yang dapat digunakan untuk mengamankan sebuah pesan rahasia, salah satu jenis metode yang dapat digunakan adalah algoritma RC4 yang digunakan untuk mengamankan pesan asli menjadi pesan rahasia yang acak agar tidak diketahui orang lain. Pada steganografi yang digunakan sebagai media untuk mengamankan pesan antara lain gambar, audio, video, dan dokumen, dimana salah satu metode yang digunakan adalah algoritma least significant bit (LSB). Berdasarkan pengujian yang dilakukan terkait pada penyisipan pesan pada media gambar dan audio didapatkan analisis terkait enkripsi dan dekripsi algoritma rc4. Pengujian aspek imperceptibility, dari histogram gambar dan spektrum audio terlihat tidak ada perbedaaan antara gambar dan audio sebelum dan setelah penyisipan. Pengujian aspek fidelity, dari PSNR dihasilkan rata-rata nilai > 30 dB. Pengujain aspek recovery, menunjukan bahwasanya aspek recovery berhasil 100 % karena tidak ada perbedaan antara pesan asli dan setelah ekstraksi. Pengujian aspek capacity, menunjukkan bahwasanya semakin besar ukuran media penampung maka semakin besar pesan yang bisa disisipkan
A Systematic Literature Review on Blockchain Technology in Software Engineering
Full text linkBlockchain technology is gaining increasing interest among software developers as a distributed and decentralized ledger for tracking the origin of digital assets. However, the application of blockchain in software engineering requires further attention. In this study, we aim to address the current challenges and explore the need for specialized blockchain practices in software engineering. Through a systematic literature review, we identify the various applications of blockchain technology in software engineering. Additionally, we conduct a thorough analysis of existing obstacles and propose potential solutions. Gathering and evaluating requirements using blockchain-based requirements engineering approaches will enhance the quality and reliability of data in software development projects. This, in turn, will improve the overall quality and dependability of software, as well as increase user interest and productivity
Unpacking Public Perceptions of Qris with Twitter Data: A Vader And LDA Methodology
Full text linkQRIS, a mobile payment transaction system standardized by Bank Indonesia, has become the subject of extensive public discourse on Twitter. Employing VADER for sentiment analysis and LDA for topic modeling, this study aims to capture the nuanced perspectives of the Indonesian public toward QRIS. Our methodology includes real human validation for tweets that have been initially labeled by VADER. Our unique contributions lie in employing a mixed-methods approach for comprehensive sentiment and topic analysis, as well as making our dataset publicly available for future research. We achieve a sentiment labeling accuracy of 81.66%, uncovering that 67% of the sentiment towards QRIS is positive, 28.2% negative, and 4.17% neutral. Positive tweets mostly cover six dominant topics with a value of 0.488037, whereas negative sentiments are concentrated around three dominant topics with a  value of 0.383938. These findings not only affirm the generally positive public response towards QRIS but also highlight areas requiring attention for its continued success. Our study translates these insights into actionable recommendations, aiming to provide a multidimensional understanding that stakeholders can leverage for system enhancement. This study serves as a foundation for future works in sentiment analysis and public opinion mining related to financial technologies, particularly in the Indonesian context
IDENTIFIKASI TANDA TANGAN MENGGUNAKAN PENGOLAHAN CITRA DIGITAL DAN METODE MACHINE LEARNING
No full textSignature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research
Random State Initialized Logistic Regression for Improved Heart Attack Prediction
Full text linkOne of the primary causes of death in Indonesia is heart attacks. Therefore, an effective method of pre-diction is required to determine whether a patient is experiencing a heart attack. One efficient approach is to use machine learning models. However, it is still rare to find machine learning models that have good performance in predicting heart attacks. This study aims to develop a machine learning model on Logistic Regression algorithm in predicting heart attack. Logistic Regression is one of the machine learning meth-ods that can be used to study the relationship between a binary response variable [0,1] and a set of pre-dictor variables, and can be used directly to calculate probabilities. In this study, a random state is ini-tialized in the Logistic Regression model in order to stabilize the training of the machine learning model and increase the precision of the proposed method. The results of this study show that the proposed model can be a method that has good performance in predicting heart attack disease
IoT Frequency Band Channelization in Indonesia as A Recommendation for Machine-To-Machine Communication Preparation in the 5G Era
Full text linkThis study aims to provide recommendations regarding frequency and channel settings for machine-to-machine (M2M) communication in preparation for the 5G era in Indonesia. In the rapid development of the Internet of Things (IoT), M2M communication is becoming increasingly important to support efficient and reliable connectivity between IoT devices. In this study, we conduct an in-depth analysis of the available frequency spectrum in Indonesia, considering existing regulatory constraints and technical requirements. The results of this study show that the frequency bands 920-925 MHz and 925-928 MHz suit M2M communication in Indonesia with the suggested channel settings. These recommendations are based on spectrum availability, M2M communication needs, and relevant technical requirements. Implementing these recommendations is expected to increase the efficiency and reliability of M2M communications in Indonesia, facilitate the further development of IoT technology, and prepare Indonesia well to face the 5G era. This study contributes to designing a regulatory framework and optimal spectrum use to support successful M2M communications in Indonesia
Smart Rice Box - The Prototype of Organic Rice Storage Anti-Rice Weevil for Food Security during Pandemic
Full text linkThe need for organic rice among the people continues to increase in line with the declining level of public health due to the COVID-19 pandemic. Consuming organic rice is one way to maintain body immunity, but organic rice is susceptible to attack by Sitophilus Oryzae L, a type of rice weevil which is the main pest in postharvest commodities. Proper storage of rice is one way to address food security during a pandemic. In this study, a prototype of an anti-rice weevil (Sytophilus Oryzae L) organic rice storage was made using a Raspberry Pi controller and several additional sensors such as a camera sensor and temperature and humidity sensors. UV Hydroponic Lamp and LED Grow Light are used to reduce the growth rate of rice bugs during storage. The results showed that the whole system was running well and the rice bugs on rice were drastically reduced within 36 hours and 18 minutes of storage.The need for organic rice among the people continues to increase in line with the declining level of public health due to the COVID-19 pandemic. Consuming organic rice is one way to maintain body immunity, but organic rice is susceptible to attack by Sitophilus Oryzae L, a type of rice weevil which is the main pest in postharvest commodities. Proper storage of rice is one way to address food security during a pandemic. In this study, a prototype of an anti-rice weevil (Sytophilus Oryzae L) organic rice storage was made using a Raspberry Pi controller and several additional sensors such as a camera sensor and temperature and humidity sensors. UV Hydroponic Lamp and LED Grow Light are used to reduce the growth rate of rice bugs during storage. The results showed that the whole system was running well and the rice bugs on rice were drastically reduced within 36 hours and 18 minutes of storage