Rancang Bangun Sistem E-learning Pemrograman Pada Modul Deteksi Plagiarisme Kode Program Dan Student Feedback System

Kompetensi utama yang harus dimiliki oleh mahasiswa jurusan ilmu komputer/informatika adalah pemrograman. Perkuliahan yang berbasis pemrograman seringkali mewajibkan mahasiswa untuk mengerjakan soal yang cukup banyak. Hal ini tentunya akan menyulitkan dosen dalam mengevaluasi hasil pekerjaan mahasiswa. Selain itu, dimungkinkan pula adanya praktik mahasiswa yang memplagiat hasil dari mahasiswa lain. Penelitian ini bertujuan untuk menjawab permasalahan tersebut. Pada penelitian ini dikembangkan sebuah sistem pembelajaran/e-learning pemrograman. Modul sistem e-learning yang dibuat pada artikel ini terbatas pada modul deteksi kemiripan kode program dan student feedback system. Modul deteksi kemiripan kode program berfungsi untuk mengecek kemiripan kode program antar mahasiswa. Kemudian kode-kode program yang memiliki tingkat kemiripan tinggi akan dikelompokkan menjadi satu menggunakan algoritma hierarchical clustering. Proses pengecekan kemiripan program dimulai dari proses transformasi kode program ke dalam Abstract Syntax Tree (AST), kemudian ditransformasi menjadi sequence dan dihitung kemiripannya menggunakan algoritma Levenshtein Distance. Modul student feedback system berfungsi untuk mengecek kemiripan kode program mahasiswa dengan dosen. Mahasiswa akan mendapatkan informasi apakah kode program mereka sudah sesuai dengan yang diinginkan oleh dosen atau belum. Sebelum sistem memberikan umpan Balik, terlebih dahulu kode program dosen dan mahasiswa diproses menjadi AST dan kemudian menjadi sequence. Sistem akan membandingkan kemiripan kode program dosen dan mahasiswa menggunakan algoritma Smith-Waterman yang telah dimodifikasi. Kemudian sistem menampilkan baris-baris kode mana saja yang sama antara kode program dosen dengan kode program mahasiswa. Dari hasil pengujian dapat disimpulkan bahwa modul deteksi plagiarisme dan student feedback system telah terimplementasi dengan baik

Nano(Q)SAR: Challenges, pitfalls and perspectives

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure–activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data

Nanomaterial-based Sensors for the Study of DNA Interaction with Drugs

The interaction of drugs with DNA has been searched thoroughly giving rise to an endless number of findings of undoubted importance, such as a prompt alert to harmful substances, ability to explain most of the biological mechanisms, or provision of important clues in targeted development of novel chemotherapeutics. The existence of some drugs that induce oxidative damage is an increasing point of concern as they can cause cellular death, aging, and are closely related to the development of many diseases. Because of a direct correlation between the response, strength/ nature of the interaction and the pharmaceutical action of DNA-targeted drugs, the electrochemical analysis is based on the signals of DNA before and after the interaction with the DNA-targeted drug. Nowadays, nanoscale materials are used extensively for offering fascinating characteristics that can be used in designing new strategies for drug-DNA interaction detection. This work presents a review of nanomaterials (NMs) for the study of drug-nucleic acid interaction. We summarize types of drug-DNA interactions, electroanalytical techniques for evidencing these interactions and quantification of drug and/or DNA monitoring

Gold Nanoparticle-Quantum Dot Fluorescent Nanohybrid:Application for Localized Surface Plasmon Resonance-induced Molecular Beacon Ultrasensitive DNA Detection

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