Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan

Penelitian kesehatan harus melalui proses telaah etik yang bertujuan untuk mengantisipasi dugaan atas risiko fisik, sosial, ekonomi dan psikologis. Secara etik penelitian kesehatan dapat diterima apabila pada penelitian tersebut mampu dibuktikan dengan metode ilmiah yang valid serta lulus uji etik sebelum penelitian dilakukan. Untuk memastikan pada ringkasan penelitian kesehatan terdapat aspek etik, dibutuhkan kata kunci yang dapat dijadikan representasi dari isi ringkasan tersebut. Salah satu pendekatan yang sering dilakukan adalah dengan menghitung frekuensi kemunculan kata dalam dokumen. Pendekatan lain yaitu pendekatan YAKE dan keyBERT yang tidak hanya menghitung frekuensi kata namun juga menghitung konteks kata. Selain melakukan ekstraksi dilakukan juga proses ekspansi kueri sebagai upaya memperluas istilah yang dapat mewakili masing-masing aspek etik. Salah satu pendekatan yang digunakan untuk ekspansi kueri adalah model word2Vec. Penelitian ini mengusulkan metode pengembangan ekspansi kueri dengan dan metode ekstraksi kata kunci seperti TFIDF,YAKE dan keyBERT dan mengombinasikannya dengan fuzzy. Hasil eksperimen menunjukkan bahwa metode paling unggul secara presisi yaitu YAKE dan gabungan antara TFIDF + YAKE + keyBERT dengan nilai tertinggi 46% kemudian dari untuk recall model YAKE mendapat nilai tertinggi dengan angka 72% dan untuk nilai F1-Score yang paling unggul adalah metode YAKE dengan nilai tertinggi 54%

FRECOMTWEET: PRODUCT RECOMMENDATION APPLICATION USING FRIENDSHIP CLOSENESS ON TWITTER

The information and communication technology development makes someone interact with each other easier. This convenience is used to exchange ideas, like using social media Twitter for product recommendations before buying it. It brings up a trend that consumers seek product recommendations through other people on social media. Social media, especially Twitter, has several features such as tweets, ReTweet and mentions to interact with other people. Users can describe the product, attach a link, and give a positive or negative rating in a tweet. These types of tweets can be used as an alternative to product recommendations. FrecomTweet is an Android-based product recommendation application that can detect close friendships based on the user’s ReTweet and mentions. This application also detects a product recommendation that appears in a conversation between users. This detection uses the keyword filtering method, which matches the conversation content with the markers in the database. If the conversation has a positive rating, it will recommend the user’s closest friends. This research uses a crawling method with the Twitter API streaming filter built using the CodeIgniter framework. The results of the black box test show that Twitter user conversations can be used as a product recommendation with a precision and recall value of 0.94 and 0.81, respectively

KAKAS BANTU PENDETEKSI KESALAHAN TANDA BACA PADA KARYA TULIS ILMIAH

Jumlah karya ilmiah yang dihasilkan oleh akademisi dan peneliti di Indonesia semakin banyak, terutama setelah diterbitkannya surat edaran Dirjen DIKTI tahun 2012 dimana karya ilmiah dijadikan sebagai syarat kelulusan mahasiswa S1, S2 dan S3. Namun demikian, tidak semua karya ilmiah tersebut memiliki kualitas yang baik. Masih banyak karya ilmiah yang belum memenuhi standar baku Ejaan Yang Disempurnakan (EYD). Pada artikel ini, penulis mengembangkan sebuah kakas bantu untuk mendeteksi kesalahan tanda baca pada karya ilmiah, khususnya yang berbahasa Indonesia, sesuai dengan EYD. Aplikasi dirancang agar dapat mendeteksi kesalahan tanda baca pada tulisan karya ilmiah dengan format .doc atau .docx serta dapat menghasilkan keluaran berupa arsip Microsoft Word dengan tambahan hasil telaah pemeriksaan tanda baca yang dibangkitkan secara otomatis. Deteksi kesalahan tanda baca menggunakan metode pencarian kata dengan algoritma BoyerMoore. Aplikasi kakas bantu telah diuji coba dengan hasil rata-rata nilai presisi sistem sebesar 0,6806, recall sebesar 0,969 dan akurasi sistem sebesar 0,9636. Hasil tersebut menunjukkan bahwa aplikasi sudah mampu mendeteksi adanya kesalahan tanda baca meskipun masih ada keterbatasan deteksi karena tidak semua aturan tanda baca dicakup dalam pemeriksaannya

Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest

The number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear with an emphasis on quality. To ensure that academic papers meet the required quality standard, the peer review process is necessary. The main objective of the assignment of reviewers is to find the appropriate reviewer who can conduct a review based on their field of research. However, there are potential obstacles when there is a conflict of interest in the process. This study aims to develop a method for assigning reviewers that overcomes such obstacles. Our approach involves combining the Latent Dirichlet Allocation (LDA), Classification, and Link Prediction methods. LDA is used to find topics from the research data of prospective reviewers to ensure that the assigned reviewers are well suited to the submitted article. These data were used as training data for classification using Random Forest. Finally, link prediction implemented to make reviewer recommendations. We evaluated and compared our proposed method with previous research that used cosine similarity as the last step in recommendation, using Mean Average Precision (MAP). Our proposed method achieved a MAP value of 0.87, which was an improvement compared to the previous approach. These results suggest that our approach has the potential to improve the effectiveness of academic peer review

Automatic scheduling using Forward Chaining for Ethics Protocol Review

Research involving humans as the object has to follow ethics protocol. This protocol has to be reviewed by the research ethics committee before the research can be conducted. Though the mechanism for determining the protocol reviewer is simple, there are several obstacles, including the unbalanced workload of each reviewer, the empty schedule because the reviewer is busy or has other tasks, and the length of time it takes to determine the schedule manually. There needs to be a system that can see various obstacles and challenges in scheduling protocol reviewers and resolve them automatically. This paper proposes an automatic scheduling mechanism with a Heuristic Forward Chaining approach that can adjust the rules for determining reviewers from real experts and avoid the constraints that exist in the manual scheduling system. The proposed method is made in the form of a web application and can practically generate accurate schedules automatically