3 10 26 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/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/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/a90a5c206576b6df9c50daf9a6b32b04.pdf 44335a66e5148988c8bf9e1952b08cc1 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 Texture Feature Extraction in Grape Image Classification Using K-Nearest Neighbor Subject The topic of the resource KNN, GLCM, grape, classification Description An account of the resource Indonesian Grapes are a vine. This fruit is often found in markets, shops, roadside. Along with the development of computer technology today, computers can solve problems by classifying objects and objects. How to apply GLCM and K-NN methods for classification of grapes. The purpose of this study is to apply the GLCM and K-NN methods in the classification of grapes. The dataset used from kaggle.com sources, the data tested are 3 types of grapes, the number of images is 2624. The fruit that will be used for data collection and classification process is limited to three types of grapes, namely grape blue, grape pink and grape white. How to apply GLCM and K-NN methods for classification of grapes. The feature extraction of GLCM used in this study is the feature contrast, energy, correlation, and homogeneity. From testing the test data, the highest accuracy value is 99.5441% with k = 2 at level 8, while the lowest accuracy value is 24.924% at each k level 2. The GLCM level value is very influential on the accuracy results, namely the higher the GLCM level value, the higher the GLCM value. accuracy is getting better Creator An entity primarily responsible for making the resource Pulung Nurtantio Andono1 , Siti Hadiati Nugraini2 Publisher An entity responsible for making the resource available Universitas Dian Nuswantoro Semarang 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/86b7c9e714702128016f02413c46987f.pdf bf32dadb87adcc89eca53e25dd661201 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 Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor Subject The topic of the resource : Asthma Disease, Certainty Factor, Expert System, Forward Chaining Description An account of the resource Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor Creator An entity primarily responsible for making the resource Mohamad Irfan1 , Pebri Alkautsar2 , Aldy Rialdy Atmadja3 , Wildan Budiawan Zulfikar4 Publisher An entity responsible for making the resource available UIN Sunan Gunung Djati Bandung, 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/3599a020edfe78b4391ea7a808778f85.pdf 9ec01b860ecf1435bc806cac731cb778 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 Vehicle Detection Monitoring System using Internet of Things Subject The topic of the resource Congestion, IHCM, Internet of Things, Vehicle detection, Web monitoring Description An account of the resource The overcapacity of vehicle numbers is one of the significant causes of the traffic congestion problem on Indonesia roadway. The government applies a One-way system (SSA) as one proposed solution to unravel the congestion. However, several congestion points are still found during the SSA implementation. Thus, this research offers an alternative method to detect congestion using IoT technology. The system automatically enumerates the number, classifies the type, and computes the speed averages of vehicles to identify the severity of congestion based on the Indonesian Highway Capacity Manual (IHCM) published by the Ministry of Public Works 2014. We utilize ultrasonic sensors to detect the vehicles and send the data to the server in real-time. The research successfully develops an IoT system for traffic congestion detection. Communication between nodes and API can be done well. Data exchange involving encryption and decryption with AES-256 is successfully done. Website application developed in this research is successfully show the severity level of the congestion and their vehicle numbers. The average accuracy of the system is 78,97%. The system detected more vehicles than actual numbers due to the misreading value of the sensors. Creator An entity primarily responsible for making the resource Yani Nurhadryani1 , Wulandari2* , Muhammad Naufal Farras Mastika3 Publisher An entity responsible for making the resource available IPB 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