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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIEET (Journal of Information Engineering and Educational Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Abdi: Media Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Journal of Informatics and Computer Science (JINACS) J-Icon : Jurnal Komputer dan Informatika Journal of Emerging Information Systems and Business Intelligence (JEISBI) Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA) Journal of Renewable Energy and Smart Device
Wiyli Yustanti
Universitas Negeri Surabaya
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Sentiment Analysis and Topic Modeling of Tourist Attractions in Gresik Regency Using the BERT Method Salsabilla Putri Saharani; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.72873

Abstract

Tourism is one of the key sectors in national economic growth as well as a pillar of regional community welfare. Gresik Regency in East Java has considerable tourism potential, with more than 100 destinations covering religious tourism, natural attractions, and family recreation. However, tourist visit data from 2022–2024 shows a declining trend that requires an in-depth evaluation of visitor perceptions and experiences. This study aims to analyze public sentiment toward tourist destinations in Gresik Regency and identify the main topics of concern for tourists. The research data was collected from Twitter and Google Maps within the period of 2021–2024 using crawling techniques. Sentiment analysis was carried out with IndoBERT, while topic modeling was conducted using BERT. The results indicate that tourism reviews are dominated by positive sentiments highlighting the uniqueness of religious destinations, natural beauty, and family recreation atmosphere. However, negative sentiments were also found, emphasizing issues related to facilities, cleanliness, staff services, and accessibility to the sites. Topic modeling successfully grouped tourist opinions into coherent themes, and evaluation with coherence scores demonstrated good quality outcomes. The study concludes that although Gresik has strong tourism appeal, challenges in facility management, services, and digital promotion need to be addressed immediately. The integration of sentiment analysis and BERT-based topic modeling has proven effective in providing comprehensive insights into tourist perceptions and can serve as a basis for formulating regional tourism development strategies.
Sentiment Analysis and Topic Modeling Using BERT And LDA Methods (Case Study of Free Meal Program on Twitter) Siti Mahmudah Putri Yanna; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.75819

Abstract

The Free Meal Program is one of the Indonesian government's strategic efforts to structurally address poverty and malnutrition (stunting). As a new policy with massive social and fiscal impacts, an in-depth evaluation is required to measure public acceptance. This study aims to categorize public sentiment into positive and negative categories and identify the dominant topics discussed on Twitter (X) regarding the program. The methodology involved crawling Twitter data, resulting in a total of 8,307 datasets. Sentiment labeling was performed automatically using the IndoBERT deep learning model, followed by topic modeling using the Latent Dirichlet Allocation (LDA) method for each sentiment category. The results of the topic modeling were validated through topic coherence tests using word instruction task and topic instruction task techniques. The results showed an imbalanced sentiment distribution, with 7,034 negative sentiments and 1,273 positive sentiments. LDA modeling successfully extracted 5 optimal topics for both sentiment categories. Positive sentiments included topics such as budget efficiency, the role of government institutions (National Police), technical implementation, and local economic empowerment. Meanwhile, negative sentiments encompassed concerns regarding state budget (APBN) priorities, health/poisoning issues, and the comparative urgency between the free meal program and the education and health sectors. The coherence test results showed an interpretation accuracy rate of 93% for keywords and 79% for topic relevance, indicating that the developed LDA model was optimal in extracting public opinion.
Sentiment Analysis and Word Association Patterns in Skincare Product Customer Reviews Aliyah Alifi; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.76813

Abstract

The growth of the skincare industry and the increasing activity of consumer reviews on e-commerce platforms have generated large text data containing customer opinions, experiences, and perceptions. This study aims to analyze sentiment and identify word association patterns in Indonesian-language customer reviews of skincare products. The literature review covers sentiment analysis, Natural Language Processing, the IndoBERT language model, Data Mining, Knowledge Discovery in Databases, as well as Association Rule Mining using the Apriori algorithm. The research method uses a quantitative approach based on KDD, which includes Data Selection, Preprocessing, Data Transformation, Data Mining, and interpretation and evaluation. Data was obtained through web scraping of skincare product reviews on the Shopee platform, resulting in 7,320 clean reviews. Sentiment analysis was conducted using IndoBERT with a Hybrid Linguistic approach to handle neutral rating ambiguities. The results of the sentiment classification were then used as the basis for analyzing word association patterns using the Apriori algorithm for each sentiment category. The findings indicate that IndoBERT is capable of classifying sentiment contextually, while Apriori successfully uncovers word patterns that represent product aspects such as quality, effectiveness, and user experience. This study concludes that the integration of sentiment analysis and word association patterns provides a more comprehensive understanding of consumer perceptions and can be utilized as a basis for strategic decision-making in the skincare industry.
Domain Gap and Context Bias Analysis in YOLOv11-Based Housing Condition Detection through Indoor–Outdoor Cross-Domain Evaluation Farendi Giotivano; Wiyli Yustanti; Ricky Eka
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.998

Abstract

Computer vision provides a promising solution to evaluate housing conditions. However, the differences between indoor and outdoor visual environments may cause domain gaps that affect the generalization ability of the model. This study explores the effect of domain gaps on the detection of housing physical conditions using You Only Look Once version 11 (YOLOv11) by proposing an integrated evaluation framework that combines cross-domain evaluation, EigenCAM-based model interpretability, and illumination variation analysis. A total of 2,712 housing images were classified into Indoor, Outdoor and Mixed datasets and evaluated under uniform experimental conditions. From the results, the Indoor model obtained a 42.77% relative mAP50 drop when evaluated on the Outdoor dataset, thus proving the existence of a significant domain gap. The Mixed model achieved the most consistent performance across all evaluation scenarios, although this improvement may also be partially influenced by the larger training dataset used in the Mixed configuration. EigenCAM analysis showed that single-domain models were more reliant on contextual visual cues while the Mixed model was consistently more attuned to relevant housing elements. The illumination analysis showed that the differences in brightness between the domains were not large, suggesting that context bias is more likely than illumination variation to explain the observed performance degradation. These results demonstrate that the combination of cross-domain evaluation with model interpretability provides a more complete understanding of domain gap effects and enables the creation of more robust computer vision models for housing condition assessment.
DIPLOMASI AKADEMIK BERBASIS DATA: KEMITRAAN BADAN PENJAMINAN MUTU UNESA DAN NAKHON PHANOM UNIVERSITY DALAM INTERNASIONALISASI SISTEM PENJAMINAN MUTU INTERNAL Widowati Budijastuti; Bertha Yonata; Bambang Dibyo Wiyono; Jaka Nugraha; Novi Marlena; Ayunita Leliana; Djoko Suwito; Pradini Puspitaningayu; Wiyli Yustanti; Advendi Kristiyandaru
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 12 No. 1 (2026): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v12i1.57142

Abstract

The internationalization of quality assurance systems is now a strategic agenda for Indonesian higher education institutions, particularly since Ministerial Regulation (Permendiktisaintek) No. 39 of 2025 pushed universities to move beyond national standards and toward global ones. Rather than reading as a standard training-evaluation report, this article frames the partnership between the Quality Assurance Agency (BPM) of Universitas Negeri Surabaya (Unesa) and Nakhon Phanom University (NPU), Thailand, as a form of academic diplomacy aimed at strengthening cross-border Internal Quality Assurance Systems (SPMI). The partnership unfolded in three stages, two online sessions followed by one in-person session, transferring Unesa's nationally proven practices in quality audit, monitoring, and Importance-Performance Analysis (IPA)-based survey analysis. The impact evaluation points to strong acceptance of both the program's substance and its facilitation: the average performance score (3.19) exceeded the average importance score (2.79), most indicators fell in the Maintain quadrant, and none landed in the Overinvest quadrant. These findings suggest that a staged online-offline collaboration model is an efficient, sustainable way to disseminate an international quality culture, and one worth replicating at other universities preparing for SPMI internationalization.
Prediksi Kenaikan Jabatan Pranata Komputer pada Kementerian X dengan Menggunakan Model Algoritma Klasifikasi Linear Discriminant Analysis (LDA) Savira Rahmania Putri Ariyanto; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 3 (2023): Vol. 04 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i3.54229

Abstract

Perbandingan Algoritma Klastering dalam Pengelompokan Penjualan Produk Komputer Naufal Aditya Rahman; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 4 No. 4 (2023): Vol. 04 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i4.56532

Abstract

Penelitian ini membandingkan tiga algoritma clustering (K-means, K-Medoids, dan Agglomerative) untuk mengelompokkan data penjualan dari CV. Media Karya Komputindo. Untuk pemilihan jumlah klaster yang paling optimum menggunakan elbow dan divalidasi dengan Silhouette Coefficient. Statistik deskriptif dan Word Cloud digunakan untuk mengevaluasi interpretabilitas dari hasil algoritma clustering yang paling optimum. Efisiensi komputasi juga dipertimbangkan, mengingat sumber daya yang terbatas. Hasil penelitian menunjukkan bahwa semua algoritma menggunakan sumber daya yang sedikit dan mudah digunakan dengan PyCaret. Dari hasil Silhouette Coefficeint, algoritma yang paling optimal untuk kumpulan data ini adalah K-Means, hasil clustering menunjukkan bahwa ada 4 kategori cluster, dimana cluster 1 berisi permintaan rendah dengan harga tinggi, cluster 2 permintaan rendah dengan harga rendah, cluster 3 permintaan tinggi dengan harga rendah, dan cluster 4 adalah permintaan cukup dengan harga rendah. Hasil clustering berhasil membentuk 4 cluster dalam segi harga maupun kuantitas penjualan.
IMPLEMENTASI METODE YOU ONLY LOOK ONCE (YOLOv5) DALAM DETEKSI PELANGGARAN HELM Martinus Ade Meidyan; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024): Vol. 05 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.60517

Abstract

Pelanggaran pada lalu lintas yang di sebabkan oleh pengendara roda dua yang sering di tindak pada saat melakukan operasi patuh pada tahun 2023 mencatat, terdapat tiga pelanggaran terbanyak yang dilakukan oleh kendaraan roda dua. Paling banyak adalah pelanggaran tidak menggunakan helm yaitu sebanyak 8.916 pelanggaran (Made et al., 2020). Tujuan penelitian ini adalah menghasilkan sistem deteksi kendaraan berdasarkan kelasnya melalui analisis video berbasis algoritma YOLOv5. Metode yang disajikan dalam penelitian ini berfokus pada optimasi dan implementasi algoritma YOLOv5 untuk mendeteksi objek berupa helm pada pengendara roda dua pada saat berkendara, menggunakan dataset berisi 2000 gambar, dengan 1200 gambar untuk pelatihan dan 800 gambar untuk pengujian. Pelatihan dilakukan hingga mencapai langkah 200 epoch dengan batch 48 dengan ukuran gambar 448. Hasil penelitian dan uji coba berdasarkan eksperimen yang penulis lakukan, penulis berhasil mencapai nilai F1 Score sebesar 0.87 dan nilai mAP 0.90 menggunakan algoritma YOLOv5 dengan arsitektur YOLOv5m. Adanya beberapa faktor yang memengaruhi hasil deteksi adalah latar belakang objek pada gambar, posisi objek, terdapat objek penghalang pada sudut tertentu, serta tinggi/jarak objek.
Cat Skin Disease Detection System Using You Only Look Once (YOLO) v8 Algorithm: Sistem Deteksi Penyakit Kulit Kucing Menggunakan Algoritma You Only Look Once (YOLO) v8 Bunga meilita; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 2 (2024): Vol. 05 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i2.60656

Abstract

Kucing adalah hewan peliharaan yang popular di Indonesia, dengan jumlah populasi mencapai 4,80 juta ekor pada tahun 2022. Meskipun menggemaskan dan menyenangkan, kucing rentan terkena penyakit, terutama penyakit kulit speerti jamur. Pemilik hewan masih banyak yang kurang memahami gejala penyakit kulit kucing, sehingga penanganan penyakit tidak tepat yang bisa memperparah kondisi kucing. Solusi untuk mengatasi permasalah tersebut dengan mengimplementasikan algoritma You Only Look Once (YOLO) v8 yang dapat dijalankan secara realtime untuk mendeteksi penyakit kulit kucing jamu, scabies, lain dan sehat melalui aplikasi android. Berdasarkan hasil uji didapatkan Map score sebesar 0.788, precission sebesar 0.727, recall sebesar 0.769, dan F1-Score sebesar 0.75. Hasil pengujian white box berhasil berjalan pada semua test case yang ada. Hasil blackbox testing yaitu aplikasi bisa berjalan sesuai yang diharapkan, selain itu hasil uji pada fitur camera detector dengan pengujian ditiga jarak yang berbeda didapatkan jarak yang paling optimal untuk melakukan pendeteksian penyakit kulit kucing secara real time yaitu 20 cm dengan akurasi pengujian sebesar 0.92. Hasil uji pada fitur import gambar menghasilkan keakuratan sebesar 0.92.
Peramalan Jumlah Incident Information Technology PT XYZ Menggunakan Artificial Neural Network (ANN): Peramalan Jumlah Incident Information Technology PT XYZ Menggunakan Artificial Neural Network (ANN) Dini Amalia; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 3 (2024): Vol. 05 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i3.61037

Abstract

Forecasting is a technique for predicting events that will occur in the future using historical data as a comparison In this research, researchers try to determine the performance of the ANN method for forecasting the number of incidents at PT XYZ and build an application for forecasting the number of incidents at PT XYZ. This research uses the Mean Absolute Percentage Error (MAPE) evaluation metric as the evaluation metric that will be interpreted. The smaller the MAPE value, the better the model architecture. The best model is the model that produces the smallest MAPE value and does not experience underfitting or overfitting conditions. Based on the research results, it was found that all the best models from each model architecture produced a MAPE value of less than 10 and did not experience underfitting or overfitting conditions. Therefore, it can be interpreted that all the models produced are very accurate to be used as incident forecasting models at PT XYZ for the next 4 weeks. The website-based incident forecasting application created to predict the number of incidents for the next 4 weeks using the best model that has been previously saved also produces a MAPE value of less than 10 and does not experience underfitting and overfitting conditions.
Co-Authors Advendi Kristiyandaru Agus Prihanto Ahmad Khoiru Shofa Ainandita Riwipapusa Aliyah Alifi Amalia Putri Andhika Edo Prasetyo Anita Qoiriah Aulia Anisa Puji Rahayu Ayunita Leliana Bambang Dibyo Wiyono Bertha Yonata Bunga meilita Calycha Kurniasari Dhiany Hana Amalia Dini Amalia Djoko Suwito Ervin Yohannes Farendi Giotivano Fatma Abiyya Fawasi Hapsari Peni Agustin Tjahyaningtijas Irhamah - Jaka Nugraha Martinus Ade Meidyan Miftakhul Alief Faturrahman Mohammad Hasan Machfoed Muhammad Dafi Bagas Muwirotul Hasanah Nabiilah Winda Kurnia Putri Nabila Putri Listyanto Naim Rahmawati Naim Rochmawati Naufal Aditya Rahman Novi Marlena Nur Iriawan Octania Sriwahyuni Pradini Puspitaningayu Rahadian Bisma Ricky Eka Ridhotul Maulidia Salsabilla Putri Saharani Savira Rahmania Putri Ariyanto Siti Mahmudah Putri Yanna Stevi Aprilianti Cahyani Sultan choirullah Susi Purwani Tama Priyadi WIDOWATI BUDIJASTUTI Windy Chikita Cornia Putri Yoyok Prastyo Yuni Yamasari