1 10 15 https://repository.horizon.ac.id/files/original/f8f1f359437d4eec315d60b3bff92485.pdf 4e3d11e4011207986b9d442c0dd85f47 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 5 NO 4 (2021) 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 PengembanganMetode Autentikasi pada Sistem PresensiBerbasis Aplikasi Mobile Subject The topic of the resource attendance system, MAC address, IP address, BSSID, Wi-Fi Description An account of the resource Research has been carried out on a mobile-based presence system authentication method using MAC addresses, BSSID and IP addresses for (Wi-Fi) networks.This study aims to develop an authentication method on the attendancesystem that meets two authentication requirements, namely the suitability of employee identity and location suitability, so that the attendance process becomeseasy, effective, fast, and can reduce fraud. The employee's identity can be obtained from the MAC address of the smartphone that has been previously registered, while the employee's location during the attendance process can be confirmed to be in the company environment by checking the BSSID data and IP of the Wi-Finetwork connected to the smartphone. The data is then compared with MAC address data from all Wi-Finetworksinstalled in thecompany area. RAD is used as a development model because it is simple and fast. Overall, employee identification and site checking as authentication of the developed system went well. Other than that, every function on the system works well. Furthermore, the results of the user experience evaluation using the UEQ questionnaire received an average score above 0.8 on 6 scales. This shows that the system has attractiveness, perspicuity, efficiency, dependability, stimulationand novelty Creator An entity primarily responsible for making the resource Komang Sri Utami1, Nyoman Putra Sastra2, Dewa Made Wiharta Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Udayana Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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/ec8f75cca9ecaf65e1d42c726619669b.pdf 0e68ec9aa8fd12a53391659b6e14b40e 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 5 NO 4 (2021) 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 Implementasi Convolutional Neural NetworkUntuk Deteksi NyeriBayi Melalui Citra Wajah Dengan YOLO Subject The topic of the resource NVDIA Jetson Nano Developer Kit, Pain, Baby, YOLO, PyTorch, CNN, Facial Expresion Description An account of the resource Pain in a baby is difficult to detect is because the method for detecting pain is self-reporting even though babies themselves still cannot describe the pain verbally, then by observing changes in behavior in the form of facial expressions. Statistically, it is also recorded that about 80% of the world's population pays less attention to pain assessment, especially for children, even though this pain gives children a bad experience so that it can interfere with pain responses in the future or psychological trauma. Based on these problems, a prototype system was made using the NVIDIA Jetson Nano Developer kit to help detect pain, especially in infants 0-12 months by using the Convolutional Neural Network (CNN) model with the PyTorch framework and the You Only Look Once (YOLO) algorithm with three detection classification is sad, neutral and sick. From the results ofthe study, it was found that the YOLO algorithm was able to detect the three classifications with mAP@0.5value of sad 97,9%, neutral 99,2%, pain 96,9%, model accuracy 70%. The result of random check and the data from Puskesam Imogiri 1 have accuracy value 90% Creator An entity primarily responsible for making the resource Tomy Abuzairi1, Nurdina Widanti2, Arie Kusumaningrum3, Yeni Rustina Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 2021 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/dc79073470f65a014645066a99c61538.pdf 60967a43b40f9efcedf055256fc2fa8f 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 5 NO 4 (2021) 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 Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA Subject The topic of the resource LDA, SVM, review, aspect Description An account of the resource During the Covid-19 pandemic,almost all community activities are conducted from home.Therefore, video conference technology is needed for people to carry out their normal activities from home. One of the video conference applications is ZOOM Cloud Meetings. Applications certainly havebeen reviewedgiven by theirusers as a reference for new users andcompanies of the application to know the application’s performance. However, in reviews, some constraints are the number of reviews as well as irregular. Therefore, a solution is needed with sentiment analysis that aims to classify the reviews of the application to be organized by categorizing positive or negative sentiment. In this study, aspect-based sentiment analysis was conducted on ZOOM Cloud Meetings app reviews from Google Play Store. The analysis’s resultof the review data obtained threeaspects,namely aspects of usability, system, and appearance. The modeling topic used istheLatent Dirichlet Allocation(LDA) method and classification using the Support Vector Machine (SVM). This research resulted in the best performance with the best parametersresulting in the performance accuracy of usability aspect is 88.83%, system aspect with 91.2%, appearance aspect with 94.78%, and performance accuracy of all aspects 91.61% Creator An entity primarily responsible for making the resource Janu Akrama Wardhana1, Yuliant Sibaroni Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 2021 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/e810b22e1ff6075b72b5cd8e941863b1.pdf dabf33d02a0a11bad56dd6ca8d5da533 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 5 NO 4 (2021) 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 Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest Subject The topic of the resource recognition system, vehicle logo, local binary pattern, random forest Description An account of the resource The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identificationtools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods.This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were asmany as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images Creator An entity primarily responsible for making the resource Alda Putri Utami1, Febryanti Sthevanie2, Kurniawan Nur Ramadhani3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Telkom Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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/f942ec9b7e619455ff4da59569ccaa4b.pdf acd88fcef56ce1ad7c53ce90291df4ed 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 5 NO 4 (2021) 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 Realtime Object Detection Masa SiapPanenTanaman Sayuran BerbasisMobile Android DenganDeep Learning Subject The topic of the resource real-time, objectdetection, vegetable, harvest,MobileNetV3 Description An account of the resource Determining the harvesting period can be done visually, physically, computationally, and chemically. Since the harvesting process is crucial, late harvesting will affect post-harvest and production quality. Leafy vegetables have a relatively short ready-to-harvest period. Visual recognition of the harvesting period combined with image processing can recognize harvesting vegetables' visual characteristics. This study aims to build a deep learning-based mobile model to detect real-time vegetable plant objectssuch as bok choy, spinach, kale, and curly kale to determine whether these vegetables are ready for harvest. Mobile-based architecture is chosen due to latency, privacy, connectivity, and power consumption reason since there is no round-trip communicationto the server. In this research, we use MobileNetV3 as the base architecture. To find the best model, we experiment using different image input size. We have obtained a maximum MAP score of 0. 705510using a 36,000 image dataset. Furthermore, after implementing the model into the Android mobile application, we analyze the best practice in using the application to capture distance. In real-time detection usage, the detection can be done with an ideal distance of 5 cm and10cm Creator An entity primarily responsible for making the resource Andri Heru Saputra1, Dhomas Hatta Fudholi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Islam Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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 Indoesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/9470e5aaebb459aa02362832fc18f76a.pdf 4c02834b36a90d3b2aad1e1a833de1d7 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 5 NO 4 (2021) 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 lasifikasi Citra Daging Menggunakan DeepLearningdengan Optimisasi Hard Voting Subject The topic of the resource Deep Leaning, Ensemble Learning, Image Recognition, Transfer Learning Description An account of the resource Meat is a staple food for some Indonesian people, apart from the taste, meat also contains vitamins and minerals that are good for the human body, however, not all meat can be consumed by the Indonesian people. the texture and color of beef, pork and mutton have similarities and tend tobe similar, therefore a system is needed to recognize the three types of meat. In this study, the authors use various types of Deep Learning architecture such as Resnet-50, VGG-16, VGG-19 and Densenet-121 with Hard Voting to improve the performance of Deep Learning in recognizing the three types of meat. The results show that Resnet-50 with Hard Voting can outperform Deep Learning Resnet-50, VGG-16, VGG-19 and Densenet-121-with f1 score 98.88%, precision 98.89% and recall 98.88%. in image classification of pork, beef and mutton Creator An entity primarily responsible for making the resource Made Bramasta Vikana Putra1, I Putu Agung Bayupati2, Dewa Made Sri Arsa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Udayana Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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/ea049502b4d25f900d492c2022764054.pdf b38f5ebbe85d277b55ad14c59ce14b1b 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 5 NO 4 (2021) 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 mplementasi Sistem Otentikasi Dokumen Berbasis Quick Response(QR) Codedan Digital Signature Subject The topic of the resource Digital Signature, Quick Response(QR) code, Secure Hash Algorithm-256 (SHA-256), Rivest-Shamir-Adleman (RSA), Error correction. Description An account of the resource The authenticityand integrityof documents areessentialindata exchange and communication. Digital documents must be verifiable for their authenticity and integrityby all parties that use the documents. Generally, digital documents can be authenticated by using digital signatures. This study aims to implement adocument authentication system based on Quick Response(QR) code and digital signature. As the case study, thedocument authentication system is implementedto generate digital signatures forstudent’scertificate documents. Furthermore, the system can also verifythe authenticityof the certificate documents. Creatinga digital signature requires ahash function algorithm forgeneratingthe message digestof the document. In addition, an algorithm togeneratethe public key and the private key used in the encryption/decryption of the message digest is also needed. Thehashfunction utilizedin this study is the Secure Hash Algorithm-256 (SHA-256), while the algorithm used for encryption/decryption is theRivest-Shamir-Adleman (RSA)algorithm. The system is evaluated by verifying 30 student certificate documents, of which 15of them were certificates with QR code signature generated by the system and the other 15were certificates with QR code signature generated usinga random QR code generator. The system’stesting results demonstrate thatthe systemcan ensurethe authenticityand integrityof the signed certificate documents toprevent document falsification. All documents that contain random QR codes were correctly identified asfalse documents Creator An entity primarily responsible for making the resource Antika Lorien1, Theophilus Wellem2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Kristen Satya Wacana Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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/c31d9bf3769fefa314b9a8496df7f44f.pdf 81cb6a387516d441235685f4d3725899 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 5 NO 4 (2021) 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 Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method Subject The topic of the resource rapid test, Twitter, LDA, sentiment, corona Description An account of the resource n 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is moreconsistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments Creator An entity primarily responsible for making the resource Viny Gilang Ramadhan1, Yuliant Sibaroni2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 2021 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/c0d24bb8a0c944c86f06e4a05d7b22ff.pdf b3acbd21a34e79ed8efca290ba7c7754 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 5 NO 4 (2021) 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 Semantic Approach for Big Five PersonalityPredictiononTwitter Subject The topic of the resource Big Five Personality, BERT, SVM,LIWC Description An account of the resource Personality provides a deepinsight of someone and hasanimportant part in someone’s job performance. Predicting personality throughsocial media has been studied onseveral research.The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personalityon Twitter usersand increase the performance of thepersonality predictionsystem. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295Twitteruserswith 511,617tweets data. In this research, we experimenton twodifferent methods using SupportVector Machine (SVM),and the combination of SVM and BERTas the semantic approach.This research also implementsLinguistic Inquiry Word Count (LIWC) asthe linguistic feature for personality prediction system.Theresults showed thatcombination of these two methods achieve 79.35% accuracyscore and with the implementation of LIWC can improvethe accuracy score up to 80.07%.Overall, these results showed thatthecombination of SVM and BERTas the semantic approach with the implementation of LIWCis recommended to gain a better performance for the personality prediction system Creator An entity primarily responsible for making the resource GhinaDwi Salsabila1, Erwin Budi Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 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 20 agustus 2021 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/9c4ab937b76854bcd6add66922e8fef1.pdf bd263585f7e7b2a138dff1a27fe7c3eb 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 5 NO 4 (2021) 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 Citra Burung LovebirdMenggunakan Decision Treedengan Empat Jenis Evaluasi Subject The topic of the resource Lovebird, ANN, Decision Tree, Precision, Recall, F-measure Description An account of the resource Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficultydistinguishing the types of lovebirds. This research is needed to improve previous studyperformance inclassifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case,also to reduce the stage of feature extraction to speed up the computational process. Based on availablecomparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000,and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841,and anaccuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 canimprove performance and produce the best accuracy Creator An entity primarily responsible for making the resource Aviv Yuniar Rahman Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24 Publisher An entity responsible for making the resource available Universitas Widyagama Malang Date A point or period of time associated with an event in the lifecycle of the resource 20 agustus 2021 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