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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
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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Penerapan Algoritma C4.5 Untuk Prediksi Churn Rate Pengguna Jasa Telekomunikasi Yohana Tri Utami; Dewi Asiah Shofiana; Yunda Heningtyas
Jurnal Komputasi Vol 8, No 2 (2020)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2647

Abstract

Telecommunication industries are experiencing substantial problems related to the migration of customers due to a large number of competing companies, dynamic circumstances, as well as the presence of many innovative and attractive offerings. The situation has resulted in a high level of customer migration, affecting a decrement toward the company revenue. Regarding that condition, the customer churn is one well-know approach that can help in increasing the company's revenue and reputation. As to predict the reason behind the migration of customer, this study proposed a data mining classification technique by applying the C4.5 algorithm. Patterns generated by the model were implemented using 10-fold cross-validation, resulting in a model with an accuracy rate of 87%, precision 87.5%, and a recall of 97%. Based on the good performance quality of the model, it can be stated that the C4.5 algorithm succeeded to discover several causes from the migration of telecommunication users, in which price holds the top place as the primary reason
Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network Kurnia Muludi; Mohammad Surya Akbar; Dewi Asiah Shofiana; Admi Syarif
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66016

Abstract

Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.809 KB) | DOI: 10.23960/pepadun.v2i1.31

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.538 KB) | DOI: 10.23960/pepadun.v2i2.56

Abstract

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.
Pelatihan Pembuatan Video Profil Usaha Kecil Menengah di Pekon Wonodadi Induk, Kecamatan Gadingrejo, Kabupaten Pringsewu Admi Syarif; Yunda Heningtyas; Aristoteles Aristoteles; Dewi Asiah Shofiana
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 1 No. 1 (2020)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v1i1.8

Abstract

Video profil menjadi media yang semakin banyak digunakan dalam menyampaikan informasi terkait suatu organisasi, produk, ataupun jasa, terutama di era digitalisasi seperti saat ini. Penggunaan video profil di bidang usaha industri dikenal sebagai salah satu teknik mempromosikan produk secara singkat dan efisien, serta mampu menjangkau kalangan yang lebih luas. Pekon Wonodadi merupakan desa binaan yang memiliki banyak usaha kecil dan menengah yang dibangun oleh masyarakat. Sayangnya, penjualan dan teknik promosi yang dilakukan oleh pelaku usaha Pekon Wonodadi masih secara konvensional dan terbatas. Kegiatan pengabdian kepada masyarakat ini memberikan pelatihan kepada masyarakat di salah satu desa binaan Universitas Lampung, yaitu Pekon Wonodadi, terkait pembuatan video profil usaha kecil dan menengah menggunakan Filmora 9. Kegiatan pelatihan yang dilaksanakan dalam kurun waktu 6 bulan telah berhasil meningkatkan pengetahuan pelaku usaha Pekon Wonodadi serta membantu mereka untuk dapat memperluas promosi produknya dengan pengembangan video yang dapat diunggah di media sosial maupun situs lainnya.
HOTSPOT PREDICTIVE MODELING USING REGRESSION DECISION TREE ALGORITHM Dewi Asiah Shofiana; Yohana Tri Utami; Yunda Heningtyas
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.2051

Abstract

Forest fires had always become an international issue influencing many life sectors, including environmental, social, and economic. The forest fire in 2013 was regarded as one of the worst forest fire tragedies in history, not only in Indonesia but also in the world. Detection of hotspots on the earth's surface by the satellite can be an indication of land and forest fire occurrence. This research aims to build a predictive model of monthly hotspots in Rokan Hilir Regency using the regression tree algorithm. Several variables related to weather information are included, such as rainfall, sea surface temperature, and southern oscillation index. This research used 245 training data and 43 testing data, resulting a predictive model with a correlation of 0.875 and an error rate of 0.166. Based on the values, we can conclude that the performance of the model is considerably good.
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

Abstract

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

Abstract

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%.
Peningkatan Keterampilan Penggunaan Fitur Mail-Merge Microsoft Word Bagi Guru dan Staf SMK Muhammadiyah Seputih Raman Dewi Asiah Shofiana; Yohana Tri Utami; Yunda Heningtyas; Rizky Prabowo
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 4, No 1 (2023): Volume 4, Nomor 1, March 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v4i1.2535

Abstract

Development of information technology plays an important role not only in the industrial economic section, but also in education. Information technology is applied at almost every level of education to support the performance of teachers and academic staff. One software commonly used for education and administration is Microsoft Word. The Microsoft Word software has many features, however most of the features are still unfamiliar to some users, including teachers and staff at SMK Muhammadiyah Seputih Raman, Central Lampung. Many teachers and staff are still unfamiliar with the tools available in Microsoft Word, including the mail-merge function. Therefore, our team provided training for 24 participants of teachers and staffs to improve their understanding of Microsoft Word features through lectures, discussions, and hands-on practice. Evaluation results show that after the training, 79% of the participants understand the material provided and almost all participants showed an improvement in the test.
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

Abstract

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 Favorisen R. Lumbanraja Heningtyas, Yunda Indah Pasaribu Kurnia Muludi 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