Jurnal Buana Informatika最新文献

{"title":"Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat","authors":"Bakhtiyar Hadi Prakoso, Ervina Rachmawati, Demiawan Rachmatta Putro Mudiono, Veronika Vestine, Gandu Eko Julianto Suyoso","doi":"10.24002/jbi.v14i01.7105","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.7105","url":null,"abstract":"One of the fundamental principles followed by the Jember Health Office for decision-making is data. Data plays a crucial role in the decision-making process. Raw data is more difficult to interpret and needs to be analyzed. Clustering is one of the techniques used for analysis. This study discusses using K-Means to cluster Public Health Center data based on AKI, AKB, and stunting prevalence. The data is processed by reducing dimensions and normalizing them. The clustering process is performed using the K-Means method, where the maximum k-value is obtained by calculating WCSS. The clustering process results in three clusters of Public Health Centers in the Jember Regency. These clusters can serve as a reference for the Jember Health Office to formulate family health and community nutrition quality policies.Keywords: data mining, K-Means, clustering, Maternal Mortality Rate, Infant Mortality Rate, the prevalence of stunting\u0000 \u0000Salah satu dasar pengambilan kebijakan oleh Dinas Kesehatan Jember adalah data. Data memiliki peran dalam proses pengambilan keputusan. Data mentah yang didapatkan lebih sulit untuk diinterpretasikan sehingga diperlukan analisis terhadap data tesebut. Salah satu analisis yang dapat digunakan adalah teknik klasterisasi. Padapenelitian ini akan dibahas penggunaan K-Means untuk klasterisasi data puskesmas berdasarkan AKI, AKB, dan prevalensi stunting. Data diproses dengan melakukan reduksi dimensi dan normalisasi. Proses klasterisasi dilakukan dengan metode K-Means dimana nilai k maksimal diperoleh dengan menghitung WCSS. Adapun hasil proses klasterisasi didapatkan tiga kelompok klaster puskesmas yang terdapat di Kabupaten Jember. Hasil klasterisasi dapat digunakan sebagai referensi Dinas Kesehatan Jember dalam mengambil kebijakan terkait kualitas kesehatan keluarga dan gizi masyarakatKata Kunci: data mining, K-Means, klasterisasi, Angka Kematian Ibu, Angka Kematian Bayi, prevalensi stunting","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124125190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blackbox Testing on Virtual Reality Gamelan Saron Using Equivalence Partition Method","authors":"Richard Gunawan, Yohanes Priadi Wibisono, Clara Hetty Primasari, Djoko Budiyanto","doi":"10.24002/jbi.v14i01.6606","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.6606","url":null,"abstract":"Pengujian Blackbox Pada Virtual Reality Gamelan Saron Menggunakan Metode Equivalence Partition. Dalam pengembangan sebuah aplikasi, testing pada aplikasi sangat penting sebelum aplikasi dirilis. Testing berdasarkan kualitas dengan menggunakan Black Box mengutamakan pengujian fitur-fitur yang terdapat pada aplikasi, sehingga dapat menemukan permasalahan yang terjadi di aplikasi Gamelan VR Saron ini. Pada metode Black Box berbasis Equivalence Partition secara menyeluruh menguji dalam aspek penggunaan aplikasi Gamelan VR Saron. Dalam pengujian ini membutuhkan seleksi penggunaan berdasarkan test case, kemudian memastikan kualitas dari fitur-fitur yang tersedia serta menemukan error function yang bisa terjadi dalam aplikasi. Sehingga ujicoba pada aplikasi ini bisa menilai apakah telah sesuai dengan harapan dan direncanakan. Berdasarkan hasil pengujian yang telah dilakukan, dapat disimpulkan bahwa aplikasi ini dapat berjalan dengan baik tanpa adanya error sesuai dengan yang telah direncanakan. Hasil pengujian ini dapat dijadikan sebagai dokumentasi serta evaluasi untuk pengembangan aplikasi kedepannya.Kata Kunci: black box, equivalence partition, test case, error function, user\u0000Blackbox Testing on Virtual Reality Gamelan Saron Using the Equivalence Partition Method. Testing is essential in application development because it helps identify and eliminate defects. One of the most used testing methods is Black Box testing, which involves deeply examining the application’s functionality without knowing its internal workings. The Equivalence Partition method is frequently used in Black Box testing to divide input values into groups and select test cases from each group. Potential errors can be identified by testing the available features with appropriate test cases, and future improvements can be made to ensure seamless application performance. In addition, testing results also serve as documentation and research for future development. By using this method, the developers of the VR Gamelan Saron application can ensure that its quality meets user expectations to improve its quality to provide an optimal user experience. In summary, proper testing is crucial in application development, and the Equivalence Partition method is an effective tool foridentifying and eliminating potential issues.Keywords: black box, equivalence partition, test case, error function, user","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114441587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning","authors":"Felix Adrian Tjokro Atmodjo, K. R. Prilianti, Hendry Setiawan","doi":"10.24002/jbi.v14i01.6997","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.6997","url":null,"abstract":"Implementation of Plant Image Quality Improvement based on Machine Learning on Camera Variation to Predict Photosynthetic Pigments. Pigments are natural dyes found in plants and animals. In photosynthesis, there are 3 essential pigments: chlorophyll, cartenoid, and anthocyanin. Pigment analysis can be performed with High Performance Liquid Chromatography (HPLC) and a spectrophotometer. However, HPLC and spectrophotometers require high resources and time. Thus, the Fuzzy Piction Android application built using the FP3Net model is the best choice in pigment prediction since it is low on cost and accessible. However, the Fuzzy Piction produces different performance, which is affected by light conditions and camera specifications. The experiment used ten sample images for Jasminum sp., P. betle, Syzygium oleina of green and red variations, and Graptophyllum pictum leaves with three smartphone cameras and three lighting levels. Improvements using 3D-TPS produced the best SSIM values in the range of 0.9191 – 0.9797 for images Syzygium oleina of green and red variations leaves, and the predicted MAE value of pigment was 0.0296 – 0.0492.Keywords: 3D-TPS, plant leaves, pigment, image quality improvement\u0000 \u0000Pigmen merupakan pewarna alami yang ditemukan pada tumbuhan dan hewan. Dalam proses fotosintesis terdapat tiga pigmen yang penting, yaitu klorofil, kartenoid, dan antosianin. Analisis pigmen dapat dilakukan dengan Kromatorafi Cair Kinerja Tinggi (KCKT) dan spektrofotometer. Namun,KCKT dan spektrofotometer membutuhkan sumber daya dan waktu yang tinggi. Sehingga, aplikasi Android Fuzzy Piction yang dibangun menggunakan model FP3Net mejadi pilihan dalam prediksi pigmen dengan biaya murah dan mudah. Akan tetapi, aplikasi Android Fuzzy Piction menghasilkan kinerja yang berbeda-beda yang dipengaruhi oleh kondisi cahaya dan spesifikasi kamera. Dilakukan percobaan dengan mengambil sepuluh sampel citra daun dari empat varietas tanaman yaitu, pucuk merah, daun ungu, melati, dan sirih. Citra diambil dengan tiga kamera smartphone dan tiga tingkat pencahayaan yang berbeda. Perbaikan yang dilakukan menggunakan algoritma 3D-TPS menghasilkan nilai SSIM terbaik pada rentang 0.9191 –0.9797 untuk citra daun pucuk merahdan nilai MAE prediksi pigmen sebesar 0.0296 –0.0492.Kata Kunci: 3D – TPS, daun tanaman, pigmen, perbaikan kualitas citra","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"38 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116800956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Algoritma Pathfinding A* dalam Game Dual Legacy berbasis Android","authors":"Felix Octavian, Latius Hermawan","doi":"10.24002/jbi.v14i01.6928","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.6928","url":null,"abstract":"A* Pathfinding Algorithm Implementation in Dual Legacy Game based on Android. Games have 2 characters, the player, and the NPC (Non-Playable Character) which cannot be controlled by the player,so the NPC movements are easy to predict. A Star (A*) algorithm is a pathfinding algorithm or finding a way to a destination, in this case searching for the closest path to the player and avoiding obstacles. The enemy NPC is tasked with chasing the player, and the enemy NPC must reduce the player's health. A* algorithm calculatesthe distance of one of the paths and then calculatesthe distance of the other paths. The algorithm will choose the shortest path when all paths have been completed. Research focuses on the NPC's task of finding the shortest route. The A* in the “Dual Legacy” 2D Side-Scrolling RPG game based on Android is expected with this algorithm NPC can search for and chase players/players via the nearest path. The conclusion is that the A Star Algorithm has been successfully implemented, the NPC approaches the player through the shortest distance by avoiding obstacles.Keywords: A Star (A*) algorithm, NPC, game, Android, 2D side-scrolling RPG\u0000Game biasanya terdapat 2 karakter yaitu player dan NPC (Non-Playable Character) yang tidak bisa dikendalikan oleh player sehingga pergerakan karakter NPC mudah ditebak. Algoritma A Star (A*) merupakan algoritma pathfinding atau mencari jalan ke tujuan, dalam kasus ini mencari jalan terdekat ke player dan menghindari rintangan yang ada. NPC musuh ini bertugas untuk mengejar player dan NPC musuhharus mengurangi darah player. Algoritme A* menghitung jarak satu jalur, menyimpannya, lalu menghitung jarak jalur lainnya. Setelah semua jalur dihitung, algoritma A* memilih jalur terpendek . Penelitian berfokus pada tugas NPC untuk pencarian rute terdekat. Menerapkan algoritma pathfinding A* pada NPC game Dual Legacy 2D Side-Scrolling RPG berbasis Android diharapkan dengan algoritma tersebut NPC dapat mencari dan mengejar pemain / player melalui jalan terdekat. Kesimpulanperancangan ini adalah Algoritma A Star berhasil diimplementasikan, NPC mendekati player melalui jarak terdekat dengan menghindari halangan yang ada.Kata Kunci: algoritma A Star (A*), NPC, game, Android, 2D side-scrolling RPG","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128482753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine","authors":"V. Fitriyana, Lutfi Hakim, Dian Candra Rini Novitasari, Ahmad Hanif Asyhar","doi":"10.24002/jbi.v14i01.6909","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.6909","url":null,"abstract":"Sentiment Analysis of Jamsostek Mobile Application Reviews Using the Support Vector Machine Method. Today's technology is evolving quickly, leading to new developments that have helped produce JMO and other mobile applications that can be useful to Indonesians. The reviews or comments in the JMO can be used as a gauge for quality and user satisfaction. This study aims to analyze the quality of JMO applications and classify reviews or opinions into positive, negative, and neutral categories through sentiment analysis. The Support Vector Machine method is used in this analysis process with a linear kernel approach to determine the level of accuracy of classifying JMO application reviews. Research shows that classifying the SVM method against sentiment analysis of reviews or JMO application reviews produces the best accuracy scores, obtaining results with accuracy of 96%, precision of 92%, recall of 96%, and f1-score of 94%, while for the results of most reviews are positive category reviews with a total of 17.571.Keywords: sentiment analysis, JMO, SVM, linear kernel\u0000 \u0000Perkembangan pesat teknologi saat ini memunculkan inovasi baru untuk menciptakan berbagai aplikasi mobile yang dapat memberi kemudahan bagi masyarakat Indonesia, salah satunya yaitu JMO. Penelitian ini bertujuan untuk menganalisis kualitas aplikasi JMO dan mengklasifikasikan ulasan atau opini kedalam kategori positif, negatif dan netral melalui analisis sentimen. Metode Support Vector Machine digunakan pada proses analisis ini dengan pendekatan kernel linear untuk mengetahui tingkat akurasi dari pengklasifikasian ulasan aplikasi JMO tersebut. Penelitian menunjukkan bahwa pengklasifikasian metode SVM terhadap analisis sentimen ulasan atau review aplikasi JMO menghasilkan nilai akurasi terbaik, didapatkan hasil dengan accuracy 96%, precision 92%, recall 96%, dan f1-score 94%, sedangkan untuk hasil ulasan terbanyak adalah ulasan berkategori positif dengan jumlah 17.571.Kata Kunci: analisis sentimen, JMO, SVM, kernel linear","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124494926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementasi Algoritma K-Nearest Neighbour dalam Menganalisis Sentimen Terhadap Program Merdeka Belajar Kampus Merdeka (MBKM)","authors":"Dewi Sartika","doi":"10.24002/jbi.v14i01.7178","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.7178","url":null,"abstract":"K-Nearest Neighbor Algorithm Implementation in sentiment analysis towards Merdeka Belajar Kampus Merdeka (MBKM) Program. Merdeka Belajar Kampus Merdeka (MBKM) is a program that supports students to improve their skills by having direct experience in the work environment to prepare for competition and a future career. MBKM program has been implemented by Indonesia's Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) since  2020. Every policy needs to be evaluated; a simple evaluation can be done through sentiment analysis to determine public responses to the MBKM program. The results are used as suggestions for program improvement. Sentiment analysis is done by applying the Natural Language Processing (NLP) algorithm to process crawled data from Twitter, then classified using the K-NN Algorithm. Based on the results, the sentiment is neutral. This illustrates that people are only partially interested in the MBKM program policy. The accuracy of the classification model using the K-NN algorithm is 95%, and an F1-score value of 0.96 for the classification model with a ratio of 80% training data and 20% test data.Keywords: MBKM, NLP, K-NN, F1-Score\u0000 \u0000Program Merdeka Belajar Kampus Merdeka (MBKM) merupakan suatu kebijakan dalam mendukung pemberian kebebasan terhadap mahasiswa untuk mengasah kemampuan dengan merasakan langsung pengalaman di dunia kerja sebagai bekal untuk menghadapi persaingan dan persiapan berkarir di masa mendatang. Program MBKM mulai diberlakukan oleh Kementerian Pendidikan Kebudayaan Riset dan Teknologi (Kemendikbudristek) Republik Indonesia sejak tahun 2020. Setiap kebijakan tentunya perlu dievaluasi, evalusi sederhana dapat dilakukan melalui analisis sentimen untuk mengetahui tanggapan masyarakat mengenai program MBKM. Hasilnya digunakan sebagai saran perbaikan untuk pengembangan program. Analisis sentimen dilakukan dengan menerapkan algoritma Natural Language Processing (NLP) untuk memproses data hasil crawling dari Twitter, selanjutnya diklasifikasikan menggunakan algoritma K-NN. Berdasarkan hasil analisis diperoleh bahwa sentimen masyarakat bersifat netral. Hal ini menggambarkan bahwa masyarakat tidak sepenuhnya tertarik terhadap kebijakan program MBKM, sedangkan untuk tingkat akurasi model klasifikasi menggunakan algoritma K-NN sebesar 95% dan nilai F1-score sebesar 0,96 untuk model klasifikasi dengan perbandingan 80% data latih dan 20% data uji.Kata Kunci: MBKM, NLP, K-NN, F1-Score","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127038255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Sound Response from Simple and Fuzzy Algorithm in Saron Virtual Reality","authors":"Ayub Her Pracoyo, Clara Hetty Primasari, Albertus Joko Santoso, Thomas Adi Purnomo Sidhi, Yohanes Priadi Wibisono, Djoko Budiyanto Setyohadi","doi":"10.24002/jbi.v14i01.6612","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.6612","url":null,"abstract":"Analisis Komparatif Respon Suara dari Algoritma Sederhana dan Algoritma Fuzzy di Saron Virtual Reality. Game virtual reality dengan konsep alat musik memerlukan respon suara yang dinamis karena musik tidak lepas dari perasaan manusia dalam memainkannya. Suara yang bagus dalam sebuah game tergantung pada kesesuaiannya dengan situasi game. Keterbatasan waktu dan tempat menjadi permasalahan dalam melakukan variasi perekaman sampel suara. Jika sampel suara yang diambil terbatas dan diterapkan dengan algoritma sederhana kemungkinan terdengar repetitif dan kurang sesuai dengan dinamika suara musik sesuai kehidupan nyata manusia. Oleh karena itu, pada penelitian ini dilakukan komparasi implementasi antara algoritma sederhana dengan algoritma fuzzy pada suara game Gamelan Saron. Metode pengolahan data yang digunakan adalah analisis komparatif dan data diperoleh dari hasil eksperimen responden. Pada skala persetujuan satu sampai lima, mayoritas responden setuju adanya perubahan signifikan yang lebih baik setelah diberikan algoritma fuzzy yang digambarkan dengan nilai rata-rata 4,1.Kata Kunci: suara, gamelan, Saron, dinamika, fuzzy\u0000Comparative Analysis of Sound Response from Simple and Fuzzy Algorithm in Saron Virtual Reality. Virtual reality games with musical instruments require a dynamic sound response because playing the instrument requires real human feelings. A good sound in a game depends on its suitability for the game situation. Time and place limitations are a problem in recording variations in sound sample recording. If the sound samples taken are limited and a simple algorithm is applied, it may sound repetitive and not match the dynamics of music according to real human life. Therefore, in this study, a comparison of a simple algorithm with the fuzzy algorithm was carried out in the Gamelan Saron game. The data processing method used is a comparative analysis obtained from the experimental results of the respondents. On the agreement scale of one to five, most respondents agree that there is a better significant change after being given a fuzzy algorithm described by a mean value of 4.1.\u0000Keywords: sound, gamelan, Saron, dynamics, fuzzy","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117218923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temu Kembali Berbasis Citra untuk Menemukan Kemiripan Merek Menggunakan Algoritma SIFT dan SURF","authors":"Eri Zuliarso, Sulastri, Yunus Anis","doi":"10.24002/jbi.v13i02.6328","DOIUrl":"https://doi.org/10.24002/jbi.v13i02.6328","url":null,"abstract":"Abstract. Image-Based Retrieval to Find Trademark Similarities Using SIFT and SURF Algorithms. In the world of trade in products and services, brands are essential. Every company wants to register a unique trademark for its products and services. Registration and evaluation to find the uniqueness of a trademark is challenging. Trademark image registration is one of the critical application areas of Content-BasedRetrieval (CBIR), which compares new brands with existing ones to ensure no dispute in the community. This study used SIFT and SURF algorithms to build a content-based brand image retrieval system. The research data used trademark data dispute cases that were decided in court. The features extracted from the SIFT and SURF algorithms are used to find similarities between the query image and the image in the database. Furthermore, the k-Nearest Neighbors algorithm with Euclidean distance measurements was used to sort the database images that were most similar to the query image. Experiments were conducted to find the algorithm and sequencing with the highest precision and recall values.Keywords: Trademark, SIFT, SURF, K-Nearest Neighbors, Euclidean.\u0000Abstrak. Dalam dunia perdagangan produk dan jasa, merek menjadi sangat penting. Setiap perusahaan ingin mendaftarkan merek dagang yang unik untuk produk dan jasanya. Pendaftaran dan evaluasi untuk menemukan kekhasan suatu merek dagang menjadi suatu pekerjaan yang sangat sulit. Pendaftaran citra merek dagang adalah salah satu area aplikasi penting Content Based Information Retrieval (CBIR) yang membandingkan merek baru dengan merek yang ada untuk memastikan tidak ada sengketa di masyarakat. Penelitian ini menggunakan algoritma SIFT dan SURF untuk membangun sistem temu kembali citra merek berbasis konten . Data penelitian menggunakan kasus sengketa data merek yang diputuskan di pengadilan. Fitur hasil ekstraksi algoritma SIFT dan SURF digunakan untuk mencari kemiripan citra query dan citra dalam basis data. Selanjutnya algoritma k-Nearest Neighbors dengan pengukuran jarak Euclidean digunakan untuk mengurutkan citra basis data yang paling mirip dengan citra query. Eksperimen dilakukan untuk mengetahui algoritma dan pengurutan dengan nilai presisi dan recall tertinggi. Kata Kunci: Merek, SIFT, SURF, K-Nearest Neighbors, Euclidean.","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116918900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prototipe Dashboard Aplikasi POSTASY Berbasis Website Menggunakan Metode Pureshare untuk Meningkatkan Kemudahan Layanan Posyandu","authors":"Salma Maghfira, Tri Sagirani, Tan Amelia","doi":"10.24002/jbi.v13i02.6117","DOIUrl":"https://doi.org/10.24002/jbi.v13i02.6117","url":null,"abstract":"Abstract. Dashboard Prototype of Website-Based Postasy Application Using the Pureshare Method to Improve the Convenience of Posyandu Services.Integrated Service Post (Posyandu) is a form of community-based health service. Recording of data in a Posyandu is the main activity. To support the main activities, Posyandu has a Postasy website that can display information, maintain data and manage every detail that will be used. The evaluation results showed that the website has deficiencies in the value of memorability and satisfaction, and the dashboard display is inadequate. The solution offered to redesign the dashboard using the pureshare method and the double diamond model as a stage in building a prototype. The final test of the prototype results using the UXT usability test tool and a questionnaire. The test results through the distribution of questionnaires are 87.3% in memorability and 84.8% in satisfaction. The results of testing using UXT obtained 100%, which means that the design is excellent, can be accepted by respondents, and becomes a recommendation.Keywords: Posyandu, redesign, usability testing\u0000Abstrak. Pos Pelayanan Terpadu (Posyandu) adalah suatu bentuk layanan kesehatan berbasis masyarakat. Pencatatan dan perekapan data di sebuah Posyandu merupakan kegiatan utama yang dilakukan. Untuk mendukung kegiatan utama Posyandu memiliki website Postasy yang dapat menampilkan informasi yang dibutuhkan, memelihara data dan mengelola setiap detail yang akan digunakan. Berdasarkan hasil wawancara terdapat beberapa keluhan terhadap tampilan website Postasy. Dari hasil evaluasi didapatkan data bahwa website memiliki kekurangan pada nilai memorability dan satisfaction dan juga tampilan dashboard yang kurang memadai. Solusi yang ditawarkan adalah mendesain ulang dashboard menggunakan metode pureshare, dan model double diamond sebagautahapan dalam membangun prototype. Pengujian akhir terhadap hasil prototipe dilakukan dengan menggunakan uji usability tools UXT dan kuesioner. Hasil pengujian melalui penyebaran kuesioner diperoleh hasil 87,3% untuk memorability dan 84,8% untuk satisfaction. Hasil pengujian menggunakan UXT diperoleh hasil 100% yang artinya dapat disimpulkan bahwa desain sangat baik, dan dapat diterima oleh responden sehingga menjadi rekomendasi pengguna.Kata Kunci: Posyandu, desain ulang, usability testing","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127686322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering","authors":"Rizal Adi Saputra, Jumadil Nangi, Ika Purwanti Ningrum, Muhamad Faza Almaliki, La Ode Rahmat Andre Pratama","doi":"10.24002/jbi.v13i02.5448","DOIUrl":"https://doi.org/10.24002/jbi.v13i02.5448","url":null,"abstract":"Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering.","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125835406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}