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All Journal JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Knowledge Engineering and Data Science Progresif: Jurnal Ilmiah Komputer Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat LEBAH Jurnal INFOTEL
Merlinda Wibowo
Institut Teknologi Telkom Purwokerto
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Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing Merlinda Wibowo; Christoph Quix; Nur Syahela Hussien; Herman Yuliansyah; Faisal Dharma Adhinata
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p105-116

Abstract

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result.
A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Merlinda Wibowo; Akhmad Jayadi
JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1047.417 KB) | DOI: 10.30595/juita.v9i1.9624

Abstract

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.
Predicting Students Graduate on Time Using C4.5 Algorithm Herman Yuliansyah; Rahmasari Adi Putri Imaniati; Anggit Wirasto; Merlinda Wibowo
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.67-73

Abstract

Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.
Pelatihan Manajemen Web untuk Membantu Program Desa Melek Internet di Desa Kabupaten Kebumen Muhammad Afrizal Amrustian; Merlinda Wibowo
Lebah Vol. 16 No. 1 (2022): September: Pengabdian
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (919.234 KB) | DOI: 10.35335/lebah.v16i1.53

Abstract

Desa Melek Internet (Desmeli) merupakan program yang dijalankan oleh pemerintah Kabupaten Kebumen untuk membantu kegiatan desa. Salah satu kegiatan dari program Desmeli adalah penyediaan sistem informasi bagi desa-desa dalam bentuk web. Kabupaten Kebumen melalui Dinas Komunikasi dan Informatika (Diskominfo) Kebumen telah memberikan fasilitas web yang digunakan oleh desa-desa di Kabupaten Kebumen untuk menyebarkan informasi. Namun terdapat masalah yang terjadi, yakni kurangnya pengetahuan pihak desa dalam mengelola web yang telah diberikan. Oleh karena itu kami menawarkan solusi untuk memberikan pelatihan atau workshop terkait pengelolaan web kepada Diskominfo sebagai bentuk pengabdian kepada masyarakat serta membantu program Desmeli. Peserta mendapatkan pelatihan pengelolaan web yang dibagi menjadi tiga materi yakni, penggunaan template serta proses kustomisasi, pembuatan posting informasi dan proses merapikannya menggunakan fitur kategori, dan pemanfaatan plugin untuk memantau aktifitas web. Hasil dari pelatihan yang dilakukan adalah bertambahnya pengetahuan peserta terkait pengelolaan web.
Pelatihan Teknologi Informasi Pada Era Pandemi COVID-19 (Studi Kasus SD Negeri 03 Ketandan Klaten) Gita Fadila Fitriana; Merlinda Wibowo
Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Vol 5, No 2 (2022): Jurnal Abdimas Berdaya
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jab.v5i2.215

Abstract

Pandemi Covid-19 yang terjadi saat ini menuntut para guru untuk memiliki inovasi pembelajaran. Inovasi pembelajaran yang dapat mengatasi pelaksanaan belajar secara online (daring) dan pembelajaran ini dilakukan di rumah. Para guru dituntut untuk dapat menguasai teknologi agar kegiatan belajar mengajar tetap berlangsung di masa pandemi. Tetapi hal ini tidak didukung dengan pengembangan diri sehingga diperlukan pelatihan teknologi informasi diantaranya Google Meet. Hasil pelatihan yang akan dicapai berupa inovasi pembelajaran meliputi ceramah, praktik dan tanya jawab. Hasil pelatihan yang dicapai berupa inovasi pembelajaran di masa pandemi. Sebelum diberikan pelatihan, para guru SD Negeri 03 Ketandan hampir belum pernah menggunakan Google Meet, namun setelah diberikan pelatihan, para guru merasa bahwa menggunakan Google Meet memberikan manfaat dan motivasi bagi para guru dalam komunikasi jarak jauh secara daring dan langsung.
Perbandingan Algoritme Naïve Bayes dan C4.5 Pada Pengklasifikasian Tingkat Pemahaman Belajar Mahasiswa Dalam Pembelajaran Daring Nora Trivetisia; Rima Dias Ramadhani; Merlinda Wibowo
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1081

Abstract

Online learning is a learning system that has been widely implemented since the Covid-19 Pandemic. This learning system is synonymous with the use of internet-based learning media. In practice, teachers often have difficulty knowing how far their students can understand the material being taught. Therefore, it is necessary to do a classification to make it easier for teachers to assess the level of understanding in terms of health, motivation, and teaching methods. Many classification algorithms can be used so that analysis is needed to find the best algorithm. This study focuses on comparative observations of two classification algorithms, namely Naïve Bayes and C4.5. The dataset used is the result of a student questionnaire at the Telkom Purwokerto Institute of Technology in the form of a Likert scale. The steps taken were data preprocessing and then classification using Naïve Bayes and C4.5. The result is that Naïve Bayes is superior to C4.5 with a Naïve Bayes testing accuracy of 99% compared to C4.5 with 91% accuracy. So, it can be concluded that Naïve Bayes is superior to C4.5 in this case.Keywords: Online Learning; Naïve Bayes; C4.5; Classification; Data Mining AbstrakPembelajaran daring adalah salah satu sistem pembelajaran yang ramai diterapkan sejak Pandemi Covid-19. Sistem pembelajaran ini identik dengan penggunaan media belajar berbasis internet. Dalam pelaksanaannya pengajar sering mengalami kesulitan untuk mengetahui sejauh mana mahasiswanya bisa menangkap materi yang diajarkan. Oleh karena itu, perlu dilakukan klasifikasi untuk mempermudah pengajar dalam menilai tingkat pemahaman dari segi kesehatan, motivasi, dan cara pengajaran. Banyak algoritme klasifikasi yang dapat digunakan sehingga dibutuhkan analisis untuk mencari algoritme terbaik. Penelitian ini berfokus pada pengamatan komparasi terhadap dua algoritme klasifikasi yaitu Naïve Bayes dan C4.5. Dataset yang digunakan adalah hasil kuesioner mahasiswa Institut Teknologi Telkom Purwokerto berbentuk skala Likert. Tahapan yang dilakukan adalah preprocessing data lalu dilakukan klasifikasi menggunakan Naïve Bayes dan C4.5. Hasilnya Naïve Bayes lebih unggul dari C4.5 dengan akurasi untuk pengujian Naïve Bayes sebesar 99% dibanding C4.5 dengan akurasi 91%. Maka, dapat disimpulkan bahwa Naïve Bayes lebih unggul daripada C4.5 pada kasus ini.Kata kunci: Pembelajaran Daring; Naïve Bayes; C4.5; Klasifikasi; Data Mining
Opinion mining indonesian presidential election on twitter data based on decision tree method Nur Ghaniaviyanto Ramadhan; Merlinda Wibowo; Nur Fatin Liyana Mohd Rosely; Christoph Quix
JURNAL INFOTEL Vol 14 No 4 (2022): November 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i4.832

Abstract

Indonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%.
Co-Authors Akhmad Jayadi Anggit Wirasto Christoph Quix Christoph Quix Diovianto Putra Rakhmadani Faisal Dharma Adhinata Herman Yuliansyah Herman Yuliansyah Muhammad Afrizal Amrustian Nora Trivetisia Nur Fatin Liyana Mohd Rosely Nur Ghaniaviyanto Ramadhan Nur Syahela Hussien Rahmasari Adi Putri Imaniati Ramadhani, Rima Dias