Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Sainteks Jurnal Mantik Journal of Islamic Philanthropy and Disaster Jurnal Sistem Informasi Journal of Computer Science Advancements
Diana Yusuf
Institut Teknologi Dan Bisnis Ahmad Dahlan
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Infographic of Internet Usage Data for Learning Process in the Province of Indonesia Nofirman; Pandu Adi Cakranegara; Diana Yusuf; Nanny Mayasari; Arifin
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of telecommunications in Indonesia has significantly affected the emergence of the digital age, as the internet has become a daily necessity for community activities. Information and communication technology (ICT)-based learning is influencing teaching methods and learning media to use the internet for the learning process in the field of education. This study aims to analyze and graphically present data on the internet usage of rural and urban residents in all Indonesian provinces concerning their education levels. The presented data is in the form of types of internet usage that support the learning process so that it can become predominant in the use of the internet by rural and urban areas and serve as a positive input for internet service providers in the improvement of education-related services. The results indicated that using the Internet to support the learning process by communities in rural and urban areas of Indonesian provinces is one of the goals of using the Internet to obtain information for the learning process is 10%. D.I.Yogyakarta has the highest percentage 58,1%, indicating the most significant number of internet users for educational purposes. When students and student users access the internet to support the learning process, they engage in a variety of activities, including doing assignments, accessing e-learning tools, accessing media information to support learning, using web browsers to display social media, and accessing e-mail to submit assignments using smartphone media, which is the most prevalent form of internet access.
Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants Vany Terisia; Widi Hastomo; Adhitio Satyo Bayangkari Karno; Ellya Sestri; Diana Yusuf; Shevty Arbekti Arman; Nada Kamilia
Sainteks Vol 20, No 2 (2023): Oktober
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v20i2.18691

Abstract

In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets.
Marketing Strategies to Support the Effectiveness of Revolving Alms Philanthropic Institution Fundraising Uki Masduki; Diana Yusuf; Arif Budimanta; Iwan Setiadi
Journal of Islamic Philanthropy and Disaster (JOIPAD) Vol. 6 No. 1 (2026): January-June 2026
Publisher : Institut Agama Islam Negeri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21154/joipad.v6i1.13244

Abstract

Introduction: Revolving alms is a model of economic empowerment in which funds collected from the community are channeled to beneficiaries in the form of productive loans, not just consumptive loans. To support this concept, an effective marketing strategy is needed, especially through digital marketing, the use of social media, diversification of fundraising methods, and transparency in fund management. Research Methods: This study uses a literature review method by analyzing articles and journals related to the marketing of philanthropic institutions during the last 10 years, especially the last 10 years. Results: The results show that digital marketing and social media are the most effective strategies to expand community reach, increase transparency, and speed up the fundraising process. A combination of traditional and modern approaches such as direct visits, crowdfunding, and collaboration with companies through CSR programs can maximize the impact of rolling alms. Conclusion: This study demonstrates that digital marketing and the use of social media significantly enhance the effectiveness of revolving alms fundraising by expanding outreach, increasing donor participation, and improving operational efficiency. Fundraising diversification and transparent fund management are essential in building public trust and ensuring program sustainability. Therefore, optimizing digital platforms and strengthening transparency are key strategies for sustainable revolving alms institutions.
PENERAPAN SUPPORT VECTOR MACHINE DAN ANALISIS ASOSIASI UNTUK ANALISIS ULASAN APLIKASI E-TICKETING (STUDI KASUS: TIKET.COM) Fikri Haikal; Diana Yusuf; Fahrul Razi
Jurnal Sistem Informasi (JUSIN) Vol. 7 No. 1 (2026): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v7i1.3200

Abstract

The development of digital technology has driven the wider adoption of e-ticketing systems on the Tiket.com application, which is increasingly used by the public. However, the increasing number of users doesn't always correlate directly with satisfaction levels, as evidenced by the diverse user reviews on the Google Play Store. This research is urgent for analyzing user sentiment as a basis for improving service quality. The purpose of this research is to classify reviews into positive and negative sentiments using the Support Vector Machine (SVM) algorithm and to analyze dominant word patterns thru the Apriori algorithm. The research data consists of 5,000 Indonesian-language reviews collected between 2023 and 2025, which were then processed thru preprocessing, TF-IDF weighting, SVM classification, association analysis with Apriori, and result visualization using Streamlit. The research results show that SVM produces a high level of accuracy in sentiment classification, while association analysis reveals dominant words that reflect user satisfaction and complaints. The integration of these two methods provides a more comprehensive understanding of user opinions and is expected to serve as a basis for developing service improvement strategies and as a reference for further research in the field of machine learning-based sentiment analysis.
PENERAPAN NATURAL LANGUAGE PROCESSING UNTUK ANALISIS SENTIMEN ULASAN APLIKASI ALLO BANK MENGGUNAKAN ALGORITMA SVM Singgih Adhi Prasetyo; Diana Yusuf; Muhajir Syamsu
Jurnal Sistem Informasi (JUSIN) Vol. 7 No. 1 (2026): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v7i1.3201

Abstract

The rapid growth of digital banking applications in Indonesia requires companies to pay close attention to user perceptions and satisfaction. This research focuses on analyzing the sentiment of user reviews of the Allo Bank application obtained from the Google Play Store, applying the Support Vector Machine (SVM) algorithm combined with a Natural Language Processing (NLP) approach. A total of 5,100 reviews in the Indonesian language were collected using web scraping methods. The analysis involved several stages, including text preprocessing, term weighting with TF-IDF, sentiment classification into three categories (positive, negative, and neutral), and performance evaluation of the model. The findings reveal that most user reviews express positive sentiment, with frequently appearing words such as "easy,” "secure,” and "good.” Negative sentiments are commonly represented by words like "complicated,” "lag,” and "slow,” while neutral reviews often include expressions such as "adequate” and "okay.” The SVM model achieved strong performance, with an accuracy of 96%, precision of 97%, recall of 75%, and an F1-score of 79%, indicating high effectiveness despite limited sensitivity to minority classes. These classification results can provide valuable insights for identifying areas that need improvement or should be maintained by the application developers. Therefore, this study is expected to serve as a reference for strategic decision-making based on user feedback to enhance the quality of digital banking services.
Application of K-Means Clustering Algorithm to Obtain Recommendations for Strategies to Increase the Number of Students in the Information Systems Study Program at ITB Ahmad Dahlan Jakarta Diana Yusuf; Xie Guilin; Deng Jiao
Journal of Computer Science Advancements Vol. 1 No. 4 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v1i4.581

Abstract

The rapid development of technology today has almost touched all sectors of life such as the economy, health and education. The technology currently used produces a lot of data every day, one of which is in the field of education. Data mining is a group of methods used to investigate and reveal complex relationships in very large data sets. Data here means information organized in a tabular format, as is often used in relational database management. This research uses data from the academic section of ITB Ahmad Dahlan, namely data on students of the Information Systems study program from 2019 to 2022. The attributes that will be used for this research are student gender, student employment status and student achievement index.  Recommendations for promotional strategies to increase the number of new students are to conduct visits to high schools or vocational schools. Not only that, the new student admission team can also promote to companies or offices.
Co-Authors Arif Budimanta Arifin, Arifin Bayangkari Karno, Adhitio Satyo Cakranegara, Pandu Adi Deng Jiao Elliya Sestri Fahrul Razi Fikri Haikal Iwan Setiadi Muhajir Syamsu Nada Kamilia Nanny Mayasari Nofirman, Nofirman Shevti Arbekti Arman Singgih Adhi Prasetyo Uki Masduki Vany Terisia Widi Hastomo Xie Guilin