1 10 26 https://repository.horizon.ac.id/files/original/49cdcf9fe2dd75e49761fcf7f79eee2c.pdf 7225549e2ab4d1ca90547ab056fe49e9 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 6 (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 A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting Subject The topic of the resource forecasting, smoothing constant, exponential smoothing, B-DES, SES. Description An account of the resource Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing (SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE Creator An entity primarily responsible for making the resource 1Hazriani, 2Yuyun, 3Mashur Razak Publisher An entity responsible for making the resource available Handayani University Date A point or period of time associated with an event in the lifecycle of the resource 29-12-2022 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/bf02ba004fecaa851cb508d3c306b1f5.pdf cabfe760302c793ee587d7eb8ab591a6 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 6 (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 Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Subject The topic of the resource task allocation, multi-agent system, multiple tasks, ACO Description An account of the resource Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm. Creator An entity primarily responsible for making the resource Iis Rodiah1 , Medria Kusuma Dewi Hardhienata2 , Agus Buono3 , Karlisa Priandana4 Publisher An entity responsible for making the resource available Iis Rodiah1 , Medria Kusuma Dewi Hardhienata2 , Agus Buono3 , Karlisa Priandana4 Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2022 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/4ac7a2e29b354a4858f33b0753ab0b96.pdf abbc7b97a11e572c5a6c9fc137724b99 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 6 (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 Best Employee Decision Using Multi Attribute Utility Theory Method Subject The topic of the resource MAUT method, criteria, performance, best employee, decision making Description An account of the resource Selection of the best employee is a form of appreciation that can be shown by the company for the achievements of its employees. This appreciation can motivate employees to be more enthusiastic in improving their performance at work. Appropriate evaluation and decision-making methods need to be taken so that the best employee selection process runs objectively, transparently, and in accordance with established standards. This study aimed to select the best employee candidates at PT Kerry Express Indonesia using the multi attribute utility theory (MAUT) method. The criteria for the selection process as follows: attendance (weight = 2), output obtained (weight = 3), discipline (weight = 3), and reporting (weight= 2). The employees in this study were 30 respondents from 150 populations. The assessment was carried out for three months from January to April 2022. The calculations were carried out using the Microsoft Access tool. The results of calculations using the MAUT method show that the highest rank among all candidates has a score of 7.75 while the lowest rank had a score of 3.25. It can be concluded that the MAUT method can be used to select the best employees at PT. Kerry Express Indonesia effectively and efficiently Creator An entity primarily responsible for making the resource Sunardi1 , Rusydi Umar2 , Dewi Sahara Nasution3* Publisher An entity responsible for making the resource available Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta Date A point or period of time associated with an event in the lifecycle of the resource 28-12-2022 Contributor An entity responsible for making contributions to the resource Sunardi1 , Rusydi Umar2 , Dewi Sahara Nasution3* 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/5a7355a897a66c50a6f483967fe8e7e6.pdf 6421edf7762c55882c6a39e0e70f0c21 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 6 (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 Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3 Subject The topic of the resource Brain Tumor Classification, Convolutional Neural Network, EfficientNet, Transfer learning Description An account of the resource Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied. Creator An entity primarily responsible for making the resource Ahmad Darman Huri1 , Rizal Arya Suseno2 , Yufis Azhar3 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 29-12-2022 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/9b651f4e2b0aa4c8f7357793c3b8f47f.pdf fdfe6995c58c4baf084b765d654ef821 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 6 (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 Comparison of Grid Search and Evolutionary Parameter Optimization with Neural Networks on JCI Stock Price Movements during the Covid 19 Subject The topic of the resource neural network, optimization, grid search, evolutionary, preprocessing, RMSE. Description An account of the resource This study aims to determine the effect of covid 19 on the movement of the JCI Stock Price by testing various combinations of the input variables of closed price stock data on the JCI. The analysis is carried out to find the best RMSE value from the combination of these input variables using the neural network method. The best RMSE results are compared using the optimization of grid search and evolutionary parameters. The data used in this study was taken from the Yahoo.finance.com page on the JCI Historical Data, during the covid pandemic, from 12/11/2019 to 12/30/2021. The data obtained are 509 records. The input variable used is the closing price data (closed price) as a target. The preprocessing data used are data cleansing, filtering, and windowing until seven days before. The results obtained an RMSE value of 0.104 five days before Close t (P=5), training cycle 9000. Momentum 0.9 and learning rate 0.2 is then optimized using the grid search parameter to produce RMSE 0.101, training cycle 100. Learning rate 1 and momentum 0.1 are then compared with evolutionary parameters, which make RMSE 0.103 at learning rate 0.029, momentum 0.68, and training cycle 86. Based on this research, optimizing grid search parameters produces better RMSE than evolutionary parameter optimization. This small RMSE result shows that investors are still safe to invest. Creator An entity primarily responsible for making the resource Wresti Andriani1 , Gunawan2 , Purwanto3 , Catur Supriyanto4 Publisher An entity responsible for making the resource available University, Semarang and STMIK YMI Tegal Date A point or period of time associated with an event in the lifecycle of the resource 30-12-2022 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/2fde583128e81a7ff0709bb0c3a42bdc.pdf f0d04fb9a415ac8c64099bceb61a0b34 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 6 (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 Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques Subject The topic of the resource machine learning, ensemble learning, classification, SMOTE, credit card fraud Description An account of the resource Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes. Creator An entity primarily responsible for making the resource Moh. Badris Sholeh Rahmatullah1 , Aulia Ligar Salma Hanani2 , Akmal M. Naim3 , Zamah Sari4 , Yufis Azhar5 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 27-12-2022 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/dc381d8efbf4435da2ce21a36d24f642.pdf 2a23bc1dc1ba57dad40f184576e57a60 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 6 (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 Digital Forensic on Secure Digital High Capacity using DFRWS Method Subject The topic of the resource Digital forensic, sdhc, dfrws, linux, foremost, dc3dd Description An account of the resource As evidenced in the trial, between 2015 and the second quarter of 2022, there were 54 cases involving secure digital high capacity (SDHC) storage hardware as evidenced in trials. In 2021 there will be an increase in cases involving SDHC. The three cases with the highest number are corruption cases, special crimes, and ITE. SDHC is an advanced technology development of Secure Digital (SD) card hardware which functions as storage. SD Card only has a capacity of up to 2 gigabytes, while the largest SDHC capacity is 32 gigabytes. As a storage device that is small, thin, and has a fairly large capacity. this research needs to be done because of the increasingly widespread increase in cases involving SDHC. This study aims to perform digital forensic analysis on SDHC evidence using forensic applications that run on Linux, namely foremost and DC3DD. This study uses the DFRWS method to retrieve valid evidence in court. Based on the research conducted, it was found that the number of files that can be restored at the examination stage using foremost is 77%, and the accuracy of recovered files is 50% with string file hash validation. From this research, it can be concluded that the processing results of DC3DD and Foremost can be used as valid evidence. Creator An entity primarily responsible for making the resource Anton Yudhana1 , Imam Riadi2 , Budi Putra3 Publisher An entity responsible for making the resource available Universitas Ahmad Dahlan Date A point or period of time associated with an event in the lifecycle of the resource 29-12-2022 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/2f967906450a070ce55e1ed81eec5f42.pdf 160402004088ec5542f613fe27eeae23 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 6 (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 DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method Subject The topic of the resource : dipeptidyl peptidase IV inhibitor, particle swarm optimization, quantitative structure-activity relationship, support vector machine Description An account of the resource Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively Creator An entity primarily responsible for making the resource Reza Rendian Septiawan1 , Bambang Hadi Prakoso2 , Isman Kurniawan2 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 29-12-2022 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/a14251b5e05f710263c0e23d4addbc84.pdf ac363689c49f0ec3e36c85be5db35649 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 6 (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 Faux Insider Hazard Investigation on Non-Public Cloud Computing by Using ADAM’s Technique Subject The topic of the resource Cloud, MITC, Forensics, ADAM, Analysis Description An account of the resource Cloud computing is a service system mechanism that businesses and organizations use to perform computerized and integrated transactions over computer networks. The service system must, of course, be”matched”with a”certain amount”of security. It is applied to” forecast the probability of cybercrime. A Cloud Service Provider (CSP) often offers cloud-based services with a basic level of security. Typically, CSPs are set up to offer their services on the open internet. Data security-focused organizations strive to shield their systems from a wide range of attackers. One of the alternatives is to construct a private cloud computing system. The issue is the potential for Man in the Cloud (MITC) assaults, which compromise and modify identities and are identified in cloud systems as phony insider threats. Based on the ISO 27032 standard research, the goal of this work is to undertake a threat analysis of MITC attack methodologies against private cloud computing services. With regards to risks to cloud services in a private cloud computing environment, it is intended that reporting and documenting the study' findings would lead to suggestions for more research and cybersecurity management procedures Creator An entity primarily responsible for making the resource Dwi Kurnia Wibowo1 , Ahmad Luthfi2 , Yudi Prayudi3 , Erika Ramadhani4 , Muhamad Maulana5 Publisher An entity responsible for making the resource available Indonesian Islamic University Date A point or period of time associated with an event in the lifecycle of the resource : 29-12-2022 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/38f0b07e5336c9d209935bb2df116244.pdf afd5958bdc8a98a21b690e1e1452263e 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 6 (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 Fire Detection on Video Using ViBe Algorithm and LBP-TOP Subject The topic of the resource fire detection, ViBe algorithm, local binary pattern-three orthogonal planes, support vector machine Description An account of the resource In this research, we built a system to detect fire using the ViBe (Visual Background Extractor) algorithm to extract dynamic targets. The ViBe algorithm is better at detecting moving target objects such as flame combustion. In this research we combined the ViBe algorithm with three frame differencing to gain better results on movement object. The HSI color space model was applied after the movement object was obtained. We used Local Binary Pattern-Three Orthogonal Planes to obtain the feature extraction to be classified with Support Vector Machine. Our result has shown that the proposed system were able to detect the fire using the LBP-TOP and ViBe algorithm methods with an average accuracy rate of 88.10%, and the best accuracy was 90.37%. The parameters used to achieve this accuracy in the feature extraction process were T=120, Radius=2, and frame gap=15, then the threshold value parameter for three-frame difference parameter was 25. Creator An entity primarily responsible for making the resource Febryanti Sthevanie1 , Gamma Kosala2 , Kurniawan Nur Ramadhani3 , Ketut Sudyatmika Putra4 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 27-12-2022 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