bluuebunny/crossref_metadata_2025 huggingface dataset:
Causal Inference for Scoliosis and Strabismus: A 2-sample Mendelian Randomization Study
Authors: Changsui Yu , Zifeng Xu , Xiaofeng Zhang , Zhongbao Yu , Kejian Lu , Fengyuan Zhan , Xinyue Zhang , Liguo Zhu
Published: July 2024

Background: Some studies have shown an association between spinal curvature and strabismus, but the genetic association has not been clarified. Therefore, the present study is proposed to be a Mendelian randomization study aiming to investigate the genetic causal association between spinal curvature and strabismus. Purpose: Genetic causal associations between strabismus, convergent concomitant strabismus (Ccs), Divergent concomitant strabismus (Dcs), Other specified and unspecified strabismus (Osus), Other strabismus (Os) and spinal curvature were investigated by a bidirectionalMendelian randomization study to provide a basis for the prevention and treatment of spinal curvature. Methods: Significant and independent Single Nucleotide Polymorphisms (SNPs) in genome-wide association studies were selected as Instrumental Variables (IVs) for Mendelian Randomization (MR) analysis. Inverse Variance Weighted (IVW), MR-Egger regression, Weighted Median (WME), Simple Mode (SM), and weighted mode (WM) were used to analyze causal association; Heterogeneity and multiplicity tests were also performed and analyzed using the leave-one-out method to assess the stability of the results. Results: MR and reverse MR were utilized to assess the impact of scoliosis on strabismus, revealing that the 95% confidence intervals of all instrumental variables’ OR values spanned 1 and the p values were all above 0.05. These results indicate a lack of evidence supporting a causal relationship between scoliosis and strabismus. Conclusion: There is currently no conclusive evidence of a genetic causal relationship between scoliosis and strabismus, including their subtypes. Further laboratory studies are needed to confirm these findings, and future research with larger sample sizes is necessary to provide more robust support.
Audit Sistem E-Learning pada Universitas ABC Menggunakan Framework COBIT 5 - Domain MEA (Monitoring, Evaluate and Assess)
Authors: Putra Kevin , Wahyuningtyas Emmy
Published: July 2024

Information technology (IT) has a very important role in an institution, both educational and non-educational to support the learning function within the institution. If the use of IT has been going well and is able to integrate well, then IT is able to encourage an increase in performance within the institution. For this reason, IT management is needed so that it can be utilized properly. ABC University implements E-Learning as an online learning medium, but its implementation still requires evaluation. Assess the level of capability and prepare recommendations for IT management that do not meet the standards. The evaluation standard usd is COBIT 5 domain Monitor, Evaluate and Assess (MEA) which focuses on supervision, assessment and regulatory compliance of the entire IT system. COBIT 5 is a framework that has a broad and more detailed spectrum of IT processes.  In this study, researchers took research to audit E-Learning from ABC University in order to determine the maturity of the system as well as monitor and evaluate to comply with the COBIT 5 MEA domain framework. From this research, the researcher can give some recommendations after auditing and calculating each domain, sub domain and gap analysis. In accordance with the existing level conditions, every recommendation given by the university can be realized by 95%. This recommendation is made in accordance with the target of achieving maturity level is 2 while currently the level achieved is 1.
Analisis Sentimen Ulasan Pelanggan Online Ubi Madu Cilembu Abah Nana Menggunakan Algoritma Naïve Bayes
Authors: Zain Muhammad Rafly Al Fattah , Kamayani Mia
Published: July 2024

This research aims to analyze the sentiment of online customer reviews for Ubi Madu Cilembu Abah Nana using the Naïve Bayes algorithm. The study has two main objectives: to classify the sentiment analysis of reviews into positive and negative categories regarding the service and products of Ubi Madu Cilembu Abah Nana, as well as to evaluate the accuracy level of the final classification results. The data was collected from online food delivery applications such as Gofood, Grabfood, and Shopeefood. The data used in this study amounts to 259 entries, with 310 positive and 49 negative data points. After conducting experiments, an accuracy result of 86.29% was obtained in Experiment 1 using the Split Data operator, and an accuracy of 86.12% was achieved in Experiment 2 utilizing Cross Validation with the assistance of language experts. The findings of this research indicate that the Naïve Bayes algorithm can be employed to classify customer sentiment towards the service and products of Ubi Madu Cilembu Abah Nana with a significantly high accuracy rate. These results can be valuable for Ubi Madu Cilembu Abah Nana in enhancing their service and product quality based on customer feedback. Additionally, this study also contributes to the field of sentiment analysis and natural language processing by applying classification algorithms to customer review data.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection
Authors: Wirawan M Wandi Dwi , Gusti Siska Kurnia , Jasril Jasril , Pizaini Pizaini
Published: July 2024

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Facial Expression Recognition inCovid-19 Pandemic EraUsing 2 Stage Convolutional Neural Network
Authors: Kurniawan Kevin , Wulandhari Lili Ayu , Gondosiswojo Antonius Rildo Pramudya
Published: July 2024

In the Covid-19 pandemic era, the use of face mask has become mandatory for all citizens to prevent the spread of the virus. This regulation has becomes a big problem for the Facial Expression Recognitions (FER) applications because face mask cover more than halfof human faces. Starting from this problem, this experiment attemps to produce a robust network which can perform well in both conditions: recognizing expressions with and without a face mask. Dataset used in this experiment is FER2013, with a preprocessing step to produce a FER2013 masked. This research uses 2-stage network, where the first network is used to recognize whether the subject is wearing a mask or not, and then the second network is used to recognize the expression based on the result from the first stage. The network in this experiment is based on Convolutional Neural Network (CNN), with Imagenet as our pretrainedmodel and EfficientNet as our architecture model.Ourproposed model has shown quite good performance in recognizing expressions, even when the data consists of subjects who use and do not use masks with an accuracy of 57.55%.
Prediksi Harga Tandan Buah Segar dengan Algoritma K-Nearest Neighbor
Authors: Bukit Silvi Joya Arditna Br , R. Rakhmat Kurniawan
Published: July 2024

Palm oil and its derivative products are a source of foreign exchange for this country, because efforts are needed to maintain and develop the sustainability of palm oil as a potential natural resource. The company carries out statistical analysis on the factors inhibiting the previous month's harvest with a correction value of 5% – 12%. However, this kind of analysis still produces inaccurate prediction results, this is because the calculation process still involves estimation techniques from personal experience, looking at previous production patterns and other determining factors such as land area, principal amount and planting age. As a result, prediction targets often experience errors and production results are excessive or less than the target. Therefore, better predictive calculations are needed in determining palm oil production targets. Accurate predictions can help companies make decisions to increase production output. To carry out forecasting, it is necessary to apply the K-Nearest Neighbor Algorithm which can be used to predict palm oil prices in the future. Based on the results of data mining calculations using palm oil FFB prices from 2018 to 2023 (May 2023), it was concluded that the prediction of palm oil FFB prices in the 67th month (July 2023) had an accuracy level of 10,667 with k=3 and 19,200 with k=5.
Penentuan Mahasiswa Berprestasi dengan Menerapkan Metode Multi Attribute Utility Theory (MAUT)
Authors: Murti Wulan Kartika , Triayudi Agung , Mesran Mesran
Published: July 2024

Being an outstanding student in higher education is certainly a positive and proud thing. Where national education aims to develop the potential of students, in order to become educated, creative students and become democratic and responsible citizens. In determining outstanding students, there are several criteria that must be met by each student as a condition for determining outstanding students. The problem that occurs is that sometimes there are obstacles when assessing the criteria set for each prospective participant. To help the evaluation team in determining outstanding students, a decision support system is needed, sometimes experiencing obstacles when assessing the criteria set for each candidate. In the assessment carried out directly there are prospective candidates who do not meet the criteria standards but excel in other criteria. The Multi Attribute Utility Theory (MAUT) method is a quantitative comparison method used to convert several interests into numerical values on a scale of 0-1 with 0 representing the worst value and 1 the best value. The result of this research is a student determination decision that has the highest score value, namely Netralman (A1) with a utility value of 0.462.
Sistem Pendukung Keputusan Perbandingan Metode MOORA Dengan MOOSRA Dalam Pemilihan Hair Stylish
Authors: Abdullah Mohammad Aldinugroho , Aldisa Rima Tamara
Published: July 2024

This study aims to compare the effectiveness of the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) and MOOSRA (Multi-objective Optimization on the Basis of Simple Ratio Analysis) methods in the context of selecting stylish hair at the barbershop. In the growing hair care industry, the selection of stylish hair does not only affect the appearance of the customer but also plays an important role in the image and success of the barbershop itself. Therefore, it is important for barbershop owners to choose the right stylish hair. The MOORA method is known for its ability to solve multi-objective decision-making problems by utilizing ratio analysis. Meanwhile, MOOSRA is another method that focuses on optimization by considering relative preferences. In the context of selecting stylish hair, both can be useful tools in guiding barbershop owners to choose stylish hair according to customer needs and preferences. This research involves collecting data regarding customer preferences and hair stylish characteristics from various barbershops. This data was then analyzed using the MOORA and MOOSRA methods to choose the most suitable hair style for each scenario. The results of the analysis will be compared to assess the relative performance of the two methods in this context. It is hoped that the results of this research will provide valuable insights for barbershop owners and the hair care industry in general. By understanding the advantages and limitations of each method, barbershop owners will be able to make more informed decisions in selecting stylish hair. In addition, this research can also contribute to the development of a methodology in more complex multi-objective decision-making, by providing concrete examples in practical applications. The final results of the calculations of the two methods are proven to produce the same highest ranking result, which is obtained by alternative 1 on behalf of Poppy Sukma.
Sistem Pendukung Keputusan Seleksi Data Terpadu Kesejahteraan Sosial Menggunakan Metode K-Means dan SAW
Authors: Praningki Tutus , Poernomo Moyo Hady , Suban Ignasius Boli
Published: July 2024

Indonesia as a developing country cannot be free from problems related to poverty, Some people still have income that is not sufficient for a decent life. The Indonesian government has several programs that are useful for reducing poverty, namely the Bantuan Pangan Non Tunai (BPNT) atau Program Keluarga Harapan (PKH). Often in the process of distributing aid there are obstacles or problems, the obstacle that often arises is determining the right family to receive assistance. Sub-districts or villages are at the forefront of the data collection process and selection for recommendation into the Data Terpadu Kesejahteraan Sosial (DTKS). This research aims to develop an application product that can help the Ngronggo Kediri sub-district to accurately determine which families are entitled to assistance. The K-Means and SAW methods are used to determine which families are included in the DTKS. The K-Means method is used for the clustering process and SAW is used for the weighting process. The final weight shows that the highest recommended family weight is 91,2%, and the lowest is 51.4%.
Analisis Perbandingan Kinerja Clustering Data Mining Untuk Normalisasi Dataset
Authors: Saqila Siti Emalia , Ferina Intan Putri , Iskandar Agus
Published: July 2024

Nowadays, the development and influence of technology in human life is very important, where the role of technology greatly influences the activities carried out by humans. In a company organization, technology is not only used as a process to speed up the processes carried out. The use of such important technology also increases the size or volume of available data information. A dataset is a collection of data obtained in a data warehouse. Data mining is a technique that is part of Knowledge Discovery in Database (KDD). Clustering is a grouping process carried out in data mining. The first problem that is central to the research is that the values obtained from the clustering process are sometimes still not considered optimal. The performance results of the data mining clustering algorithm cannot yet be fully used as a basis for decision making. Comparisons made in clustering data mining are used to assist in the decision making process. In this research, the algorithms that will be used for comparison of performance are the K-Means and K-Medoids algorithms. Another problem that needs special attention is the problem of data quality. The results obtained from the data mining process can be seen from the quality of the data stored or used in the data processing process. Normalization is part of preprocessing data mining which aims to re-reason it based on a new scale. Z-Score is a normalization carried out on data based on statistical functions. The results obtained in the research The role of normalization in the research is very important, this is because using Z-Score normalization can improve the performance of the K-Means and K-Medoids algorithms, this can be seen from the DBI value obtained which is smaller when normalization is carried out compared to before it is carried out normalization, which indicates that performance is better after normalization. In the comparison of algorithms, the K-Medoids algorithm gets better performance, this can be seen from the DBI value obtained at 0.773 at K=9 after normalization. Meanwhile, the K-Means algorithm obtained a value of 0.783 at K=9 after normalization as well
Implementasi Dempster-Shafer Theory Sebagai Mesin Inferensi Pada Sistem Pakar Diagnosa Penyakit Cerebral Palsy
Authors: Erkamim Moh. , Tonggiroh Mursalim , Munti Novi Yona Sidratul , Rahmanto Yuri
Published: July 2024

Early diagnosis and appropriate intervention are very important to minimize the long-term impact of Cerebral Palsy in children. Currently, the diagnosis of Cerebral Palsy in children is often based on clinical observations, developmental tests, and brain imaging. It requires medical knowledge and careful observation by an experienced health professional, which is often difficult to access in many areas. For this reason, early diagnosis by parents is very important for taking action against children suffering from Cerebral Palsy. This research aims to develop an expert system that can diagnose Cerebral Palsy in children using the Dempster-Shafer Theory algorithm as an inference engine to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach works by calculating the level of confidence or belief in a hypothesis or certain event based on existing evidence. An expert system built on a website has the ability to make diagnoses based on symptoms and display diagnosis results, definitions of the type of Cerebral Palsy disease in children, as well as actions or methods of treating it. Based on the test results, the accuracy level obtained was a value of 90% and was classified as "Good" criteria.
Otomatisasi Pengontrolan Kualitas Air Pada Akuarium Ikan Arwana Berbasis IOT Menggunakan Logika Fuzzy Tsukamoto
Authors: Nurhadi Ade Muhamad , Midyanti Dwi Marisa , Suhardi Suhardi
Published: July 2024

Arowana fish is an ornamental fish that has a beautiful body shape and color that makes it have a high price, and requires excellent care. The quality of the water in the aquarium must be maintained so that the Arowana fish are not stressed, sick, or die. However, many Arowana owners do not monitor water conditions closely. Therefore, a system is needed that can monitor the state of the aquarium environment, including automatic and manual water changes from the website. In this study using Arduino uno as a microcontroller connected to a pH and turbidity sensor, then NodeMCU ESP32 was used to send data to the internet and to perform the calculation process of Tsukamoto's fuzzy logic. The test results obtained an average accuracy on pH sensors of 96.62%, on turbidity sensors an average accuracy of 92.84%, on ultrasonic sensors an average accuracy of 99.62%. The system also tested Tsukamoto's fuzzy logic where the results were fuzzy with an accuracy of 83%. Testing of the system as a whole was carried out for 4 days. From the tests carried out, the average water pH per day results are 7.1 and turbidity of 18 NTU and all water quality conditions can be met according to the desired output using 9 rule bases.
Sistem Keamanan Kendaraan Bermotor Berbasis IoT dan Web dengan Fitur Pelacakan GPS dan Pemutusan Aliran Listrik Secara Otomatis
Authors: Hidayat Tofiq Nur , Ardiani Farida
Published: July 2024

In general, motorcycle vehicles only have security such as conventional safety locks or only an alarm system. Unlike vehicles such as modern cars that already have GPS (Global Positioning System) devices. There are many ways to improve motorized security systems, one of which is embedding GPS devices in vehicles that do not yet have similar security. So Internet of Things (IoT) technology and GPS devices such as Ublox Neo are used to help monitor the whereabouts of vehicles in real time. IoT technology allows smart sensors to be connected to the internet network. Tracking systems that use IoT can utilize data from sensors to provide information to users about the whereabouts of their vehicles. the use of this technology aims to increase efficiency and security on vehicles so that cases of vehicle theft rates can be suppressed. The results of the test there are several conditions when the device cannot work properly. However, 80% of the test results show the success of the device in tracking vehicles. The average response time in receiving satellite data is about 1-5 seconds. The user can also turn off the vehicle so that the user can more easily chase the perpetrator so as to minimize the occurrence of vehicle theft. the response time of the device in turning off the vehicle is about 5-10 seconds due to alternating in sending and receiving data with the GPS module. It is hoped that criminals who steal vehicles will find it more difficult to steal motorcycles equipped with IoT-based security systems.
Klasifikasi Sentimen Masyarakat Terhadap Prabowo Subianto Bakal Calon Presiden 2024 di Twitter Menggunakan Naïve Bayes Classifier
Authors: Dwitama Raja Zaidaan Putera , Yusra Yusra , Fikry Muhammad , Yanto Febi , Budianita Elvia
Published: July 2024

The Indonesian President who has served for 2 consecutive terms cannot nominate again to become President. The public's attitude towards the three presidential candidates, Prabowo Subianto, Anies Baswedan, and Ganjar Pranowo, who are predicted to run for the 2024 presidential election, is also a matter for netizens' opinions from which conclusions can be drawn. Testing will be carried out in this research using information collected from tweets posted by Twitter users. Naïve Bayes Classifier is a technique that will be applied for sentiment assessment. In the upcoming presidential election, this research will be a source when determining the presidential choice. 2100 tweets with the search keywords "Presidential Candidate" and "Prabowo Subianto" are data collected by dividing 1050 positive data and 1050 negative data. Then implementation was carried out using Google Colab starting from data processing (cleaning, case folding, tokenizing, normalization, negation handling, stopword removal, stemming) followed by classification using the Naïve Bayes Classifier. According to test findings using the Confusion Matrix with three experimental test data 90:10, 80:20 and 70:30. Obtained the highest accuracy results of 89%, with a precision value of 89.7%, 88.6% recall and 88.9% f1-score in the 90:10 trial test.
Penerapan CRISP-DM dalam Klasifikasi Sentimen dan Analisis Perilaku Pembelian Layanan Akomodasi Hotel Berbasis Algoritma Decision Tree (DT)
Authors: Singgalen Yerik Afrianto
Published: July 2024

The Cross-Industry Standard Process for Data Mining (CRISP-DM) approach is very relevant in identifying business challenges and producing recommendations in the form of appropriate models to face various business challenges. Sentiment classification is needed to identify and analyze consumer trends and preferences in order to plan risk mitigation strategies related to business sustainability. This study adopts the CRISP-DM method in classifying hotel guest sentiment through review data on the Agoda platform and analyzing sentiment data based on the purchase behavior of related products and services. Meanwhile, the stages in the CRISP-DM method are as follows: the stage of understanding the business context (business understanding), the stage of understanding data characteristics (data understanding), the modeling stage (modeling), the evaluation stage, and the implementation stage (deployment). The results of this study show that ten words are the attention of hotel guests and are dominated by positive sentiment, namely shopping, great, stay staff, clean, location, room, good, mall, and hotel. The classification results using the DT algorithm showed good performance with an accuracy value of 93.91%, a precision value of 90.98%, and a recall value of 97.77%.  In addition, the AUC value is 0.943 or 94.3%, and the f-measure value is 94.18%. Furthermore, sentiment analysis data can be developed into a Customer Relationship Management (CRM)  supporting application to analyze guest purchase history data related to sentiment, country of origin, guest type, room type, and length of stay by day, month, and year. Thus, the marketing strategy of hotel accommodation services can be optimized for personalization and increase interest and intention of returning stays.
Studi Kinerja Jaringan LoRa: Optimalisasi dan Analisis Efisiensi Komunikasi Nirkabel
Authors: Astutik Liya Yuni
Published: July 2024

LoRa is a long-range wireless connectivity technology, considered an innovative solution for implementing the Internet of Things (IoT). This research encompasses a series of performance tests conducted under various conditions, including Line of Sight (open) and Non-Line of Sight (closed), utilizing the ISM 915 MHz frequency. The importance of testing optimization and efficiency analysis of LoRa is a key factor in ensuring the optimal performance and sustainability of this technology. The testing process involves several stages. The first test assesses the communication range of LoRa SX1276 within a building, determining its communication capabilities indoors. The second test focuses on the maximum communication range, identifying the farthest distance at which LoRa SX1276 can maintain a stable and reliable connection. The third test evaluates the performance of data transmission in a closed environment, specifically Non-Line of Sight conditions, to assess how well LoRa SX1276 maintains data transmission performance in situations with obstacles or hindrances. The fourth test assesses the resilience of LoRa SX1276 in long-term operations, evaluating the network's durability and reliability over an extended period. The fifth test conducts a comprehensive analysis of the system's performance, including aspects such as delay, data delivery, and data loss. This research aims to provide a deeper understanding of the capabilities of LoRa SX1276 in various usage scenarios. The results are expected to contribute to the development and implementation of LoRa SX1276 technology in diverse applications requiring wireless connectivity with extensive range and high efficiency.
Analisis Dan Visualisasi Data Penjualan Menggunakan Exploratory Data Analysis dan K-Means Clustering
Authors: Sari Shinta Permata , Putri Raissa Amanda
Published: July 2024

Many innovations in the retail sector are closely related to digitalization and technological advances. The value of sales data is increasing as a resource for businesses in today's digital era. Sales data consists of details about the items sold, the clients they serve, sales patterns, and other elements that influence how well a business operates. However, effectively handling and understanding vast and complex sales data can be difficult and confusing. So with this problem the author will determine the annual sales level of the products that are most widely marketed by applying Exploratory Data Analysis (EDA) and the K-Means Clustering Algorithm, the method used is to determine the sales level based on the attributes used, the sales level will later be divided into 3, namely clusters 1 is high, cluster 2 is medium and cluster 3 is low. Based on the results of the EDA and K-Means methods, the results of a sales comparison for 4 years have a average value, indicating that sales in 2019 have large average values between 2020, 2021 and 2022. From the visualization results it can be concluded that there are 27 products in the cluster-1 category, 10 products in the cluster-2 category, and 5 products in the cluster-3 category.
Penerapan Algoritma K-Nearest Neighbor Untuk Pengelolaan Efisien Tata Letak Buku Dalam Lingkungan Perpustakaan
Authors: Dollar Dzulfikri Akbar , Zufria Ilka
Published: July 2024

The library plays a central role in providing efficient access to information tailored to the interests of the community. The current system of organizing the library at the Medan City Library and Archives Department involves using call numbers. This system isn't very efficient for readers looking for interesting books, which can decrease people's interest in reading and perceive the library as a dull place. Hence, researchers have attempted to optimize book layout by prioritizing the most popular books. Using the K-Nearest Neighbor algorithm to optimize the book layout at the Medan City Library and Archives Department can assist in arranging book patterns based on historical borrowing data and facilitate better access to collections favored by library visitors. The research method involves collecting book borrowing data from previous months and processing it using the K-Nearest Neighbor Algorithm with Euclidean Distance calculation. The research results demonstrate that implementing this algorithm successfully predicts book arrangement patterns that align with reader interests. An adaptively arranged layout enhances the availability of popular books in easily accessible areas of the library for visitors. This is evident from the research findings in May, where the priority for the following month was the general collection. Similarly, in June, the priority was indeed the general collection, confirming that the algorithm's use of K-Nearest Neighbor is successful in optimizing book layout.
Pemanfaatan Algoritma K-Means Dalam Menentukan Potensi Hasil Produksi Kelapa Sawit
Authors: Wahyuni Ayu Sri , Haerani Elin , Budianita Elvia , Afrianti Liza
Published: July 2024

Considering the importance of oil palm cultivation now and in the future, as well as the increasing demand for palm oil by the world population, it is necessary to think about efforts to increase the quality and quantity of palm oil production appropriately in order to achieve the desired and achievable goals. Based on data on palm fruit production results from PT Salim Ivomas Pratama Tbk, it can be seen that fruit production varies in several places. The potential yield of oil palm fruit is based on the harvested area, actual production and year of planting. K-Means welding can help identify the potential of oil palm, with results that vary from day to day. This process allows locations with similar production patterns, which facilitates management decisions and production strategies. In this research, potential fruit planting areas were grouped using the K-Means algorithm. K-Means aims to facilitate the grouping of blocks with high and low fruit production. The data used is 180 data for the last 5 years, namely from 2018 to 2022, with the attributes Harvest Block, Area, Sheet Weight, and Product Realization or quantity. This research uses the help of Rapidminer and Google Colab software. The results of this research show that C1 (the highest) is 125 Harvest Block data in the sense that the first group is included in the good or high harvest yield category in 2018-2022, and C0 (the lowest) is 55 Harvest Block data in the sense that the second group is included low yield category 2018-2022.
Sistem Deteksi Dini dan Pemadaman Kebakaran Otomatis di Rumah Berbasis IoT Menggunakan NodeMCU ESP32
Authors: Saputra Dimas Bagus , Hidayati Rahmi , Suhardi Suhardi
Published: July 2024

Fire is a serious disaster that causes significant losses in terms of both material assets and loss of life, especially in densely populated residential areas. Fires that occur in one house can easily spread to other houses. To prevent this issue, an early fire detection and automatic fire suppression system has been developed for homes based on the Internet of Things (IoT) using NodeMCU ESP32. This system has the ability to detect early signs of fire, gas, and temperature conditions, and takes automatic action when potential fire hazards are detected. Additionally, the system sends real-time push notifications to users' Android applications to provide warning messages. Test results indicate that the fire sensor can detect flames up to a distance of 130 cm with an average notification delay of approximately 2,37 seconds. The MQ-2 gas sensor can detect butane gas up to a distance of 170 cm with an average notification delay of about 2,84 seconds, while the DHT22 temperature sensor has an average accuracy of approximately 98,52% with a notification delay of about 2,03 seconds.
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier
Authors: Dermawan Jozu , Yusra Yusra , Fikry Muhammad , Agustian Surya , Oktavia Lola
Published: July 2024

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Penerapan Data Mining Dalam Analisis Penilaian Kinerja Pegawai Menerapkan Metode K-Means
Authors: Sahibu Supriadi , Bambang Rismawati , Taufik Imran , Agusriandi Agusriandi
Published: July 2024

The organization, including local government agencies, in the face of increasingly fierce competition, must improving employee performance if they want to exist. As an effort to improve employee performance, various strategies were needed, one of which is clustering analysis of employee performance. Clustering analysis is very important in an effort to gain knowledge from employee activities quickly compared to manual methods. Therefore, in this study an analysis of employee performance was carried out based on the Employee Performance Targets (SKP), which was divided into 2, namely the 2020 SKP Printout and the 2021 SKP questionnaire. From the results of the K-Means Cluster it produced a more convincing questionnaire SKP cluster because there was a distribution that was more dominated by employee performance that good and the outliers are of little value, and the difference in the Sum of Square values of the SKP printout and questionnaire clusters that not too far away, only 21% so that the characteristics of the cluster results are almost identical. Cluster results described the real conditions of employee performance.
Simulasi Pengukuran Mutu Perguruan Tinggi: Principal Component Analysis (PCA) pada Model Integrasi BANPT - COBIT
Authors: Faradillah Faradillah , Alie Muhammad Fadhiel
Published: July 2024

This study integrates two measurement instruments, namely the BAN PT assessment matrix as a measure of quality and COBIT 5 as a measurement of higher education IT governance through the application of Principal Component Analysis to reduce variables with the same components in order to obtain a measurement model that can accommodate IT quality and governance equally. Based on the PCA results obtained 2 Main Components with variables VMTS, Civil Service and Curriculum on Main Component Factor 1 and Build, Acquire and Implement, Deliver, Service and Support, Monitor, Evaluate, and Score on Main Component Factors 2 with a total of 44 indicator items. Furthermore, a simulation of measuring higher education accreditation was carried out with the variable using the average value. This research was conducted at a private university in South Sumatra, the simulation results obtained showed a score of 3.62 or at the "Established" level. The results of this simulation can then be used by these universities in preparing the actual accreditation process.
Penerapan Metode SMART Pada Sistem Pendukung Keputusan Rekrutmen Karyawan Baru
Authors: Hasugian Humisar , Umar Hamdani Agus , Wulandari Wulandari , Nofiyani Nofiyani
Published: July 2024

At non-bank institutions, the recruitment process of new employees starts with the selection stage of the curriculum vitae, if they meet the requirements then process followed by psychological tests, interviews and programming tests. The determination of employees who are accepted to be hired by the companies is still subject to interviews results without considering other tests, therefor the hired employees are not ready for the work due to lack of technical ability. To overcome this problem, it is proposed to use a simple multi-attribute rating technique method for the processing of employee selection data because this method can process the data quantitatively and qualitatively with various assessments for each criteria and making the decision-making process easier and faster. The research objective is to assist the employee recruitment process using a web-based decision support system application by simple multi attribute rating technique method. The result of this study is in the form of a decision support system application website for recruiting new employees, using the criteria for Psycho-test Results (C1) weighting 20%, Age (C2) weighting 10%, Work Experience (C3) weighting 20%, Interview (C4) weighting 20%, and Mastery of Technical Aspects (C5) weighting of 30%. As for the alternative results of data processing using the above method are Febila Manda Dewi worth 0.666, Dini Apiani worth 0.447, Miqhiyal Noer Sopyani worth 0.890, and Irvan Egiawan worth 0.042, then based on the result it can be concluded that the prospective employee is Miqhiyal Noer Sopyani who got the highest score so he is recommended as the alternative new employee. The results of this study are objective due to the data processing is based on all criteria, so that the selected employees are certain to be ready to work.
Optimasi Biaya Distribusi Kusen Pintu Menggunakan Model Transportasi Northwest Corner Method, Russel Approximation Method, dan Stepping Stone
Authors: Andriani Poppy , Cipta Hendra
Published: July 2024

The transportation method is a method used to calculate distribution costs based on the origin that provides the same products to the destination location optimally using minimum costs. Previously, Cv Prima had to pay Rp 72,356,720 for transportation costs. So that the CV Prima company experienced swelling in distribution costs. Expenses for excess distribution costs result in the quality of door frame delivery not being optimal. In this study, the Northwest Corner Method and the Russell Approximation Method were used as the initial solution and continued with the Stepping Stone method as the optimal solution. So that this research produces the lowest costs after applying the Northwest Corner method revealing transportation costs of IDR 69,256,630 and using the Russel Approximation Method, transportation costs are IDR 67,304,680. For Stepping Stone optimization, the transportation cost is IDR 66,641,020. Therefore, the Northwest Corner and Russell Approximation Methods and continued with the Stepping Stone method for this problem are more efficient, because the company found a reduction in transportation costs of 7.899% or IDR 5,715,700.
Improvement Ranking Accuracy of Weighted Aggregated Sum Product Assessment With Lambda Variable
Authors: Gultom Muhadi M. Ilyas , Nababan Erna Budhiarti , Situmorang Zakarias
Published: July 2024

Conventional methods are still used in selecting  the best students in the various institutions depending on the subjectivity of each member of the assigned committee. In order to make an objective decision, it is necessary to have a method that can consider the criteria used to select the candidates to be elected. The decision-making method used in this study is Weighted Aggregated Sum Product Assessment(WASPAS). This study aims to analyze the increase in accuracy of the WASPAS method that occurs in the implementation of the lambda variable in the process of combining the Weight Product Method(WPM) and Weight Sum Method(WSM). This method is use because it is suitable for the case studied where the application of this method focuses on weighting criteria with a dynamic number of alternatives and low computational complexity providing good performance in handling large amounts of data.The application of this method uses data from students from Engineering Faculty of Universitas Islam Sumatera Utara which is tested on 10 students with criteria adapted from student data attributes that can be used as parameters for decision making. The results of this study show an increase for each alternative with an average value of 23.6% for each alternative. From this study it can be concluded that accuracy is highly dependent on variations in lambda values which are affected by the determinant operator in the equation used. Therefore it is possible to find an absolute equation to give optimal effect on a single value without variation by considering the bias of the effect of the WASPAS method on the lambda variable in future research.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
Tagging Efficiency Analysis of Part of Speech Taggers on Indonesian News
Authors: Nugraha Djatnika Widia , Richasdy Donni , Ihsan Aditya Firman
Published: July 2024

Part of speech tagging (POS tagging) is a part of Natural Process Language (NLP). POS tagging is the process of automatic labeling of a word in a sentence according to the word class. There are various tagger methods in POS tagging, each tagger method has its own characteristics in its application. The research method used is Conditional Random Fields and Hidden Markov Model. The training of the two method models uses the Indonesian language corpus and Indonesian news texts as test data to determine which method is the most efficient based on the results of the accuracy and training time of each model. The method that has the best value is the CRF method with an accuracy value of 97.68 on the evaluation of the corpus test data and 90.02% for the sample Indonesian news dataset with a training time of 146.90 seconds, then there is the HMM method which has the highest accuracy value with a value of 94.25 % and shorter training time relatively shorter at 32.45 seconds and for the sample sentences containing 116 tokens, CRF method produces 90.05% accuracy which is higher than the HMM method which produces 79.31% accuracy.
AI Explanation related Covid Hoax Detection Using Support Vector Machine and Logistics Regression Methods
Authors: Luthfi Naufal Haritsah , Hartoyo Agus
Published: July 2024

Hoax news about Covid is still circulating in society. Especially on social media, this phenomenon still occurs. The existence of this disinformation can cause divisions between communities. Currently, technology can classify hoax news and non-hoax news. But no system can see the reasons for a model to classify hoax news and non-hoax news. Therefore, in this study, a system was developed that can see words on a system that detects hoax and non-hoax news using the Support Vector Machine and Logistic Regression methods. Meanwhile, the Explainable AI method is Local Interpretable Model-agnostic Explanations (LIME). The test results show that the SVM and Logistic Regression methods have the highest accuracy of 91% and 95%. The words collected in the dataset are sufficient to differentiate between a hoax and non-hoax news. It was found that hoax news about Covid-19 has many words related to Covid-19, religion, politics, medical, and words that are not related to Covid-19. Among them are "lockdown", "masjid", "rezim", "ventilator", and "kiamat". Meanwhile, non-hoax news about Covid-19 has many words related to Covid-19, government, and medical. Among them are "protokol", "isolasi", "infeksi", "menteri", and "nakes".
Sistem Pakar Untuk Mendiagnosa Penyakit Paru-Paru dengan Menggunakan Metode Teorema Bayes
Authors: Wenda Alex , Kraugusteeliana Kraugusteeliana , Suryanto Andik Adi , Alam Sitti Nur , Suhada Karya
Published: July 2024

Health is one of the most expensive assets that no one can buy. So that health is the most important thing for every human being, which is priceless. One of the important organs in the human body that greatly affects health is the lungs. The lungs are a part of the human body that plays a role in the respiratory system. Where the respiratory system holds the main control in human life after the heart. at the time of examination of the lungs, the costs required are relatively expensive. So because of the high cost of inspection and the lack of funds owned by the community, so that in the end the community becomes hesitant to carry out an examination. To help the community in dealing with these problems, a system is needed that can help the community diagnose or find out the disease that is being experienced by the community. The system is an expert system. An expert system is a system developed by experts using technology. Expert systems need methods. The method used in this study is the Bayes Theorem. The main function of this Bayes Theorem is to calculate the probability of an event or event occurring through the basis of the effect resulting from an observation or observation. After carrying out calculations based on this method, information was obtained that 90% of patients who consulted had Lung Cancer.
Penerapan Support Vector Machine dan FastText untuk Mendeteksi Hate Speech dan Abusive pada Twitter
Authors: Zikri Afdhal , Agustian Surya
Published: July 2024

Hate speech and abusive language are negative tendencies that often appear on social media recently. In addition, due to the advancement of technology and the rapid growth of the internet, anyone can now engage in hate speech or even offensive expression such as in Twitter, which eventually leads to fights on that social media platforms. Automatic detection of offensive contents and hate speech is recommended to be applied, especially on the user application’s side, to filter tweet contents which destruct social life in the real world. The purpose of this research is to create a classification model using Support Vector Machine with FastText word embeddings features, to classify if a tweet contains hate speech and/or offensive language. Our contribution in this research is an improvement in performance from the baseline SVM (support vector machine) with FastText word embeddings features input. The experiment results will also be compared with several machine learning method that have been reported using the same dataset of 13,167 tweets. The experiment using the most optimal SVM model, yields an average accuracy of 82.65%, with the accuracies of the hate speech class, abusive language class and hate speech level, are 84.92%, 86.60% and 76.43% respectively. These results are better than conventional machine learning, but cannot exceed the results achieved by deep learning.