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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Science and Technology Indonesia Jurnal Komputasi Jurnal Teknoinfo Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Journal Social Science And Technology For Community Service Jurnal Pepadun Integra: Journal of Integrated Mathematics and Computer Science

Shofiana, Dewi Asiah

Jurusan Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung

Author-ID : 3250411

Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics

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PERANCANGAN DAN IMPLEMENTASI SISTEM MANAJEMEN DALAM PENGELOLAAN DATA AKADEMIK BERBASIS WEB DI SMA NEGERI 1 LIWA Aldo Pradipta; Machudor Yusman; Dewi Asiah Shofiana; Aristoteles Aristoteles
Jurnal Pepadun Vol. 2 No. 1 (2021): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

SMA Negeri (SMAN) 1 Liwa is a secondary education school located in the West Lampung Regency of Lampung Province. The current management of academic data in SMA Negeri 1 Liwa still implements manual methods. Thus, this research is conducted to build a web-based academic data management system for SMA Negeri 1 Liwa. It aims to digitally process administrative data, such as the data of lessons, students, teachers, classes, mutations, and student grade reports. The infamous waterfall method was applied to develop the system with the PHP and MySQL programming languages. Performance of the system was examined using the black-box testing method. From this study, a web-based system was successfully developed and can be accessed directly by the teachers and administrative staff.

KLASIFIKASI KEJADIAN HIPERTENSI DENGAN METODE SUPPORT VECTOR MACHINE (SVM) MENGGUNAKAN DATA PUSKESMAS DI KOTA BANDAR LAMPUNG Indah Pasaribu; Favorisen Rosyking Lumbanraja; Dewi Asiah Shofiana; Aristoteles Aristoteles
Jurnal Pepadun Vol. 2 No. 2 (2021): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Hypertension is a condition in which a person experiences an increase in blood pressure above the normal value which causes pain and even death. Normal human blood pressure is 120/80 mmHg. Patients with hypertension cannot be cured, but prevention and control can be done. The hypertension cases are always increasing in Indonesia. The Bandar Lampung City Health Service stated that hypertension is a disease that always ranks in the top ten diseases in Bandar Lampung City. Diagnosis of hypertension is currently manually performed by requiring a lot of energy, materials, and time. Based on the condition, there is an idea to apply the field of biomedical data analysis to help diagnosing hypertension using the support vector machine (SVM) method in Bandar Lampung City. This study classifies and measures the accuracy of the support vector machine method in hypertension. The data comes from five health centers in Bandar Lampung City from 2017 to 2019 with 10-fold cross validation data sharing. The kernels used are linear, gaussian, and polynomial kernels. This study successfully classifies hypertension sufferers in Bandar Lampung City. The result of the highest feature correlation analysis is 0.90. The results of the classification using the support vector machine method get the highest accuracy, which is 99.78% on the gaussian kernel.

SENTIMENT ANALYSIS PROTOKOL KESEHATAN VIRUS CORONA DARI TWEET MENGGUNAKAN WORD2VEC MODEL DAN RECURRENT NEURAL NETWORK LEARNING Ni Putu Ayu Anesca; Kurnia Muludi; Dewi Asiah Shofiana
Jurnal Pepadun Vol. 2 No. 3 (2021): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v2i3.86

Sentiment analysis is a computational study of opinion from various opinions, which is part of the work that conducts a review related to the computational treatment of opinions, sentiments, and perceptions of the text. To solve various problems in sentiment analysis, needed a good text representation method. In this study, a deep learning analysis was carried out using the Recurrent Neural Network (RNN) method and the Word2Vec Model as word embedding in sentiment classification. The sentiment dataset used comes from user reviews on Twitter (tweets) on the health protocols implemented by the public from the government's appeal. The results showed that the RNN model using sigmoid activation resulted in the greatest accuracy of 66%. The training process in this test uses 10 epochs and 32 batch sizes so that the precision value for negative sentiment is 54% and for positive sentiment is 67%.

IMPLEMENTASI ALGORITME SUPPORT VECTOR MACHINE DAN FITUR SELEKSI MRMR UNTUK PREDIKSI GLIKOSILASI Favorisen Rosyking Lumbanraja; Naurah Nazhifah; Dewi Asiah Shofiana; Akmal Junaidi
Jurnal Pepadun Vol. 3 No. 1 (2022): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i1.96

During the protein formation process, there are post-translational modifications that provide additional properties and produce new R groups in the polypeptide chain. One of the results of post-translational modifications is glycosylation. Glycosylation reactions occur between protein and glucose at high concentrations. There are 3 categories of glycosylation found in the human body, namely N-glycosylation, O-glycosylation and C-glycosylation. To determine the functional role of glycosylation, that is by predicting the substrate of each glycosylation site. A computational approach is a way to predict the glycosylation site, using the Support Vector Machine (SVM) algorithm. In this study there are 2 types of data, namely independent data and benchmark data. The features used are feature extraction and feature selection using Maximum Redundancy Minimum Relevance (MRMR) of 25, 50 and 75 columns. SVM classification test using 5-fold cross validation. The highest accuracy result lies in the use of the 75 column MRMR selection feature. In Independent N data, the greatest accuracy lies in the sigmoid kernel with a causation value of 86.66%, while for independent C data, the accuracy is 87.5% in the sigmoid kernel and for independent O data, the largest accuracy is 89.31% which is in the RBF kernel. For benchmark N data, the highest accuracy is 70.54% in the RBF kernel, for benchmark C data the greatest accuracy lies in the RBF kernel with a value of 95.06% and for benchmak O data it is in the RBF kernel with the greatest accuracy, which is 92.64%.

Sistem Penilaian Angka Kredit Pegawai pada Program Pelatihan Mandiri di BPKP Provinsi Lampung Dewi Asiah Shofiana; Muhammad Ridho Restu Alam Sobri; Mulia Kesuma Putri
Jurnal Pepadun Vol. 4 No. 1 (2023): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v4i1.141

The Financial and Development Supervisory Agency (BPKP) of Lampung Province routinely holds independent training programs for employees. However, activity data collection is still carried out conventionally which makes it difficult for employees to see the history of the training attended. In fact, these activities have credit numbers that must be recorded carefully because they are related to performance appraisal and have an impact on employee careers. Based on this problem, this research developed a credit scoring information system for self-training programs using a prototyping approach that runs on a web-based platform. The system can provide information about the implementation of independent training activities and employees can make attendance of training activities through the system. Employees can also see the history of the training that has been followed along with the credit score. System testing uses a user acceptance test with a Likert scale which achieves a customer satisfaction index of 92.5%, which shows that it is very satisfying for users and has functioned according to the standards desired by the BPKP of Lampung Province.

Co-Authors Admi Syarif Akmal Junaidi Aldo Pradipta Aristoteles Caniadi, Melan Heningtyas, Yunda Indah Pasaribu Kurnia Muludi Lumbanraja, Favorisen R Machudor Yusman Manurung, Yunita Rosalina Mohammad Surya Akbar Muhammad Ridho Restu Alam Sobri Mulia Kesuma Putri Naurah Nazhifah Ni Putu Ayu Anesca Prabowo, Rizky Sholehurrohman, Ridho YOHANA TRI UTAMI, YOHANA TRI

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