bluuebunny/crossref_metadata_2025 huggingface dataset:
Metode TOPSIS Untuk Penerima Bantuan Pendidikan Bagi Mustahik Fakir
Authors: Satria Devit , Desyanti Desyanti , Suarlin John
Published: July 2024

Education is mandatory for all levels of society, every parent wants their children to be able to go to school properly, but the roots of education problems generally lie in financing for parents who have relatively low incomes. If conditions like this continue, Indonesia will lose the best generation if there are still many children who do not go to school due to the cost factor. The Dumai City National Amil Zakat Agency (BAZNAS) is one of the Amil Zakat Agencies in Riau Province. The Dumai City Baznas created an educational assistance program for poor families in the form of money to buy school supplies. Mustahik who wish to receive educational assistance must fill out a form and other predetermined conditions. After that, Baznas will select the incoming proposals and carry out the selection process. The large number of proposals caused Baznas to take a long time to make a decision because they had to check the submitted documents one by one, because it took a long time, the proposal for submitting assistance was approved based on the results of the meeting and agreement with the chairman of BAZNAS. So that the results decided are not as optimal as the actual conditions. For this reason, a system is needed that can assist BAZNAS in carrying out the document selection process in accordance with the requirements so that the assistance provided is right on target and can determine priorities for recipients of educational assistance for mustahik. The TOPSIS method is able to provide solutions to problems that occur, from the results of research conducted by Sutjiati Gita Lestari, it is ranked 1st with a value of 0.8041
Sistem Pendukung Keputusan Dalam Menentukan Calon Nasabah Penerima Pinjaman Dana Menerapkan Metode TOPSIS dan AHP
Authors: Siregar Sri Yuslina , Putri Raissa Amanda
Published: July 2024

PT. FIFGroup is a company that has obtained permission from the Minister of Finance, where this company carries out business in the field of providing loans in the form of funds. PT. FIFGroup Cikampak is one of several branches in other cities, as a prospective customer there are 5 criteria that must be considered and have been determined, namely according to the prospective customer's income, collateral for the prospective customer, employment, needs and term of borrowing funds. However, when determining potential customers who will receive loan funds, PT.FIFGroup Cikampak still uses manual methods, such as analyzing the conditions attached when applying for funds. In order to avoid errors in customer decision making, a web-based decision support system is needed to provide information quickly and precisely regarding the criteria for prospective customers. This decision support system uses a combination method, namely Topsis (Technique for orders preference by siilatyt ideal solution) and AHP (Analytical hierarchy process), this system can automatically recommend potential loan recipient customers who comply with predetermined criteria. Prospective customers who receive loan funds in this system will produce a ranking based on Topsis and AHP calculations. Based on calculations using the AHP method from the five criteria elements, the alternative weightings use a satty scale weighting of 1-9 according to the provisions of the AHP method. Then the ranking was carried out using the topsis method, resulting in the first rank being the name of the Misno customer with a manual priority of 0.729 and a system of 0.729, the lowest value or lowest ranking of the 15 alternatives, namely Sri Irma Naibaho manual priority of 0.204 and system of 0.204. The design of the decision support system has been successfully built using the Topsis and AHP methods, based on the results of Black Box testing, the system runs very well as desired.
Perbandingan Metode Dempster Shafer Dan Teorema Bayes Dalam Sistem Pakar Mendiagnosa Moyamoya Disease
Authors: Rifqi Naufal , Iskandar Agus
Published: July 2024

The main aim of this research is to compare two analytical approaches, namely the Dempster-Shafer Method and the Bayes Theorem, in the context of a system developed for diagnosing Moyamoya disease. Moyamoya is a rare condition involving the narrowing or blocking of blood vessels in the brain, which can lead to disrupted blood flow and an increased risk of stroke. In the medical field, diagnosing Moyamoya disease is a crucial initial step for appropriate treatment planning. The Dempster-Shafer Method is an approach used to address uncertainty and combine uncertain information into a conclusion. On the other hand, the Bayes Theorem is a statistical principle that connects the probability of a hypothesis before and after new evidence emerges. Both of these approaches are vital in the medical diagnostic process. In this study, both methods are implemented in an expert system specifically developed for diagnosing Moyamoya disease. Data from Moyamoya cases are used to evaluate the performance of both methods. Performance measurement is conducted by observing diagnostic accuracy, computational time, and resource usage. The results of this research provide valuable insights into the effectiveness and performance of the Dempster-Shafer Method and the Bayes Theorem in medical applications, particularly in diagnosing Moyamoya disease. Strengths and weaknesses of each approach are revealed, aiding in understanding situations where each method is most suitable. The Dempster-Shafer Method is effective in dealing with complex uncertainties and combining uncertain evidence. Meanwhile, the Bayes Theorem excels in probability calculations. The implications of this research are important in developing more advanced medical expert systems. In the medical realm, where diagnostic decisions impact patient care, a better understanding of these approaches helps in selecting the most appropriate method for specific situations. The results of comparing both methods indicate that the Dempster-Shafer Method yields a high probability of around 91%, indicating a substantial likelihood that the patient is suffering from this disease. Conversely, the Bayes Theorem yields a low probability of around 22%, suggesting a relatively small likelihood that the patient has Moyamoya Disease.
Clustering Pecandu Narkoba Menggunakan Algoritma K-Means Clustering
Authors: Amal Ikhlasul , Putri Raissa Amanda
Published: July 2024

With the rise of increasingly sophisticated technology in this time of globalization, it is exceptionally simple for the more extensive local area to manage exchanges (drugs). For this reason, the government is constantly trying to stop the spread of drugs among Indonesian people by using any media, ranging from verbal invitations, banners, posters, videos and photos displayed in schools, government and public places. The maltreatment of opiates and perilous medications (drugs) in Indonesia as of late has turned into a difficult issue and has arrived at a condition of concern with the goal that it has turned into a public issue.In order to make it easier for BNN to conduct monitoring and counseling to areas where there are many drug addicts, it is necessary to cluster data on drug addicts in Medan city. To solve the problem, it can be solved by clustering drug addicts in Medan city using the K-Means Clustering Algorithm. The data used comes from the BNN of North Sumatra Province, the data used is data on drug addicts in Medan City in 2020-2023. The purpose of clustering drug addict data in Medan city is to find out areas that are very high, high, low and very low levels of drug addicts.This study found that there are 2 sub-districts with the highest level of drug addiction, 7 sub-districts with a high level of addiction, 7 sub-districts with a low level of drug addiction, and 5 sub-districts with a lowest level of addiction.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes
Authors: Hamwar Syahbudin , Nazir Alwis , Gusti Siska Kurnia , Iskandar Iwan , Insani Fitri
Published: July 2024

Livestock is becoming one of the important animal protein source providers, along with the fisheries sector, to meet the protein needs of the community at large. One type of livestock business that is popular is the maintenance of broiler chickens because of the potential for meat yield. Today, many breeders run a partnership pattern with large companies where breeders play the role of the main supplier and the company as the core. This step helps maintain the stability of production and income of farmers. The success of farmers in broiler chicken production can be measured by looking at the performance index (IP), if the performance is not good then coaching from the core company is needed. The large amount of data obtained from farmers makes it difficult for core companies to model the success rate of farmer production, this can make it difficult for core companies to choose farmers who need coaching. The application of data mining methods using the Naïve Bayes algorithm classification model has the potential to provide solutions to this problem. The purpose of this study was to predict how much success rate of broiler chicken production in Riau region by utilizing the Naïve Bayes Classifier algorithm. This study utilizes a production data set involving 952 broiler chicken farmers in Riau, with 3 scenarios dividing the data ratio of 90:10, 80:20, and 70:30. The results of the analysis showed that through the evaluation of the confusion matrix, it was best found in a data ratio of 90:10 with accuracy results reaching 89,58%, precision reaching 89,89%, and recall reaching 90,16%.
Kontruksi Sosial Network Analysis Untuk Menilai Kepopuleran Brand Sepatu di Twitter
Authors: Prayoga Setyo Hendro , Handoko Widiyanto Tri
Published: July 2024

Shoes are one of the most popular products to buy. Therefore, many well-known shoe brands such as Adidas, Converse, New Balance, Nike, Reebok and Skechers are competing to create the latest shoe models. Many shoe brand users share their experiences on the Twitter platform. The aim of this research is to assess the level of popularity of the Adidas, Converse, New Balance, Nike, Reebok, Skechers shoe brands on the Twitter platform. This research will analyze the social network patterns of Twitter users in the period January - March 2023 which discusses the shoe brands Adidas, Converse, New Balance, Nike, Reebok, Skechers and will be analyzed using Social Network Analysis (SNA). SNA is a method that can be used to analyze interactions between individuals that occur on the Twitter platform by looking at network properties. This research will also use the Weighted Aggregated Sum Product Assessment (WASPAS) method which is used to support decision making about which shoe brands are the most popular in the period January – March 2023. The results of this research show that New Balance obtained rank 1 with a value of 0.985172467, Adidas was ranked 2nd with a value of 0.946380309, Nike was ranked 3rd with a value of 0.851347847, Reebok was ranked 4th with a value of 0.83674501, Converse was ranked 5th with a value of 0.831588591, and Skechers was ranked 6th with a value of 0.759538181.
Analisis Sentimen Tentang Penggunaan Galon Bebas BPA di Indonesia Menggunakan Algoritma Support Vector Machine
Authors: Tri Atmojo Muhammad Iqbal , Sinduningrum Estu
Published: July 2024

This research employs the Support Vector Machine algorithm to classify sentiment in comments on the X, Youtube, and Tiktok platforms regarding the use of BPA-free water gallons in Indonesia. From a total of 1200 data points, post-labeling, 772 data points were obtained, with 552 classified as positive and 220 as negative. The experimental results reveal that SVM achieves an accuracy of 96.15%, while Naïve Bayes achieves an accuracy of 84.55%. These findings indicate that SVM is effective in classifying sentiment with a high accuracy rate, providing valuable insights for manufacturers, government entities, and consumers regarding the use of BPA in water gallons in Indonesia. This study contributes to a better understanding of the role of social media in shaping public opinion and policies related to environmental and health issues.
Implementasi Metode CRISP-DM dalam Analisis Model Pendukung Keputusan Simple Additive Weighting dan Pengembangan Basis Data Riwayat Pembelian Layanan Akomodasi Hotel
Authors: Singgalen Yerik Afrianto
Published: July 2024

The development of studies on implementing Simple Additive Weighing (SAW) decision support models in purchasing hotel accommodation services or making stay decisions is limited to hotel recommendations calculated from consumer assessments of criteria with predetermined weights. However, it is necessary to develop a database with interactive visualization of hotel accommodation services and make it easier for consumers to compare and provide ratings. Considering this, this study uses the CRISP-DM method to develop a database based on the purchase history of hotel accommodation services in a business operational area, then uses SAW as a decision support model in the calculation process to produce the best hotel recommendations based on purchase data. The CRISP-DM method consists of business understanding, data understanding, modeling, evaluation, and deployment stages. At the business understanding stage,  the customer's purchase history data is collected in the Agoda website review column. At the data understanding stage, the data collection process is carried out based on the supporting data of the criteria used. At the modeling stage, the SAW algorithm is used in the calculation process to get the best recommendations. In the Evaluation phase, hotels with the best recommendations are analyzed based on the guest's country of origin, guest sentiment, type of guest staying, room type used, length of stay, and month and year. In the Deployment stage, the database is developed using Oracle Apex and visualized interactively so that system users can understand consumer trends and behavior, especially in making overnight decisions based on purchase history data. Based on data obtained from the Agoda platform, it can be seen that A2 ranks first with a value weight of 0.983, then A3 ranks second with a value weight of 0.982, and A1 ranks third with a value weight of 0.946. Meanwhile, based on data obtained from the Booking.com platform, it can be seen that A3 ranks first with a value weight of 0.983, then A2 ranks second with a value weight of 0.974, and A1 ranks third with a value weight of 0.951. Thus, the SAW decision support model implementation output is not limited to the results of calculations and recommendation tables but includes a database with interactive visuals.
Klasifikasi Sinyal EEG Untuk Mengenali Jenis Emosi Menggunakan Recurrent Neural Network
Authors: Utari Aspirani , Rini Dian Palupi , Sari Winda Kurnia , Saputra Tommy
Published: July 2024

This research focuses on in-depth exploration and analysis of the application of two types of Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The two models are drilled with the same parameters, consist of three layer, use the relu activation function, and apply 1 dropout level. In order to compare the performance of the two, experiments were carried out using five groups of datasets for training and performance evaluation purposes. The evaluation includes metrics such as accuracy, recall, F1-score, and area under the curve (AUC). The dataset used is Eeg Emotion which contains 2458 unique variables. In terms of performance, LSTM succeeded in outperforming GRU in the task of classifying emotional data based on EEG signals. On the other hand, GRU shows advantages in accelerating the training process compared to LSTM. Although the accuracy of both methods is almost similar in all data divisions, in the evaluation of the ROC curve, the LSTM model demonstrates superiority with a more optimal curve compared to GRU.
Pemanfaatan Metode Decision Tree dengan Algoritma C4.5 Untuk Prediksi Potensi Kunjungan Wisatawan
Authors: Iman Hadad Karsa Nur , Latifah Noor , Supriyono Supriyono , Nugraha Fajar
Published: July 2024

This research aims to predict potential tourism visits in Pati Regency, Indonesia, utilizing data mining methods, specifically the Decision Tree with the C4.5 algorithm. The significance of the tourism sector in a region's economy and sustainability, along with the potential of data in formulating more effective and targeted strategies and decisions, motivated this objective. The initial experiment results demonstrated the superior performance of the Decision Tree C4.5 method in predicting potential tourism visits in Pati Regency, with an accuracy of 96.42%, precision of 96.42%, and recall of 96.66%. This performance exceeded the Naive Bayes method, which yielded an accuracy of 82.14%, precision of 84.49%, and recall of 83.07%. The research highlights the potential of data mining methods in the tourism sector, especially for predicting tourism visits. The results are expected to assist stakeholders in formulating more effective strategies and decisions, contributing positively to the wider development of the tourism sector.
Implementasi Support Vector Machine untuk Kendali Lampu Ruangan
Authors: Adam Chairul , Hidayati Rahmi , Suhardi Suhardi
Published: July 2024

The use of lights in daily activities is a necessity for everyone. Lights are useful as lighting when the room has minimal light, so lights are needed in the room in the house. If there are many rooms in a house, there are also many lights used. Often users forget to turn on and off the lights in each room if done manually. An automatic control is needed to make it easier for users to turn on and off lights according to user habits. Support Vector Machine is used in this in this study to classify the condition of lights based on user habits when turning on and off the lights. User habit data used for 15 days with a total of 620 data. Testing was carried out for 3 days with a total of 72 data. The results of system testing obtained the accuracy value of kitchen lights by 87.50%, accuracy of main room lights by 95.83%, accuracy of second room lights by 91.67%, accuracy of living room lights by 95.83%, accuracy of terrace lights by 95.83%, and accuracy of toilet lights by 94.44%.
Sistem Pemilihan Supplier Obat Menerapkan Metode Additive Ratio Analysis (ARAS)
Authors: Al Khadzik Fahmi , Huda Baenil , Novalia Elfina , Hilabi Shofa Shofiah
Published: July 2024

Qita Sehat pharmacy provides a wide range of medicines that are supplied by more than 30 suppliers and 100 buyers every month, but not all suppliers can meet the criteria set by pharmacies and suppliers are often late in the process of supplying drugs to pharmacies so that the stock in pharmacies is running low. From these problems, a solution is made, namely a drug supplier selection system is made by determining the priority order of drug suppliers with several criteria that match the availability of drugs at Qita Sehat pharmacies. The method used is the method of ARAS (Additive Ratio Analysis). The criteria considered are price, quality, lead time, communication systems, performance history and repair services. The result of this method is the order of priority of drug suppliers and knowing the results of the questionnaire through the sensitivity test that is the influence of changes in the value of the importance of the criteria. From the data generated in research using the ARAS method, the results obtained are that PT Javas Karya is the best supplier with the first rank of alternative A6 with a total value of 0.120.
Toxicity Analysis and Sentiment Classification of Wonderland Indonesia by Alffy Rev using Support Vector Machine
Authors: Singgalen Yerik Afrianto
Published: July 2024

The music industry's increasing reliance on digital platforms like YouTube for dissemination raises concerns about the potential impact of music videos on viewer sentiment and well-being. This study seeks to assess the toxicity and sentiment of the Wonderland Indonesia music video by Alffy Rev through Support Vector Machine analysis, contributing to our understanding of the effects of music content on online audiences. This research addresses the challenge of sentiment classification in digital content by leveraging the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The study aims to enhance sentiment classification accuracy by applying a Support Vector Machine (SVM) with a Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues. The research problem revolves around the need for robust sentiment analysis models capable of accurately discerning sentiment polarity within diverse datasets. Through the systematic application of CRISP-DM phases - business understanding, data understanding, data preparation, modeling, evaluation, and deployment - the study examines the efficacy of SVM with SMOTE in sentiment classification tasks. The findings demonstrate notable performance metrics, including accuracy (96.50%), precision (95.75%), recall (99.00%), and F-measure (97.34%). The AUC value substantially increases from 0.642 without SMOTE to 0.997 with SMOTE, highlighting its effectiveness in improving sentiment classification accuracy. In addition, The comparative analysis of toxicity values between the first and second videos demonstrates distinct patterns: the first video showcases a Toxicity score of 0.05290, with notable metrics such as Profanity registering at 0.04815. Conversely, the second video exhibits a slightly lower Toxicity score of 0.04744, with varying metrics such as Severe Toxicity at 0.01386.
Implementation of Global Vectors for Word Representation (GloVe) Model and Social Network Analysis through Wonderland Indonesia Content Reviews
Authors: Singgalen Yerik Afrianto
Published: July 2024

Integrating the Global Vectors for Word Representation (GloVe) Model with Social Network Analysis presents a promising approach for extracting nuanced semantic relationships from Wonderland Indonesia's content reviews. However, the lack of comprehensive studies exploring the effectiveness of this integration, specifically within the context of Wonderland Indonesia's content reviews, necessitates focused research to uncover its potential impact and applications. This study investigates the reception and impact of the "Wonderland Indonesia" video content by Alffy Rev ft. Novia Bachmid (Chapter 1) within the YouTube community using a comprehensive methodology based on CRoss-Industry Standard Process for Data Mining (CRISP-DM), topic analysis, and Social Network Analysis (SNA). Through topic analysis, the content's main themes and narrative elements were identified, shedding light on its storytelling effectiveness. Furthermore, sentiment analysis using Vader was conducted on 2204 out of 24185 posts, revealing that 1369 (92%) exhibited positive sentiment, 427 (31.19%) had neutral sentiment, and 850 (62.09%) contained negative sentiment. Additionally, sentiment analysis using TextBlob was performed on the same subset of posts, with 1369 (40) posts exhibiting positive sentiment, 599 (43.75%) with neutral sentiment, and 730 (53.32%) expressing negative sentiment. Notably, metrics such as toxicity (highest value: 0.90780) and severe toxicity (highest value: 0.95021) exhibited varying prominence within the analyzed content. These findings enable targeted interventions and content moderation strategies to promote healthier online discourse. The SNA uncovered intricate social dynamics and interaction patterns among viewers, emphasizing the video's ability to foster engagement and community interaction. This study underscores the significance of creative storytelling and community engagement strategies in digital content creation, with implications for audience participation and community development within the digital sphere. Future research could explore the longitudinal effects of such content strategies on audience retention and community engagement.
Efek Transformasi Wavelet Diskrit Pada Klasifikasi Aritmia Dari Data Elektrokardiogram Menggunakan Machine Learning
Authors: Nugrahadi Dodon Turianto , Mulyani Tri , Kartini Dwi , Herteno Rudy , Faisal Mohammad Reza , Budiman Irwan , Abadi Friska
Published: July 2024

Arrhythmia is one of the abnormalities of the heart rhythm, and some patients who suffer from arrhythmia do not feel any symptoms. Automating the early detection of arrhythmia is necessary by using an electrocardiogram. Previous research that had been done conducted classifications using several methods of data mining. In this research, the transformation for processing signals used is Discrete Wavelet Transformation, where a filtering process occurs that separates signals into high and low-frequency signals without losing the information from signals and is carried out with a two-level decomposition. After that, data normalization was performed using min-max normalization and was put into the model classification using the Support Vector Machine method with a Gaussian Radial Basis Function kernel of Naïve Bayes and K-Nearest Neighbor. Each data that was being used consisted of 140 data with a total of 35 data for each label. This research shows that at level 1 decomposition, the highest accuracy was obtained at db7 for the classification using Support Vector Machine with an accuracy of 73,57%, 68,57% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 59,64%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 63,57% while at level 2 decomposition the highest accuracy was obtained at db6 dan db8 for the classification using Support Vector Machine with an accuracy of 70,71%, 67,50% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 66,07%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 65%. From this research, it can be concluded that the highest accuracy is produced by decomposition level 1 using Support Vector Machine classification and that the Daubechies wavelet type has better results than the Haar wavelet.
Analisis Metode WASPAS Dalam Pemilihan Pimpinan Perusahaan
Authors: Anwar Badrul , Giatman M. , Maksum Hasan , Nasyuha Asyahri Hadi
Published: July 2024

The head of the company is an important figure in a business. In selecting a leader, criteria are needed that reflect a good leader. Some companies still do not use the system in selecting leaders, and even this is still nepotism, in this case the selected leaders are related to the owner of the company. A decision support system is a system that can assist in making decisions. Decision support systems are very effective in providing decisions on a problem, because this system has alternative problems and criteria according to the problems that occur. The WASPAS method in a decision support system provides the right solution for selecting prospective leaders with appropriate criteria because the WASPAS method is a unique combination of the known MCDM (Multi Criteria Decision Making) approach, namely the WSM (Weighted sum model) and the WPM weighted product model. (Weighted Product Method) initially requires linear normalization of the elements of the decision matrix by using two equations. From the results of Qi calculations using the WASPAS method, of the 6 alternatives calculated in the selection of company leaders, the best results are obtained with the highest score, namely 2.3732.
Klasifikasi Tingkat Kesegaran Ikan Nila Menggunakan K-Nearest Neighbor Berdasarkan Fitur Statistis Piksel Citra Mata Ikan
Authors: Widadi Rahmat , Arifwidodo Bongga , Masykuroh Kholidiyah , Saputra Ariyatno
Published: July 2024

Healthy tilapia will experience a decrease in quality when stored out of water even with the refrigerator. The decline in fish quality can be seen from the fish's eye. In this study the aim was to develop a tilapia freshness classification system based on fisheye images utilizing image processing and machine learning. Fisheye images were taken at intervals of 0 to 16 hours after being removed from the water. Ten tilapias were used in this study. The distance between the camera and the fish has also been changed with a variation of 4 distances. The total data obtained is 640 fisheye images. The method used in fisheye image classification consists of two stages, namely feature extraction and classification. The four types of statistical features used from image pixel values are the mean, standard deviation, skewness, and kurtosis. While at the classification stage using K-Nearest Neighbor. The scenario that has been determined is then used at the system implementation stage using the Python Programming Language. Testing and analysis using k-fold cross validation and confusion matrix. In the results of the study, the accuracy rate was obtained with an average of 100% using 2 classes, namely 0-2 hours and 2-4 hours. The accuracy rate using 4 classes was obtained with an average of 75%, namely classes 0-2 hours, 2-4 hours, 10-12 hours, and 14-16 hours, and when using all classes, an average accuracy rate of 45% is obtained.
Analysis of Distributed Denial of Service Attacks Using Support Vector Machine and Fuzzy Tsukamoto
Authors: Paradise Paradise , Prabowo Wahyu Adi , Rijanandi Teguh
Published: July 2024

Advances in technology in the field of information technology services allow hackers to attack internet systems, one of which is the DDOS attack, more specifically, the smurf attack, which involves multiple computers attacking database server systems and File Transfer Protocol (FTP). The DDOS smurf attack significantly affects computer network traffic. This research will analyze the classification of machine learning Support Vector Machine (SVM) and Fuzzy Tsukamoto in detecting DDOS attacks using intensive simulations in analyzing computer networks. Classification techniques in machine learning, such as SVM and fuzzy Tsukamoto, can make it easier to distinguish computer network traffic when detecting DDOS attacks on servers. Three variables are used in this classification: the length of the packet, the number of packets, and the number of packet senders. By testing 51 times, 50 times is the DDOS attack trial dataset performed in a computer laboratory, and one dataset derived from DDOS attack data is CAIDA 2007 data. From this study, we obtained an analysis of the accuracy level of the classification of machine learning SVM and fuzzy Tsukamoto, each at 100%.
Implementasi Data Mining Tingkat Kepemimpinan Siswa dengan K-Nearest Neighbor, Decision Tree, dan Naïve Bayes
Authors: Sayhidin Didin , Haris Gendhi , Juliane Christina
Published: July 2024

The process of monitoring and evaluating high school student leadership is deemed necessary because the level of student leadership is one of the prerequisites for high school students to face real challenges in the future. Data mining can be used to classify the level of leadership among high school students. The purpose of the research conducted in this case is to apply data mining using the K-NN, Decision Trees, and Naive Bayes models. This research is located in two different public high schools, namely SMA A as training data and SMA B as test data. This data was obtained in the same year, namely 2022. The data obtained were analyzed with the help of the Rapidminer application using K-NN, Decision Tree, and Naive Bayes. Student data that is processed is Basic Education Data (DAPODIK) in excel format. Before being analyzed, the text is processed first, namely tokenization, case folding, stop words, and details. The main goal of the steps above is also the main goal of this study to get the most accurate algorithm for classifying student leadership levels and knowing the results for comparison. The conclusion of this study is when measuring the performance of the three algorithms, the test results use confusion matrix validation. The K-NN algorithm was found to have the highest accuracy score compared to the Decision Tree and Naive Bayes. The accuracy value of the K-NN method using a dataset of high school students is 95.86%, the accuracy value of the Decision Tree algorithm is 94.65%, and the accuracy value of the Naïve Bayes algorithm is 79.55%.
Explainable AI: Identification of Writing from Famous Figures in Indonesia Using BERT and Naive Bayes Methods
Authors: Kurniyanto Firdaus Putra , Hartoyo Agus
Published: July 2024

Identifying the writings of well-known figures in Indonesia is a form of appreciation for the writing itself. By knowing the language style used by every famous figure in Indonesia, we can know the uniqueness of each writer, and it can help us understand the thoughts, ideas, and ideas they convey. This research has yet to be done, so it is still interesting to do further research. In this study, only a few writers were used, so it is still impossible to know the overall language style used by every famous figure in Indonesia. In this study, a system was built to determine the language style used by well-known figures in Indonesia based on their writing using the BERT, Naïve Bayes, and LIME algorithms for explainable AI processes. The results are that the BERT algorithm is better at classifying text with an accuracy of 92% compared to Naïve Bayes, which has an accuracy of 90%. From this study, it was also found that KH. Abdurrahman Wahid and Emha Ainun Nadjib have almost the same style of language in which their writings contain many words with political and religious elements. Dahlan Iskan, his writing contains many words with political and socio-cultural elements, while Pramoedya Ananta Toer's writing uses many pronouns.
Reduce Inventory Cost by Implementation of Just In Time Method In Raw Materials Inventory Control Website Application
Authors: Kholik Naila Hida , Rahmawati Endra , Sudarmaningtyas Pantjawati
Published: July 2024

The production process is the main activity of a manufacturing company, and one of the success factors of production is good raw material inventory management. One of the problems is the inefficiency of using raw materials that impacted raising inventory costs caused by the large number of raw materials left in the warehouse. This study aims to reduce inventory costs by embedding the Just in Time (JIT) method in the inventory application to calculate the raw material inventory. The application of the JIT method will consider 5 things, namely optimal number of shipments, order quantity per order, delivery quantity, frequency of raw material purchases and total inventory costs with JIT.The analysis used data from 2019-2021, proving the JIT could reduce the total inventory cost by 92%. In 2019 the total inventory cost decreased from Rp. 6.773.533 to Rp. 829.976. JIT is able to lower the total inventory cost in 2020 and 2021 by 93% and 96%. The application testing results using Black Box shows that all application features can be used properly and accordingly. It’s supported by the percentage of the trial pass value reaching 100%. Based on those results, it can be concluded that the established built inventory application was appropriately used, accordingly, and effectively to reduce the inventory cost.
Penerapan Convolutional Neural Network pada Timbangan Pintar Menggunakan ESP32-CAM
Authors: Rahman Hanung Pangestu , Indra Jamaludin , Rahmat Rahmat
Published: July 2024

Scales are needed by traders, including vegetable traders, but the scales created in the market can only determine the weight. That way traders need time to calculate the price based on the weight and type of vegetables. In previous research there has been research on smart scales that can calculate the total price based on the weight and type of vegetables being weighed, this study used the Raspberry Pi 3 Model B and the Convolutional Neural Network (CNN) as a method for the scales to be able to identify the types of vegetables that are on it. Along with the rapid development of technology, the price of the Raspberry Pi for all variants has increased in price. Therefore the need for research on smart scales with components that have relatively cheaper prices. In this study, researchers used the ESP32-CAM microcontroller, which is priced relatively cheaper than the Raspberry Pi 3 Model B. This research still uses the Convolutional Neural Network (CNN) method and a load cell equipped with the HX711 module as a sensor to obtain the weight value of an object. The dataset collected totaled 600 image data with 150 image data for each type of vegetable, classes in the training data consisted of tomatoes, cabbage, carrots, and potatoes. Smart scales using the ESP32-CAM get results of a classification accuracy of 90% and the average difference of the tools built is 0.8 grams compared to the SF-400 brand digital scales.
Klasifikasi Gerakan Yoga dengan Model Convolutional Neural Network Menggunakan Framework Streamlit
Authors: Nur Syahbani Mohammad Fikri , Ramadhan Nur Ghaniaviyanto
Published: July 2024

Indonesian people are not fit and lack sports activities, therefore one of the alternative sports activities is yoga. Yoga is a type of exercise that has two important components, namely breathing and movement. Yoga movements also vary and can be distinguished from body curves, but ordinary people may not be familiar with yoga movements. With advances in technology and computer performance intelligence, it is now possible for computers to recognize an image for object recognition, namely detecting yoga movements using the digital image classification method. To make it easier to classify yoga movements, you can use the CNN model. Convolutional Neural Networks (CNN) are a combination of artificial neural networks with deep learning methods. The CNN process will carry out a training and testing process for yoga movements so that an image classification can be determined from the type of yoga movement. The image of the yoga movement is divided into 80% for training and 20% for testing. The training process is carried out using two different scenarios by differentiating the input image size, batch size, optimizer. The dataset consists of goddess, plank, tree, warrior2, downdog movements. The highest accuracy results are 94.10% using 170 x 170 image input, batch size 32, RMSprop optimizer. The results of testing a total of 40 images of yoga movements, 37 images were correctly guessed. The model that has been trained is implemented into the website using the Streamlit framework.
Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection
Authors: Prayoga Imam , Purbolaksono Mahendra Dwifebri , Adiwijaya Adiwijaya
Published: July 2024

The advancement and development of the internet is used by the people to support various sectors, one of which is the film industry. Nowadays, people can easily access various movies from available sites. This convenience had led to many reviews about a movie that can be obtained easily. This movie review is very influential on the variety of movies. Freedom of expression on the internet, makes the reviews of a movie vary. For this reason, it is necessary to analyze the sentiment of he movie reviews that are positive or negative. In this research, a sentiment analysis model is build using chi-square selection feature with the KNN algorithm. The final result of this research is able to provide the best classification model with the implementation of stemming. The value of k = 267 in selectkbest at the feature selection stage using chi-square, and using the value of K = 11 in the KNN parameter. This model produces f1 score value of 86.98%.
Elliott Wave Prediction Using a Neural Network and Its Application to The Formation of Investment Portfolios on The Indonesian Stock Exchange
Authors: Rifqi Arrahim Natadikarta Muhammad , Saepudin Deni
Published: July 2024

Predicting stock trends is a complex problem because their movements constantly fluctuate and are affected by many factors such as political elements, world economic situation, investor expectations, and psychological factors. One way to analyze stock prices is Technical Analysis. This method focuses on stock indicators and patterns formed. Elliott Wave is one of the Technical Analysis methods. This study suggests an approach based on the combination of Neural Networks and Elliott Wave theory which will predict the possible direction of future trends. This model uses Fast Fourier Transform (FFT) coefficients to look for similarities with Elliott Wave patterns. When the dataset is similar to Elliott Wave patterns, the Neural Network will predict the direction of the stock trend. This study was conducted to confirm that the Elliott Wave-based Neural Network method helps predict stock trends. Experimentation is being carried out in some stocks on the Indonesian Stock Exchange, with profits exceeding 90%.
Healthy Menu Recommendation for Malnutrition Patients Based on Ontology
Authors: Virgiani Igga Febrian , Baizal Z K A , Dharayani Ramanti
Published: July 2024

A healthy diet is one of the keys to creating a healthy lifestyle, but at this time the selection of a healthy and nutritious meal menu in the society is difficult to do because of the limited nutritional information contained in a food. A healthy diet can help a person to get balanced nutrition, good nutritional intake can increase the body's immunity, and make a normal or healthy body weight so that it can increase work productivity and prevention of chronic diseases. To overcome this problem, we propose the use of ontology and Semantic Web Rule Language (SWRL) to build a healthy menu recommendation system in the form of a chatbot to make it easier for users to determine the daily meal menu. These recommendations are personalized by considering the user's needs. Ontology is used to represent the required knowledge and the reasoning process uses SWRL. From the results of system testing, the recommendations get the accuracy of the F-Score value of 0.951
Pengelompokkan Produksi Tanaman Jagung di Sumatera Utara Menggunakan Algoritma K-Medoids
Authors: Safruddin Safruddin , Sitopu Joni Wilson , Manurung Azwar Anas , Satria Indra , Wanto Anjar
Published: July 2024

Corn is a strategic commodity with bright marketing prospects, especially in North Sumatra. Therefore efforts to increase corn production need great attention because, with sufficient availability, it is hoped that the community's need for corn can be fulfilled and the selling price remains stable. This study aims to classify corn production in North Sumatra based on districts/cities so that districts/cities can be identified and developed into corn production centers to reduce food imports, specifically corn crops. This research uses a corn production dataset based on districts/cities in North Sumatra consisting of 25 regencies and eight cities in 2019-2021 obtained from the Food Crops and Horticulture Service of North Sumatra Province. The algorithm used is the K-Medoids algorithm with Rapid Miner Studio tools. The results of this study were grouping corn production which was divided into 5 (five) groups, including Group 1 was an area with very high corn production consisting of 1 Regency, Group 2 was an area with high corn production consisting of 2 Regencies, Group 3 was an area with moderate corn production consisting of 4 regencies, Group 4 is an area with low corn production consisting of 3 regencies, and Group 5 is an area with very low corn production consisting of 15 regencies and seven cities. Based on these results, Karo, Dairi, and Simalungun districts can be used as centers for corn production in North Sumatra because these three districts alone produce corn production of 65.7% of the total corn production in North Sumatra.
Analisis Sentimen Kendaraan Listrik Pada Media Sosial Twitter Menggunakan Algoritma Logistic Regression dan Principal Component Analysis
Authors: Pratama Youga , Murdiansyah Danang Triantoro , Lhaksmana Kemas Muslim
Published: July 2024

Twitter sentiment analysis is a method for identifying a person's opinions, reactions, judgments, evaluations, and emotions towards certain topics on Twitter social media. Opinions or can be called opinions can be classified as positive or negative. This research was conducted to find out public opinion about electric vehicles on Twitter social media, which is more positive or negative. The data obtained was 1874 tweets with data divided into training data and testing data at a ratio of 80:20. Data is classified using the Logistic Regression (LR) method, and Principal Component Analysis (PCA) as an optimization to improve accuracy. In this study it was found that around 86.9% of the opinions were positive, and 13.1% of the opinions were negative on the topic of electric vehicles. The results of research conducted using the Logistic Regression algorithm obtained the best accuracy of 87.9%, and after being optimized using Principal Component Analysis the best accuracy obtained increased to 90%.
Cluster Analysis using K-Means and K-Medoids Methods for Data Clustering of Amil Zakat Institutions Donor
Authors: Siregar Hotmaida Lestari , Zarlis Muhammad , Efendi Syahril
Published: July 2024

Cluster analysis is a multivariate analysis method whose purpose is to classify an object into a group based on certain characteristics. In cluster analysis, determining the number of initial clusters is very important so that the resulting clusters are also optimal. In this study, an analysis of the most optimal number of clusters for data classification will be carried out using the K-Means and K-Medoids methods. The data were analyzed using the RFM model and a comparative analysis was carried out based on the DBI value and cluster compactness which was assessed from the average silhouette score. The K-Means method produces the smallest DBI value of 0.485 and the highest average silhouette score value of 0.781 at k=6, while the K-Medoids method produces the smallest DBI value of 1.096 and the highest average silhouette score value of 0.517 at k=3. The results show that the best method for data clustering donations Amil Zakat Institutions is using the K-Means method with an optimal number of clusters of 6 clusters.
Prototipe Sistem Monitoring Rumah Walet Berbasis IoT
Authors: Ariandi Muhamad , Alvinser Jerry
Published: July 2024

Swallow's nest is one of the areas of business carried out by residents of the Tulung Selapan area, where many residents of the area make their houses part of the swallow's house, so that the houses where people live and the swallow's house merge. Problems often occur in swiftlet houses which cause unstable temperatures, poor air quality, and the sound of swallow call speakers that often turn off without a known cause. Where to carry out maintenance of the swallow house cannot be done at any time, only certain times that can be done by the swallow entrepreneur in checking the swallow house, namely when the swallow comes out. From these problems, the researcher aims to make a prototype of a swallow house monitoring tool which has several components such as temperature so that the condition of the swallow house is maintained at a temperature of 260C - 290C using a DHT22 sensor and a mistmaker. The heater automatically turns on at temperatures below 260C to keep the swiftlet house hot and if the temperature is above 290C a mistmaker is used as a cooler which will automatically turn ON. To maintain air quality using the MQ-2 sensor, and monitoring the current flowing to the swiftlet calling speaker using a DC voltage sensor with the help of Esp32 as a microcontroller. So with the existence of this swiftlet house prototype tool, swallows will feel comfortable and produce good quality swallow nests for entrepreneurs to make it easier to monitor swallows in their swallow houses. From the results of measurements and percentage calculations performed on each component of the tool sensor, nothing exceeds the tolerance value of 5% and the components used in this tool can work properly according to their functions. This monitoring system prototype tool can help swallow house entrepreneurs more easily control the condition of swallow houses and produce good quality swallow nests than before, so they have quite expensive selling prices.