bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementasi Deep Learning Menggunakan Metode You Only Look Once untuk Mendeteksi Rokok
Authors: Harun Ahmad , Mustakim Mustakim , Kharisma Oktaf Brilian
Published: July 2024

Cigarettes are processed products from tobacco products which are used by burning and then smoked. Smoking activities are often found in everyday life, including in public infrastructure. The approach taken to prevent this activity generally uses manual information or human intervention. In terms of this approach, there are often many problems and failures due to the lack of manpower and supporting rules. Therefore, this study was structured with the aim of being able to detect smoking objects in real time using the You Only Look Once (YOLO) method. YOLO which is based on deep learning is very good at detecting objects, this model provides a single convolution neural network in assigning location and classification. So that in its application, YOLO is very fast in detecting and recognizing objects. This study conducted experiments on the training dataset in testing the YOLOv3, YOLOv3-Tiny and YOLOv4 models. The best training results were obtained in the YOLOv4 model with a composition of 80% training and 20% validation data sharing with a Mean Average Precision (mAP) of 92.54% and an F1-Score of 0.89. This study also conducted experiments on testing to detect cigarettes in real time, where the system can detect cigarettes up to a distance of 4.5 meters, and the highest detection accuracy is obtained at a distance of 1 meter, namely 99.03%.
Penerapan Metode Transportasi dan Transhipment Menggunakan Linear Programming dalam Efisiensi Biaya Distribusi Barang
Authors: Batubara Fikri Husin , Widyasari Rina
Published: July 2024

Goods delivery services or expedition services are now increasingly in demand by people in Indonesia every day. One of the most popular shipping services is the company PT. Tiki Jalur Nugraha Ekakurir (JNE), delivery of goods cannot be separated from the important role of the distribution channel, so that problems that arise in the distribution of goods at PT. JNE are the very expensive cost of shipping goods, which causes the company to incur large costs and suffer losses if the problem occurs. This distribution is not complete. The transportation model is used to solve distribution problems experienced by PT. JNE by applying the north west corner, least cost, vogel's approximation and russell's approximation methods as a comparison solution for problem solving. Then the efficient method will be proven by the linear programming method with the help of LINDO software and a sensitivity test to prove the correctness of the transportation model. With the completion of this model, the aim is to streamline the cost of distribution of goods at PT. JNE, from the results of the research on the distribution of goods that is most efficient with the transportation model, namely the vogel's approximation method with a total cost of Rp. 1.018.500.
Penerapan Fuzzy Logic Untuk Menentukan Indeks Massa Tubuh (IMT) Berbasis Internet of Things (IoT)
Authors: Apriansyah Ardi , Fauzi Ahmad , Faisal Sutan
Published: July 2024

A person's Body Mass Index (BMI) can be determined using the parameters of body weight and height. In manual measurements, to determine BMI, measurements of body weight and height must be carried out and then the BMI results are calculated, so it is felt that it still requires effort and is less effective. Then a tool is made to determine BMI using the Fuzzy Logic method by measuring body weight and height in one step. This Fuzzy Logic method is used to determine the membership status of weight and height. Fuzzy set of weight and height that has been adjusted to the threshold in Indonesia, including weight 35-55 low status, 44-66 normal status, 54-80 heavy status, and height 145-165 low status, 149-176 status normal, 159-190 high status. Software testing results have successfully displayed the results of both sensors, namely, body weight 69.43 Kg with Fuzzi weight status and 161 cm height with normal Fuzzi status. The results of the measurements of the two sensors will also be stored in a database and then displayed on a web containing historical information to find out when the body was last weighed. Then the test results on the LCD Hardware have been able to display a person's weight with a weight of 53 Kg and a height of 171 cm. After testing, these scales have been able to determine the results of a person's body BMI and the two sensors on the scales have been able to determine a person's weight and height.
Analisis Terhadap Tagar #LGBT di Twitter Menggunakan Analisis Jaringan Sosial (SNA)
Authors: Radjah Erwianta Gustial , Iriani Ade , Manongga Daniel H.F.
Published: July 2024

Twitter is utilized to express opinions and respond to social phenomena in real life. Responses to events through Twitter conversations are relatively fast and create popular topics. Popular topics using hashtags are often used to attract, invite and influence other users to respond as well. In May 2022, the hashtag "#LGBT" was widely discussed, which was due to the invitation of a gay couple to attend a podcast by Youtuber Deddy Corbuzier. This resulted in a decrease in Deddy Corbuzier's subscribers and the blocking of one of the TikTok accounts of a gay couple. This study aims to analyze opinions on social media that influence the decisions of other social media users. Negative impacts on content creators and affects the real-life community of religious Indonesians. An analysis of network structure, group, and actors involved was conducted using Social Network Analysis to detect and study the opinions that occurred. Network structure mapping of Density, Diameter, and Reciprocity. Group detection using Modularity and Centrality to identify influential actors. Crawling Dataset of 10,000 tweets with 7,761 nodes (actors) and 8,371 edges. The results of the network structure research showed the farthest distance to reach other accounts is 18 (Diameter), the low-Density value is 0.000164 and the low Reciprocity value between accounts is 0.049480. The results of the research show that the value of the group formed is relatively high, 0.868000. Centrality identification shows @brn as the account with the most connections of 287 and @sop as the account with the highest intermediary among other accounts at 0.000303, and @aiy as the account with the closest distance to other nodes at 1.0. Based on the quality of the node to other nodes, @brn is the highest at 1.0
DevOps Implementation with Enterprise On-Premise Infrastructure
Authors: Alvin Muhammad , Aji Rizal Fathoni
Published: July 2024

Digital transformation is essential in today's VUCA era (volatile, uncertain, complex, ambiguous). As the primary driver of digital transformation, the software has widely adopted agile concepts with agile software development. Its short and iterative cycle makes it adaptable to change. Instead of producing significant changes simultaneously, the development team produces small but frequent changes. The operations team is overwhelmed with implementing these changes, and bottlenecks arise. DevOps comes to remove these bottlenecks and allow the development and operations teams to work together to release the software to users quickly. As part of the digital transformation, PT Logistik Pangan has started to implement DevOps with on-premise infrastructure, which is yet to be optimal. This qualitative research aims to understand the steps taken by the company for implementing DevOps with on-premise infrastructure using GitLab and offers suggestions on how to maximize its implementation. The results show that implementing DevOps with on-premises infrastructure requires additional works to manage the supporting infrastructures for DevOps. Implementation was done incrementally, by adopting DevOps practices one by one at a time. Version control (also known as source control or source code management) is implemented by using GitLab, and requires self-managed GitLab as supporting infrastructure. Continuous integration and continuous delivery are implemented by using GitLab CI/CD, and requires GitLab Runner as supporting infrastructure. Besides the DevOps practices, the company also implement container technology by using Docker that is upgraded to Docker Swarm later, and requires local Docker Registry as supporting infrastructure. All the supporting infrastructures are installed on-premise in company’s data center. It includes servers, storage, and networking that must be managed separately. Some improvements are ensuring mindset and culture have been adjusted, implementing other principles alongside automation, and should continuing beyond these stages: Code, Build, Test, Release, and Deploy. This research has limitation of using GitLab products only. Future research can use other DevOps tools or combine GitLab products with other products.
Penerapan Metode Technique For Order Preference by Similarity To Ideal Solution (TOPSIS) Dalam Pengembangan Desa Terbaik Menggunakan Pembobotan Rank Order Centroid (ROC)
Authors: Putra Lakry Maltaf , Widjaja Warkianto
Published: July 2024

The best village development is a village development where the village can be used as an example for other villages. However, there are many villages that appear to be the best village developers, but they are not at all. In this day and age there are many people who are reluctant to develop a village, there are also many people who are narrow-minded that there is no need for village development because whether there is or not will definitely remain the same. Therefore the author wants to conduct a study in choosing the best village development by having 5 criteria, namely the number of productive age, the number of umkm, the area of the village, the number of schools, and the number of tourist destinations. So in this research a helper system is really needed to solve the problems that occur in the research. The system referred to as an auxiliary tool in solving problems is (SPK). Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the TOPSIS method and ROC weighting where the method is very helpful in generating weight values from alternative data and criteria so as to get the final results obtained in Alternative Q6 with a value of 1.00000.
Collaborative Filtering with Dimension Reduction Technique and Clustering for E-Commerce Product
Authors: Riyadi Daffa Barin Tizard , Baizal Z K A
Published: July 2024

The rapid development of internet users over the last decade has led to an increase in the use of electronic commerce (e-commerce). The existence of a recommender system influences the success of e-commerce. Collaborative Filtering (CF) is one of the most frequently used recommender system methods. However, in real cases, sparsity problems generally occur. This is generally caused because only a small number of users give ratings to items. In this study, we propose the combination of clustering and dimension reduction methods on the Amazon Review Data to overcome the sparsity problem. The clustering method with K-Means is used to group users based on item preferences. Meanwhile, we used Singular Value Decomposition (SVD) for dimension reduction to improve the performance of the recommender system in sparse data. The results show that the combination of SVD and K-Means is successful in predicting ratings with an RMSE value of less than 2, significant performance increase compared to previous study. The use of SVD is proven to be able to overcome sparsity, with a decrease in RMSE of 9.372%.
Music Recommender System using Autorec Method for Implicit Feedback
Authors: Irawan Muhamad Faishal , Baizal Z K A
Published: July 2024

As the number of music and users in music streaming services increases, the role of music recommender systems is getting important to make it easier for users to find music that matches their tastes. The collaborative filtering paradigm is the most commonly used paradigm in developing recommender systems. Many studies have proven that deep learning is able to improve the performance of matrix factorization. One such method in deep learning that has been adapted for use in Recommender Systems is Autorec, which is a variation of the Autoencoder technique. Autorec shows that it performs better than the baseline matrix factorization using Movielens and Netflix datasets. Therefore, in this study we propose the use of Autorec to develop a recommender system for music. The experimental results show that Autorec performs better than Singular Value Decomposition (SVD), with an RMSE difference of 0.7.
COVID-19 Misinformation Detection in Indonesian Tweets using BERT
Authors: Nugraha Fahmi Adi , Fudholi Dhomas Hatta
Published: July 2024

The COVID-19 pandemic has seen a marked increase in the spread of misinformation throughout various media channels, most notably social media. This is particularly true of Indonesia where a combination of middling digital literacy and the slow speed of fact-checking contributes to the continued spread of misinformation. Many of the solutions proposed by other researchers to address this problem do not use transformers despite the existence of Indonesian language BERT models. Thus, in order to both provide a potential solution to the problem of misinformation as well as a baseline for future research we propose an IndoBERT-based model for detecting misinformation in Indonesian language Tweets. For model training, we use the "small" version of the MuMiN dataset which is a comprehensive multi-lingual dataset containing fact checked Tweets. The authors of MuMiN provide a baseline LaBSE model which achieves a macro average F1-score of 54.5% when trained on the MuMiN "small" dataset. We train and evaluate our proposed model on this dataset in order to compare it to the LaBSE model. We also train and evaluate our model on a subset of the dataset containing only Tweets related to COVID-19 that we first translate into Indonesian. Our model achieves a best macro average F1-score of 59.5% on the MuMiN dataset and 79.04% on the subset.
Penerapaan Metode TOPSIS dengan Pembobotan ROC dalam Seleksi Penerimaan Auditor Internal Perusahaan
Authors: Aldisa Rima Tamara
Published: July 2024

Cheating or fraud is a major problem that must continue to be routinely monitored at the company. Fraud is a form of fraud that is carried out intentionally by hiding a truth or fact so that it can incite other people to commit acts that are detrimental to the company. In preventing fraud at the company, routine audit activities must be carried out at the company with a fast and precise time. For the company's operational system to be better, the audit activity is carried out by an internal auditor. Internal auditors are someone who works in activities with confidence with the aim of being able to advance values and improve company operations in an objective and independent manner. In the selection of candidates for internal auditors, there are several criteria as an internal auditor that have been determined by the company, namely General Knowledge, Skills, Expertise Certificates, Personal Qualities, Traits and Education. Currently the company does not have a system that can carry out the selection of appropriate and objective internal auditor candidates. The selection of internal auditors is carried out objectively, so the authors use a decision support system (SPK). SPK is an information system that functions as an aid in decision making within an organization. This system is useful in solving problems encountered through the use of predetermined methods. In this study, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and the ROC (Rank Order Centroid) method were used to obtain the data needed in the selection of internal auditor candidates to get Ratih Budi with a score of 1.0000 in alternative A6 as an internal auditor. on the company.
Sistem Pendukung Keputusan Penentuan Aplikasi Jasa Pemesanan Makanan Online Terbaik Dalam Menerapkan Metode OCRA
Authors: Abdullah Mohammad Aldinugroho , Triyono Gandung , Aldisa Rima Tamara
Published: July 2024

In using online food ordering service applications, the existence of a Decision Support System (DSS) with the OCRA method is very helpful in choosing the best alternative. The application of a Decision Support System with the OCRA method gives important value to the predetermined criteria, so that the best alternative can be selected objectively and measurably. In choosing the best online food ordering service application. the use of DSS can increase the effectiveness and efficiency in decision making, thus bringing a positive impact on the development of the business. So from the results obtained from research regarding determining the best online food ordering service application is alternative A5 (GoFood) with a value of 0.2000.
Implementasi Metode Preference Selection Index (PSI) dalam Seleksi Penerimaan Content Creator Media Sosial
Authors: S Usanto
Published: July 2024

Currently social media in the world of work has an important role and can also be a place of business competition. Social media is now being used as an easier and more efficient promotional platform designed to capture the attention of the audience so as to expand the target market for higher profits. Content creators have an important role for business actors, content creators are responsible for social media owned by business actors. The selection process for accepting content creators is still carried out conventionally so that the selection process is sometimes not appropriate in accepting the best content creators. So with that, business actors must choose quality content creators so that their social media continues to be active and get a wider target audience. In selecting content creators, several criteria have been determined, namely skills, work experience, number of followers, interviews and education. Because of these problems, a system is needed that can assist in obtaining reliable and targeted recommendations. The application of a decision support system in this study was used by implementing the PSI (Preference Selection Index) method in which this method is very helpful in producing the best weight and preference values from alternative data and criteria so that the final result is the recommendation for the best content creator in Medan city in the Alternative A3 is Andriana with a value of 0.9236.
Penerapan Metode Multi-Attribute Utility Theory (MAUT) pada Pemilihan Broadcasting Terbaik
Authors: Suhada Karya , Saludin Saludin , Sadikin Abdurrahman , Dewi Indah Kusuma , Nugroho Fifto
Published: July 2024

In an increasingly sophisticated era and increasingly modern life, entertaining content makes Broadcasting a promising job field. This resulted in many people being interested in broadcasting. Broadcasting is a job related to broadcasting. All parties who participate in broadcasting, both cameramen, presenters, content creators, writer creators and so on. Broadcasting is usually useful for those who want to upload videos to YouTube, Tiktok, Instagram and so on. Broadcasting selection is done manually, it will take a long time and a lot of effort. Therefore a decision support system is needed in order to assist interested parties in choosing the best Broadcasting. In using a decision support system using this ranking method, the method used is the Multi-Attribute Utility Theory (MAUT) method. Based on the calculation results from the MAUT method, a result of 0.6731 is obtained with the alternative A7 On behalf of Bintang Simanjuntak as the best alternative that deserves to be accepted as Broadcasting.
Implementasi Metode Convolutional Neural Network Untuk Identifikasi Citra Digital Daun
Authors: Nasrullah Asmaul Husnah , Annur Haditsah
Published: July 2024

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.
Optimasi Perawatan Pasien Rawat Inap Berbasis Android Untuk Pemantauan Cairan Infus
Authors: Sumarno Sumarno , Wisaksono Arief , Hakim Mochammad Luthfan , Yulianto Riski , Hindarto Hindarto
Published: July 2024

Good and thorough patient care at the hospital is very necessary, so that someone who is treated at the hospital can be served properly, one of which is giving infusions to patients. So far, many hospitals or puskesmas provide infusions manually, relying on nurses or those who look after the patient. This study aims to provide Android-based infusion fluids, so that nurses or caregivers can find out the condition of the patient's infusion. The research used is quantitative research with experimental methods. The location of the research was carried out at the Siti Suaibah clinic located in Tempel Village, Kec.Krian, Kab.Sidoarjo. Data processing methods are used to sort data according to the topic where the data produces something from a research and also Coding data. The research carried out consisted of two stages, the first was making software and the second was making hardware. The results of the research are sending infusion fluid data every 10-12 seconds, and auto refreshing on the blynk application every 5 seconds with a measurement error percentage of 2.15 from the results of manual measurements as well as sending notifications on smartphones if the volume of intravenous fluids is below 6 mm by 70 time. The data sharing process was successful because the data held by the ultrasonic and Mlx90614 temperature of the infusion fluid were successfully stored in the Blynk database via the Arduino Wemos D1R2.
Penerapan Algoritma Association Rules Dalam Penentuan Pola Pembelian Berdasarkan Hasil Clustering
Authors: Octavia Sania Fitri , Mustakim Mustakim , Permana Inggih , Monalisa Siti
Published: July 2024

Zanafa Bookstore is one of the bookstores in Pekanbaru city that is required to meet customer needs and has the right focus in developing sales strategies every day. During the new school year there is an increase in sales, it is known that in July there are the most purchase transactions which are the beginning of the new school year for students and students. In addition, the placement of the book layout is only based on the employee's estimated shelf so that it will affect the convenience of consumers in choosing and finding books if the books are arranged far apart. By placing the layout in accordance with consumer purchasing patterns, it can improve the quality of customer service in bookstores. The book layout can also be used as a reference when adding book stock, information is needed by utilizing transaction data using data mining, namely by using Association rules commonly called Market Basket Analysis. This research uses K-Medoid for clustering on Apriori and FP-Growth in generating rule patterns on large-scale data. Several experiments were conducted on K-Medoid starting from cluster 2 to cluster 7, each of which will be applied to Apriori and FP-Growth with 30% support and 70% confidence. By comparing the evaluation results of each algorithm with each other, it is known that FP-Growth has superior results to Apriori with a total strength of rules of 1.2012. So that the results of the association rules obtained can be used as a reference in the placement of book layouts in the Zanafa bookstore.
Meningkatkan Kemampuan Model dalam Memprediksi Penyakit Jantung dengan Algoritma NCL dan GridSearchCV
Authors: Ahmadi Zulfan , Abdullah Asrul , Fakhruzi Izhan
Published: July 2024

Heart disease is the main cause of death in the world. To reduce this high mortality rate, accurate prediction capabilities are needed in warning people with heart disease to prevent and manage this condition. This study uses a machine learning model to predict heart disease. The purpose of this research is to improve the ability of a machine learning classification model, namely Logistic Regression (LR), in predicting heart disease. So that prediction errors that can harm patients can be significantly reduced. To achieve this goal, research is carried out using two important approaches, namely data preparation and model optimization. At the data preparation stage, data imbalance problems were found between people with heart disease and non-heart disease sufferers. To deal with this problem, the Neighborhood Cleaning Rule (NCL) algorithm is used to correct data imbalances. The use of NCL in the data preparation stage has a significant impact on improving the performance of the prediction model. Furthermore, at the model optimization stage, the GridSearchCV method is used to find the best hyperparameter combination in the Logistic Regression (LR) algorithm. By finding optimal hyperparameters, the performance of the prediction model can be improved. In addition, this study also implemented Weighted Logistic Regression which allows setting class weights, which also contributes to improving model performance. The results of testing the model using the evaluation metrics Accuracy, Recall, and Area Under Curve (AUC) show an increase in the ability of the model. The recall score increased from 0.10 to 0.93, and the AUC score increased from 0.83 to 0.98. This study used a dataset obtained from Kaggle from the Centers for Disease Control and Prevention (CDC). With better predictive ability in identifying heart disease, it is hoped that it can provide accurate early warning to individuals at risk, thereby significantly reducing mortality from heart disease.
Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan
Authors: Carnegie M Devid Alam , Chairani Chairani
Published: July 2024

One of the impacts of the threat caused by heavy rain is flooding, which can have negative effects on human life. There are many factors that contribute to heavy rain, and predicting the intensity of rainfall issued by BMKG (Meteorology, Climatology, and Geophysics Agency) is an initial solution for planning and taking actions to mitigate the impacts of natural disasters. Machine learning methods can be used to predict weather parameters, especially time series rainfall. Deep learning, a branch of machine learning that can understand patterns and make weather parameter predictions with high accuracy, includes several algorithms commonly used for analyzing and predicting weather parameters, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research aims to compare both algorithms and determine which one performs best in predicting rainfall at the North Lampung Geophysics Station. From the evaluation results with RMSE (Root Mean Square Error) value of 16.81, MSE (Mean Square Error) value of 282.55, and MAD (Mean Absolute Deviation) value of 10.43, it is known that the LSTM model 1 with a dataset split of 7:3 has the best performance in predicting rainfall. As for the rain prediction, the GRU model 1 with a dataset split of 7:3 performs best with an accuracy value of 62%, precision of 58%, recall of 66%, and f1score of 62%.
Sentiment Analysis using Random Forest and Word2Vec for Indonesian Language Movie Reviews
Authors: Rafif Fahriza Ichsani , Purbolaksono Mahendra Dwifebri , Astuti Widi
Published: July 2024

The film industry in recent years has become one of the industries that people are most interested in. The convenience of watching movies through streaming services is one of the reasons why watching movies is so popular. This ease of access resulted in a large selection of available movies and encouraged the public to look for movie reviews to find out whether the movies was good or bad. Freedom of expression on the internet has resulted in many movie reviews being spread. Therefore, sentiment analysis was conducted to see the positive or negative of these reviews. The method used in this research is Random Forest and Word2Vec skip-gram as feature extraction. The Random Forest classification was chosen because Randomforest is a highly flexible and highly accurate method, while Word2Vec Skip-Gram is used as a feature extraction because it is an efficient model that studies a large number of word vectors in an irregular text. The best model obtained from this experiment is a model built with stemming, Word2Vec with 300 dimensions, and a max_depth value of 23, achieving an f1-score of 83.59%.
Prediksi Curah Hujan Bulanan Berdasrkan Parameter Cuaca Menggunakan Jaringan Saraf Tiruan Levenberg Marquardt
Authors: Setiyaris Setiyaris , Hariyadi Mokhamad Amin , Crysdian Cahyo
Published: July 2024

Accurate prediction of rainfall is very important for warning services for hydrometeorological disasters or disasters caused by rain, so high accuracy is required in making predictions of rainfall. Artificial Neural Networks are becoming a trend in the field of computers because they provide the best accuracy in making predictions. Artificial neural networks are very powerful in recognizing data patterns to model and predict rainfall. The purpose of this research is to predict rainfall using the Levenberg Marquardt algorithm artificial neural network method. The data used for analysis are 120 data consisting of temperature, humidity, pressure, wind speed and solar radiation. To get accurate predictions, calculations are carried out by varying the amount of input and output data as well as varying the number of neurons in the hidden layer. The best performance of a model is measured from the value of MSE or Mean Square Error. The result shows that the network with a data composition of 90% input data, 10% output data and 25 neurons in the hidden layer is the best architecture with an MSE value of 0.029.
Implementation of Collaborative Filtering Algorithms in Mobile-Based Food Menu Ordering and Recommendation Systems
Authors: Siregar Nurini , Samsudin Samsudin
Published: July 2024

In the business world, the application of technology is becoming common, including in the process of buying or ordering food products which can now be done through a mobile application. Makecents Coffee is a startup in the city of Medan that provides solutions for ordering food and drinks at Android-based restaurants using the QR Code ordering system. To make it easier for buyers to place orders, an automatic recommendation system is needed. One method that can be used to develop an ordering application with a recommendation system is a collaborative filtering algorithm. In this study, a collaborative filtering algorithm was used to work by storing and processing data provided by buyers, such as ratings or comments on the food menu ordered. Using buyer data provides results for users in placing orders because they use an application that has them, as well as making it easier to choose a menu to order because of a recommendation system. The level of accuracy of the prediction of the collaborative filtering algorithm itself has been tested using the MAE and RMSE tests. Where the MAE test obtained a value of 0.67 points, while the RMSE test obtained a value of 0.58 points. The two test results were fairly good when compared to the range of points which only ranged from 1 to 5 points. The results of the recommendations can be implemented in applications designed to increase sales and make it easier to place orders that have been recommended to users.
Effectiveness of Word Embedding GloVe and Word2Vec within News Detection of Indonesian uUsing LSTM
Authors: Adrian Muhammad Ghifari , Prasetyowati Sri Suryani , Sibaroni Yuliant
Published: July 2024

In recent years the use of social media platforms in Indonesia has continued to increase. The increasing use of social media has several advantages and disadvantages. The advantage is that the news is easily accessible by anyone, while the disadvantage is that much information that is spread is hoax news. Hoax news must be detected because hoax news spreads false and misleading information. This undermines the integrity of the information and needs to be clarified for the public. By detecting hoax news, we can ensure the information being disseminated is accurate and trustworthy. In this study, the author will detect hoax news on Indonesian news media on Twitter using LSTM with word embedding GloVe and Word2Vec and compare the two-word embeddings to find the best performance in the LSTM model. The reason for choosing the GloVe and Word2Vec extraction features to be compared is that both are useful for representing vectors of words. Their performance may vary. Word2Vec might better capture semantic relationships between words, whereas GloVe might better capture distributional relationships and word co-occurrence. This study shows that LSTM with Word2Vec performs better than LSTM and GloVe in detecting Indonesian language news. LSTM and Word2Vec produced an average accuracy value of 95%, while LSTM with GloVe produced an average accuracy value of 90%.
Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier
Authors: Muslimin Muhammad , Lusiana Veronica
Published: July 2024

Basic necessities are the main needs that are important for people’s lives. The increase in the price of basic necessities certainly has a huge impact on the operational costs of the community and has become a very crucial issue. This event gave rise to pro and con responses from the public expressed through social media Twitter. From this event, sentiment analysis research was conducted related to the increase in the price of basic commodities. The amount of data used for this research is 2070 tweet data. The analysis results show that negative sentiment appears more than positive sentiment, with a percentage of 2,8% positive sentiment and 97,2% for negative sentiment. Retrival of tweet data is done through the netlytic.org website with the keyword staples. The classification method uses the naïve Bayes Classifier method. Furthermore, data division is carried out on the dataset with a ratio of 6:4. The data is divided into 60% training data and 40% test data. The size of the test data as much as 40% of the overall data produces the best accuracy rate. From the results of testing the model with the Naïve Bayes Classifier method the evaluation value results are the highest accuracy score of 94,38%, precision of 59,67%, recall of 67,93%, and F-measure of 62,32%. It can be concluded that the results of sentiment analysis on the increase in the proce of basic commodities get a negative response from the public. This research proposes a method of sentiment analysis of rising prices of basic commodities by considering the level opinion sentiment on Twitter.
Group Recommender System using Matrix Factorization Technique for Book Domain
Authors: Saputro Moh Naufal Mizan , Baizal Z. K. A.
Published: July 2024

A recommender system helps users to select the desired items by analyzing the user's habit of interacting with the system. Recommender system also help the group of users for selecting items due to information overloads. Group Recommender System (GRS) is designed to identify all preferences within a group. An aggregation strategy is needed to accommodate all user preferences in a group. GRS is required in many cases, for example in the book domain, a bookstore recommends a list of books through a display for a group of visitors. We design a GRS for the book domain using Matrix Factorization technique. We utilize three methods to design GRS, such as After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). These three approaches were applied to three different group categories, i.e., small groups, medium groups, and large groups. We aim to find the best approach for each group category in this research. The evaluation metrics used are precision and recall in building this GRS. The results of this research indicate that a small group is suitable for using all three approaches, AF methods is the best approach methods for medium groups, and the best approach method for large groups is WBF.
Optimisasi Biaya Distribusi Furniture dengan Metode Tocm-Sum Approach dan Lowest Supply Lowest Cost
Authors: Hasibuan Elpita Sari , Sari Rina Filia
Published: July 2024

PT. Jipta Rimba Djaja is a company engaged in the manufacture of furniture, one of which is the manufacture of plywood. The key to the success of a company can be determined from how the company's marketing, so as to get maximum profit. In order to achieve this goal, the program that must be carried out for the company is to distribute goods or services produced to consumers. In solving transportation problems, there are two solutions, namely the initial solution and the optimal solution. Initial settlement is a solution to find an allocation of goods/products from one source to several destinations. There are many methods that can be used to determine the initial solution, including the Lowest Supply Lowest Cost (LSLC) method and the Total Oppurtunity Cost Matrix-SUM (TOCM-SUM) Approach method. Which is used to determine the most effective allocation of goods and aims to find out the results of optimizing transportation costs from sources to certain destinations with the minimum possible cost. Based on calculations using the TOCM-SUM APPROACH method and the Lowest Supply Lowest Cost (LSLC) method on the transportation costs of distributing furniture (plywood) at PT. Tjipta Rimba Djaja in January 2023 of Rp. 2,507,898,000, - and in February 2023 the optimal cost of distribution is the same, namely Rp. 1,457,324,000. Where the difference between the two is that the calculation steps of the Lowest Supply Lowest Cost (LSLC) method are not as many as those of the TOCM-SUM APPROACH method. So that the completion is faster than the TOCM-SUM APPROACH method.
Metode Naive Bayes Classifier dan Forward Selection Untuk Deteksi Berita Hoaks Bahasa Indonesia
Authors: Chandra Prasetiyo Danang Bagus , Andono Pulung Nurtantio , Supriyanto Catur
Published: July 2024

Presently, hoaxes or fake news have become a serious threat to human life. Hoax news can not only cause material harm and chaos in society, but now fake news can also affect a person's psychology by causing fear and terror, and at worst, it can break national sovereignty. To process the classification, data miming is used so that it can be seen whether a news item is hoax or genuine news. In this study, researchers used naïve Bayes as a classification method. Then the researcher also uses the forward selection function used in the Naïve-Bayes method. Forward selection is the best regression model formation method based on an approach by selecting variables by including the independent variables that have the largest correlation values. While the naïve Bayes algorithm works conditionally independent between predictions. Based on the tests that have been carried out on the classification of Indonesian hoaxes using Naïve Bayes and Forward Selection to obtain an accuracy of 84%, and a recall of 63.72% while the precision increases to 91.19% with an increase in accuracy of 8.8% and a recall of 8.19% and precision increased by 20.98%. It is hoped that the level of accuracy in the classification of Indonesian hoax news using the naïve Bayes method using forward selection can be increased.
Sistem Pakar Diagnosa Kerusakan Pada Mesin ATM Menggunakan Metode Naive Bayes
Authors: Pratika Mutia Dwi , Samsudin Samsudin
Published: July 2024

Detection of damage contained in ATM Machines carried out by employees still has drawback, especially if there are new employees who are still confused about finding damage to the ATM machine. This study aims to build an expert system for diagnosing damage found in ATM machines at PT Advantage SCM Medan which is equipped with solutions for indicated damage. This system was built using the Naive Bayes method where this method will be able to solve this problem, this is because it is able to predict opportunities in the past and this method only requires small training data to determine the estimated parameters it need in the classification process. The design of an expert system for diagnosing damage contained in this ATM machine has 29 symptoms and 6 damages. This expert system is designed using the MySQL database and the PHP programming language. The results of this study are in the form of an accuracy of 90% which is calculated from the comparison obtained between manual data and data in the system.
Sistem Optimalisasi Pengadaan Alat Kesehatan Menggunakan Metode Fuzzy Time Series
Authors: Sari Febrina , Mahmud Soni Fajar , Faisal Rudi
Published: July 2024

Controlling the procurement of Medical Devices is an important matter for the Pharmacy industry to pay attention to in order to win highly competitive competition, therefore an appropriate model and strategy is needed so that the number of sales can increase, the right solution is to maintain optimal stock availability. this is in line with the Regulation of the Minister of Health of the Republic of Indonesia Number 35 of 2014, concerning pharmaceutical service standards at pharmacies for pharmaceutical preparations, medical devices and consumables which includes procurement and control. The purpose of this study is to assist pharmacies in optimizing the procurement of medical devices by applying the fuzzy time series Chen model, so that they can overcome stock emptiness and over stock, besides that the pharmacy has a system that can predict the optimal number of medical device product purchases for the next period. which has an impact on the ability to control stock. The results showed that the fuzzy time series method of the Chen model has very good performance. This can be seen from the value of the accuracy of the forecasting results which is calculated using the AFER (Average Forecasting Error Rate) formula with a value of 4%. The number of medical devices that will be provided for the January 2023 period is 15 pieces.
Perbandingan Keefektifan Metode Case-Based Reasoning dan Certainty Factor dalam Sistem Pakar Diagnosis Penyakit Multiple Sclerosis
Authors: Ekawati Hanifah , Arfyanti Ita , Bustomi Tommy
Published: July 2024

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.
Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index
Authors: Soepriyono Gatot , Triayudi Agung
Published: July 2024

Mutual funds are a container that can be used to accommodate funds from the public which will later be distributed to the owners of the company. The ease of investing in share prices cannot be separated from the ease of obtaining information. The share price that is very popular with the public is the share price for banks, whether privately owned or government owned. However, even though banks are very close and popular with capital market players, this does not rule out the possibility of a decline in share prices. This problem is not a problem that can be considered trivial and ignored, if you continuously experience losses from the capital market it will certainly give rise to distrust or a lack of interest in the public to participate in investing in companies. Predictions for stock prices must be done well and correctly and get accurate results, therefore it is necessary to use a special technique or method to help carry out the prediction process until results are obtained with a good level of accuracy. The expected prediction process is in line with the concept of data mining. The process of applying clustering for predictions is also considered very suitable, this is because in stock prices there is no target class for each data. The K-Means algorithm and K-Medoids algorithm are part of cluster data mining to be used to make predictions based on cluster formation. The purpose of the comparison is to get more reliable results, where these results can be seen from better algorithm performance. The performance measurement process for the K-Means and K-Medoids algorithms will later be assessed based on the Davies Bouldin Index (DBI). The results of the research show that the performance results of the K-Means algorithm are better than the K-Medoids algorithm. This is proven by the DBI value obtained from the K-Means algorithm being no more than 0.6, while in the K-Medoids algorithm the DBI value obtained is up to 5.822. Overall, each stock data has an optimal cluster based on the clustering process with the K-Means algorithm. The optimal cluster results in BMRI stock data, the optimal cluster is at K=4 with a DBI value of 0.501. In the BBNI stock data, the optimal cluster is at K=4 with a DBI value of 0.500. In the BBCA stock data, the optimal cluster is at K=3 with a DBI value of 0.441. In the BNGA stock data, the optimal cluster is at K=2 with a DBI value of 0.263. In the BDMN stock data the optimal cluster is at K=2 with a DBI value of 0.028 and in the MEGA stock data the optimal cluster is at K=4 with a DBI value of 0.353.