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Dewi Fatmarani Surianto

Universitas Negeri Makassar

Author-ID : 4084971

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Implementasi Kriptografi Vigenere Cipher untuk Keamanan Data Informasi Desa Erva Irianti; Dewi Fatmarani Surianto; Ainun Zahra Adistia; Muh. Juharman; Jumadil Ahmad Safi’i
Progressive Information, Security, Computer, and Embedded System Vol. 1, No. 1 Maret (2023)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v1i1.24

Data security on village information is a very important issue because it contains a lot of village information data. Important data cannot be denied that data can be edited or changed by unauthorized persons. So the data becomes insecure. Therefore, a technique is needed to secure data, namely using the classic Vigenere cipher cryptography technique, to maintain the security and confidentiality of messages or information so that it cannot be read by anyone, a desktop-based application is designed using the Java programming language which is carried out on the Netbeans IDE software to implementing the vigenere cipher algorithm as a technique for securing village information.

Evaluating Synonym Augmentation Impact on SBERT Performance for Indonesian Social Media Style Classification Jessicha Putrianingsih Pamput; Aindri Rizky Muthmainnah; Dewi Fatmarani Surianto; Nur Azizah Eka Budiarti; Abdul Wahid
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2580

Language on social media reflected the identity and characteristics of its users, including differences in language style between generations. Millennials and Generation Z were two dominant demographic groups in digital communication that exhibited linguistic variations, which often caused gaps in understanding during online interactions. Variations in language structure and expression posed challenges in understanding the context of cross-generational communication. Therefore, this study aimed to classify linguistic styles across generations in social media texts by combining Sentence-BERT (SBERT). FastText-based synonym augmentation in Indonesian, and Support Vector Machine (SVM) as a margin-based classification model that utilizes embedding representations from SBERT. The results showed that synonym augmentation improved model accuracy from 85% to 93%, with a similarity threshold of 0.7 providing the best balance between data diversity and semantic consistency. These findings confirmed that synonym-based augmentation and SBERT semantic adaptation were effective in capturing generational linguistic differences in informal Indonesian. This approach had the potential to be applied in other NLP tasks that required contextual understanding of social language variation, such as sentiment analysis and cross-generational dialect detection.

Langkah-Langkah Bijak di Era Digital: Pelatihan Dasar Keamanan Data Pribadi bagi Masyarakat Fadhlirrahman Baso; Andika Isma; Nurul Fadhilah; Muhammad Fajar B; Dewi Fatmarani Surianto
Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Volume 1 Issue No. 1: Juli 2023
Publisher : Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/kreativa.v1i1.202310

Dalam era digital, keamanan data pribadi menjadi aspek krusial yang memerlukan perhatian serius, terutama dengan kemajuan teknologi yang terus berkembang. Artikel ini mengulas hasil kegiatan pengabdian masyarakat di Dusun Matteko, Desa Erelembang, Kecamatan Tombolo Pao, Gowa, yang bertujuan meningkatkan literasi keamanan data pribadi masyarakat. Metode pelaksanaan mencakup observasi, pemberian materi, diskusi, dan penutup. Observasi awal membantu tim memahami kebutuhan masyarakat dan mengidentifikasi risiko keamanan data. Pemberian materi dilakukan secara interaktif, mengenalkan konsep keamanan data pribadi dengan contoh konkret, dan mendorong partisipasi aktif peserta. Kendala utama dalam pelaksanaan adalah ketidakmerataan koneksi internet di Dusun Matteko, yang diatasi dengan pembimbingan tambahan dan evaluasi menyeluruh. Hasilnya mencerminkan peningkatan pemahaman masyarakat terkait keamanan data pribadi, meskipun tantangan koneksi internet. Kesimpulan menyoroti keberhasilan kegiatan dalam menciptakan lingkungan kolaboratif, memberikan pengetahuan bermanfaat, dan meningkatkan kapasitas masyarakat dalam mengelola keamanan data pribadi secara efektif. Upaya lebih lanjut perlu dilakukan untuk mengatasi kendala teknologi demi kesinambungan pemahaman masyarakat dalam era digital.

Pendampingan Literasi Riset Sosial bagi Mahasiswa Baru Ilmu Komunikasi Universitas Hasanuddin Nurul Fadhillah S; Nurjayanti Nurjayanti; Dewi Fatmarani Surianto; Sri Riski Wulandari; Rahmat Kurniawan
Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Vol. 4 No. 3 (2026): April 2026
Publisher : LPPM Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/madani.v4i3.12121

Kegiatan pengabdian ini bertujuan memperkenalkan literasi riset sosial kepada mahasiswa baru Program Studi Ilmu Komunikasi Universitas Hasanuddin melalui workshop dan presentasi interaktif. Latar belakang pengabdian ini didasarkan pada kebutuhan untuk membangun kesiapan berpikir kritis, kemampuan membaca data, dan keberanian mengajukan pertanyaan penelitian sejak tahap awal studi. Metode yang digunakan adalah workshop partisipatif yang melibatkan ceramah interaktif, presentasi PowerPoint, diskusi terbuka, dan sesi tanya jawab, dengan partisipasi aktif mahasiswa dan koordinasi dengan pengurus Korps Mahasiswa. Hasil kegiatan menunjukkan peningkatan keterlibatan mahasiswa, pemahaman awal terhadap konsep riset sosial, serta kemampuan mereka untuk menghubungkan teori dengan praktik nyata. Pengabdian ini juga berhasil menumbuhkan kesadaran reflektif dan partisipatif, membuka ruang dialog, dan membangun fondasi budaya akademik berbasis bukti yang lebih kokoh. Kegiatan ini menjadi intervensi awal yang efektif untuk mempersiapkan mahasiswa menghadapi proses penelitian lebih lanjut dan membentuk sikap akademik yang proaktif.

Outlier-Aware Clustering for Mapping Computer Science Students’ Career Readiness: A Hybrid DBSCAN–K-Means Approach Eriyani, Nindy Sri; Surianto, Dewi Fatmarani; Nurhidayat; Salam, Fitria Nur Dina; Muliadi
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28865

Career readiness assessment increasingly requires competency-based profiling beyond academic achievement, yet conventional clustering methods remain vulnerable to outliers that distort student classification. This study proposes an outlier-aware hybrid DBSCAN–K-Means framework to map the career readiness of computer science students using eight non-academic competency dimensions. Data were collected from 566 students across 21 Indonesian universities using a validated 35-item questionnaire covering leadership, collaboration, time management, self-directed learning, goal setting, adaptability, problem-solving, and technical skills. Cluster quality was evaluated using the Elbow Method, Silhouette Score, and Davies–Bouldin Index. DBSCAN identified 113 outliers (19.96%), and removing these observations improved clustering performance, increasing the Silhouette Score from 0.292 to 0.326 while reducing the Davies–Bouldin Index from 1.229 to 1.122. The hybrid approach identified four meaningful career readiness profiles, including highly prepared students, students requiring competency development, critically underprepared outliers, and exceptionally high-performing outliers overlooked by conventional clustering. These findings demonstrate that outlier-aware clustering produces more robust competency profiles and provides a replicable analytical framework for evidence-based career development strategies in higher education.

Optimizing Student Graduation Prediction Using XGBoost with SMOTE-ENN, Hyperparameter Tuning, and Threshold Adjustment Fadillah, Nur; Safira, Wahyuni Edsa; Suherman, Muhammad Ilham; Surianto, Dewi Fatmarani; Zain, Satria Gunawan
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28867

Predicting students at risk of delayed graduation is essential for enabling timely academic intervention, yet educational datasets are often characterized by class imbalance that limits predictive performance. This study proposes and evaluates an optimized XGBoost framework that integrates SMOTE-ENN, hyperparameter tuning, and decision threshold adjustment for student graduation prediction. A quantitative machine learning approach was conducted using academic records from 315 alumni across multiple Indonesian universities. Six classification algorithms were systematically compared to identify the most suitable baseline model before optimization. Model performance was assessed using multiple classification metrics to ensure comprehensive evaluation. The findings demonstrate that XGBoost consistently outperformed the competing algorithms and achieved its strongest predictive performance after integrating all three optimization strategies. Compared with applying each optimization technique individually, the combined framework produced more balanced classification results, improved minority-class recognition, and reduced prediction bias caused by imbalanced data. Feature analysis further revealed that academic variables, particularly cumulative grade point average, accumulated credits, and course repetition history, were the strongest predictors of timely graduation, whereas social and non-academic variables contributed comparatively less. These findings provide an effective and replicable machine learning framework for early identification of students at risk of delayed graduation and offer practical support for data-driven academic intervention and decision-making in higher education

Students Understanding of Network Virtualization in Computer Network Courses at Universitas Negeri Makassar Afrisal Arifin; Muh. Alief Rezky Anisar; Fadhlirrahman Baso; Salsa Dillah; Muliadi Muliadi; Dewi Fatmarani Surianto; Jumadi Mabe Parenreng
EDUKASIA Jurnal Pendidikan dan Pembelajaran Vol. 6 No. 2 (2025): Edukasia: Jurnal Pendidikan dan Pembelajaran
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v6i2.1653

Students’ understanding of network virtualization concepts in Computer Network courses remains limited, particularly in institutions with restricted access to advanced practical facilities. This study aims to analyze students’ comprehension of network virtualization and its relationship with learning experiences, technical skills, and perceptions of simulation tools. A quantitative cross-sectional survey was conducted with 202 students who had completed the Computer Network course and gained experience using Cisco Packet Tracer. The questionnaire was validated through expert judgment and construct validity testing via Exploratory Factor Analysis, and its reliability was confirmed. Findings indicate that four dimensions conceptual understanding, learning experience, technical skills, and perception of simulation tools consistently form a framework explaining students’ understanding of virtualization. Correlation analysis revealed that technical skills exert the strongest influence, while learning experiences and perceptions contribute additional support to conceptual comprehension. These results highlight the importance of integrating simulation media such as Cisco Packet Tracer into Computer Network courses, not only as a means of developing practical skills but also as a strategy to enhance students’ conceptual grasp of virtualization technologies increasingly demanded in the digital industry. This study contributes to the literature by emphasizing the cognitive dimension of simulation-based learning, an aspect often overlooked in prior research that mainly focused on technical performance.

Article Retrieval And Automatic Summarization System Using BERT-Based Neural Network Model On Chatbot Awaluddin, Muhammad Ghazali; Aksa, Muhammad; Arifky, Reza; Bakri, Muhammad Fajar; Surianto, Dewi Fatmarani; Edy, Marwan Ramdhany; Zain, Satria Gunawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4463

The rapid growth of online scientific publications presents challenges in efficiently filtering relevant information. Many search systems still rely on keyword matching, which is often ineffective in understanding the context of user queries. This study develops a chatbot system based on BERT (Bidirectional Encoder Representations from Transformers) for scientific article retrieval and automatic summarization. The system is designed to comprehend user intent and generate summaries of relevant articles. The evaluation was conducted on a dataset of 506 scientific articles, assessing search accuracy based on topic, abstract, author name, and time range. Results show 100% accuracy in searches by author and abstract, with varying performance in topic-based and time-based searches. This system is expected to enhance the efficiency and relevance of scientific literature retrieval and support the productivity of researchers across various fields.

Performance Comparison of SVM in Sentiment Analysis of Israel-Palestine Comments Using Lsa and Word2vec Akbar, Muh. Arsan; Syam, Abd. Azis; Al Amanah, Muh. Nur Hidayat; Risal, Andi Akram Nur; Surianto, Dewi Fatmarani; Budiarti, Nur Azizah Eka; Wahid, Abdul
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4601

This study compares two feature extraction techniques, namely Latent Semantic Analysis (LSA) and Word2Vec, in the sentiment classification of comments related to the Israeli-Palestinian conflict using Support Vector Machine (SVM). The dataset consists of 1000 YouTube comments and 158 news paragraphs, categorized into pro and con Palestinian sentiments. The preprocessing process includes casefolding, special character and stopword removal, lemmatization, and tokenization. The results show that SVM with Word2Vec has better performance than SVM with LSA in the classification of positive and negative comments. SVM model with Word2Vec recorded a precision value of 92% and F1-Score of 93% on negative comments. Meanwhile, SVM with LSA recorded 90% precision and 92% F1-Score. On positive comments, SVM with Word2Vec recorded 92% recall and 93% F1-Score. While SVM with LSA recorded 89% recall and 91% F1-Score. Word2Vec's strength lies in its ability to capture word context and nuance more effectively, thanks to training using richer contextualized comment and news data. In conclusion, although both methods show good ability in sentiment classification, the use of Word2Vec provides more consistent and accurate results. This research contributes to the advancement of sentiment classification methods in the context of complex socio-political issues and can serve as a reference for applying machine learning to more accurate and contextual public opinion analysis.

Improving Sentiment Classification of Kredit Pintar Reviews Using IndoBERT, SMOTE, and Stacking Ensemble Safitri, Ayu; Risaldi, Muhammad; Alwi, Muh Naufal Ramadhani; Surianto, Dewi Fatmarani; Fadilah, Nur; Parenreng, Jumadi M
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5342

Kredit Pintar is one of the most widely used fintech applications in Indonesia, generating millions of user reviews on the Google Play Store that reflect diverse user experiences. These reviews provide valuable insights into application performance; however, extracting sentiment from such unstructured and imbalanced textual data remains a challenging task. This study aims to improve sentiment classification of Kredit Pintar user reviews by proposing a hybrid approach that integrates IndoBERT, SMOTE (Synthetic Minority Over-Sampling Technique), and a stacking ensemble model. From 2020 to 2024, 2,278 user reviews were classified into positive, neutral, and negative categories based on star ratings. SMOTE was employed to rectify class imbalance, whereas IndoBERT gathered contextual representations of the Indonesian language. Furthermore, a stacking ensemble combining IndoBERT, Random Forest, and SVM (Support Vector Machine) was implemented to enhance classification performance. Experimental results show that IndoBERT without data balancing achieved an accuracy of 84%, whereas the proposed combination of IndoBERT, SMOTE, and stacking ensemble consistently produced superior performance, achieving 92% accuracy, precision, recall, and F1-score. The findings demonstrate that integrating language-specific transformer models with data balancing and ensemble techniques effectively improves sentiment classification. This study contributes to the advancement of Indonesian-language natural language processing in the fintech domain and provides practical insights for fintech developers in understanding user perceptions and improving digital financial services.

Co-Authors A. Arianugerah Ilham AA Sudharmawan, AA Abd. Rahman Patta Abdal, Nurul Mukhlisah Abdul Muis Mappalotteng Abdul Wahid Abdul Wahid Adiba, Fathiah Adiba, Fhatiah Adnas, Diny Anggriani Afrisal Arifin Agusyana, Nurrahmah Ahmar, Ansari Saleh Ahnaf Riyandirga Ariyansyah Putra Helmy Aindri Muthmainnah Aindri Rizky Muthmainnah Ainun Zahra Adistia Akbar, Mohammad Arsan Akbar, Muh. Arsan Akmal Hidayat Akmal Hidayat Akmal, Muhammad syafruddin Aksa, Muhammad Al Amanah, Muh. Nur Hidayat Alwi, Muh Naufal Ramadhani Amanda Putri Lestari Amiruddin Amri, Muh. Aidil Amukune, Stephen Ana Sulistiana Alwi Andi Akram Nur Risal Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Andi Dio Nurul Awalia Andi Tenri Ola Rivai Andi, Tenriola Andika Isma Anwar Wahid Arifiyanti, Fitria Arifky, Reza Asis Nojeng Asmaul Husnah Nasrullah Asri Ismail Athiyyah Anandira Awalia, Andi Dio Nurul Awaliah, Widiarti Awaluddin, Muhammad Ghazali Ayu Hasnining Ayu Safitri Ayu Safitri Azis, Putri Alysia Azzah Ulima Rahma B., Muhammad Fajar Bahar, Muhammad Mahdinul Bakri, Muh. Fajrin Baso, Fadhlirrahman Cahyana Resky, Andi Aulia Clarisha, Windi Dary Mochamad Rifqie Della Fadhilatunisa Dhaffa Mulya Rahman Dilla, Salsa Dillah, Salsa Dwi Rezky Anadari Sulaiman Edi Suhardi Rahman Edy, Marwan Ramdhany Eriyani, Nindy Sri Erva Irianti Fadhlirrahman Baso FADIAH, NUR Fani, A. Astri Merilsa Fathahillah Fhatiah Adiba Fhatiah Adiba Firdaus Firdaus Firdaus Fitriani Dzulfadhilah Fitriyanty Dwi Lestary Fizar Syafaat Furqan Ali Yusuf Haekal Febriansyah Ramadhan Hanum Zalsabilah Idham Hardy M, Galang Hartini Ramli Hidayat M., Wahyu Ilyas, Sitti Nurhidayah Indanasufya Indanasufya Inez Sri Wahyuningsi Manguling Irwandi Ivan Fadillah Akram Iwan Suhardi Jariah S.Intam, Rezki Nurul Jariah, Rezki Nurul Jasruddin Jessicha Pamput Jessicha Putrianingsih Pamput Jumadi Mabe Parenreng Jumadil Ahmad Safi’i Jusniar . Khaerunnisa Nur Fatimah Syahnur Khalil Mubaraq Darwing Kurnia Prima Putra Lapendy, Jessica Crisfin Lutfiah Tri Syahyaningsih Lutfiah Tri Syahyaningsih M. Miftach Fakhri M. Syahid Nur Wahid Makmur, Haerunnisya Mappangara, Surianto MARDIAH, AINA Marwan Ramdhany Edy Meisaraswaty Arsyad Muh. Alief Rezky Anisar Muh. Juharman Muhammad agung Muhammad Akil Musi Muhammad Ayat Hidayat Muhammad Fadhil Hani Muhammad Fadhullah Muhammad Fahrul Rosi Ishaq Muhammad Fajar B Muhammad Fardan MUHAMMAD ILHAM Muhammad Nur Yusri Muhammad Rafli Aditya H. Muhammad Rakib Muhammad Try Dharsana Muharni Muharni Muhtadi, Nashiruddin Sahal Muliadi Muliadi Muliadi Musda Rida Mulia Muthmainnah, Aindri Muthmainnah, Aindri Rizky Mutmainnah R Nafil Rizqullah Rajab Nashiruddin Sahal Muhtadi Nasrullah, Asmaul Husnah Natsir, Nasrah Ninik Rahayu Ashadi NIRMALA, PUTRI Nur Fadiah NUR FADILAH Nur Fadilah NUR FADILLAH Nur Fadillah, Nur Nur Isma Nur Risal, Andi Akram Nurhidayat Nurhidayat Nurhidayat Nurjannah Nurjannah Nurjayanti Nurjayanti Nurrahmah Agusnaya Nurul Fadhilah Nurul Fadhillah S Nurul Fadhillah S Nurul Mukhlisah Abdal Pamput, Jessicha Pamput, Jessicha Putrianingsih Parenreng, Jumadi M. Putri Nanda Sari Putri Nirmala Putri Zhachilia Susanto Raden Mohamad Herdian Bhakti Rahman, Dhaffa Mulya Rahmaniar Rahmat Kurniawan Ramadhan, Haekal Febriansyah Resky, Andi Aulia Cahyana Rezki Angriani Pratiwi Kadir Rezki Nurul Jariah Ridwan Daud Mahande Ridwan Daud Mahande Risaldi, Muhammad Rosidah Rosidah Rusli, Risvan S, Aprilianti Nirmala S, Muh. Rizal S, Nurul Fadhillah S.Intam, Rezki Nurul Jariah Safira, Wahyuni Edsa Salam, Fitria Nur Dina Salsa Dillah Sari Wulandari Sasmita Sasmita Setialaksana, Wirawan - Shabrina Syntha Dewi Shasa Inayah Vega Siti Syarifah Wafiqah Wardah Soeharto Soeharto Sri Riski Wulandari Suherman, Muhammad Ilham Surianto, Dewi Fatmawati Syahrul Syahrul Syahrul Syam, Abd. Azis Syamsurijal Syamsurijal, Syamsurijal Tenriola, Andi Udin Sidik Sidin Wahid, M Syahid Nur Wahid, M. Syahid Nur Wahid, Yokogeri Abdullah Wahyu Hidayat M Wahyu Hidayat M Wahyudi Wahyudi Warda Wahyuni Wardani, Ayu Tri WULANDARI Wulandari Wulandari Zsolt Lavicza Zulfikar, Muh Ihsan Zulhajji, Zulhajji

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