Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Sahria, Yoga; Fudholi, Dhomas Hatta

doi:10.30865/mib.v4i3.2183

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…As seen in Figure 1, the research uses an experimental method in making the model, which starts from the idea obtained after going through the problem identification, literature study, and data collection stages, then proceeds with model building and implementation, the last stage is evaluating the results of the experiments carried out. [20], [21]. This research begins with identifying problems and finding solutions through literature studies.…”

Section: Methodsmentioning

confidence: 99%

Modelling Metadata and Data from Censuses and Surveys with Graph Databases

Faradila,

Lutfi Rahmatuti Maghfiroh

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Relational database users are switching to non-relational databases because non-relational databases are better able to handle dynamic data storage. One of the institutions that require dynamic data storage is Statistics Indonesia (BPS). Currently, data storage for census and survey activities at BPS is done using a relational database, even though there are metadata changes in each activity. Accommodating metadata changes in each activity requires one database, which creates problems when retrieving some raw data. There is an opportunity for convenience if the data collected is stored in a non-relational database, one of which is a graph database. This research discusses the modeling of metadata and data from censuses and surveys at BPS using a graph database. Followed by implementation on Neo4j DBMS and comparing the proposed model with the relational model on Microsoft SQL Server DBMS. Then a comparison of the features and characteristics of each DBMS is done, and finally, performance testing is done with Apache JMeter. Modeling has been able to handle dynamic data structure changes, but Neo4j's performance is still lagging behind Microsoft SQL Server.

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Section: Methodsmentioning

confidence: 99%

Modelling Metadata and Data from Censuses and Surveys with Graph Databases

Faradila,

Lutfi Rahmatuti Maghfiroh

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Dalam penelitian ini, kami menggunakan implementasi database grafik Neo4j untuk mengusulkan pemodelan pengetahuan yang berguna di bidang medis. Kajian ini menggunakan grafik dalam CQL (Chyper Query Language) untuk menampilkan kajian kesehatan Indonesia, judul kajian, topik kajian, penulis, dan afiliasi pada tahun tertentu (Sahria & Fudholi, 2020). Penelitian lain seperti "Graph Database Untuk Knowledge Management System Penyakit Epidemis Yang Ditularkan Oleh Nyamuk Di Indonesia" (Maharlika et al, 2018) dan "Visualisasi Tematik Al-Qur'an Berbasis Knowledge Graph" (Hakim et al, 2020) menggunakan Knowlegde Graph untuk menampilkan visualisasi dalam bentuk grafik pencarian surat dan ayat serta menyimpan pengetahuan penyakit epidermis dengan Graph Database Neo4j.…”

Section: Pendahuluanunclassified

“…Studi literatur digunakan sebagai bahan referensi penulis dalam melakukan penelitian. Referensi didapat dari jurnal, artikel ilmiah atau penelitian terdahulu yang terdapat kaitan dengan pembahasan pada peneltian ini (Sahria & Fudholi, 2020).…”

Section: Studi Literaturunclassified

Penentuan Penerima Beasiswa Sekolah Berbasis Visualisasi Graph Database Menggunakan Metode Analytical Hierarchy Process (AHP)

Khasanah,

Fauzan,

Prabowo

2023

JSCE

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Banyak masyarakat kalangan menengah yang tidak mampu memberikan biaya pendidikan anaknya mencapai 9 tahun. Di sekolah terdapat program pemberian beasiswa PIP, namun sistemnya masih dijalankan secara manual yang menyebakan penyaluran beasiswa kurang tepat. Hal tersebut dikarenakan pihak yang diberi kepercayaan untuk menentukan beasiswa melihat kriteria-kriteria masih terpisah dan juga dipengaruhi calon penerima beasiswaa yang jumlahnya cukup banyak. maka dibutuhkan sistem yag bisa membantu sekolah dalam mengambil keputusan berdasarkan kriteria. Graph database sendiri bisa diartikan sebagai suatu model basis data yang didalamnya berisi node, relasi dan property. IMetode iAHP imerupakan isalah isatu imodel ipendukung ikeputusan, instrumen iutamanya iadalah hirarki ifungsional, dan iinput iutamanya iadalah ipersepsi imanusia. Dalam hal ini, mereka adalah ahli dalam menangani bab Beasiswa. Dalam AHP, prioritas terdiri dari berbagai pilihan yang berbeda dan berupa kriteria yang telah terstruktur sebelumnya, sehingga metode AHP dapat diandalkan. Jadi penentuan priioritas didasarkan pada proses yang hierarkis dan masuk akal. Pada penelitian ini penentuan bobot pada setiap kriteria seleksi beasiswa diambil dari yang paling tinggi nilainya yang akan diprioritaskan menjadi penerima beasiswa. Dengan perhitungan ini dihasilkan perankingan yag menunjukkan kualitas siswa yang layak atau belum layak sesuai dengan kriteria yang telah ditentukan. Dari perhitungan tersebut kemudian divisualisasikan kedalam graph database dengan Neo4j. Hasil dari visualisasi menunjukkan urutan ranking siswa yang layak menerima beasiswa dari yang tertinggi ke terendah.

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“…In addition to extracting content, data and information, this technique is also used to automate the data retrieval process or called a robot (Adila, 2022). In addition, how to develop web scraping techniques, namely the maker of the scraping script must first learn the HTML document from a website that will be enclosed in information to use HTML tags (Sahria, 2020).…”

Section: Introductionmentioning

confidence: 99%

Implementation Of K-Nearest Neighbor Algorithm With SMOTE For Hotel Reviews Sentiment Analysis

Mahmud

Hermanto

Nugroho

2023

SinkrOn

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Indonesia has considerable tourism development potential, this phenomenon is in accordance with the number of foreign tourist visits to Indonesia from January to September 2022 recorded by Badan Pusat Statistik many as 2,397,181 visitors. This research focuses on super-priority destinations in Labuan Bajo, East Nusa Tenggara, based on the government's plan that the focus of developing this destination is to increase hotel development to meet the need for an additional 2,000 hotel rooms. Thus, the available hotel rooms are still limited. Then for need to choose a hotel based on the November 2021 survey by the Populix website, 76% of 1,012 respondents chose to book hotels online with the majority using the Traveloka website. However, making decisions in choosing hotels using the reviews feature in the Traveloka website still raises various problems, such as biased information and even the rating values given do not match the reviews submitted. So that users to know what becomes the perception of positive and negative ratings, it is necessary to do in-depth research on satisfaction factors to find out positive and negative sentiments of hotel visitors. This study uses the k-nearest neighbor algorithm with SMOTE on the research objects of the three most popular hotels in Labuan Bajo. Data testing uses a value of k = 3 so that it produces an accuracy value of 87.71% - 93.47% with a maximum error tolerance of 12.29%. In addition, the performance of accuracy results is validated by the appropriate AUC value, namely the good classification category.

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Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Cited by 4 publications

References 11 publications

Modelling Metadata and Data from Censuses and Surveys with Graph Databases

Modelling Metadata and Data from Censuses and Surveys with Graph Databases

Penentuan Penerima Beasiswa Sekolah Berbasis Visualisasi Graph Database Menggunakan Metode Analytical Hierarchy Process (AHP)

Implementation Of K-Nearest Neighbor Algorithm With SMOTE For Hotel Reviews Sentiment Analysis

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