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All Journal Jupiter Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Indonesian Journal of Computer Science
Rona Nisa Sofia Amriza
Institut Teknologi Telkom Purwokerto
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning Supriyadi, Didi; Safitri, Sisilia Thya; Amriza, Rona Nisa Sofia; Kristiyanto, Daniel Yeri
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 1 (2022): Volume 8 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i1.49300

Abstract

E-Learning merupakan salah satu produk layanan berbasis teknologi informasi yang dikembangkan dengan tujuan untuk meningkatkan kualitas pembelajaran pada perguruan tinggi. Kesuksesan implementasi sistem e-learning tidak lepas dari peran aktif dan kesetiaan pengguna (customer loyalty) untuk memberikan penilaian maupun feedback untuk peningkatan kualitas layanan yang meliputi efektivitas, efisiensi dan kepuasan dari kegunaan e-learning secara terus menerus. Kepuasan pelanggan berdampak positif terhadap retensi pelanggan, hingga pembelian produk atau jasa lanjutan pelanggan dan kepuasan pelanggan dianggap sebagai faktor utama loyalitas pelanggan. Kegunaan e-learning dapat diukur menggunakan kerangka kerja System Usability Scale (SUS). Sedangkan untuk mengetahui tingkat loyalitas pengguna e-learning dapat menggunakan pendekatan Net Promoter Scale (NPS). Penelitian ini bertujuan untuk membandingkan algoritma Decision Trees, Naïve Bayes, dan K-Nearest Neighbor (KNN) untuk klasifikasi tingkat loyalitas pengguna e-learning dengan pendekatan kategori berdasarkan NPS. Dataset terdiri atas 100 data yang berasal dari penilaian kepuasan pengguna dari dosen dan mahasiswa sebagai pengguna e-learning. Dataset dibagi menjadi 80:20 untuk data training dan data testing. Penerapan metode 10-fold cross validation pada pengujian ketiga model algoritma berhasil menghindarkan model dari kondisi underfitting maupun overfitting. Pengujian kinerja dari tiap – tiap model algoritma machine learning menggunakan confusion matrix yang meliputi parameter accuracy, sensitivity, dan precision.  Hasil pengujian menunjukkan bahwa algoritma Decision Trees memiliki tingkat akurasi terbaik yaitu sebesar 95%, diikuti dengan Naïve Bayes dengan tingkat akurasi sebesar 90% dan KNN dengan tingkat akurasi sebesar 85%.
Komparasi Metode Machine Learning dan Deep Learning untuk Deteksi Emosi pada Text di Sosial Media Rona Nisa Sofia Amriza; Didi Supriyadi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 2 (2021): JUPITER Edisi Oktober 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/3603.jupiter.2021.10

Abstract

Emotion Detection is the process of human emotions recognition, it extracting emotions such as happy, sad, and angry, which are obtained from human natural language. Linguistic Style has a wide range, emotional representations occur to millions of people and makes it difficult to infer a person's emotion in a concrete way. Multilabel datasets are also a challenge to deal in emotion detection. Therefore, an in-depth study of the appropriate method for emotional detection is needed. This study performs a comparative analysis between machine learning methods and deep learning methods. The machine learning methods used are Naïve Bayes, Random Forest, SVM, Gradient Boosting and Logistic Regression. The deep learning methods used in this study include LSTM, CNN, MLP, GRU and RNN. This research discovered that Deep learning has a better performance than machine learning, it seen from the accuracy values ​​of LSTM, CNN, MLP, GRU and RNN which exceed the accuracy values ​​of Naïve Bayes, SVM, Logistic Regression, Gradient Boosting and Random Forest.
Implementasi Firebase Realtime Database pada Aplikasi FeedbackMe sebagai Penghubung Guru dan Orang Tua Khairun Nisa Meiah Ngafidin; Artika Arista; Rona Nisa Sofia Amriza
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.861 KB) | DOI: 10.29207/resti.v5i2.2909

Abstract

The necessity of learning assistance for elementary student is to ensure that students can absorb the learning well. In order to keep track of the student's progress, the teacher needs to know how and what the student has done while at home. The FeedbackMe application was created to become a liaison between teachers and parents during distance learning. Firebase Realtime Database is implemented to support messages to be delivered quickly. The purpose of this study is to implement the Firebase Realtime Database into the FeedbackMe application to support remote student learning. The system development method used is the Waterfall method which is a systematic and sequential method. The results of this study indicate that all the features in the application and also the application of Firebase can run properly and correctly. Meanwhile, testing of respondents regarding user satisfaction results in the amount of 89.28% from teacher, and respondents from parents got 89.73% satisfaction.
ANALISIS PENGARUH PLATFORM SOSIAL MEDIA TERHADAP PENYEBARAN INFORMASI BENCANA Rona Nisa Sofia Amriza; Khairun Nisa Meiah Ngafidin
Jurnal Sistem Informasi Vol. 8 No. 2 (2021)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v8i2.3639

Abstract

Abstrak- Sosial media menjadi platform yang sangat berperan dalam menyebarkan informasi, berita, dan memberikan informasi bencana secara cepat dan tepat. Banyak informasi berharga yang dapat diperoleh dalam platform ini. Penelitian ini menganalisis pengaruh platform sosial media terhadap intensi masyarakat untuk menyebarkan informasi bencana yang dipengaruhi oleh mediator personal yaitu altruisme dan efikasi diri. Penelitian ini mengobservasi penyebab seseorang memiliki intensi untuk menyebarkan informasi bencana. Structural Equation Modeling Partial Least Squares (SEM-PLS) digunakan untuk melakukan uji hipotesis. Dari penelitian ini kami menemukan bahwa mediator altruisme dan efikasi diri dalam platform sosial media berpengaruh secara signifikan terhadap intensi seseorang untuk menyebarkan informasi bencana. Kata Kunci: Sosial Media, Penyebaran Informasi, Penyebaran Informasi Bencana, Structural Equation Modeling, Partial Least Squares, SEM-PLS
The Classification of Anxiety, Depression, and Stress on Facebook Users Using the Support Vector Machine Tsania Maulidia Wijiasih; Rona Nisa Sofia Amriza; Dedy Agung Prabowo
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1273

Abstract

Social media remains an essential platform for connecting people with friends, family, and the world around them. However, when events spread on social media are primarily negative, it will cause depression, anxiety, and stress that tend to increase. This study aims to classify depression, anxiety, and stress using the Support Vector Machine. The data in this study were obtained from active Facebook users using the Depression Anxiety Stress Scale (DASS 21) questionnaire. This study adopted the Knowledge Discover Database process. The result of this study is an evaluation of the performance of the Support Vector Machine classification of depression, anxiety, and stress. The accuracy of the Support Vector Machine in this study is 98.96%.
Perancangan Sistem Informasi E-Catalogue Berbasis Website Menggunakan Metode Waterfall Jonatan Maruli Butarbutar; Darmansah Darmansah; Rona Nisa Sofia Amriza
Jurnal Sistem Komputer dan Informatika (JSON) Vol 3, No 4 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4165

Abstract

The problem faced by CV. Ramos Ponsel is that competitors have just started the same business as Cv. Ramos Ponsel, so that it has the impact of decreasing consumers in meeting their needs at Cv. Ramos Ponsel. With the situation for the store to innovate in customer service. The innovations in question are, among others, digitizing business processes based on a website, one of which is using an electronic catalog. The purpose of this research is to produce an E-Catalogue that can support the business processes of CV. Ramos Ponsel. The method used in this research is the Waterfall method. Waterfall every step of the work process is completed first to move to the next stage. The Waterfall Stage itself consists of Planning, Analysis, Design, Implementation, Testing, and Maintenance/Maintenance. With the existence of an e-catalogue that is designed to have superior value than e-catalogue owned by other institutions, by adding features that benefit sellers and buyers in the business process. The result of this research is to produce an E-Catalog information system on Cv. Ramos Mobile based web site.
Peran Pembelajaran Daring, Komunikasi, Dan System E-Learning Terhadap Motivasi Belajar Mahasiswa Achmad Rizky Fambudianto; Rona Nisa Sofia Amriza; Dwi Januarita Ardianing Kusuma
Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): Indonesian Journal of Computer Science Volume 11. No. 2 (2022)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3049

Abstract

Online learning is the main learning system amid government policies that have closed face-to-face learning activities as a result of the emergence of Covid-19. Based on pre-study data, it shows that the obstacles that interfere with the learning process are learning content that is less attractive, internet networks that are less supportive, and inadequate digital devices. In the communication aspect, problems that arise, such as students who cannot ask questions directly to lecturers, and online learning which is not as interactive as face-to-face learning. Meanwhile, from the aspect of the E-Learning System, the problems that arise are the content, content, or appearance that is less attractive and interactive, information that is not optimal, not up-to-date, and E-Learning is less stable. This research was conducted to analyze the role of online learning, communication, and E-Learning systems on learning motivation. The approach is carried out using quantitative methods and processed using Structural Equation Modeling Partial Least Squares (SEM-PLS). As a result, it was found that the facilities and knowledge of the teacher had a significant positive effect on students' learning motivation with p-values ​​of 0.00.
Analisis Forecasting Penjualan Gula Merah di Jatilawang Menggunakan Metode Weighted Moving Average Safhira Nanda Rahmadhani; Logiandani Logiandani; Raihan Zidane Ramadhan; Rona Nisa Sofia Amriza; M. Yoka Fathoni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 3 (2022): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i3.1433

Abstract

Gula Merah berasal dari air nira yang disadap langsung dari pohon kelapa dan kemudian dilakukan proses memasak. Perubahan musim memberikan dampak berupa air nira yang dihasilkan oleh pohon kelapa tidak stabil oleh karena itu kuantitas produksi serta penjualan gula merah juga terdampak. Adanya permasalahan tersebut maka penelitian ini membahas tentang analisis forecasting penjualan gula merah di Kecamatan Jatilawang menggunakan Metode Weighted Moving Average (WMA) dengan pengukuran akurasi menggunakan Mean Absolute Error (MAE). Penelitian ini bertujuan untuk meramalkan penjualan gula merah sebagai tolok ukur petani dalam memproduksi gula merah di musim yang terus berubah. Sumber data berasal dari tiga petani gula merah di Kecamatan Jatilawang, yaitu Petani A, Petani B, Petani C. Hasil penelitian ini menunjukan penjualan Gula Merah pada bulan Juni akan mengalami peningkatan. Hasil forecasting WMA pada bulan Juni 2022 mendapatkan forecasting penjualan paling tinggi pada Petani Gula Merah B dengan nilai peramalan 264.80, selanjutnya nilai peramalan pada peringkat kedua ada pada Petani Gula Merah C berada pada tengah – tengah dengan nilai peramalan 263.61, sedangkan  penjualan terendah pada Petani Gula Merah A dengan nilai peramalan 253.52
Analisis Kepuasan Pengguna BIMA Menggunakan Integrasi Model EUCS Delone Mclean dan Self-Efficacy Nurul Fitriani; Sarah Astiti; Rona Nisa Sofia Amriza
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2658

Abstract

Technology modernization is developing so fast that people have become dependent on it. Technology has a major impact on human life. It plays a larger role in the life of a wider community, especially in education, where it plays a role as a knowledge provider. The information system is an integrated unit of information technology and an activity to support operations and management to use the technology. One of the information systems used in education is the Research and Community Service Information Base (BIMA). BIMA is an information system that reports research activities such as planning, implementation, and reporting. BIMA implementation is within the Academic Community of the Telkom Purwokerto Institute of Technology. However, BIMA still has several problems with the infrastructure and systems, creating dissatisfaction when users access it. Based on these conditions, an analysis of user satisfaction with BIMA is needed. This study uses the EUCS, Delone Mclean, and Self-Efficacy Model Integration. This study analyzes user satisfaction levels and determines which factors significantly affect BIMA user satisfaction. The testing technique used is SEM-PLS. Based on the test results, there are five variables with a significant effect. Information Quality, System Quality, and Self-Efficacy significantly affected User Satisfaction. Technical Support significantly affected System Quality, and System Quality variables significantly affected Information Quality. One hypothesis has no significant effect on User Satisfaction, namely the Technical Support variable with p-values > 0.05, which is 0.478 and the t-statistic value <1.64, which is 0.032.
The Impact of Personal, Environmental, and Information Platform Factors on Disaster Information Sharing on Twitter Rona Nisa Sofia Amriza; Khairun Nisa Meiah Ngafidin; Wiwit Ratnasari
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 8 No. 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.2540

Abstract

Twitter has become a major platform for disseminating disaster news, providing people with disaster information quickly and precisely. A lot of essential and valuable information can be obtained from this online platform. Twitter users might be able to help with warnings and submit specific and accurate information in a disaster situation. This research attempts to examine factors that affect disaster information-sharing behavior. Furthermore, this study aims to integrate personal, environmental, and information platform factors to gain more insight into the factors influencing Twitter users' willingness to share disaster information. The hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The result showed that Altruism, Self-efficacy, Community Identity, and Information Platforms significantly influence people's decisions to share disaster information on Twitter.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Achmad Rizky Fambudianto Arista, Artika Astiti, Sarah Citra Wiguna Darmansah Dedy Agung Prabowo Didi Supriyadi Dwi Januarita Ardianing Kusuma Jonatan Maruli Butarbutar Khairun Nisa Meiah Ngafidin Logiandani Logiandani M Yoka Fathoni Nurul Fitriani Raihan Zidane Ramadhan Safhira Nanda Rahmadhani Sisilia Thya Safitri, Sisilia Tsania Maulidia Wijiasih Wiwit Ratnasari