bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of CRISP-DM for Social Network Analysis (SNA) of Tourism and Travel Vlog Content Reviews
Authors: Singgalen Yerik Afrianto
Published: July 2024

Technological developments have facilitated the process of creating and publishing digital content on various platforms while influencing application user behavior in terms of consumption. In the context of tourism and travel vlog content, the publication of travel content influences perceptions and triggers the intention of visiting the destination in which the video is taken. In addition, content reviews can be seen in the comment column, which shows the response from content creators to tourists related to the topics discussed in the video content. This study aims to analyze content reviewers' social network patterns and sentiments using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach. Meanwhile, the model used in this study is Social Network Analysis (SNA) and Sentiment Classification based on analyzing network patterns of "whom, mention whom," and "who replies who," based on diameter, density, reciprocity, centralization, and modularity. The stages in the CRISP-DM method consist of business understanding, data understanding, modeling, evaluation, and deployment. The results of this study show that at the business understanding stage, tourism and travel vlog content reviews with Waseda Boys Indonesia Trip video comment datasets and Waseda Boys Trip to Manado and Likupang video and Waseda Boys Trip to Labuan Bajo video. Scraping content review data from the YouTube platform is carried out at the data understanding stage based on author, description, Global Unique Identification (GUID), like_count, link, pub_date, and author channel URL. In the modeling stage, the interaction pattern between authors in the form of networks is visualized and analyzed based on clusters. At the evaluation stage, an evaluation is carried out based on sentiment related to content related to tourism activities. At the deployment stage, recommendations for digital tourism marketing strategies based on tourism and travel vlogs can be known. Thus, tourism and travel vlog content play an important role in triggering tourism intentions so that it is effectively used in destination marketing strategies in the digital era.
Sentiment Classification of The Capsule Hotel Guest Reviews using Cross-Industry Standard Process for Data Mining (CRISP-DM)
Authors: Singgalen Yerik Afrianto
Published: July 2024

Technology advancements empower hotel accommodation service managers to undertake innovative initiatives to enhance guest appeal and ensure safety and comfort. One manifestation of such innovation is exemplified by The Capsule Hotel, which offers novel experiences to both domestic and international tourists. This research seeks to assess the sentiments of guests at The Capsule Malioboro, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Support Vector Machine (SVM) technique with Synthetic Minority Over-sampling Technique (SMOTE) operators. The findings demonstrate that when operated without SMOTE, the SVM algorithm yields a confusion matrix displaying an accuracy of 99.01%, precision of 99.00%, recall of 100%, AUC of 0.944, and an f-measure of 99.49%. With the integration of SMOTE, there is a notable enhancement across all metrics, with accuracy, precision, recall, AUC, and f-measure, all achieving perfect scores of 100%. In addition, an analysis of the top 10 frequently used words in guest reviews, such as "solo," "good," "place," "staff," "comfortable," "room," "clean," "hotel," "capsule," and "Malioboro," provides additional insights. Examining guest profiles within the dataset uncovers a strong inclination among Indonesian individuals to opt for The Capsule Malioboro's services, with solo travelers being the predominant guest type and most stays lasting only a single day. The capsule accommodations cater to various gender preferences, and an examination of overnight data indicates a rising trend, particularly in December 2022 and 2023. These insights enable the hotel to discern guest preferences, offering valuable information for enhancing service ratings and addressing specific needs.
Perbandingan Metode Dempster Shafer Dan Teorema Bayes Untuk Mendeteksi Penyakit Ensefalitis
Authors: Mustaqim M. , Rakasiwi Galih , Iskandar Agus
Published: July 2024

The aim of this study was to evaluate how well Bayes' Theorem and the Dempster-Shafer Method identify encephalitis. Inflammation of the brain, or encephalitis, can be caused by several things, such as bacterial or viral diseases. The main aim of this study was to assess how well both approaches perform in identifying this disease using clinical data. The main problem faced in detecting encephalitis is the complexity of the variations in symptoms and causal factors. This research focuses on analyzing clinical data of encephalitis patients, including medical history, laboratory test results, and clinical symptoms. The Dempster-Shafer method, a belief theory approach that allows the integration of information from uncertain sources, will be compared with Bayes' Theorem, a classical statistical approach frequently used in medical diagnostics. The research method involves collecting clinical data from medical records of patients diagnosed with encephalitis. This data will then be analyzed using the Dempster-Shafer Method and Bayes' Theorem to compare their accuracy in detecting disease. In addition, evaluation of method performance will also be carried out by comparing the sensitivity, specificity, and positive and negative predictive values of each method. The results of this research are expected to provide better insight into the effectiveness of the Dempster-Shafer Method and Bayes' Theorem in detecting encephalitis. The implications of these findings can be used to improve existing diagnostic methods and increase the ability of early detection of this disease. This research has the potential to make an important contribution to the development of the field of diagnostic medicine and can help medical practitioners make better decisions in the management of encephalitis patients. Using the Dempster Shafer method, the encephalitis diagnosis rate reached 99.8%, while applying Bayes' Theorem gave a diagnosis rate of only 3.5%. From these results it can be concluded that the application of Dempster Shafer is more powerful and provides a higher level of confidence in the encephalitis diagnosis process compared to the Bayes Theorem method.
Analisis Sentimen Pengguna Twitter Terhadap Bus Listrik Menggunakan Naïve Bayes
Authors: Verawati Ike , Jaelani Syarif Nurwahid
Published: July 2024

Twitter sentiment analysis is one method of identifying and classifying opinions into positive or negative sentiment in tweets. One of the topics that is being widely discussed on Twitter and has received various opinions for and against is electric buses. All of these opinions are still random and their sentiments have not been classified so sentiment classification needs to be carried out.Naïve Bayes can be used to classify sentiment and is easy to implement. The aim of this research is to classify whether sentiment regarding electric buses leads to positive sentiment or negative sentiment using Naïve Bayes and calculate the accuracy obtained. Several steps were taken, namely data collection, preprocessing, lexicon labeling, word weighting, naïve Bayes classification, and confusion matrix evaluation. The results of this stage from 4 trials of different data sharing ratios showed that the highest sentiment was positive sentiment which reached 77.31% with 22.69% negative sentiment at a data sharing ratio of 6:4 with the evaluation results using the confusion matrix obtaining an accuracy of 74.4%. After naïve Bayes was optimized with hyperparameter tuning, the accuracy increased to 78%. At a data sharing ratio of 9:1, the accuracy obtained after optimization shows a decrease to 71.5%, whereas initially Naïve Bayes obtained an accuracy of 75.6%, this shows that the data split ratio can influence the accuracy obtained by the classification model.
Komparasi Metode LSTM dan GRU dalam Memprediksi Harga Saham
Authors: Meri Aryati Ni Wayan , Wiguna I Komang Arya Ganda , Putri Ni Wayan Suardiati , Widiartha I Komang Kurniawan , Ginantra Ni Luh Wiwik Sri Rahayu
Published: July 2024

The rapid development of technology has an impact on the economy of society, one of which is investing in stocks. Stocks are evidence of ownership of an individual's assets in a company. However, stock prices have very high levels of fluctuation, requiring accurate methods to assist in predicting stock prices. LSTM and GRU were chosen for their intrinsic ability to handle long-term and short-term problems in time series data. LSTM has a complex memory structure that allows decision-making based on long and short-term information. Meanwhile, GRU has a simpler structure with a focus on gate mechanisms to control information flow, resulting in lighter and faster models. Therefore, this study will compare two RNN methods, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting stock prices using MAPE and RMSE evaluation metrics. The combination of parameters used to evaluate the MAPE and RMSE values in this study includes learning rate, timestamps, batch size, and epoch. The results of this study show that the GRU method is more accurate compared to the LSTM method. This is evidenced by the evaluation results of the LSTM method with the lowest MAPE value of 2.42% and the lowest RMSE value of 0.01807, while the evaluation results of the GRU method with the lowest MAPE value of 2.14% and the lowest RMSE value of 0.01775. The combination of parameters used in this study also has an influence on the final MAPE and RMSE results, especially in the use of learning rates of 0.001 and 0.0001. Therefore, it can be concluded in this study that the GRU method is more accurate and effective compared to the LSTM method in predicting stock prices.
Improving Infant Cry Recognition with CNNs and Imbalance Mitigation
Authors: Indrawan Michael , Luthfiarta Ardytha , Fahreza Muhammad Daffa Al , Rafid Muhammad
Published: July 2024

The classification of baby cries using machine learning is essential for developing automated systems that can assist caregivers in identifying and responding to the needs of infants promptly and accurately. This study aims to improve upon previous research relating to the Cry Baby Dataset, which has highly imbalanced data. We combine oversampling and undersampling techniques using SMOTE and ENN, along with data augmentation through pitch shifting and noise addition to address the data imbalance issue. The processed data was then modeled using Convolutional Neural Networks (CNN). The study yielded an overall accuracy of 88%, with balanced accuracy observed across all classes, effectively mitigating data imbalance. This represents a notable advancement compared to previous research, which often encountered challenges with unbalanced accuracies across classes. The classes identified include recordings of baby cries attributed to belly pain caused by colic, recordings related to burping, recordings associated with discomfort or other symptoms, recordings of hungry baby cries, and recordings indicating fatigue or the need for sleep. This shows a significant improvement from previous studies, which had very unbalanced accuracy for each class.
Perbandingan Kernel Polynomial dan RBF Pada Algoritma SVM Untuk Analisis Sentimen Skincare di Indonesia
Authors: Yunanda Vinsensius Dendi , Hendrastuty Nirwana
Published: July 2024

Skincare is considered a crucial need in maintaining and caring for skin health, given its role not only in cosmetics but also in contributing to the well-being and protection of the skin from external factors. Sentiment analysis of skincare reviews on social media helps understand consumer perspectives, guiding skincare manufacturers in product improvement. The comparison of polynomial and RBF kernels in SVM is relevant to enhance sentiment analysis of skincare in Indonesia, ensuring the model's accuracy in classifying product sentiments. The dataset used consists of 2168 data obtained through social media scraping. After obtaining the data, text preprocessing processes such as case folding, cleaning, tokenization, stemming, and data labeling were performed. The data was divided into an 80:20 ratio for comparison, with 1734 training data and 434 testing data. The accuracy results using the SVM method with RBF and Polynomial kernels were obtained, with the highest accuracy found in the RBF kernel at 86,17%, and the polynomial kernel achieving an accuracy result of 84,56%.
Implementasi Metode Naive Bayes Classifier Terhadap Klasifikasi Topik Kemacetan Lalu Lintas Indonesia Melalui Tweet
Authors: Romindo Romindo , Barus Okky Putra , Pangaribuan Jefri Junifer
Published: July 2024

The causes of traffic congestion in Indonesia include traffic accidents, poor road infrastructure, and the increasing number of motor vehicles. In 2023, the number of vehicles reached 152.6 million, exceeding half of Indonesia's population of 276 million, according to the Indonesian Traffic Police Corps data. Twitter has a user base of approximately 4.23% of the total global population, which amounts to 436 million user and Indonesia is one of the countries with the largest number of Twitter users. Twitter data will be used to determine the sentiment level of traffic congestion in Indonesia using the Naïve Bayes Classifier method to evaluate overall accuracy performance, precision, recall, and f1-score. The research classified two groups, negative and positive. Classification is carried out through several stages, including data pre-processing, data training, data testing, and evaluation. After evaluating the Naive Bayes algorithm, the highest results achieved an overall accuracy of 77%, precision of 86%, recall of 82%, and f1-score of 84%.
Enhancing Sentiment Analysis of Garden by the Bay Reviews on TripAdvisor Platform Using CRISP-DM through DT and SVM with SMOTE
Authors: Singgalen Yerik Afrianto
Published: July 2024

This research aims to improve sentiment analysis of reviews related to Garden by the Bay, a prominent tourist destination in Singapore, by leveraging the CRISP-DM methodology and Synthetic Minority Over-sampling Technique (SMOTE). The study employs a comprehensive approach, integrating CRISP-DM phases to systematically collect, clean, and analyze data from online reviews. The dataset comprises a substantial number of reviews, reflecting diverse visitor experiences. Using SMOTE, class imbalance issues within the dataset are addressed, leading to enhanced performance of sentiment analysis algorithms. The evaluation of Decision Tree (DT) and Support Vector Machine (SVM) algorithms, both with and without SMOTE, reveals significant improvements in accuracy, precision, recall, and F-measure metrics when SMOTE is applied. These findings underscore the efficacy of SMOTE in optimizing sentiment analysis algorithms for the Garden by the Bay dataset, thereby facilitating a deeper understanding of visitor sentiments and experiences, which inform strategies for enhancing the tourism experience at Garden by the Bay.
Klasterisasi Perguruan Tinggi LLDIKTI V Berdasarkan Indikator Kinerja Utama dan PDDIKTI Menggunakan K-Means Clustering
Authors: Fatmawaty Virdiana Sriviana , Riadi Imam , Herman Herman
Published: July 2024

Pertumbuhan jumlah perguruan tinggi yang terus meningkat menjadi salah satu faktor krusial untuk memastikan mutu pendidikan tinggi agar berdaya saing. Komposisi perguruan tinggi di Provinsi Daerah Istimewa Yogyakarta terdiri atas 77% Perguruan Tinggi Swasta (PTS) dan sisanya adalah Perguruan Tinggi Negeri (PTN). Masing-masing perguruan tinggi memiliki Indikator Kinerja Utama (IKU) yang wajib dilaporkan dan dipenuhi, serta melakukan pendataan aktivitas pembelajarannya pada Pangkalan Data Pendidikan Tinggi (PDDIKTI). Data IKU dan data PDDIKTI ini menjadi  bahan evaluasi dan analisis untuk menentukan baseline dalam aktivitas pembinaan di LLDikti Wilayah V khususnya bagi PTS. Salah satu model analisis yang dapat dilakukan adalah dengan metode klasterisasi. Metode ini biasa digunakan pada data mining untuk mengelompokkan data berdasarkan kesamaan karakteristik data. Penelitian ini melakukan klasterisasi PTS di LLDIKTI Wilayah V menggunakan algoritma K-Means Custering. Hasil penelitian ini menunjukkan bahwa berdasarkan kesamaan karakteristik data IKU dan data PDDIKTI terbentuk empat klaster PTS, yaitu klaster 1 terdiri dari 4 PTS, klaster 2 terdiri dari 46 PTS, klaster 3 terdiri dari 21 PTS, dan klaster 4 terdiri dari 33 PTS.  Hasil analisis ini akan sangat bermanfaat bagi LLDIKTI Wilayah V dalam melakukan fungsi pembinaan kepada PTS.
Peningkatan Performa Model Machine Learning XGBoost Classifier melalui Teknik Oversampling dalam Prediksi Penyakit AIDS
Authors: Wicaksono Duta Firdaus , Basuki Ruri Suko , Setiawan Dicky
Published: July 2024

The data shows that HIV (Human Immunodeficiency Virus) has caused tens of millions of global deaths, with 630,000 people dying from HIV-related illnesses in 2022 and 1.3 million people newly infected with HIV. Without treatment, HIV can progress to AIDS (Acquired Immune Deficiency Syndrome), weakening the immune system and increasing the risk of infections and other diseases. Despite advancements in treatment, early detection of AIDS remains a priority. This research develops an AIDS prediction model using machine learning, which proves to be an effective solution in providing future health predictions. However, data imbalance issues challenge the model in predicting rare AIDS cases. To solve this problem, oversampling techniques are employed to balance the distribution of minority classes. This study explores oversampling techniques such as SMOTE, ADASYN, and Random Oversampling, combined with the XGBoost algorithm. The results show that the combination of Random Oversampling technique with the XGBoost Classifier yields the best performance with an accuracy of 94.44%, precision of 90.72%, recall of 98.74%, and an f1_score of 94.65%. This research is expected to provide valuable insights for healthcare practitioners and the public in efforts to control the spread of AIDS globally.
Kolaborasi Metode Naive Bayes dan MPE dalam Pengambilan Keputusan Pemilihan Supplier Ban Motor
Authors: Agustina Nani , Sutinah Entin , Martini Martini
Published: July 2024

Almayra Jaya Ban is a company operating in the trading business sector which faces complexity in managing various products originating from several different suppliers. The main problem faced by this company is the difficulty in processing different supplier data for optimal evaluation in terms of supplier potential, quality of goods, and availability of goods from various suppliers. Supplier selection is an important decision that can affect operational performance and profits for the company. To increase accuracy and objectivity in decision making, this research uses collaboration between the Naive Bayes Method and the Exponential Comparison Method (MPE). Through experiments and sensitivity analysis, the results show that the integration of these two methods produces more stable and reliable decisions. Validation is carried out using historical data and trials to ensure accuracy. So by collaborating between the Naive Bayes Method and MPE it can be an effective approach in the decision making system for selecting motorbike tire suppliers, by providing more accurate, objective and accountable decisions for this company. The results of this research obtained a decision in selecting suppliers by selecting 7 alternative suppliers to be retained in the motorbike tire procurement cooperation, namely: Dermaga Jaya Motor with a score of 218.90, Ardendi Jaya Sentosa with a score of 217.90, Kumala Motor with a score of 213.08, Sinar Maju Motor got a score of 209.90, Raissa Jaya Motor got a score of 200.90, Nias Jaya Motor got a score of 198.18, Ciruas Jaya Motor got a score of 195.00.
Implementasi dan Analisis Model Machine Learning Decision Tree untuk Deteksi Akun Palsu di Twitter
Authors: Taufik Risqa , Jimah Risti , Solichin Achmad
Published: July 2024

In this digital era, social media platforms have become an integral part of daily life, facilitating social interaction, information exchange, and participation in public discussions. However, the emergence of bot or fake accounts on social media, especially Twitter, has posed a new challenge. These accounts are often used to disseminate inaccurate or misleading information, which can negatively impact social and political dynamics. This research focuses on the problem of detecting fake accounts on Twitter. We conducted an in-depth analysis of user profiles and their posting behavior. We collected data from various Twitter accounts, both real and fake, and categorized them for model creation. The Decision Tree model is used in this research for fake account detection. We chose this method because of its effectiveness in identifying and distinguishing between real and fake accounts based on predetermined features. The model creation process involves training and testing the model with the categorized dataset. As a case study, we applied this model to the followers of the Twitter account of Budi Luhur University. The result is, this model is able to identify fake accounts with an accuracy rate reaching 99%. This shows that our approach in using the Decision Tree Model is effective in dealing with the problem of detecting fake accounts on Twitter.
Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan
Authors: Kamilah Nur Azizah , Rohana Tatang , Rahmat Rahmat , Fauzi Ahmad
Published: July 2024

The number of women's violence in Indonesia is increasing. In West Java alone, 58,395 cases of violence against women were recorded. Violence against women that occurs in West Java is among the most common compared to other provinces. This high number shows that violence against women is still not being handled seriously. Therefore, clustering is carried out to achieve a more structured solution so that it can assist the government in providing appropriate and appropriate responses to the conditions of each region, so that case handling can be more focused. The aim of this research is to group districts or cities in West Java in cases of violence against women using the K-Means and K-Medoids algorithms into two clusters, namely, high and low. In this research, data grouping was carried out using 2 methods, namely the K-Means and K-Medoids algorithms to find out which comparison between the two algorithms is more optimal. It is hoped that this research will produce the best cluster, the results of this cluster can help the government and related agencies to determine which districts or cities should be prioritized in handling cases of violence against women in West Java. The results of this research produced 2 clusters. Cluster 0 (high) and cluster 1 (low). The number of cluster 0 (high) is 14 districts and cities, while cluster 1 (low) is 13 districts and cities. Comparing the clustering evaluation between K-Means and K-Medoids, the best cluster evaluation value was obtained using the K-Medoids Algorithm with a Silhoutte Coefficient evaluation of 0.43, while the Davies Bouldin Index evaluation results showed the best cluster results using the K-Means Algorithm with a DBI value of 0.95.
Decision Support System for Selecting a Camera Stabilizer Using Rank Reciprocal and ARAS Approaches
Authors: Erkamim Moh. , Daniarti Yeni , Shalahudin Mohammad Imam , Mulyadi Mulyadi , Soares Teotino Gomes
Published: July 2024

The selection of camera stabilizers is a crucial aspect in the photography and videography industry, especially with the increasing use of cameras. Typically, to select a camera stabilizer, decision-makers must be aware of all specifications of the available options. However, this necessarily results in a lengthy decision-making process, and various considerations lead to imprecise decisions. The aim of this research is to develop a Decision Support System (DSS) for choosing the appropriate and swift camera stabilizer through a combination of the Rank Reciprocal weighting approach and the Additive Ratio Assessment (ARAS) method. The Rank Reciprocal approach is utilized to obtain the criteria weight values, and the ARAS method is employed to evaluate and select the best alternative based on a number of criteria. This research produced a website-based DSS that can recommend the best alternative in the form of an alternative ranking. The results from the case study conducted obtained rankings from the highest to the lowest as follows: Zhiyun Tech Weebill S with a score of 0.9428, Beholder DS1 with a score of 0.8497, Gudsen Moza AirCross S with a score of 0.8205, and Feiyu Tech Scorp C (A3) with a score of 0.7197. The system built has been tested with a usability test scoring 88.75%, indicating that the system has met its users' needs.
The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture
Authors: Aprilla Diah Mitha , Bimantoro Fitri , Suta Wijaya I Gede Pasek
Published: July 2024

The palmprint is a part of the human body that has unique and detailed characteristics of the pattern of palm lines, such as the length and width of the palm (geometric features), principal lines, and wrinkle lines. It began to be developed as a tool for recognize a person. The palmprint dataset used comes from Kaggle, namely BMPD. The palmprint images in this dataset were taken in 2 sessions. In the first session, there was not much variation in rotation compared to the second session. This research uses Convolutional Neural Network (CNN) models with Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 architectures to see the best performance. The results of this research showed that the MobileNet model had the best performance with an accuracy of 96.6% and a loss of 14.3%. For Precision results of 94%, Recall 96%, and F1-Score 94%. Meanwhile, Xception obtained an accuracy of 88.3% and a loss of 52.9%, VGG16 70.8% and a loss of 109.8%, ResNet50 5.8% and a loss of 307.9%, and EfficientNetB0 3.3% and a loss of 340.1%.
Analisis Model Penilaian Sebagai Penunjang Keputusan Kinerja Dosen Penerima Hibah Penelitian Menggunakan Pendekatan PROMETHEE II
Authors: Mahessya Raja Ayu , Trisna Novi
Published: July 2024

A research grant is a fund awarded by an institution allocated to another researcher. The analysis applies an approach from PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluations) to evaluate grantee lecturers and provide recommendations for final weighted scores according to passing grade graduation. The current system for calculations still uses weight percentages that are not processed using one of the decision support system methods, so it takes a long time for reviewers to assess the performance of grant recipient lecturers. Of the existing problems, the closest solution to this problem is the PROMETHEE II method. The PROMETHEE II method is used in complex decision making with many criteria, so that objective and accurate evaluation results are obtained for grant recipient lecturers. The criteria consist of a writing template, research results, mandatory outputs 1 and 2, additional outputs, and the contents of the proposal. The proposals collected are assessed by reviewers and analyzed using the PROMETHEE II method to produce a final score for each grant recipient lecturer. The results of the research show that the PROMETHEE II method provides appropriate final grade recommendations based on the performance and contribution of each lecturer in various aspects of research assessment. The recommendation for the final PROMETHEE II score is where the score above is equal to 0, the results of the lecturer receiving the grant are accepted while the score below 0 is rejected. In this way, recommendations from the final grades obtained can provide an overview of lecturers' contributions in supporting institutional research.
Pemantauan Kelembapan dan Pengendalian Suhu Serta Pemberian Nutrisi Selada di Prototype Greenhouse dengan Nutrient Film Technique Berbasis IoT
Authors: Derafi Ryan , Ullah Aulia , Maria Putut Son , Faizal Ahmad
Published: July 2024

Nutrient Film Technique (NFT) is a hydroponic plant cultivation method in which plant roots grow in a shallow, particulated nutrient layer that allows plants to obtain water, nutrients and oxygen. In order to maintain a stable condition, kasturi farm hydroponic farmers experience problems in controlling and monitoring the condition of their plants, without having to sacrifice other activities. This research was carried out through a series of processes, starting from analyzing the needs, designing, and testing the design in providing nutrition, temperature control and monitoring the humidity of rockwool foam based on ESP 32. The hardware used are TDS (Total Dissolved Solids) Sensor, Ultrasonic Sensor, LDR (Light Dependent Resistor) Sensor, DHT 11 Sensor, Soil Sensor and DC Pump. The control used is ESP 32, and the application used is Blynk. The optimal control system in maintaining nutrients is 560 to 660 ppm and otherwise experiences a significant change of 1185 ppm. optimal temperature control with the highest temperature of 29.20 ˚C, and not using control will be more unstable. Rockwool humidity experienced an ideal stable increase of 50% - 56%. The average height of lettuce from the test results is about 22.07 cm with an average weight of 119.805 g. Measurement of lettuce plants indicated variations in fresh weight and height, with 3 prominent plants. The system effectively monitors and controls plant growth conditions, creating an optimal hydroponic environment. Based on the test results, the use of IoT technology in hydroponic NFT systems has proven to be very helpful for farmers in monitoring humidity, temperature and nutrients in real-time. Farmers can easily access plant condition information through remote devices, allowing farmers to immediately take necessary actions to maintain the health and productivity of hydroponic plants.
Analisis Perbandingan Metode Teorema Bayes dan Certainty Factor Pada Diagnosis Gangguan Kecemasan
Authors: Gobel Citra Yustitya , Lasena Marlin , Puspa Misrawati Aprilyana , Utiarahman Siti Andini
Published: July 2024

Anxiety disorders are a mental health problem that is increasing in prevalence, but the lack of public knowledge about anxiety disorders means that many people are not aware of anxiety disorders, so information technology is needed to understand symptoms and diagnosis using more relevant methods in intelligent systems. Intelligent systems are able to help in analyzing various symptoms and identifying initial diagnosis results with wider accessibility, but the problem of this research is focused on selecting the most effective method for intelligent systems as a basis for clinical data analysis, so in this research we will compare the level of accuracy of applying the method, namely Bayes' theorem and Certanty factor for the diagnosis of anxiety disorders. Bayes' Theorem is a classic statistical approach, offering a structured and measurable framework for calculating the probability of disease based on clinical evidence, while the Certainty Factor is a method for proving the certainty value of a fact in the form of a metric in an intelligent system. The aim of this research is to analyze the performance of the Bayes Theorem method and certainty factor by examining the percentage results obtained by applying the two methods. that the percentage result of the Bayes Theorem calculation method is higher, namely 84%, compared to the percentage result of the certainty factor, namely 70%, so it can be concluded that the application of Bayes' theorem is better than the certainty factor, especially in the diagnosis of people with anxiety disorders.
Prototype Resusitasi Jantung Paru (RJP) menggunakan Motor Nema 23 dan Sensor Detak Jantung untuk Memudahkan Media Interface
Authors: Maili Ramadoni , Jufrizel Jufrizel , Ullah Aulia , Maria Putut Son
Published: July 2024

The growth of technology has an important role in supporting medical equipment facilities, especially for tools for providing compression or cardiopulmonary resuscitation (CPR). CPR is a first aid measure for victims of sudden cardiac arrest in which cardiac compression is carried out for 4 cycles, each cycle consisting of 30 compressions. CPR given manually can endanger the victim due to certain factors such as insufficient or excessive pressure applied. So that the above can be realized, a tool is needed that can make it easier for someone to check their heart rate on their skin quickly and effectively. The MAX30102 sensor is a sensor that can calculate heart rate. This research aims to create an optimal automatic cardiopulmonary resuscitation device to help victims continue to breathe. This RJP tool uses a Nema 23 stepper motor as the driving motor, the motor driver uses a TB6600 driver as the motor controller. During testing, 1 minute produced 103 compressions and 30 compressions in 18 seconds. The torque strength of the stepper motor is only capable of lifting a load of 7 kg, the torque produced is 2.06 Nm. This research also uses sensors to detect heartbeats. This allows the device user to monitor the patient's heartbeat while undergoing RPJ.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression
Authors: Idriani R Nova , Permana Inggih , Salisah Febi Nur , Megawati Megawati , Rahmawita M Medyantiwi
Published: July 2024

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Analisis Metode Smoote pada Klasifikasi Penyakit Jantung Berbasis Random Forest Tree
Authors: Yulianto Satria Pradana Rizki , Fanani Ahmad Zainul , Affandy Affandy , Aziz Mochammad Ilham
Published: July 2024

Cardiovascular disease is the number one cause of death globally. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels. At present, there are many predictive tools that use machine learning as a basis, including predictions on heart disease in particular. There are many methods in machine learning to predict heart disease, as well as many parameters to look for to find the highest level of accuracy. This study, aims to obtain the best methods and parameters for the classification of heart disease.
Analisis Perbandingan CNN dan Vision Transformer untuk Klasifikasi Biji Kopi Hasil Sangrai
Authors: Leonardi M. Anjas , Chandra Albert Yakobus
Published: July 2024

Coffee is a globally cherished beverage, popular among various segments of society, including in Indonesia. The coffee processing procedure plays a pivotal role in determining the taste and final quality of the beverage. One crucial stage in this process is selecting the maturity level of coffee beans after the roasting process. However, determining this maturity level often faces challenges, particularly in large-scale processing contexts. This research focuses on evaluating two main approaches in classifying the maturity level of roasted coffee beans: Convolutional Neural Network (CNN) and Vision Transformer (ViT). The main issue encountered is the lack of studies comparing the effectiveness of these two methods in the context of coffee bean classification. Therefore, this study aims to fill this knowledge gap and provide better insights into which method is more suitable for this purpose. In this study, two CNN models, namely Xception and InceptionV3, as well as one ViT model, ViT-B16, are utilized. The dataset includes images of roasted coffee beans, raw coffee beans, and non-coffee bean images, with image sizes of 224 x 224 pixels. A comparison analysis is conducted based on classification accuracy and the ability of each model to capture characteristic features in coffee bean images. The experimental results show that the ViT-B16 model outperforms both CNN models with an accuracy of 99.33%, while the Xception and InceptionV3 models achieve accuracies of 96.67% and 96.00%, respectively. ViT-B16 demonstrates better capability in capturing global features in images, while CNN is more effective in detecting local features. However, both approaches face some challenges, including computational requirements and training time. In conclusion, although both methods have their own advantages, ViT-B16 offers significant potential for more accurate and efficient classification systems for images of roasted coffee beans. This research provides a crucial contribution to the development of coffee bean classification technology, which can enhance efficiency and consistency in the coffee processing industry.
K-Means Clustering Algorithm for Grouping Eligible Community Recipients of Government Assistance
Authors: Helmy Muzaqqi Farid , Rakhmawati Desty , Wijaya Anugerah Bagus
Published: July 2024

The government assistance program is financial support provided to the community with the aim of helping to improve the quality of life and welfare of almost all provinces in Indonesia including Central Java Province, especially in Ajibarang District, Banyumas Regency. This program aims to overcome the gap between the upper middle economic community and the lower middle economic community. However, the implementation of aid so far has not been even and fair, so it has not achieved its goal of helping people who really need it. In the process of providing assistance, it is important to have a strong basic foundation in determination and decision making. The program must ensure that aid is provided to truly deserving communities. The problem is that aid programs often do not work as intended, and are not effective in addressing economic inequality. One way to resolve this issue is to review the recipient's previous data. This research uses data mining to extract data from people who deserve assistance or not by applying clustering methods. Clustering, which is the process of grouping data without a class target (unsupervised learning), was used in this study. The algorithm used in this research is K-Means clustering. The results showed that there were two clusters of K-Means algorithm application, with 6 people in cluster 1 and 4 people in cluster 2. This research was successful and had an impact on aid providers to conduct more thorough data collection not just choosing the community to be given assistance.
Implementasi Ensemble Deep Learning Untuk Analisis Sentimen Terhadap Genre Game Mobile
Authors: Cahyadi Marcelinus Fajar , Rochadiani Theresia Herlina
Published: July 2024

The rapid growth of the online gaming industry in Indonesia has prompted developers to address various challenges in creating successful mobile games. This study aims to evaluate the effectiveness of ensemble learning techniques, particularly soft voting, in enhancing sentiment analysis accuracy across 17 genres of mobile games. Additionally, it identifies the most effective deep learning model for sentiment classification. The research compares the performance of CNN-LSTM, BERT, and CNN-GRU models, as well as an ensemble of these models. Review data was collected from the Google Play Store, then labeled and cleaned to improve data quality, categorized into positive, neutral, and negative sentiments. Data preprocessing techniques included cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Word embedding techniques used were Word2vec for CNN-LSTM and CNN-GRU models, and IndoBERT for BERT model. Ensemble learning combined predictions from these models, significantly improving classification accuracy. Results indicate IndoBERT achieved an accuracy of 89%, while CNN-GRU and CNN-LSTM showed accuracies around 84-85%. The ensemble approach using soft voting successfully increased overall accuracy to 90% by combining predictions from all three models. The study concludes that ensemble learning effectively combines individual model strengths to enhance sentiment classification accuracy. Furthermore, user preference visualization for game genres revealed high popularity for "Strategy", "Word", and "Trivia" genres, while "Sports" genres were less favored. This research is expected to contribute to game developers in determining which genres to develop to enhance success chances and user satisfaction.
Optimasi Algoritma KNN dengan Parameter K dan PSO Untuk Klasifikasi Status Gizi Balita
Authors: Rochman Bagus Fathur , Rahim Abdul , Siswa Taghfirul Azhima Yoga
Published: July 2024

The toddler years are a crucial phase that requires constant nutritional monitoring, because rapid growth and development require optimal nutritional intake. Nutritional problems in toddlers can hinder physical growth and can even be fatal. In assessing the nutritional status of toddlers, it is important to use efficient methods. One approach that can be used is machine learning, which can help determine the nutritional status of toddlers. K-Nearest Neighbors (KNN) is an algorithm commonly used in object classification based on nearest neighbors. Even though it is simple, determining the correct K value is very important because it can significantly influence KNN performance. This research emphasizes the importance of choosing the right parameters to increase the accuracy of the KNN model in classifying the nutritional status of toddlers. The test results show that the optimal combination for KNN is at K=4, using the 'distance' weight and distance metric p=1, producing the highest accuracy of 91.15% on the test data. Furthermore, the research applied Particle Swarm Optimization (PSO) to optimize KNN parameters, and it was found that the optimal combination was with K=6, 'distance' weight, and distance metric p=1, achieving a mean accuracy of 93.44% and a test accuracy of 93.98%. PSO is proven to be effective in finding the best parameters that increase model generalization to test data. Test results with a training data ratio of 80% and testing 20% show the best accuracy of 93.98%. .The use of PSO for parameter optimization succeeded in increasing model accuracy by 3.10% compared to the model without optimization
Optimasi Performa Random Forest dengan Random Oversampling dan SMOTE pada Dataset Diabetes
Authors: Hasbi Hasbi , Sasongko Theopilus Bayu
Published: July 2024

Diabetes, or high blood sugar, is a chronic condition that needs careful monitoring. If left untreated, it can lead to severe complications. This research aims to accurately diagnose diabetes, addressing the issue of class imbalance in the dataset, which can affect the model's classification accuracy. The goal is to improve diabetes classification accuracy using balancing methods, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and Random Oversampling. These methods are applied to data from patients diagnosed with diabetes and those who do not have the disease.The initial step in the research involved addressing class imbalance by applying SMOTE and random oversampling to generate synthetic samples for the minority class. This was followed by data normalization using the min-max normalization method. Subsequently, the Random Forest Classifier was used to train the model for classification. The results demonstrate that this approach enhances the model's ability to identify diabetes cases, achieving an accuracy of 96%. This represents a 1% improvement over the accuracy of 95% reported in previous research.
Optimasi Random Forest dengan Genetic Algorithm dan Recursive Feature Elimination pada High Dimensional Data Stunting Samarinda
Authors: Satria Bima , Siswa Taghfirul Azhima Yoga , Pranoto Wawan Joko
Published: July 2024

Stunting is a chronic malnutrition problem that disrupts children's growth, with long-term impacts on physical growth, cognitive development, and productivity in adulthood. In Indonesia, the prevalence of stunting is still above the WHO threshold, reaching 24.4% according to the 2021 Indonesian Nutritional Status Study (SSGI), and in Samarinda City, the prevalence reached 24.7% in 2021 with 1,402 toddlers identified as stunted. Addressing this problem requires a more structured data-driven approach to provide targeted interventions. This study uses data from the Samarinda City Health Office, encompassing 150,474 stunting data points, and involves data collection, data cleaning, feature selection, and classification model application. This study aims to improve the accuracy of stunting data classification in Samarinda City in 2023 using the Random Forest algorithm enhanced with Recursive Feature Elimination (RFE) feature selection techniques and Genetic Algorithm (GA) optimization. The feature selection results using RFE show that the most influential features are Weight, ZS TB/U, ZS BB/U, and BB/U. The application of RFE increased the model's average accuracy from 91.91% to 93.64%, while GA optimization further increased the average accuracy to 98.39%. The definite accuracy increased from 94.23% (baseline model) to 97.10% (with RFE) and reached 99.70% (with RFE and GA). The combination of RFE and GA has proven effective in tackling data complexity and improving the reliability of stunting predictions. This study significantly contributes to the development of machine learning techniques for high-dimensional data analysis in health and is expected to be the foundation for more effective intervention programs in addressing stunting issues in Indonesia.