bluuebunny/crossref_metadata_2025 huggingface dataset:
Deteksi Penyakit Epilepsi Melalui Sinyal EEG Menggunakan Metode DWT dan Extreme Gradient Boosting
Authors: Agustin Erlina , Eviyanti Ade , Azizah Nuril Lutvi
Published: July 2024

Epilepsy is a disorder of the central nervous system due to excessive patterns of electrical activity in the brain. This disease causes patients to experience repeated seizures in one or all parts of the body. Therefore, epilepsy must be detected early so that the patient immediately gets the right treatment so that the condition does not get worse. This study proposes the detection of epilepsy using the Discrete Wavelet Transform method for feature extraction and Extreme Gradient Boosting for classification. Detection results are classified into two classes, namely seizures and non-seizures. The EEG recording data used came from CHIB MIT Hospital Boston which was obtained online. In the classification process, this study uses four comparisons of the percentage of training data and test data as well as tuning parameters which are processed by Randomized Search Cross Validation. The combination of these methods produces the highest accuracy, namely 85.15% which is produced by the percentage of 80% training data and 20% test data. However, these results experienced a high overfitting of 13.54%. As for the most fit results produced by the research, namely an accuracy value of 81% with a training score of 88.65% and a test score of 81.20% resulting from a percentage of 80% training data and 20% test data.
Analisis Optimasi Pada Algoritma Long ShortTerm Memory Untuk Memprediksi Harga Saham
Authors: Faridah Nur , Sugiantoro Bambang
Published: July 2024

Stocks are the most popular financial market instrument at the moment, indicated by an increase in investors' shares of 27.15% from the previous year. The biggest risk for stock investors in investing is the risk of falling prices (capital loss) and the risk of liquidation. To minimize this risk, before investing, you should do an analysis first, one of which is by predicting stock price movements. The best method for predicting stock prices is to use Long Short Term Memory (LSTM). In order to predict optimally, it is important to select an optimization algorithm before creating a model. Of the eight optimization algorithms studied, namely SGD, RMSProp, Adam, AdaGrad, AdaMax, AdaDelta, Nadam, and Ftrl. Adam's optimization has the highest level of accuracy in predicting stock prices, where the accuracy value between predicted stock prices and actual stock prices is 98.88% with the average difference between the predicted price and the actual price of IDR 46. This research is expected to provide benefits in predicting stock prices as accurately as possible using the Long Short Term Memory (LSTM) model with the selection of the right optimization algorithm.
Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning
Authors: Yuliani I Gusti Ayu Putu Sintha Deviya , Sibaroni Yuliant , Setiawan Erwin Budi
Published: July 2024

One form of information and communication technology development is a smartphone. Today's popular smartphone products are the iPhone and the social media used to share opinions is Twitter. One of the topics that is often discussed on Twitter is related to iPhone reviews which can refer to different aspects. Therefore, aspect-based sentiment analysis can be applied to iPhone reviews to get more detailed results. This study applies TF-IDF feature extraction as a weighting vocabulary and the Support Vector Machine classification method. This study also uses hyperparameter tuning to optimize parameters to get the best performance. The results of this study obtained the highest accuracy performance results by using the Support Vector Machine classification on the linear kernel and TF-IDF feature extraction on the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 96.82%, price aspect with accuracy 98.62%, and specification aspect with accuracy 97.07%. As well as getting an increase in the results of the highest accuracy performance by using hyperparameter tuning on the linear kernel for the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 97.02%, price aspect with accuracy 98.82%, and specification aspect with accuracy 97.22%.
Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes
Authors: Puspitarini Ni Made Dwipadini , Sibaroni Yuliant , Prasetiyowati Sri Suryani
Published: July 2024

A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities based on Indonesian tweets and increase its performance by combining machine learning with deep learning, which is Gaussian Naive Bayes and IndoBERT model. The proposed combined model in this research is summing the log probability vector on each model. Gathered 3.342 tweets from 111 Twitter accounts that were used as a dataset. This research also implemented min-max normalization to rescale the data. The result showed that for the entire dataset, the combined model has more accuracy score than Gaussian Naive Bayes by 5.42% and IndoBERT by almost 2%, which indicates the combined model is better than the Gaussian Naive Bayes and IndoBERT models.
Pengamanan Pada Citra Digital dengan Menggunakan Modifikasi Blok Data Algoritma AES - Rijndael
Authors: Haris Muhammad , Lydia Maya Silvi , Sutarman Sutarman
Published: July 2024

Digital Image Security is one of the most important information security topics today. Along with the increasing use of digital images both for communication and documentation purposes, the security of information contained in digital images needs serious attention. Rijndael is a cryptographic method that is not only used for text encryption but also for digital image encryption. Just like block-based cryptographic methods in general, encoding data bytes only has an effect on the internal environment of the block, because the transformation process in Rijndael is done separately for each input block. In digital images, this can result in visible patterns or shapes of objects contained in the image, especially when using the Rijndael standard block size of 4x4. To improve the quality of digital image encryption on Rijndael, several studies have made modifications, especially at the transformation stage. In terms of data, the statistical values obtained from the encryption results such as the correlation coefficient do show an increase, but visually the pattern of objects is still visible and modifications tend to be high. This research proposes a modification of Rijndael which focuses on increasing the input block size from 4x4 to 8x8 with minimal changes to the transformation function. The results showed that the value of the correlation coefficient was better and the results of the encryption visually disguised the shape of the object more than the usual Rijndael, especially in text images, logos and caricatures. From the process carried out there is an increase in the quality of security for the encryption process by 13.22% to 91.48%.
Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA
Authors: Mutmainah Siti , Fudholi Dhomas Hatta , Hidayat Syarif
Published: July 2024

The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.
Smart Packgaes Box Berbasis Internet Of Things Menggunakan Telegram Bot
Authors: Putri Sri Ayu Nur Hidayati , Kharisma Oktaf Brillian , Simaremare Harris , Abdillah Abdillah
Published: July 2024

The trend of online shopping increases the use of delivery services. However, packages ordered online are often delivered when the customer is away from home, so the package is usually placed outside and unattended, which is not secure. Therefore, a safe place is needed to secure the package before received by the owner. Based on the problem, this study designed a Smart Packages Box based on the Internet of Things using the Telegram Bot, Wemos D1 R32 that connects the system with Telegram, Barcode Scanner GM66 to scan the barcode on the shipping label, Solenoid Door Lock, and ESP 32 Cam to monitor the condition of the package. Based on the Smart Packages Box test, the door lock can be opened if the barcode on the shipping label matches the receipt number that the owner has registered. The owner will get a notification status if the package has arrived through Telegram Bot. The barcode scan's response time test by courier was 3.20 to 4.50 seconds with a distance of 5 cm to 40 cm until the door locks open and locks again in 5 seconds. Based on the tests carried out there is a graph that illustrates a decrease at a distance of 50 cm which is caused by the distance being too far.The receipt number can be registered by writing "Daftar 1" on the Telegram bot with a limit of 10 receipts, and the owner can check the information status by writing "Cek" on the Telegram Bot. Hopefully, Smart Packages Box can improve the safety of packages even though it has not been received directly by the owner.
Pemanfaatan Algoritma BFGS Quasi-Newton untuk Melihat Potensi Perkembangan Luas Tanaman Kopi di Pulau Sumatera
Authors: Safruddin Safruddin , Efendi Elfin , Mawarni Rita , Wanto Anjar
Published: July 2024

Coffee is one of Indonesia's essential export commodities and a foreign exchange source for the country. One crucial factor in coffee production development is the planted land area. Therefore, the availability of land for coffee plants in Indonesia needs to be maintained for the continuity of coffee production today and in the future. This study aimed to see the potential for the widespread development of coffee plants on the island of Sumatra. This is because the island of Sumatra is the largest coffee producer in Indonesia, so information about the potential for the development of this plant area needs to be known as early as possible, especially for the agriculture/plantation service and for coffee farmers, so that coffee production can be maintained. The algorithm proposed in this study is the Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton algorithm which can be used to solve data prediction (forecasting) problems. This study uses a dataset of coffee plant areas sourced from the Directorate General of Plantations for 2012-2021. This study was analyzed using 3 (three) network architecture models (4-9-1, 4-18-1, and 4-27-1). Based on the analysis, the results obtained from model 4-18-1 as the best architecture with 100% accuracy with minor MSE testing, which is 0.00036764820. Meanwhile, based on predictions made using the best architecture (predictions for 2022 and 2023), the area of coffee plantations has decreased slightly. So this needs serious attention from the respective provincial governments.
People Entity Recognition for the English Quran Translation using BERT
Authors: Ningtias Retno Diah Ayu , Bijaksana Moch. Arif
Published: July 2024

The Quran is a holy book for Muslims all over the world. Therefore, the Quran is not only translated into Indonesian but also into many other languages, including English. The contents of the Quran are a collection of thousands of verses, each verse having different topics and entities. Sometimes, someone may find it difficult to understand and study the contents of the Quran. Therefore, to make it easier, it is done by extracting information and identifying various entities in the Quran, such as human entities. An important thing to do in order to extract information on human entities is to extract information related to the human entity itself first. Because it can help in the search process, particularly the search for names of people in the Quran. The extraction of human entities is commonly known as Named Entity Recognition (NER). With NER, it can automatically recognize important entities such as people's names, group names, and other entities in a sentence or verse in the Quran. Currently, research on the Quran's English translation is not widely done. Therefore, in this research, we are building an information extraction system model for human entities based on a pre-trained deep learning model called Bidirectional Encoder Representations from Transformer (BERT). The dataset used is made up of 19473 tokens and 720 entities taken from the website tanzil.net. The development of the model shows that BERT can be used to extract information for NER on the Quran translation in English by obtaining a F1-score value of 53 %.
Comparative Analysis of Personality Detection using Random Forest and Multinomial Naive Bayes
Authors: Azifa Azka Zainur , Maharani Warih , Gani Prati Hutari
Published: July 2024

Personality is a difference that is owned by each individual in thinking, feeling, and behaving. Personality is an individual characteristic that is formed based on biological parents and environmental influences. Personality type is one of the determinants of the type of work performed. The Big Five personality is a method used to detect personality. This theory divides characteristics into five dimensions, namely Openness, Conscientiousness, Extraversion, Neuroticism, and Agreeableness. Several studies have shown that personality identification can be done through social media, one of which is by using Twitter. Much research related to personality detection has been carried out using machine learning, but only focuses on one machine learning model. In the case of text detection, multinomial naive bayes have a more stable performance than random forest, while random forest has better accuracy than multinomial naive bayes. therefore this study focuses on conducting a comparative analysis using random forest and multinomial naive Bayes. the best accuracy is produced by a system with a random forest model of 60.71% and a precision value of 62% for openness personality and 57% for agreeableness personality.
Personality Detection On Twitter User With RoBERTa
Authors: Khusuma Rianda , Maharani Warih , Gani Prati Hutari
Published: July 2024

Social media provides a service where users can make status updates about themselves. One of the social media that has such a facility is twitter. Twitter allows its users to express themselves easily by uploading tweets to their Twitter accounts. These activities on social media can indirectly describe the personality of the account owner. One form of personality classification that can be used is the big five personality. This theory classifies individual characters into five personality types, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. In the work environment, personality will significantly affect the work that is suitable for someone to do. To do a personality test, a test that is done manually, certainly takes longer and costs more. Therefore the use of machine learning to detect personality from social media is needed. By using the RoBERTa model to perform personality classification and dataset support from Twitter tweets, a system can be formed to detect personality. In the RoBERTa model, by determining the optimal ratio of training data and test data, as well as performing hyperparameter tuning, accuracy results can be obtained in classification activities, reaching 57.14%.
Penggunaan Algoritma K-Means Pada Aplikasi Pemetaaan Klaster Daerah Pariwisata
Authors: Marini Lion Ferdinand , Suhendra Christian Dwi
Published: July 2024

The Teluk Wondama Regency has various potentials in the field of religious and natural tourism. There are 13 districts in Teluk Wondama, where some districts are included in the Teluk Cendrawasih National Park area. This makes Teluk Wondama regency visited by many tourists every year. However, this potential is not well maximized by the local government. This is due to the long distance between each district, with a travel time of about 1-2 hours. This research will group districts that have potential to be maximized by the local government using the K-Means clustering algorithm. This algorithm will use the Elbow and Silhouette methods in the process of determining the most ideal cluster. The cluster results obtained will be presented in the form of web-based tourism area maps. The results obtained from the two cluster determination methods are 2 clusters. Of the 13 districts, after the normalization process is carried out by removing districts that do not have tourist data, only 7 districts remain. Based on the cluster analysis, there are 3 districts in cluster 1 and 5 districts in cluster 2. The cluster of tourism areas is presented in the form of a map created using the Shiny Web with R programming language.
Aspect-Based Sentiment Analysis on Twitter Using Long Short-Term Memory Method
Authors: Putri Siti Inayah , Setiawan Erwin Budi , Sibaroni Yuliant
Published: July 2024

Twitter is one of the most popular social media among Indonesian people. Due to the high number of users and the intensity of their use, Twitter can also be used to dig up information related to a topic or product with sentiment analysis. One of the most frequently discussed topics on Twitter is related to movie reviews. Everyone's opinion of movie reviews can refer to different aspects. So, aspect-based sentiment analysis can be applied to movie reviews to get more optimal results. Aspect-based sentiment analysis is a solution to find out the opinions of Twitter users on movie reviews based on the aspects. In this study, a system for aspect-based sentiment analysis was built with a dataset of Indonesian language movie reviews consisting of 3 aspects: plot, acting, and director. The classification model uses Long Short-Term Memory (LSTM) method with the application of TF-IDF feature extraction, fastText feature expansion, and handling of imbalanced data using SMOTE. The results of this study for the plot aspect obtained an accuracy score of 74.86% and F1-score of 74.74%, the acting aspect obtained an accuracy score of 94.80% and F1-score of 94.74%, and the director aspect obtained an accuracy score of 94.02% and F1-score of 93.89%.
Perbandingan Metode K-NN Dan Metode Random Forest Untuk Analisis Sentimen pada Tweet Isu Minyak Goreng di Indonesia
Authors: Yanti Christina Purnama , Eva Agustini Ni Wayan , Sri Rahayu Ginantra Ni Luh Wiwik , Putri Wulandari Dewa Ayu
Published: July 2024

Along with the development of technological advances, a lot of social media is used by humans, one of which is Twitter social media. On Twitter social media, we can find a lot of text data, opinions and public opinion, as the issue of cooking oil is currently hot in Indonesia. In this study, the K-NN and Random Forest methods were used, and the purpose of this study was to compare the two methods in sentiment analysis on the issue of cooking oil. The results of the accuracy of these two methods are not too far apart. Each of the two methods used will be divided into three research scenarios, the first is scenario 1, a collection of 500 data, scenario 2, a collection of 800 data, and scenario 3, a collection of 1,000 data, where the ratio of training data and test data is 80:20. The test results for the K-NN method in scenario 2 are superior with an accuracy presentation of 74.58%, 56.75% precision and 44.57% recall and the lowest result is the K-NN method scenario 1 with an accuracy presentation of 71. 50%, 47.83% precision and 37.45% recall. The average test results for the K-NN method are 72.86% accuracy, 52.26% precision and 41.04% recall. While the average results of the random forest method are 73.37% accuracy, 52.26% precision and 34.28% recall
Pemanfaatan Metode Preference Selection Index Untuk Penilaian Dosen Terbaik Yang Diambil Dari Pengisian Kuesioner
Authors: Devi Putri Aisyiyah Rakhma
Published: July 2024

Muhammadiyah University conducts evaluations of the performance of the best lecturers using norms requiring students to fill out online surveys. But still using ordinary calculations and the occurrence of these calculations is not effective. Student assessment of lecturer teaching performance is the most frequent evaluation used in large learning to measure how well the course is being taught. The research phase begins with case identification, followed by data collection in the form of literature studies, surveys, and interviews. After data collection is complete, analysis is carried out using the PSI method to aid in calculations. Reports are then made according to the research results obtained. The criteria that were tried to evaluate the best Muhammadiyah University lecturers based on the assessment of the results of the questionnaire filled in by students were 4, namely Personality Competence, Pedagogic Competence, Professional Competence, and Social Competence. Based on the mechanism of the Preference Selection Index procedure to provide more efficient decisions, the implementation of this procedure is very simple and very easy to understand because each step helps in making decisions. The final result obtained from the PSI mechanism is 11.747.
Penerapan Algoritma Certainty Factor pada Sistem Pakar Diagnosis Penyakit Creutzfeldt-Jakob Disease
Authors: Wenda Alex , Saludin Saludin , Alam Sitti Nur , Winarno Edy , Mahendika Devin
Published: July 2024

Indonesia is a country that is active in the livestock sector, one of which is cattle farming. However, not many cows are affected by mad cow disease where this disease can be transmitted to humans by consuming the beef itself. This Gina cow disease, if it has been infected with humans, is called Creutzfeld-Jakob disease, where this disease can attack any part of the human body, namely the nervous tissue in the human brain so that it can destroy human brain cells and cause small holes in the brain automatically. gradually, the severity of this disease can claim a person's life if proper medical treatment is not carried out. This disease can cause death within one year after the symptoms are indicated as creutzfeld-jakob disease. To make it easier for an expert to diagnose a disease, a system can be used, namely an expert system with a certainty factor algorithm that determines the confidence value based on the percentage of the final Cfcombine value of the various types of symptoms that exist, so that the percentage value of the resulting confidence level is 89%.
Sistem Pendukung Keputusan Dalam Penentuan Wisata Alam Terbaik Menerapkan Metode Operational Competitiveness Rating Analysis
Authors: Khailizah Khailizah , Bangun Budianto , Rohayani Hasibuan Elysa
Published: July 2024

Tourism is an activity on vacation or traveling with the aim of enjoying a new atmosphere and visiting tourist attractions such as mountains, beaches, parks and others. Nature tourism is a form of tourism that involves nature as the main object or place, which takes advantage of the beauty and uniqueness of nature such as national parks, forests, mountains, beaches, lakes, rivers, and so on. In selecting nature tourism, ecological aspects must be considered to maintain environmental balance and conserve biodiversity. Social and cultural aspects must also be considered to ensure that the development of natural tourism does not harm the local community, does not damage cultural heritage and traditions and mistakes often occur in determining good nature tourism. In determining natural tourism there are various criteria, namely: Natural beauty, Cost, Atmosphere, Cleanliness and Sustainability and Facilities. From the test results obtained the best alternative with a value of 0.32125.
Sistem Pakar Dalam Mendiagnosis Penyakit Leishmaniasis Menerapkan Metode Case-Based Reasoning (CBR)
Authors: Nasyuha Asyahri Hadi , Syahra Yohanni , Perangin-Angin Moch Iswan , Habibie Dedi Rahman , Subagyo Aloysius Agus
Published: July 2024

Leishmaniasis caused by protozoa of the genus Leishmania, is one of the neglected zoonoses. Sand flies (mosquitoes) of the genus Phlebotomus transmit Leishmaniasis. Leishmaniasis has attacked 98 countries and is widespread in tropical, subtropical and Mediterranean regions. Because it primarily affects endemic areas in developing countries, which often have dense populations, malnutrition, poor sanitation, and a lack of human resources for disease control, prevention, and treatment, leishmaniasis is considered a neglected tropical disease. Leishmaniasis is one of the neglected tropical diseases, it is based on the low level of public awareness and scarcity of funds for research to develop effective disease control methods. Leishmaniasis is a difficult condition to treat because the general public is not well aware of it. Based on these problems, an expert system for the diagnosis of leishmaniasis was studied. An expert system is a program that can simulate the thought process of a computer expert and solve problems that are usually handled by experts. Knowledge stored in expert systems is often obtained from human subject matter experts. By using the help of expert systems and calculations carried out using the Case-Based Reasoning (CBR) approach, this study aims to facilitate the diagnosis of Leishmaniasis based on the patient's perceived input. Approach (CBR) can facilitate diagnosis then produce more precise diagnostic results. The test results with the Case-Based Reasoning approach found that the type of disease Cutaneous Leishmaniasis had the highest similarity value with a similarity value of 73%.
Analisis Dalam Pendukung Keputusan Seleksi Reporter dengan Menerapkan Metode EDAS dan Pembobotan ROC
Authors: Adytia Pitrasacha , Fahmi Muhammad , Andrea Reza
Published: July 2024

The community's need for curiosity about the latest information makes news something that is always sought after and never goes extinct. One of the important elements in the world of news is the reporter. Reporters are people who are involved or play an important role in finding the latest information and covering everything that happens and conveying news or facts that happened at the scene directly to the public. In addition, reporters are also tasked with compiling various important information in a systematic and easy-to-understand manner so that the public can understand well about whatever is conveyed by the reporter. However, the amount of data makes it difficult for companies to select potential reporters. Because if done manually it is less effective. To solve these problems, a decision support system was created to assist the company in determining which applicants should be accepted. A decision support system (DSS) is a system that was created with the aim of helping parties who have difficulty making a decision or making an election with large amounts of data. In this study the method used is the EDAS method. the EDAS method is a method that functions to produce a ranking value from several choices so that a calculated value is obtained from each criterion attribute value. Based on the results of the research that has been carried out and has been carried out regarding the selection of prospective reporters who are eligible to be accepted by companies by optimizing the Decision Support System function by implementing the EDAS (Evaluation Based On Distance From Average Solution) method and the Rank Oder Centroid (ROC) weighting method, it is obtained the result is equal to 1.3093 with code B4 on behalf of syahputra as a reporter candidate that the company deserves to accept.
Penerapan Sistem Pendukung Keputusan dalam Penerimaan Pengajar Desain Grafis Menerapkan Metode Preference Selection Index (PSI)
Authors: Rahman Ben , Hasian Irene , Yudi Arifin Nofri , Hutahaean Jeperson , Atmoko Rachmad Andri
Published: July 2024

Graphic design teacher is one of the teachers who is really needed in various schools or universities, because graphic design is really needed to provide knowledge that can be used to get jobs according to abilities. However, in accepting graphic design instructors, they must really master the field of design and have a lot of experience in teaching that field. In this research, several criteria are needed including Education, Experience, Creative, Innovative, and Communicative. So with that the criterion data can support this research in solving problems and getting accurate and detailed results. And this research really needs a supporting system to solve this research. The system is a Decision Support System (DSS) and the method used to produce an appropriate ranking, this method is the Preference Selection Index (PSI). So the application of this method can produce the highest rank that will be accepted as a graphic design teacher on behalf of Putra Andiyono with a total score of 1.0331.
Collaborative Filtering Based Food Recommendation System Using Matrix Factorization
Authors: Siddik Muhammad Bayu Samudra , Wibowo Agung Toto
Published: July 2024

A recommendation system is a method that provides suggestions of items that might users like. There are many domains that can be recommended, one of the most demanded domains by users today is food. In the era of big data, food choices from the large amount of data make it difficult for users to choose the right food for them. The collaborative filtering (CF) approach is considered capable of providing accurate and high quality item suggestions. One of the algorithms that can provide good performance results from the CF approach is Matrix Factorization (MF). This study aims to test a dataset that contains product ratings of food items using three MF algorithms, which are Singular Value Decomposition (SVD), SVD with Implicit Ratings (SVD++), and Non-Negative Matrix Factorization (NMF). Different latent factors are also used for the purpose of improving the performance of the proposed recommendation system algorithm. The dataset used is Amazon Fine Food Reviews. The study shows NMF and SVD++ as the best algorithm for generating user rating predictions for items. NMF has the smallest average prediction error as measured by MAE which is 0.7311. While SVD++ obtains the smallest prediction error value of 1.0607 as measured using RMSE. In addition to these results, the top-n evaluation also shows that the proposed algorithm performs quite well. The hit ratio value for each different n-item always increases proportionally to the number of recommended n-items. The highest hit ratio value is generated from the SVD++ algorithm of 0.0025 on n-item recommendations of 25 items. Overall it can be said that the proposed algorithm has good performance in providing item recommendations.
Klasterisasi Jenis Tanah pada Tanaman Cabai Menggunakan Algoritma K-Means
Authors: Verawati Ike , Rebecca Agnes Lucky
Published: July 2024

Chili is one of the ingredients of spices that are widely used in Indonesia. Sales opportunities in the market are also very wide so that many are competing to grow chili with the best quality. There are several parameters that can improve the quality of chili, one of which is soil. Where if seen by the eye, soil that is suitable or unsuitable can be distinguished from color and texture, but not many people know the difference. Therefore, in this study, research will be conducted on the clustering of soil images based on color features and texture features using the K-Means algorithm which previously selected features using information gain feature selection. The first stage in this research is image acquisition and then the results will be processed first. From the results of pre-processing, RGB color feature extraction and first-order texture feature extraction will be carried out which is then followed by feature selection using information gain which is expected to produce the best features which will then proceed to clustering using the K-Meaning algorithm. The final step is to conduct an analysis to obtain the results of this study. The results obtained from the clustering that have been carried out can be obtained that the K-Means algorithm can cluster suitable and unsuitable soil images with information gain, resulting in 63 suitable soil images and 37 unsuitable soil images from 100
Analisis Sentimen Pembatalan Indonesia Sebagai Tuan Rumah Piala Dunia FIFA U-20 Menggunakan Naïve Bayes
Authors: Setiawan Harry , Zufria Ilka
Published: July 2024

The FIFA U-20 World Cup is a prestigious event for young footballers around the world. Indonesia was originally to host the event in 2023, but FIFA eventually had to cancel the World Cup in Indonesia because some Indonesian public figures did not accept the presence of the Israeli national team in Indonesia at the football match. The refusal was made on security grounds, because the participation of the Israeli national team was considered a potential threat to Indonesia's security, especially considering the Palestinian conflict. Another reason, Indonesia does not have diplomatic relations with Israel, FIFA's decision is enough to cause a variety of opinions both positive and negative. The purpose of this research is to function so that people can be tabayyun by providing solutions by conducting sentiment analysis, namely collecting Twitter user opinions automatically so that they can be useful for the community. The data obtained is 946 tweets after going through the preprocessing stage then the data can be used in the labelling stage using the Lexicon Based method. Grouping is divided into positive sentiment and negative sentiment which is classified by the Naïve Bayes algorithm reinforced by Lexicon Based weighting resulting in positive sentiment as many as 150 tweets with a percentage of 15.86% and negative sentiment as many as 796 tweets with a percentage of 84.14%. From the confusion matrix results, the classification performance with the Naïve Bayes algorithm reinforced with Lexicon Based weighting then produces an accuracy percentage of 84%, with a precision of 86%, a recall of 95% and an f-measure value of 90%.
Evaluasi Performa Algoritma Naïve Bayes Dalam Mengklasifikasi Penerima Bantuan Pangan Non Tunai
Authors: Alfitri Mohammad Mastur , Nurahman Nurahman , Minarni Minarni , Rusda Depi
Published: July 2024

The improvement of the standard of living of the community in Bapinang Hulu Village is carried out through various social assistance programs. However, the realization of the implementation of social assistance programs did not go smoothly. Social jealousy often occurs among the community during the distribution of social assistance. The distribution of assistance is carried out based on the assessment of the village officials and the Village Consultative Body, which is then validated by the heads of RT and RW. The quota provided by the government is often not in accordance with the actual number of eligible recipients in the village. Another difficulty is determining the criteria or attributes used for the selection of Non-Cash Food Assistance recipients. This study aims to obtain a classification model from which the classification pattern can be applied to the population data of Bapinang Hulu Village for the selection of social assistance recipients. To solve this problem, the classification method is applied using the Naive Bayes Algorithm. The research results show that the performance of the Naive Bayes algorithm model before feature selection had the highest accuracy in the 8th test with an accuracy of 89.80%. Meanwhile, after feature selection, the highest accuracy was found in the 3rd test with an accuracy of 88.37%. The feature selection using the Information Gain algorithm reduced the number of attributes from 16 to 6. Therefore, it is known that the highest accuracy is obtained before feature selection, but in selecting social assistance recipients, more criteria need to be applied, which is time-consuming. Meanwhile, after feature selection, only 6 criteria are used to determine social assistance recipients.
Penerapan Algoritma Apriori dan FP-Growth Untuk Market Basket Analisis Pada Data Transaksi NonPromo
Authors: Pabendon Andrew Aquila Chrisanto , Purnomo Hindriyanto Dwi
Published: July 2024

This research aims to find association rules based on the transactions of Aksesmu members on non-promo items. The method in this study uses Association rules using the a priori algorithm and FP-Growth to obtain Frequent Itemsets. The data analysis phase is carried out starting with Exploratory Data Analysis, Pre-Processing Data, Transformation Data, and Data Mining, to evaluate the results of the formed association rules. Researchers conducted 4 experiments with a minimum support of 0.02 and a minimum confidence of 0.25 on a priori and FP-Growth was the best by producing 52 frequent itemsets and 17 association rules. With a dataset of 379,635, a priori is faster in processing frequent itemsets with a time of 1.10 seconds while FP-Growth is with 1.86 seconds. Apriori and FP-Growth produce the same frequent itemset, namely the highest category is obtained by SKT with a support of 0.32 and SKM with a support of 0.26, but the best association rules are produced by the Extruded & Pellet and Sweetened Condensed Milk categories with a confidence of 0.47, which if items in the Extruded & Pellet category are purchased together with Sweetened Condensed Milk category items have a success rate of 47%.
Penerapan Algorimta Backpropagation Untuk Prakiraan Cuaca Harian Dibandingkan Dengan Support Vector Machine dan Logistic Regression
Authors: Zulfiani Ayu , Fauzi Chairani
Published: July 2024

To anticipate the impacts caused by extreme weather, the Meteorology, Climatology, and Geophysics Agency (BMKG) issues weather forecasts so that the community can be prepared when such extreme weather occurs. The application of Artificial Neural Network (ANN) techniques in weather forecasting significantly enhances the ability to explore vast amounts of big data in obtaining the necessary information, serving as a reliable assistant for forecasting and policymaking. The data used in this study consists of weather elements such as pressure, air temperature, humidity, wind direction and speed, as well as rainfall, obtained from the Radin Inten II Lampung Meteorological Station. The observational data has a data density per hour, spanning a period of 5 years from January 1, 2018, to December 31, 2022. The method employed in this research is Backpropagation Neural Network (BPNN). The research results indicate that BPNN can effectively predict classified rainfall compared to other methods, within recall value when slight rain 0.68, moderate rain 0.17, and heavy rain 0.03, meanwhile Support Vector Machine (SVM) and Logistic Regression (LR) method can predict only slight rain with recall value when slight rain is 0.51 and 0.47.
Analisis Penerapan Metode MOORA dan WASPAS dalam Keputusan Pengajuan Kredit Pemilikan Rumah (KPR)
Authors: Alinse Rizka Tri , Sari Venny Novita , Sallaby Achmad Fikri
Published: July 2024

To fulfill the necessities of life, humans need a place to shelter from the hot sun and the dark of the night even if it rains, the role of residence is very important. Not only that, the house can also be a place for gathering and carrying out family activities such as carrying out daily activities and communicating with each other. A house is an asset whose price is not cheap, so not a few people buy a house by credit. Therefore, the bank created a mortgage program to make it easier for people with limited economic conditions. Mortgages are made with payments in installments so it is likely that the bank will lose if the mortgage is given to the wrong person. So we need a system that facilitates the bank in making a decision. The Decision Making System (DSS) can be carried out using several methods such as MOORA, MOOSRA, WASPAS, TOPSIS, PSI, VIKOR, WP, SAW and others. In this study, the method used in determining the recipient of a Home Ownership Loan is the MOORA and WASPAS methods. These two methods will be applied separately based on existing criteria and alternatives. The final result after applying this method is that the best alternative is obtained by A7 with a Qi value of -0.04131 (MOORA method). Likewise with the application of WASPAS, the highest value was obtained for alternative A7 with a Qi value of 2.91829.
Perbandingan Algoritma Naïve Bayes Classifier Dan K-Nearest Neighbor Pada Sentimen Review Aplikasi Mobile JKN
Authors: Annisa Citra , Afdal M. , Ahsyar Tengku Khairil
Published: July 2024

BPJS Health must provide health services for the people of Indonesia. With the availability of the Mobile JKN application, it is useful to facilitate services for participants of the National Health Insurance-Indonesian Health Card (JKN-KIS). Mobile JKN is an innovation in electronic government health insurance services, making it easier for the public to access services and information quickly in the palm of their hand. With this innovation, many pros and cons flowed from the community, various comments appeared in the Play Store review column, sentiment analysis could be used to assess and rate applications. Therefore, these sentiments can be analyzed into information that can be used as material for evaluation and consideration by BPJS Kesehatan regarding Mobile JKN. This study aims to look at the results of the accuracy comparison between the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms on the sentiment review of the Mobile JKN application on the Play Store. This study used the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods with data scrapping techniques to collect Play Store data for the past year, namely 2,847 data and divided into 3 classes, namely positive, neutral and negative. Distribution of data using 10 K-Fold Cross Validation so that a comparison of the accuracy level of the Naïve Bayes Classifier (NBC) is 61.15%, while the accuracy level of K-Nearest Neighbor (KNN) is 87.59%.
Implemantasi Case Based Reasoning (CBR) Untuk Pengembangan Sistem Pakar Diagnosa Penyakit Gigi
Authors: Rosmini Rosmini , Syafiqoh Ummi , Asmah Asmah
Published: July 2024

Dental disease is a condition that can interfere with the teeth in carrying out their functions properly. Dental disease in humans ranks first on the list of the top 10 diseases that most people complain about in Indonesia. Dental disease can affect anyone of any age and gender. Dental disease initially does not cause problems and if allowed to continue can become a big problem. Dental disease has almost similar symptoms, therefore an expert system application is needed that can help diagnose dental disease so that you can get the right treatment before the dental disease develops into a more serious disease. The Case Based Reasoning (CBR) method is a method for solving problems by remembering the same/similar (similar) events that have occurred in the past then using that knowledge/information to solve new problems or in other words solve problems by dealing with solutions. - solutions that have been used. The results of the CBR method test resulted in a diagnosis of dental and oral disease with a similarity rate of 82%. So this method can be used to diagnose dental disease experienced by patients, thereby helping specialist doctors make decisions in disease management.
Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron
Authors: Hamdi Ikhsanul , Budianita Elvia , Syafria Fadhilah , Afrianty Iis
Published: July 2024

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.
Optimasi K-means Clustering Dengan Menggunakan Particle Swarm Optimization Untuk Menentukan Jumlah Cluster Pada Kanker Serviks
Authors: Setiaji Indrawan , Affandy Affandy , Fanani Ahmad Zainul
Published: July 2024

Cervical cancer is one of the most common cancers among women in the world. It is most common in developing countries. Cervical cancer develops slowly in the body. Clustering is needed so that cervical cancer can be treated quickly. The K-means method was chosen because of its ability to group large amounts of data and fast computation time. The K-means method is also very easy to implement, flexible, and uses simple principles, which can be explained non-statistically. The many advantages that K-means has, also has weaknesses because it uses random clustering numbers and the results are not optimal. The difficulty in accurately determining the amount of clustering in the dataset. The K-means method cannot provide an optimal solution for determining the number of clustering, so it needs to be improved in order to obtain an optimal solution. PSO was chosen because it has several advantages, namely requiring few parameters, easy to implement, fast convergence, more efficient because it requires little computation and is simple. The results showed that the PSO - K-means method can prove to provide a significant contribution by directly obtaining optimum clustering results without having to do repeated experiments with a Silhouette Coefficient value of 0.83 and a Davies Bouldien Index value of 1.91.