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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika Scientific Journal of Informatics POSITIF JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Algoritma
S, Vinna Rahmayanti
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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PENGEMBANGAN SISTEM INFORMASI DAN MAJALAH DIGITAL DI PIMPINAN CABANG MUHAMMADIYAH LAWANG Nastiti, Vinna Rahmayanti Setyaning; Akbi, Denar Regata
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Volume 5 No 1 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i1.24743

Abstract

Pimpinan Cabang Muhmmadiyah (PCM) Lawang merupakan salah satu organisasi keagamaan yang memiliki peran penting dalam meningkatkan kualitas dakwah di Lawang. Salah satu upaya yang dilakukan oleh PCM Lawang adalah memanfaatkan teknologi informasi dan komunikasi (TIK). Namun, PCM Lawang belum memiliki website untuk dokumentasi kegiatan-kegiatan dakwah. Tim pengabdi melakukan pengabdian kepada masyarakat di PCM Lawang dengan tujuan untuk mengembangkan system informasi dan memberikan pelatihan pembuatan majalah digital. Sistem informasi berbasis website tersebut berfungsi untuk website resmi kepengurusan PCM Lawang dan majalah digital berfungsi untuk dokumentasi kegiatan dari kegiatan dakwah PCM Lawang. Hasil dari pengabdian ini adalah prototype system informasi PCM Lawang dan pelatihan konten majalah digital yang diikuti oleh perwakilan dari PCM Lawang.
Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree Nadya, Fhara Elvina Pingky; Ferdiansyah, M.Firdaus Ibadi; Nastiti, Vinna Rahmayanti Setyaning; Aditya, Christian Sri Kusuma
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48145

Abstract

Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.
Pendekatan Linguistik dalam Klasifikasi Emosi Depresi untuk Deteksi Dini Kesehatan Mental di Reddit Fitriyani, Annisaa Salsabila Shafiyyah; Setyaning Nastiti, Vinna Rahmayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2927

Abstract

In the digital era, social media has become a primary means for individuals to express emotions, including symptoms of depression. Posts reflecting feelings of despair and loneliness are increasingly common, particularly on platforms like Reddit. This phenomenon underscores the importance of automatically detecting depressive emotions at an early stage through technology-based approaches, to mitigate negative impacts on mental health. This study employs three linguistic approaches—Lexical Base, WordNet, and GLUE—to enrich semantic understanding and enhance model performance in multilabel classification of depressive emotions. A total of 6,037 text data points were used and split into training, validation, and test sets with a ratio of 70%:15%:15%, following initial processing and linguistic preprocessing stages. Evaluation was conducted using precision, recall, and F1-score metrics on both macro and micro averages. Overall, the study indicates that while linguistic approaches such as Lexical Base, WordNet, and GLUE can enrich text representation, their performance does not always surpass BERT without preprocessing. This suggests that the effectiveness of integrating linguistic information is highly dependent on data context, and further research could explore combining it with multimodal data or advanced mechanisms such as attention to improve depressive emotion classification performance. These findings have potential applications in AI-based mental health monitoring systems, such as chatbots or early detection platforms, to assist in automatically identifying depression symptoms in social media users.
EVALUASI USABILITY DAN REKOMENDASI PERBAIKAN WEBSITE SIP BRO MENGGUNAKAN METODE SUS DAN THINK ALOUD Naila Hidayah, Tia Cahyani; Dwi Wahyuni, Evi; Rahmayanti, Vinna
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 11 No 2 (2025): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v11i2.14338

Abstract

In terms of governance, websites have a strategic function as tools that support government activities, where their existence plays a role in digital engagement with the public. One of the government agencies in Blora City, namely the Regional Development and Planning Agency (BAPPEDA), has utilized and implemented an administrative website in the field of research and development, called SIP Bro. Based on observations, after the implementation of the SIP Bro website, there are still issues or weaknesses identified. So far, there have been no efforts to conduct a minimum evaluation to assess its Usability and whether the intended goals are achieved. Therefore, this study aims to determine the Usability analysis scores of the SIP Bro website using the System usability scale method. To further enhance the effectiveness of evaluating and developing the SIP Bro website, this research also incorporates the Think Aloud approach. The research findings conclude that the Usability score of the SIP Bro website obtained a score of 61.071, which falls under the "Ok" category, supported by a grade scale value in the D range and acceptability ranges categorized as marginal low, indicating that the website is acceptable but with a relatively low level of acceptance. The final analysis of the Think Aloud method resulted in 23 recommendations for improvement. The recommendations made are to enhance and develop the SIP Bro website for better performance in the future.
Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status Vinna Rahmayanti Setyaning Nastiti; Yufis Azhar; Riska Septiana Putri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4868

Abstract

This study aims to classify COVID-19 patients based on the results of their hematology tests. Hematology test results have been shown to be useful in identifying the severity and risk of COVID-19 patients. Specifically, this study focuses on classifying COVID-19 patients based on their vital status, namely Deceased and Alive. The dataset used in this study contains four variables: white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), and Neutrophil Lymphocyte Ratio (NLR). Logistic Regression algorithm was used to solve the problem, and hyperparameter optimization was implemented to obtain the best model performance. The objective of this study was to build the best parameter in classifying the patients’ vital status. The proposed model achieved an accuracy score of 78%, which is the best performance among the tested models. The results of this study provide a key component for decision making in hospitals, as it provides a way to quickly and accurately identify the vital status of COVID-19 patients. This study has important implications for managing the COVID-19 pandemic and should be of interest to researchers and practitioners in the field.
English English Pratama, Farriel Arrianta Akbar; Arief, Muhammad Eka Nur; Nastiti, Vinna Rahmayanti Setyaning
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28346

Abstract

The exponential growth of scientific literature poses a significant challenge for manually identifying thematic trends, necessitating automated analysis methods. This study aims to determine an optimal topic modeling pipeline by conducting a comparative analysis to maximize the coherence of topics extracted from scientific research. Three distinct pipelines were implemented and evaluated on a corpus of 20,972 scientific article abstracts. These included a custom pipeline combining SBERT, UMAP, and HDBSCAN; a second configuration using RoBERTa, PCA, and KMeans; and a third using the integrated BERTopic model. Performance evaluation, quantitatively benchmarked using the C_v coherence score, revealed that the integrated BERTopic model achieved the highest score of 0.7012. This result significantly surpassed the custom SBERT-based pipeline and the RoBERTa-based pipeline, which scored 0.6079 and 0.4756, respectively. The findings demonstrate that an integrated, purpose-built model like BERTopic is superior for generating highly coherent and interpretable thematic structures from scientific text. This research provides empirical guidance for researchers, benchmarking how integrated models offer a more robust solution for large-scale literature analysis compared to modular pipeline designs.
IMPLEMENTATION OF TRANSFORMER MODEL FOR FINE-GRAINED EMOTION DETECTION ON SOCIAL MEDIA "X" Nadhira Ulya Nisa; Vinna Rahmayanti Setyaning Nastiti
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4316

Abstract

Deteksi emosi secara fine-grained pada teks media sosial merupakan salah satu tantangan dalam bidang pemrosesan bahasa alami (Natural Language Processing/NLP), terutama karena sifat data yang tidak terstruktur dan multi-label. Penelitian ini bertujuan untuk mengevaluasi performa tiga model berbasis arsitektur Transformer, yaitu EmoBERT, RoBERTa, dan EmoRoBERTa, dalam tugas klasifikasi emosi pada teks dari dataset SenWave. Dataset ini terdiri dari 10.001 tweet berbahasa Inggris yang telah dilabeli ke dalam sepuluh kategori emosi, namun penelitian ini berfokus pada empat label utama: anxious, annoyed, empathetic, dan sad. Proses penelitian meliputi prapemrosesan data, tokenisasi, pembagian data latih dan uji, pelatihan model, serta evaluasi menggunakan metrik akurasi, presisi, recall, dan f1-score. Hasil evaluasi menunjukkan bahwa model EmoBERT dan EmoRoBERTa memiliki performa terbaik dengan nilai f1-score sebesar 0,81, sedangkan RoBERTa memperoleh nilai f1-score sebesar 0,73. Temuan ini menunjukkan bahwa penyesuaian arsitektur Transformer khusus untuk emosi dapat meningkatkan akurasi klasifikasi emosi pada teks media sosial.
Co-Authors Abdy Yoga Syantara Achmad Rizal Yogaswara Adnan Burhan Hidayat Kiat Ahmad Junjung Sudrajad AINA, LISTYA NUR Akbi, Denar Regata Amelia Deastu Amelia Dwi Deastu Andriani Eka Pramudita Anhar Aria Maulana Arief, Muhammad Eka Nur Asty Try Yuliandari Bagas Aji Aprian Bagus Wicaksono Basuki, Setio Bella Chintia Merita Chintia Eka Merita, Bella Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dede Nor Alfiansyah Dewi Aprilitasari Diah Risqiwati Dini Tri Purwaningsih Dirfas, Naufal Angling Evi Dwi Wahyuni Ferdiansyah, M.Firdaus Ibadi Fitriyani, Annisaa Salsabila Shafiyyah Gian Bagus Sadewa Giffari Zakawaly Gita Indah Marthasari Hilman Hilman Imsa Nur Dwijayanti Kenanga, Larynt Sawfa Lailiyah, Asmaul Lidya Fankky Oktavia Putri Maryaeni Maryaeni Muhammad Gofur Muhammad Rivaldi Asyhari Muhammad Robby Fathuda Muhammady, Dignifo Nauval Nadhira Ulya Nisa Nadya, Fhara Elvina Pingky Naila Hidayah, Tia Cahyani Naila, Tia Cahyani Naufal Adiftya Falah Novita Daian Marlissa Nugraha, Haidar Aldy Eka Nur Hayatin Nuryasin, Ilyas Oktaviani, Chintya Tria Diana Pratama, Farriel Arrianta Akbar Pratama, Ramadan Qori Raditya Damayanti Ramadan Pratama Reikisyifa, Clarissa Sanindita Rifky Ahmad Saputra Riska Septiana Putri Saputri, Indah Sari, Zamah Setiawan, Muhamad Jodi Sirril Asrory Titin Eka Puspitawati Wahyu Andhyka Kusuma Wildan Amru Hidayat Wildan Suharso Wiyono, Briansyah Setio Yufis Azhar Zamah Sari