1 10 23 https://repository.horizon.ac.id/files/original/3e14f591b600d8120a794804e22cc616.pdf d2aec48c3a3988bd57bd261b034b1e3a 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 1 (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 Management Administration Smartphone Application as a Strategy to Increase Accreditation Score in Primary Healthcare Facilities Subject The topic of the resource administration, management, Puskesmas, accreditation, smartphone application. Description An account of the resource Health facility accreditation has been in great demand in recent years. Mobile applications can serve as a tool for community healthcare center’s (Puskesmas) to assess the value of their accreditation, especially regarding administration and management. In the context of preparation for accreditation, it can be used as a self-assessment tool for elements of management administration. This case-based article focuses on smartphone application development using the input-process�output-outcome scheme as a framework. We developed a checklist and assessment for the management administration component by reviewing Permenkes No. 46 the Year 2015 concerning Puskesmas accreditation. This is the first available mobile application on the Google Play Store, targeting Puskesmas staff to improve the quality of administration and management services based on accreditation criteria. This smartphone application functions as an independent assessment tool for Puskesmas to complete the accreditation criteria as stated in the regulation of the minister of health. About 20 Puskesmas staff showed a very good response and positive. All stated that they were very helpful and satisfied with this application, so they will continue to use and distribute it to other Puskesmas staff Creator An entity primarily responsible for making the resource Agus Sugiharto1 , Boy Subirosa Sabarguna2 , Ajeng Pramastuty3 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 : 27-02-2022 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/0a1fcaeca01c7cc9b1edac14883fc957.pdf 93e4560f438e34f708ef4993921cd1eb 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 1 (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 Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter Subject The topic of the resource sentiment analysis, feature expansion, word2vec, public policy Description An account of the resource Social media users, especially on Twitter, can freely express opinions or other information in the form of tweets about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion Word2vec method was applied to overcome when the vocabulary mismatches occur. This study implements and compares the Twitter sentiment analysis using the feature expansion Word2vec method and the baseline model. To perform classification on this sentiment data, two different machine learning algorithms including Support Vector Machine (SVM) and Logistic Regression (LR) are used to compare the model. The result is feature expansion Word2Vec with SVM classifier has a slightly better performance which succeeded in increasing the system accuracy up to 0,99% with 78,99% accuracy score, rather than LR classifier which achieved 78,31% accuracy score. Creator An entity primarily responsible for making the resource Alvi Rahmy Royyan1 , 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 27 Februari 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/45e3cddd8d5cada000122ac433346377.pdf 51dd9309b03430107b961ac593ff4d1d 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 1 (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 Monitoring dan Kendali Tegangan Jaringan Listrik Fase-tiga melalui Smartphone Subject The topic of the resource : electric voltage, a three-phase network, Ethernet shield, Arduino microcontroller, Android smartphone Description An account of the resource This paper describes the creation of a minimum system for monitoring and controlling the voltage on a three-phase electrical network. Making a minimum system based on the result of previous research that has been implemented in the forms of device assembly, programming, and performance measurement. The research objectives are (i) assembling the hardware and programming based on Arduino software version 1.8.10 and (ii) measuring the minimum system performance. The research method for achieving the objective of assembling a minimum system is carried out through integrated wiring as an effort to get the hardware achievement, while for programming is an effort to get the software achievement. The implementation of the research method for measuring the performance as an effort to get the achievements of hardware and software is carried out by giving the orders to activate the paths of each phase. The result of the assembly is the integration of the Arduino UNO R3 module, Ethernet Shield type of W5100, MikroTik RouterBoard, relay modules, and Android smartphone, while the results of the programming are compiling and uploading the syntax to the Arduino module and making applications in the .apk format for a smartphone. Performance measurements are carried out by activating conditions for the three phases of phase-R, phase�S, and/or phase-T. The conclusion can be obtained, that the manufacture of a minimum system is appropriate for the fulfillment with respect to the presence of an electronic device for monitoring and controlling the voltage on a three-phase electrical network Creator An entity primarily responsible for making the resource Arief Goeritno1 , Febby Hendryan2 Publisher An entity responsible for making the resource available Universitas Ibn Khaldun Bogor Date A point or period of time associated with an event in the lifecycle of the resource 1 februari 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/99124dbbbb9704350c3d9239cc62ec32.pdf 048f3e4996a05ca0059e294b8d5ffb1a 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 1 (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 Perbandingan Model Proses Algoritma Alpha dan Alpha++ Pada Aplikasi E-commerce Subject The topic of the resource Alpha, Alpha++, Process Discovery, Process mining, Event log, E-commerce Description An account of the resource Utilization of information technology is currently growing rapidly in helping activities especially in storing an event log. The activity which is behavior of the user can be analyzed using process mining. The process mining purpose to extract information from event logs on business processes that working. Discovery technique is used in this research. The purpose of this study is to compare two algorithms applied by creating an e-commerce application that is aware of the processes. E-commerce applications require event logs to read the behavior of visitor activities against the application. This research method starts from understanding the business processes that working, then designing a website by creating the application used. Furthermore, data collection through applications that are promoted through social media. The application will be recorded user activity and formed an event log. The event log that formed then discovered using alpha and alpha++ algorithms by utilizing the ProM Lite 1.2 tools. The evaluation results show that the alpha algorithm has shortcomings, namely length one loop, length two loop and non-free choice. And the alpha++ algorithm fixed this deficiency Creator An entity primarily responsible for making the resource Bambang Jokonowo1*, Miskah Alfiyyah Kulsum2 , Nita Komala3 , Publisher An entity responsible for making the resource available Universitas Mercu Buana Date A point or period of time associated with an event in the lifecycle of the resource : 27-02-2022 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/84d0d45d3956748a9210dfbc0883e7e5.pdf 93ce4dc41a229d48001d51c540646110 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 1 (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 E-commerce Recommender System Using PCA and K-Means Clustering Subject The topic of the resource recommender system, collaborative filtering, principal component analysis, k-means, e-commerce Description An account of the resource Recently, recommender system has an important role in e-commerce to market products for users. One of recommender system approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity problems, namely the system is difficult to provide recommendations because of the lack of important information needed. Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed system has a lower RMSE value compared to other methods. For the number of neighbors of 10, 20, 30, and 40, our system obtains values of 0.771806, 0.75747, 0.75304, 0.75304, and 0.75270 Creator An entity primarily responsible for making the resource Dendy Andra A.N1 , Z. K. A. Baizal2 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 01-02-2022 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/a685411700c71a49124f584b8d280a52.pdf ee803f3bf84359edbeeb3d15640fadc6 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 1 (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 Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data Subject The topic of the resource ensemble method, microarray, schizophrenia, disease detection Description An account of the resource Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively Creator An entity primarily responsible for making the resource Diya Namira Purba1 , Fhira Nhita2 , Isman Kurniawan3 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 01-02-2022 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/e82f9cea772da62729540de359a86623.pdf 33297eb039e34b7a36a3557bf35fb831 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 1 (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 Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM Subject The topic of the resource Pap Smear, ThinPrep, Non-ThinPrep, RepoMedUNM, K-Means, GLCM Description An account of the resource Cervical cancer’s a gynecological malignancy in women that’s very dangerous, even causes death. Prevention through early detection of Pap smear test. It was carried out by pathologists with the help of a microscope still have obstacles in observations. There’re many studies on Pap smear image processing for helping pathologists in cell identification. Availability of Pap smear image dataset is needed in cervical cancer early detection research. The purpose of this study was to segment, feature extraction and classify 180 Pap smear images of RepoMedUNM. The method used to identify Pap smear images begins with preprocessing, namely changing the color in the image to L*a*b color, segmentation using the K-means method, extraction of 6 features, namely metric, eccentricity, contrast, correlation, energy, and homogeneity, and then identified by calculating the closest distance between the training data features and the test data features with the Euclidean distance. The result of identification ThinPrep Pap smear images in 3 classes achieve average accuracy of 93.33%, Non-ThinPrep Pap smear images in 2 classes achieve 90% average accuracy and the average accuracy of the overall in the 4 classes reached 92%. These results indicate that the proposed method can identify Pap smear images well. Creator An entity primarily responsible for making the resource Dwiza Riana1 , Sri Rahayu2 , Sri Hadianti3 , Frieyadie4 , Muhamad Hasan5 , Izni Nur Karimah6 , Rafly Pratama7 Publisher An entity responsible for making the resource available , Universitas Nusa Mandiri 2,3,6,7Informatika, Fakultas Teknologi Informasi, Universitas Nusa Mandiri 4,5Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusa Mandiri Date A point or period of time associated with an event in the lifecycle of the resource 1 februari 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/7d47a716ad056a9b160666c4a3076792.pdf cfe3d034112fa553076d0d45917e44e4 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 1 (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 Optimization Prediction of Big Five Personality in Twitter Users Subject The topic of the resource Big Five Personality, SVM, TF-IDF, LIWC, Optimization Description An account of the resource Various kinds of information can be acquired from social media platforms; one of them is on Twitter. User biographical information and tweets are the essential assets for research that can describe the Big Five Personality, including openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several previous studies have tried the prediction of Big Five Personality. However, the authors found problems in how to optimize the work of the personality prediction system. So, in this study, Big Five Personality predictions were carried out on users of Twitter and improved the performance of the personality prediction system. We implement optimization techniques such as sampling, feature selection, and hyperparameter tuning to enhance the performance. This study also applies linguistic feature extraction, such as LIWC and TF-IDF. By using 287 Twitter users that have permitted their data to be crawled acquired from an online survey using Big Five Inventory (BFI), and applying all optimization techniques, the average accuracy result is 84.22% which is a 74.44% gain over the specified baseline. Creator An entity primarily responsible for making the resource Gita Safitri1 , 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 27 februari 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/dedcf890602bce19f7210a38d310d90d.pdf c730c9b7f02c6cd78754d62106ec8cac 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 1 (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 Klasifikasi Kematangan Tanaman Hidroponik Pakcoy Menggunakan Metode SVM Subject The topic of the resource Classification, Pakcoy, Machine learning, SVM Description An account of the resource Pakcoy is a type of vegetable plant belonging to the Brassica family. Pakcoy plants can be cultivated using hydroponic techniques, namely plant cultivation techniques without soil media. The advantage of cultivating Pakcoy plants using hydroponic techniques is that it does not require a large area of land, so it is easy to apply in the yard. However, cultivation with hydroponic techniques has drawbacks such as farmers need to make regular observations to determine the harvest readiness of each plant. This causes a lack of effectiveness of farmers in cultivating Pakcoy plants. With the development of Machine Learning technology, a model can classify the maturity of Pakcoy plants based on digital image data. By applying the Support Vector Machine (SVM) Algorithm, the Machine Learning model can learn to classify a digital image of Pakcoy plants with the category "Small" to represent immature Pakcoy plants and "Large" to represent mature Pakcoy plants which results in an accuracy level of above 79%. It can be concluded that Machine Learning can be implemented in Pakcoy cultivation activities to support hydroponic farmers. Creator An entity primarily responsible for making the resource Hanin Latif Fuadi1 , Lukman Priyambodo2 , Tasya Enjelika Saputri3 , Naura Nazhifah4 , Angga Bagus Prawira5 , Ibrohim Huzaimi6 , Mas Aly Afandi7 , Eka Setia Nugraha8 , Agung Wicaksono9 , Petrus Kerowe Goran10 Publisher An entity responsible for making the resource available Institut Teknologi Telkom Purwokerto Date A point or period of time associated with an event in the lifecycle of the resource 27-02-2022 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/f87335747a384efa76e3c27fb0d58950.pdf a9862538a56f53ba5e6f129b846f08bd 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 1 (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 Gradient Boosting Machine, Random Forest dan Light GBM untuk Klasifikasi Kacang Kering Subject The topic of the resource GBM, RF, LightGBM, Bean Classification, BoxCox Description An account of the resource Bean seed classification is critical in determining the quality of beans. Previously, the same dataset was tested using the MLP, SVM, KNN, and DT algorithms, with SVM producing the best results. The purpose of this study is to determine the most effective model through the use of the BoxCox transformation selection feature and the random forest (RF) algorithm, as well as the gradient boosting machine (GBM), light GBM, and repeated k-folds evaluation model. The bean dataset is available on the UCI Repository website. The BoxCox transformation and repeated k-folds improved the classification prediction's accuracy. The model is used in the optimal training phase for a random forest with decision tree parameters 50 and depth 10, a gradient boosting machine model with a learning rate of 1, and a light gradient boosting machine model with a learning rate of 0.5 and estimator of 500. The best training accuracy results are obtained with light GBM. which is 99 percent accurate, but only 91 percent accurate in terms of validation. According research, the Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira beans classes provided accuracy values of 91 percent, 100 percent, 92 percent, 92 percent, 95 percent, 94 percent, and 84 percent, respectively Creator An entity primarily responsible for making the resource Indrawata Wardhana1 , Musi Ariawijaya2 , Vandri Ahmad Isnaini3 , Rahmi Putri Wirman4 Publisher An entity responsible for making the resource available UIN Sulthan Thaha Saifuddin Jambi Date A point or period of time associated with an event in the lifecycle of the resource 27 februari 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