1 10 26 https://repository.horizon.ac.id/files/original/faf29a06dee646b9e1f9f97e73261a90.pdf 51633612796843c612a2ab6283cf21ae Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Topic Classification of Quranic Verses in English Translation Using Word Centrality Measurement Subject The topic of the resource Quran, Topic classification, Multilabel, Word Centrality, SVM, Naïve Bayes, KNN, Decision Tree Description An account of the resource Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term weighting in feature extraction can improve the performance of the classification system Creator An entity primarily responsible for making the resource Achmad Salim Aiman1 , Kemas Muslim Lhaksmana2 , Jondri3 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/d0c4f4d6bff31f9c31f2bcc08b50576b.pdf 0ac934f06523bb562fea67be3b889696 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification Subject The topic of the resource Recommender System, User-based, Item-based, Collaborative Filtering, Support Vector Machine Description An account of the resource Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95% Creator An entity primarily responsible for making the resource Anang Furkon Rifai1 , Erwin Budi Setiawan2 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource : 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/085cdcd7d91621b7dafce92ed78d0a83.pdf a417dacb4df0e6f002e94b301710cf87 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Disease Detection in Banana Leaf Plants using DenseNet and Inception Method Subject The topic of the resource Deep learning, Disease Detection, DenseNet, Inception Description An account of the resource Diseases that attack banana plants can affect the growth and productivity of the fruit produced. The disease can be identified by looking at changes in the pattern and color of the leaves. Infected leaves will experience an increased transpiration process and the photosynthesis process is almost non-existent. Furthermore, disease on banana leaves can cause yield losses of up to 50%. Therefore, early detection is needed so that diseases on banana leaves can be overcome as soon as possible by using deep learning. This study aims to compare the performance of DenseNet and Inception methods in detecting disease on banana leaves. DenseNet is a transfer learning architecture model with fewer parameters and computations to achieve good performance. Inception, on the other hand, is a transfer learning architectural model that applies cross-channel correlation, executes at lower resolution inputs, and avoids spatial dimensions. In conducting the test, this study uses several data handling schemes to test the two methods, namely without data handling, under-sampling, and oversampling. Furthermore, the data is separated into training data and test data with a ratio of 80:20. The result is that the model using the DenseNet method with an oversampling scheme is superior to other models with a percentage value of 84.73% accuracy, 84.80% precision, 84.73% recall, and 84.62% f1 score. In addition, the machine learning model using the DenseNet method in all schemes is also superior to the machine learning model using the Inception method. Creator An entity primarily responsible for making the resource Andreanov Ridhovan1 , Aries Suharso2 , Chaerur Rozikin3 Publisher An entity responsible for making the resource available University of Singaperbangsa Karawang Date A point or period of time associated with an event in the lifecycle of the resource 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/a0ff65ce88384384aeac425913575f6d.pdf 5e0d206b91d4f662a9f4e837b00a3a34 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Integration of AHP and TOPSIS Methods for Small and Medium Industries Development Decision Making Subject The topic of the resource Decision Making, AHP, TOPSIS, Criteria, SMIs Description An account of the resource Financial problems are one of the reasons why small and medium-sized industries (SMIs) in West Kutai have not developed optimally. Government assistance programs are one of the solutions. This program must be appropriate, so a decision-making tool is needed to help choose the right SMIs to be assisted later. The weight of the criteria was determined using the Analytical Hierarchy Process (AHP) technique, and the priority of the SMIs as the preferred proposal for the recipients of development assistance was determined using the Technique for Other Reference by Similarly to Ideal Solution (TOPSIS) approach. Labor, investment, production capacity, production value, and raw materials were used to determine the priorities of SMIs beneficiaries. Furthermore, TOPSIS prioritizes the development of alternative small and medium-sized industries with types of handicraft commodities. Integration of AHP and TOPSIS methods has been successfully used in the IKM Development Priority Determination Application, with 83.3% precision and 96.4% accuracy achieved by using a confusion matrix so that the IKM ranking can be known. The results of the study found that integration of the two methods was successfully used for Small and Medium Industries Development Decision Making Creator An entity primarily responsible for making the resource Anton Yudhana1 , Rusdi Umar2 , Aldi Bastiatul Fawait3 Publisher An entity responsible for making the resource available Universitas Ahmad Dahlan, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/e6b6fda4de3fd557f3e8774c0ba985f4.pdf 63925314381bdad6e2718c9645c3d4e8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation Subject The topic of the resource Malaria, Image Classification, Convolutional Neural Network, Fine-Tuning, ResNet50 Description An account of the resource Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%. Creator An entity primarily responsible for making the resource Aris Muhandisin1 , Yufis Azhar2 Publisher An entity responsible for making the resource available University of Muhammadiyah Malang Date A point or period of time associated with an event in the lifecycle of the resource 02-11-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/8c4ac8c567ca52f48142dad777ee57c0.pdf 346b14cb26402b5d81520142df3c1983 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Big Five Personality Assessment Using KNN method with RoBERTA Subject The topic of the resource Big Five Personality, K-Nearest Neighbours (KNN), RoBERTa, LIWC, Information Gain Description An account of the resource Personality is the general way a person responds to and interacts with others. Personality is also often defined as the quality that distinguishes individuals. Social media was created to help people communicate remotely and easily. These personalities fall into five categories known as the Big Five personality traits, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN). The use of K-Nearest Neighbour (KNN) is a method of classifying objects based on the training data closest to them. To overcome the data imbalance during training data, we use K-Means SMOTE (Synthetic Minority Oversampling Technique). Other features such as LIWC (Linguistic Inquiry Word Count), Information Gain, Robustly Optimized BERT Approach (RoBERTa), and hyperparameter tuning can improve the performance of the systems we build. The focus of this study is to present an analysis of Twitter user behavior that can be used to predict the personality of the Big Five Personality using the KNN method. The Important aspect to consider when using this method, namely accuracy in classifying the Big Five Personalities. The experimental results show that the accuracy of the KNN method is 72.09%, which is 95.28% gain above the specified baseline Creator An entity primarily responsible for making the resource Athirah Rifdha Aryani1 , Erwin Budi Setiawan2 Publisher An entity responsible for making the resource available Universitas Telkom, Bandung Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/d261eca6f6a55820d10269ff3ec152ff.pdf 19c8a8b684e38d522a60a2e8589c3219 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Network Security Analysis Simulation at the GCS in the UCAV to support the Indonesian Defense Area Subject The topic of the resource Unmanned Combat Aerial Vehicle; Quality of Service; Defense; Ground Control Station; OPEN VPN Description An account of the resource An unmanned Combat Aerial Vehicle (UCAV) is an unmanned aircraft that has a serial control communication device that can be seen directly in real-time. In carrying out UCAV flights, it requires good and stable data transmission security so that signal loss does not occur during the communication process. Researchers create a concept of a security scheme in communication at the Ground Control Station (GCS) that can be used for the use of UCAV communication at long distances, using the Quality of Service (QoS) method from OPEN VPN with parameters Throughput, Packet Loss, Delay (Latency) and Jitter can determine the reliability of a UCAV communication network. Based on the results of Quality of Service (QoS) testing with OpenVPN Autopilot UCAV objects on the ICMP protocol have the smallest packet loss value of 0%, the delay parameter is 5.2ms, the jitter parameter gets a high value of 4.68 ms higher than the TCP protocol and UDP. The TCP protocol has a relatively small packet loss value of 0.3% and ranks second to the ICMP protocol, then the delay value is 8.48 ms greater than the ICMP and UDP protocols, and the jitter parameter value is 0.0013 ms smaller than the ICMP and UDP protocols. The use of VPN OVPN is a good recommendation. Still, researchers suggest that should use not only OPEN VPN but also L2TP VPN and PPTP VPN for security at the Ground Control Station at UCAV as a comparison. Creator An entity primarily responsible for making the resource Bita Parga Zen1 , Anggi Zafia2 , Iwan Nofi Yono Putro3 Publisher An entity responsible for making the resource available Telkom Purwokerto Institute of Technology Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/2a49aefa5451b47bff016d6352829a5e.pdf a42880f3b969d4a56ca4029d461c4578 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Time Series Forecasting of Significant Wave Height using GRU, CNN-GRU, and LSTM Subject The topic of the resource wave height, pelabuhan ratu, cnn-gru, long short-term memory, gated recurrent unit Description An account of the resource Predicting wave height is essential to reduce significant risks for shipping or activities carried out at sea. Waves inherit a stochastic nature, mainly generated by wind and propagated through the ocean, making them challenging to forecast. In this paper, we design time series wave forecasting using a deep learning model, which is a hybrid Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) or CNN-GRU. We use two time series of wave data sets, i.e., reanalysis data from ERA5 by ECMWF and GFS from NOAA. As a study area, we choose Pelabuhan Ratu, located in the south of West Java which is connected to the open Indian Ocean. Moreover, we also compare the results by using other deep learning models, i.e., the Long Short-Term Memory (LSTM) and GRU. We evaluated these models to forecast 7, 14, and 30 days. Models' performance is assessed using RMSE, MAPE, and Correlation Coefficient (CC). For predicting 30 days, using the ERA5 data, the CNN-GRU model produces relatively accurate results with an RMSE value of 1.8844 and CC of 0.9938, whereas for the GFS data, results in RMSE value of 1.8852 and CC of 0.9915. Creator An entity primarily responsible for making the resource Cornelius Stephanus Alfredo1 , Didit Adytia2 Publisher An entity responsible for making the resource available Telkom University Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/6b80a7cfa83902a072544d4eccfd205a.pdf 0c77eb95e2b3de4fccccbe16ca2689ad Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation Subject The topic of the resource Intermittent Demand, LSTM, ADIDA, MAPA Description An account of the resource Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of intermittent data forecasting using deep learning with several levels of aggregation and a combination of several levels of aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM model without aggregation and disaggregation. Creator An entity primarily responsible for making the resource Fityan Azizi1 , Wahyu Catur Wibowo2 Publisher An entity responsible for making the resource available Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 31-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/fe50aebf1f008bfc796559f08973f1c5.pdf 9f381e4c0003bd163c341ffc5066011f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 6 NO 5 (2022) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Improving AI Text Recognition Accuracy with Enhanced OCR For Automated Guided Vehicle Subject The topic of the resource : OCR, AGV, AI, Image Processing, Computer Vision Description An account of the resource AGV (Automated Guided Vehicle) with artificial intelligence (AI) is expected to change the industry's development in Indonesia, this artificial intelligence robot uses a mini-computer to operate it and uses mechanical movement like a four-wheeled vehicle with a 2WD drive system. In this article, a control strategy of the AGV robot will be shown and implemented to detect the location. This research Uses OCR (Optical Character Recognition) for the OpenCV library itself which has been enhanced/modified. This enhanced OCR is the main library used in text recognition. This research produces very accurate text detection compared to the default OCR that was previously used on the AGV robot in our university. After the process of reading this text is passed, it will produce text previously read through the camera which will then provide output in the form of text where the AGV robot is located. After the reading is validated, the AGV robot will move to the next point until it returns to its starting point. Based on hardware implementation through testing in the AGV laboratory with artificial intelligence, it can work according to the algorithm and minimize reading errors with a 95% success rate. Creator An entity primarily responsible for making the resource Florentinus Budi Setiawan*, 2Farrel Adriantama, 3Leonardus Heru Pratomo, 4Slamet Riyadi Publisher An entity responsible for making the resource available Universitas Katolik Soegijapranata, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource : 01-10-2022 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text