bluuebunny/crossref_metadata_2025 huggingface dataset:
Sistem Pakar Deteksi Dini Status Stunting Pada Balita Menggunakan Metode Naive Bayes
Authors: Prasasti Marina Indah , Normawati Dwi
Published: July 2024

A child is defined as stunted if their height for their age is less than -2 Standard Deviations (SD) below the WHO growth criteria Median, and Stunting is caused by inadequate nutrition, frequent illness, and other multidimensional factors. Based on this problem, a study was conducted to develop an early detection Expert System for Stunting status in toddlers using Naive Bayes Method. Data collection was done through a literature review and interviews at the Bantul Health Office. The system development stage includes knowledge acquisition, creating a knowledge base, determining rule bases, creating a decision model with Naive Bayes, and designing the implementation of the system into a computer program. The final stage is to test the feasibility of all functional and non-functional systems through black-box testing, expert judgment, and the system usability scale. The research conducted resulted in an expert system that can detect early Stunting in toddlers and provide appropriate handling solutions and provide probability values for the status of Stunting in toddlers. Based on black-box testing, all functional systems run according to requirements and accuracy with expert judgment produces an accuracy rate of 100% and a system usability scale score of 90, it can be concluded that the expert system that has been built is feasible to use.
Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier (NBC) dan Decision Tree (DT)
Authors: Singgalen Yerik Afrianto
Published: July 2024

This study aims to implement a classification method using the Nave Bayes Classifier (NBC) algorithm on Lake Toba visitor review text data. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology comprises the following stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The findings of this study indicate that during the phase of business comprehension, the context of the discussion focuses on the tourism sector, specifically tourist perceptions of the quality of products and services at Lake Toba tourist destinations. At the data comprehension stage, the source of review data used was the Tripadvisor website, which contained as many as 858 reviews with the following rating classification: 8 reviews with abysmal ratings; 22 reviews with poor ratings; 81 reviews with neutral ratings; 304 reviews with good ratings; 443 reviews with excellent ratings. Data cleansing is performed at the data preparation stage so that 382 data are processed by dividing training data by 70 percent and test data by 30 percent. During the modeling phase, the performance of the NBC and DT algorithms was evaluated using and without SMOTE UPsampling operators. The comparison of NBC and DT algorithm values indicates that the model with the best performance is DT using SMOTE UPsampling operators with accuracy values (98.27 percent), precision values (98.83 percent), recall values (97.71 percent), f-measure values (98.26 percent), and AUC values (98.27 percent) (0.982). At the evaluation stage, the importance of excellent service (Quality Human Resources) and supporting infrastructure was highlighted by analyzing the results of ranking the five most frequently used terms in Lake Toba visitor review data (tourism facilities and infrastructure). At the deployment stage, it is necessary to balance the development of attractions, accessibility, lodging, and tourism-supporting amenities to generate visiting intention and revisit motivation to Lake Toba.
Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors
Authors: Pramana Dedi , Afdal M , Mustakim Mustakim , Permana Inggih
Published: July 2024

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Penerapan Metode TOPSIS Sebagai Pendukung Keputusan Pemilihan Layanan Akomodasi di Destinasi Wisata Pulau
Authors: Singgalen Yerik Afrianto
Published: July 2024

The selection of accommodation services in a tourist destination can be adjusted to the preferences of tourists. This study aims to recommend the TOPSIS method as a decision support system in the selection of accommodation services in tourist destinations, based on case studies of Morotai Island tourist destinations. The application of the TOPSIS method as decision support for the selection of accommodation services in Morotai Island tourist destinations consists of four stages as follows: the stage of making a normalized matrix, weighted normalized matrix manufacturing stage; steps determine the matrix of positive and negative ideal solutions; The step determines the preference value for each alternative. The criteria set are as follows: room price as a cost with an importance weight of 5; distance of the hotel to the destination as a benefit with an importance weight of 3; room type/class as a benefit with an importance weight of 4; hotel amenities as a benefit with an importance weight of 4; Service rating as a benefit with importance weight 4. Specifically, the accommodation service providers focused on are Daloha Resort; Metita Beach and Dive Resort; Moro Ma Doto; Molokai by Sahid Hotel; and Ria Hotel. The results of this study showed that the alternative with the highest preference value was Metita Beach and Dive Resort, with a value of 0.764348724, then Molokai by Sahid Hotel, with a value of 0.626002478. In addition, Daloha Resort has a value of 0.616994938, and Ria Hotel has a value of 0.61630669. Also, Moro Ma Doto has a value of 0.363441753. This suggests that travelers prioritizing price as the dominant factor with weight critical (5) will choose Metita Beach and Dive Resort. If the importance weight of each criterion is changed according to the traveler's perspective, the system will recommend different accommodation services. Thus, tourists can use the TOPSIS method in choosing accommodation services and give importance weight according to individual preferences. Therefore, the TOPSIS method can be used in selecting accommodation services in various tourist destinations.
Evaluasi Perbandingan Kinerja Convolutional Neural Networks untuk Klasifikasi Kualitas Biji Kakao
Authors: Rahadjeng Indra Riyana , Siregar Muhammad Noor Hasan , Windarto, M.Kom Agus Perdana
Published: July 2024

The assessment of cocoa bean quality plays a crucial role in the chocolate industry, and automated approaches utilizing image processing techniques and classifiers have become increasingly appealing. In this study, we implemented and compared the performance of image classifiers using Convolutional Neural Network (CNN) architectures for cocoa bean quality classification. By employing this approach, we developed a system capable of accurately and efficiently classifying cocoa bean images, reducing dependence on human evaluation. We compared several CNN architectures, including VGGNet, to evaluate their performance in cocoa bean image classification. Experimental results demonstrated that CNN-based classifiers can provide accurate assessments of cocoa bean quality, with significant success rates. This research contributes to the development of efficient and accurate image classification systems for cocoa beans, which can enhance efficiency in the chocolate industry and ensure product quality. Additionally, our testing results indicate that the model with a batch size of 64 achieved the highest accuracy of 98.44%, outperforming the other three tested batch sizes in cocoa bean classification performance.
Penerapan Metode Hybrid Case Based dalam Diagnosis Penyakit pada Tanaman Karet
Authors: Ningsih Lucky Suriyah , Sitorus Sahat Parulian , Pane Rahmadani
Published: July 2024

Rubber cultivation utilizing Hevea brasiliensis as the primary source of raw material has established itself as a profitable commercial crop. However, diseases can damage rubber plants and hinder their growth and latex production. To mitigate the resulting losses, it is crucial to develop an efficient expert system for identifying infections in rubber plants. An expert system is a computer program designed to simulate human intelligence in problem-solving and analysis within a specific domain. In this scenario, diseases in rubber plants are identified using an expert system. The Hybrid Case-Based technique, which combines the benefits of Case-Based Reasoning (CBR) method with other methodologies, has proven to be a successful strategy for constructing expert systems. In the Hybrid Case-Based approach, CBR is integrated with other approaches such as Rule-Based Reasoning (RBR). The Hybrid Case-Based technique utilizes information from the existing case base, as well as recognized rules and models, in the context of disease detection in rubber plants. This enables the expert system to diagnose with high accuracy and provide appropriate solutions. The advantages of the Hybrid Case-Based method include its ability to handle complex situations and find optimal solutions. By integrating various approaches, this method overcomes the weaknesses of each individual method. Furthermore, the Hybrid Case-Based approach allows for an overall improvement in the performance of the expert system, resulting in better outcomes in diagnosing diseases in rubber plants. The objective of this research is to utilize the hybrid case-based approach to create an expert system for identifying diseases in rubber plants. It is expected that this research will yield a useful expert system in identifying diseases in rubber plants, which will support farmers in addressing rubber plant health issues. The significance of this research stems from the importance of preserving rubber resources and creating a sustainable rubber economy. When a readily available expert system is capable of accurately identifying infections in rubber plants, farmers can swiftly take necessary steps to halt disease spread and reduce production losses. Additionally, this research can serve as a foundation for developing expert systems in the field of agriculture and other plant diseases. The results of this research indicate that the Hybrid Case-Based method is capable of diagnosing diseases in rubber plants, revealing a low likelihood of White Root Rot disease in the rubber plants with a percentage value of 76%.
Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal
Authors: Nurjulianty A. , Purnawansyah Purnawansyah , Darwis Herdianti
Published: July 2024

Indonesia is a country with various types of herbal plants that have the potential to be very effective medicines. Herbal plants have been used since ancient times as natural medicines. One part that has health benefits is the leaves, however, there are many similarities between the different types of leaves. This research aims  to classify digital images of herbal leaves implementing the Naïve Bayes and K-Nearest Neighbor (KNN) methods with Gray Level Co-occurrence Matrix (GLCM) feature extraction. The dataset consisted of sauropus androgynus and moringa leaves with data collection in bright and dark scenarios. A total of 480 data which was divided into two parts, namely 80% for training data and 20% for testing images. The KNN distances used for comparison are Euclidean, Manhattan, Chebyshev, Minkowski, and Hamming. Meanwhile, Naïve Bayes uses Gaussian, Multinomial, and Bernoulli kernels. The results of the study showed that the KNN method with the Manhattan distance obtained the best results with an accuracy rate of up to 94% in bright scenarios.
Pengenalan Potensi Racun dan Peningkatan Keamanan Pangan Dalam Jamur Menggunakan Convolutional Neural Network
Authors: Pramutighna Ilham Rafiedhia , Hermawan Arief
Published: July 2024

One promising advancement in the field of food agriculture is the cultivation of mushrooms. Mushrooms can be broadly classified into two groups: edible mushrooms and non-edible mushrooms. Edible mushrooms serve various purposes, including as food, medicine, and other applications, while non-edible ones can lead to poisoning. However, distinguishing between edible and non-edible mushrooms is a complex task. Even a slight error in selecting suitable mushrooms for consumption can have health repercussions for consumers. The progress in science and technology, particularly in digital image processing, aids in the classification of mushrooms. Image classification using Convolutional Neural Networks (CNNs) presents an alternative to address this issue. This research primarily focuses on identifying potential toxins in mushrooms using CNNs, aiming to contribute to a more efficient and accurate approach in classifying mushrooms fit for consumption. The results demonstrate that the model trained with data augmentation achieved the highest accuracy, with 96.53% for training data and 93.22% for validation data, accompanied by lower loss rates. This underscores that CNNs are an efficient and accurate approach in classifying mushrooms based on their genus. Furthermore, this study also discovered that parameters such as the number of epochs, batch size, optimizer, image size, and image augmentation influence the model training process.
Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting
Authors: Lonang Syahrani , Yudhana Anton , Biddinika Muhammad Kunta
Published: July 2024

Stunting is a serious problem caused by chronic malnutrition in children under five, causing stunted growth and having a negative impact on long-term health and productivity. Therefore, early detection of stunting is very important to reduce its negative impacts. Previous studies utilizing machine learning have proven the success of this method in various health applications, such as disease detection and the prediction of medical conditions. The results of this research are a comparative evaluation of five classifications, namely Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in classifying stunted toddlers. The dataset used contains four important attributes: age, gender, weight, and height of toddlers, as well as a binary class label that differentiates between toddlers who are stunted and those who are not. The evaluation results show that KNN at K = 3 produces the highest accuracy of 94.85%, making it the best model for classifying stunting in toddlers. Apart from accuracy, other metrics such as precision, recall, and F1-score are used to analyze the algorithm's ability to solve this problem. KNN stands out as the best model, with the highest F1-score of 89.47%. KNN also manages to maintain a balance between precision and recall, making it an excellent choice for treating stunting in toddlers. Apart from that, the use of the AUC metric from the ROC curve also shows the superiority of KNN in differentiating between stunted and non-stunting toddlers. With a combination of consistent evaluation results, both in terms of accuracy and other evaluation metrics, this research proves that KNN is the best choice for overcoming the task of classifying stunting in toddlers.
Stress Detection Due to Lack of Rest Using Artificial Neural Network (ANN)
Authors: Nurwahidin Lukman , Gunawan Putu Harry , Wijaya Rifki
Published: July 2024

Currently, many people feel symptoms of stress due to lack of adequate rest. Which at this time the person will carry out activities that are very heavy both from tasks that are too heavy, work pressure that accumulates, and much more. People who experience stress symptoms sometimes don't know what causes stress. Through this research a learning machine will be made, using the Artificial Neural Network algorithm, will analyze heart rate data or BPM from 7 patient data per day, using a Fitbit smart watch will display several data such as falling asleep, waking up, REM (Rapid Eyes Movement) and, well, from the results of the data collected from the patients. Total data in this research are 36224. This research process will show the best accuracy results from several types of Artificial Neural Network algorithms. At the processing stage of the patient's heartbeat dataset, a comparison will be made between the types of Artificial Neural Network algorithms. The research will obtain the highest accuracy value of 81% from the results the Artificial Neural Network algorithm.
Weight-Based Hybrid Filtering in a Movie Recommendation System Based on Twitter with LSTM Classification
Authors: Ilyas Muhammad Nur , Setiawan Erwin Budi
Published: July 2024

With the era of digitalization, movie-watching has gained immense popularity, with platforms like Disney+ offering easy access to a variety of films. After watching, users frequently share their opinions on social media platforms such as Twitter, because of it is freedom of expression. With numerous movies available, users frequently encounter challenges in deciding what to watch. To address this, a recommendation system is proposed to streamline the decision-making process for users. Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering are common techniques used in recommendation systems. However, CF and CBF techniques face issues like cold start, sparse data, and overspecialization. To overcome these, this research constructs a Hybrid Filtering recommendation system, with a weight-based of CF-CBF coupled with Long Short-Term Memory (LSTM) classification. The classification uses various optimizers, including Adam, SGD, Nadam, RMSprop, and Adamax. Dataset is sourced from Kaggle website, which includes movie-related tweets linked to the Disney+ platform. The results indicate that Weight-Based Hybrid Filtering utilizing Adamax optimizer in LSTM classification yields superior performance metrics, by having 78% Precision, 79% Recall, 79% Accuracy, and 77% F1-Score value.
Klasifikasi Masalah Pada Komunitas Marah-Marah di Twitter Menggunakan Long Short-Term Memory
Authors: Hani Dian Sukma , Ratnasari Chanifah Indah
Published: July 2024

The rapid development of social media goes hand in hand with the increase in social media users. Among the social media platforms widely used in Indonesia, Twitter is one of the most popular. On Twitter, users are free to share every moment they experience or what they think. Many users use Twitter as a medium to express their emotions, as is what happens in angry communities. There are no special requirements to join as a community member other than getting admin approval. This community provides a place for its members to vent all kinds of anger they feel. This research classifies angry community tweets to find out the types of problems in these tweets. The results of this research can help in understanding communication and behavior patterns in angry communities, which can provide deeper insight into the social dynamics within them.Text data is retrieved via web scraping techniques, and then processed through a series of preprocessing steps, including unnecessary character removal, normalization, and tokenization. The classification uses the Long Short-Term Memory (LSTM) algorithm with six problem category classes, namely Study, Romance, Family, Career/Work, Person/Personal, and Swearing. After modeling, the model accuracy was 91.94%. The model was built using an embedding layer, Long Short-Term Memory (LSTM) layer, dense layer, and dropout layer which was run for 10 epochs. Model evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to measure model performance. The value resulting from the evaluation results using the confusion matrix is more than 50, this indicates that the LSTM model is able to classify the problem well.
Penerapan Metode Weighted Aggregated Sum Product Assessment (WASPAS) dengan Rank Order Centroid (ROC) Dalam Rekomendasi Barbershop Terbaik
Authors: Wahyuni Wahyuni , Lailiyah Siti , Andrea Reza
Published: July 2024

Barbershop is an innovation or development of a service previously known as a barber or barbershop. Barbershop operations are led by a barber or hairstylist, also known as a hairstylist, who has special skills in shaving and creating a variety of men's hairstyles. The barbershop business is experiencing rapid growth in this era. While there may be different variations of the name or brand, this business promises very attractive prospects in the long term. With so many barbershop options available, finding the best one can be a daunting task. In choosing a barbershop, there are several criteria to consider, such as the skill of the hairstylist, price, services offered, facilities provided, and level of cleanliness. To organize data and provide recommendations regarding the best barbershop, it is necessary to use an effective information system. The term "Decision Support System" (DSS) is often used to describe these information systems. The main objective of the DSS system is to improve the decision-making process and make it more effective and efficient by providing information, analysis and data modeling. The data needed to provide the best barbershop recommendations in Samarinda City were collected using the WASPAS (Weighted Aggregated Sum Product Assessment) and ROC (Rank Order Centroid) methods in this study. The replacement for the BS4, Sir Salon Barbershop, has the highest rating of 0.9815, making it a top barbershop recommendation.
Analisis Perbandingan metode Teorema Bayes dan CF dalam Mendiagnosa Gejala Penyakit Demam Berdarah Dengue
Authors: Harpad Bartolomius , Andrea Reza
Published: July 2024

Dengue Hemorrhagic Fever (DHF) is a potentially hazardous condition and a health concern in various tropical countries. To accurately and swiftly detect this disease, various diagnostic methods have been developed. The incidence of DHF can fluctuate from year to year, influenced by factors such as weather changes, vector control efforts, and socio-economic aspects. In Indonesia, there have been significant outbreaks of DHF in certain years. In this study, the Author conducted a comparison between the Bayesian Theorem method and the Certainty Factor (CF) method to diagnose DHF symptoms. The Bayesian Theorem calculates the probability of the disease based on symptoms, while the Certainty Factor employs a confidence level to link symptoms with the disease. Symptom data from previous DHF patients were collected, and both methods were utilized to diagnose these cases. The analysis results indicate that both methods have their respective strengths and limitations in terms of accuracy and speed. The Bayesian Theorem is accurate when complete symptom data is available, while the Certainty Factor is useful when data is incomplete or uncertainty exists. Both methods can be used concurrently based on context. This research illustrates the application of statistical analysis and data-driven approaches to enhance DHF diagnosis, also stimulating the development of advanced combined methods in the future. This study provides insights into the use of probabilistic approaches and confidence-based logic in DHF diagnostic development. Both methods can be applied interchangeably or in conjunction, depending on data and case characteristics. The results of applying the Bayesian Theorem and Certainty Factor show that the Bayesian Theorem yields 57.29%, while the Certainty Factor achieves 94.47% accuracy in diagnosing DHF.
Optimasi SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online
Authors: Ikhsani Rismawati Nurul , Abdulloh Ferian Fauzi
Published: July 2024

With the development of technology, many applications and social media make it easier for users to do various desires, one of which is borrowing money online on the Online Loan Application with easy terms. The convenience provided causes many violations committed by irresponsible people, such as the breach of important information and data of online loan application users. This causes many people to express their comments and opinions on social media, especially on twitter. Sentiment analysis is conducted to see the tendency of public opinion to fall into negative, neutral, or positive sentiment. Furthermore, public opinion will be classified using two algorithms, namely the Support Vector Machine and Decision Tree algorithms. The aim of this research is to compare the performance of SVM and Decision Tree classification algorithms on the tendency of public opinion on twitter regarding online loans. Furthermore, optimization is carried out using SMOTE to optimize the accuracy of the two algorithms. The results obtained neutral sentiment as much as 78.96%, positive sentiment as much as 14.98%, and negative sentiment as much as 6.06%, people are more inclined to neutral sentiment. Then classification using SVM gets an accuracy of 87% and on Decision Tree gets an accuracy of 89%.. Then to optimize the performance results of the two algorithms, optimization using SMOTE is carried out. After SMOTE optimization, the accuracy produced by SVM is 99% and Decision Tree is 97%. Optimization using SMOTE proves that the SVM algorithm is better than Decision Tree.
Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN
Authors: Pipin Sio Jurnalis , Sinaga Frans Mikael , Winardi Sunaryo , Hakim Muhammad Noor
Published: July 2024

The advent of OpenAI's ChatGPT, a large language model (LLM) proficient in various fields including artificial intelligence (AI) and natural language processing (NLP), has ignited a plethora of opinions and discussions, especially on social media platforms like Twitter in Indonesia. This research seeks to delve into the intricate dynamics of these discussions, aiming to map both the commendations and criticisms surrounding ChatGPT's technological advancements and potential negative impacts. Utilizing deep learning-based sentiment analysis techniques, the study employs Convolutional Neural Network (CNN) and Fast Region-based Convolutional Network (Fast R-CNN) to analyze a dataset consisting of 7,604 tweets categorized into "Positive", "Negative", and "Neutral" sentiments. The objective is to provide a comprehensive understanding of the societal perceptions towards this artificial intelligence technology in the Indonesian context. The methodology encompasses several stages including data collection from Twitter, data cleaning, and pre-processing, followed by the application of CNN and Fast R-CNN models for sentiment analysis. The findings indicate a superior performance of the Fast R-CNN model, achieving an accuracy rate of 94.5%, compared to the CNN model with an accuracy rate of 86%. In conclusion, the research highlights the effectiveness of integrating Fast R-CNN in sentiment analysis to extract deeper insights from Twitter data in Indonesia. This study not only contributes to the scientific literature in the fields of sentiment analysis and natural language processing but also aids in formulating informed strategies to navigate the challenges and opportunities presented by artificial intelligence technology in the Indonesian landscape. Future research avenues should focus on optimizing this sentiment analysis model and exploring other potential applications of this technology in the dynamically evolving digital landscape in Indonesia.
Penerapan Algoritma C4.5 Untuk Prediksi Jumlah Produksi Kelapa Sawit
Authors: Yuni Rahma , Putri Raissa Amanda
Published: July 2024

Palm oil is one of the important commodities in Indonesia today and has quite promising development prospects, Pt. Toton Usaha Mandiri is an oil palm plantation, oil palm plantations are very profitabel so many forests are being converted into oil palm plantations, even though oil palm is very profitabel, it is very difficult to determine the amount of palm oil production in the future, therefore the author will predict the amount of palm oil production going forward with factors that influence palm oil production including demand, supply, production efficiency and variables related to palm oil production such as total income, total supply and total demand needed to achieve good production levels. One way to predict the rate of palm oil production is to use the C4.5 method. The C4.5 algorithm is an algorithm that can form a decision tree, where the decision tree will then produce new knowledge. In the target attribute, the amount of palm oil production is Satisfactory and Not Satisfactory. The analysis used is the C4.5 Algorithm which has a data sample of 54 data from 2019 to 2023 June. By processing the data using the Rapid Miner application, the system accuracy value was 96.30% the unsatisfactory prediction was 16 and the satisfactory prediction was 36 with a precision value of 94.12% and a recall value of 97.30%.
Penerapan Metode Fuzzy Tsukomoto untuk Mengukur Tingkat Kelayakan Produk
Authors: Desyanti Desyanti , Wulandari Denok , Anjani Dewi
Published: July 2024

Micro, Small and Medium Enterprises are one of the business fields that are in great demand by the public. MSMEs have their own advantages, namely that they can more easily carry out product development and can survive during an economic crisis. However, product feasibility is something that must be considered in MSME activities because it can provide benefits in terms of financial and social benefits. With a feasibility analysis, it is hoped that the risk of failure in marketing the product can be avoided. One of the MSMEs in the city of Dumai is X Cookies, a business that was built in 2019 and has many consumers who come to buy products from this home business, both in small and large quantities. To improve the quality of product suitability, you must evaluate the quality of the product provided to consumers whether it meets the expectations and desires of consumers or not. By using Tsukomoto's fuzzy logic, the level of suitability of X Cookies products can be determined. From the research carried out, the product feasibility level was not feasible = 0.39155 and feasible = 0.60845. So it can be said that the feasibility level for the X Cookies brownie product is at a feasible level.
Perbandingan Metode ARIMA Box-Jenkins dengan Moving Avarage Untuk Peramalan Harga Emas
Authors: Roma Putra Lubis Desmi , Zufria Ilka
Published: July 2024

Gold is a type of investment that is starting to be popular with many people. Gold price forecasting has an important role for investors and business people in making smart investment decisions. The problem in this research is displaying the price of gold in the market which influences many factors, for example global supply and demand, fluctuating exchange rates, the global situation, and the US dollar exchange rate. And another problem that can be taken up is forecasting gold prices which change from time to time which results in potential losses for investor trading in terms of profit risk in the market. Therefore, it is necessary to create a system that can determine gold price forecasts for investors in the market. uses the Box-Jenkins ARIMA comparison method with precise Moving Average and careful analysis to forecast gold prices. The comparison method is used to determine the trend pattern of the series and provide accurate data, namely the ARIMA Box-Jenkins method, while the Moving Average method determines the movement of the average value to estimate the next value. By calculating the prediction results of the Moving Average and ARIMA, then comparing the accuracy values of ARIMA and Moving Average, we get the accuracy value of ARIMA, namely 98.74% and Moving Average, namely 97.73%. It is known that the difference is not too different, but ARIMA gets the highest accuracy. The role of the application was carried out by conducting research at the H. ST Gold Shop. Martua by collecting gold price data, after the collected data is calculated for coding then entered into an application that has been built using the Moving Average and ARIMA methods.
Klasifikasi Penyakit Daun Padi Menggunakan KNN dengan GLCM dan Canny Edge Detection
Authors: Verawati Ike , Aunurrohim Ridwan Al Akhyar
Published: July 2024

Rice plants have an important role in human survival, especially in Indonesia where rice plants are the staple food source for most of the population. The Central Statistics Agency reported that rice consumption in Indonesia reached 28.69 million tons in 2019. In the same year, rice production in Indonesia reached 31.31 million tons. However, production results decreased compared to the previous year, which amounted to 33.94 million tons. One of the factors causing the decline in quality and even death of rice plants is pests and disease. According to the International Rice Research Institute, every year farmers lose an average of 37 percent of their harvest due to pest and disease attacks. The Food and Agriculture Organization also reported a similar thing, where 20 to 40 percent of world food production failures were caused by pests and diseases. Farmers' lack of knowledge and the limited number of experts result in ineffective disease diagnosis. Therefore, a step or method is needed so that the disease detection process in rice plants becomes more effective. This research uses the K-Nearest Neighbor classification algorithm with Gray Level Co-Occurrence Matrix and Canny Edge Detection to classify diseases in rice plants. The result is that Canny Edge Detection has a positive influence on method performance with accuracy reaching 91.67%, precision 87.37% and recall 87.50% at k=7.
Sentiment Analysis of the Waste Problem based on YouTube comments using VADER and Deep Translator
Authors: Yuliansyah Herman , Mulasari Surahma Asti , Sulistyawati Sulistyawati , Ghozali Fanani Arief , Sudarsono Bambang
Published: July 2024

The waste problem is a severe problem that significantly affects the environment and public health. To effectively determine the public’s perception of the waste problem, it is necessary to examine public sentiment toward waste management. This research aims to develop a sentiment analysis model using VADER and deep-translator and analyze the Yogyakarta waste emergency problem. This research was conducted in two phases, namely, the first phase was developing a sentiment analysis model by evaluating its performance based on public data. Then, the second phase classifies public comments from YouTube regarding the waste problem to understand public perceptions and evaluations by identifying positive, negative, and neutral sentiments. The model evaluation results show that sentiment analysis using VADER and deep translator can achieve Accuracy, Precision, Recall, and F1-score values of 0.716, 0.837, 0.853, and 0.738, respectively. The sentiment results from YouTube comments obtained positive, neutral, and negative sentiments of 30.0%, 31.7%, and 37.3%, respectively. The results of the sentiment analysis are neutral sentiment discussing waste management, disappointment in negative sentiment, and hope for waste management in positive sentiment.
Alat Penghitung Jumlah Gerakan Pull Up dan Push Up Menggunakan Sudut Kemiringan pada Sensor MPU6050 Berbasis Internet Of Things
Authors: Putra Dimas Ade , Jufrizel Jufrizel , Ullah Aulia , Maria Putut Son
Published: July 2024

The pull-up and push-up counter device is designed to automatically count the number of pull-up and push-up repetitions performed by an individual. The problem faced is errors in performing pull-up and push-up movements and the limitation of manual counting, which solely relies on remembering the total count of movements from the previous day. This can lead to uncertainty in monitoring the progress of the exercise. The purpose of this research is to develop a pull-up and push-up counter device that utilizes the Internet of Things (IoT) and is connected to the Blynk application on a smartphone to provide ease and accuracy in recording exercise repetitions, eliminate the difficulties of manual counting, and provide real-time data to users for monitoring and tracking their exercise progress. This device is equipped with an MPU 6050 sensor that can detect changes in angular acceleration in the arms. The methodology applied in this research is the Research and Development (R&D) method, which includes product efficiency testing and product development. The research results show that this device can connect to Wi-Fi networks effectively, and its status can be viewed through the LCD display on the device as well as the Blynk application, which displays an online status when the device is connected and an offline status when it is not. The MPU 6050 sensor works effectively in detecting the user's inclination at the specified angles, forming a 70 angle for pull-up movements and a 90 angle for push-up movements. Testing was conducted using a comparative method between manual counting and counting using the device. In the tests conducted at three different movement speeds, it was found that fast movements in pull-ups resulted in a difference in counting of 7.6%, while in push-ups, there was a difference in counting of 6.8%. These differences were due to the device's inability to detect the intended angles of inclination accurately. On the other hand, movements at moderate and slow speeds showed a 0% difference in counting for both pull-ups and push-ups.
Implementasi Teknologi Web Geospasial dan Decision Tree untuk Klasifikasi Sebaran Pengumpulan Zakat
Authors: Susanti Pratiwi , Asyhari Moch Yusuf , Juwari Juwari , Ahmad Khairul Adila , Shamsuddin Norin Rahayu , Tajuddin Taniza
Published: July 2024

As one of the five pillars of Islam, zakat significantly contributes to socio-economic development and growth in a region. Zakat collection in the city of Madiun has various challenges, including the motivation of muzakki as zakat payers. Zakat is an obligation for someone with assets or income exceeding basic needs to fulfill their living needs. One way to increase motivation to pay zakat is using geospatial web technology to map muzakki in the city of Madiun. Muzakki report data, which previously had the form of a graphic diagram, was changed to a more interactive geospatial form. Several essential advantages in its implementation include embedding precise coordinate points to facilitate the geospatial-based monitoring process, important notes and information about muzakki, and the classification of zakat collection, including collecting zakat in high, medium, and low amounts. This method can represent muzakki mapping more easily and clearly so that zakat institutions (BAZNAS) can quickly determine their policies to increase zakat collection.
Decision Support System for Determining New Branch Locations Applying the Multi Attribute Utility Theory (MAUT) Method
Authors: Sallaby Achmad Fikri , Triayudi Agung , Sussolaikah Kelik , Lubis Muhammad Zakaria
Published: July 2024

The location of new branches that are close to community activities and have adequate facilities makes it easier for consumers to get the services and products they need. Determining the feasibility of new branch locations from several product or service producers still uses a system that is not accurate, which can cause problems in determining the location of new strategic and targeted branches. However, there are several obstacles in the selection of new branch locations, so technological assistance is needed in determining the location, product analysis, marketing management, and other matters concerning the development of the business being carried out. Technology that is considered efficient, easy, and flexible and is used by entrepreneurs, especially in determining the location of new branches using a decision support system using the MAUT method, is expected to help the location of new branches that are efficient and strategic. The decision support system is a conclusion and determination of the best using some data and computerized testing in each criterion so as to get valid results. After calculating each criterion and alternative, the best ranking is obtained in alternative A1 with a value of 0.7925 on Pertahanan Street.
Comprehensive Sentiment Analysis of Religious Content Naive Bayes Algorithm Model
Authors: Listiyono Hersatoto , Budiarso Zuly , Susilowati Susi , Windarto Agus Perdana
Published: July 2024

This paper delves into sentiment analysis of online religious content utilizing the Naive Bayes algorithm to decipher the array of sentiments present in religious discussions. By tailoring this algorithm to the complexities of religious language, the study reveals hidden sentiments, offering valuable insights for researchers, policymakers, and communities. The findings demonstrate that the sentiment analysis model performs robustly, with a precision of 84.78%, a recall of 82.98%, and a balanced F1 Score of 83.87%, indicating high accuracy in sentiment identification and effectiveness in capturing a significant portion of actual sentiments. The overall accuracy of the model stands at 75.10%, affirming its successful adaptation to the intricacies of religious discourse. These results not only deepen our understanding of sentiment analysis in the realm of faith and spirituality but also have practical implications for enhancing interfaith dialogue, fostering mutual understanding, and guiding decision-making in religious and social organizations. This research makes a significant contribution to the growing field of sentiment analysis, providing a methodological framework for exploring the nuanced sentiment landscape within the domain of faith and spirituality.
Control and Monitoring System of Growing Media for Cucumber Plants Based on the Internet of Things
Authors: Enriko I Ketut Agung , Dewi Mela Kartika , Indriyanto Slamet , Gustiyana Fikri Nizar
Published: July 2024

Cucumber is one of the horticultural commodities that has good prospects for cultivation because cucumber plants can be marketed domestically and abroad. Soil condition and fertility a very important factors in increasing agricultural growth and production. The ideal data for a good soil pH for planting cucumbers is 6-7, for a soil temperature of 18-30C and humidity of 50-60%. Ignorance of farmers about the value and condition of the land can lead to poor production of cucumber plants. Therefore the authors created a control and monitoring system to monitor soil conditions or growing media in cucumber plants. In this system, there is a soil pH sensor, temperature sensor, soil moisture sensor, and automatic sprinkler for fertilizer when the soil pH value is less than the specified limit. This system also applies the Internet of Things concept for sending data on the Telkom IoT Platform platform. Based on the test results of testing the soil temperature sensor, it gets an average error value of 0.67% and an accuracy value of 99.33%. Testing the soil moisture sensor obtains an error value and accuracy of 4.80% and 95.20%, respectively. Whereas in testing the pH sensor which was calibrated using the linear regression method, it obtained an average error value of 1.69% and an accuracy of 98.31%.
Enhancing Machine Learning Accuracy in Detecting Preventable Diseases using Backward Elimination Method
Authors: Dliyauddin Muhammad , Shidik Guruh Fajar , Affandy Affandy , Soeleman M. Arief
Published: July 2024

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.