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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik IJISTECH (International Journal Of Information System & Technology) JUTEKIN (Jurnal Manajemen Informatika) METIK JURNAL Building of Informatics, Technology and Science Journal of Information Systems and Informatics Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Media Jurnal Informatika Bulletin of Information Technology (BIT) J-Icon : Jurnal Komputer dan Informatika Sasambo: Jurnal Abdimas (Journal of Community Service)
Wahyuni - Wahyuni
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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Transformasi Digital UMKM Budidaya Ikan Rundan Ali Sejahtera untuk Pengelolaan Manajemen dan Peningkatan Produktivitas Wahyuni Wahyuni; Pitrasacha Adytia; Rizky Zakariyya Rasyad; Yunita Yunita
Sasambo: Jurnal Abdimas (Journal of Community Service) Vol. 7 No. 1 (2025): February
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/sasambo.v7i1.2301

Abstract

Pengabdian kepada masyarakat dengan judul Transformasi Digital UMKM Budidaya Ikan Rundan Ali Sejahtera Untuk Pengelolaan Manajemen dan Peningkatan Produktivitas bertujuan untuk meningkatkan efektivitas dan efisiensi proses bisnis yang terjadi pada UMKM tersebut. Adapun hasil yang diharapkan pada program pengabdian ini adalah tercapainya tujuan untuk menerapkan transformasi digital pada UMKM Rundan Ali Sejahtera untuk pengelolaan manajemen dan peningkatan produktivitas. Transformasi digital yang dilakukan adalah: (i) Pendigitalan proses. (ii) Kolaborasi digital. (iii) Peningkatan keterampilan digital. Sistem manajemen yang dibuat sangat membantu para anggota untuk melakukan pencatatan keuangan. Selain itu sistem juga dapat dimanfaatkan untuk pemesanan tempat dan waktu untuk memancing. Kegiatan ini juga memperkenalkan platform E-Fishery dan sosial media Instagram. Sedangkan sosial media Instagram digunakan sebagai media promosi kolam pemancingan. Dibuatkan pula alat pemberi pakan ikan otomatis yang diberi nama WFish Feeder. Peningkatan keterampilan digital oleh anggota POKDAKAN Rundan Ali Sejahtera rata-rata berkisar antara 20% - 50%. Pertanyaan terkait penggunaan perangkat digital dalam pekerjaan sehari-hari serta kepercayaan diri dalam menggunakan aplikasi online mengalami peningkatan sekitar 25%. Pertanyaan yang terkait dengan akses internet untuk informasi budidaya dan kemampuan mengiklankan produk secara online memiliki persentase peningkatan sekitar 30%. Keterampilan membuat konten digital dan menggunakan aplikasi keuangan sederhana menunjukkan peningkatan sekitar 25%. Digital Transformation of Rundan Ali Sejahtera Fish Farming UMKM for Management and Increasing Productivity Community service entitled Digital Transformation of Rundan Ali Sejahtera Fish Farming UMKM for Management and Increased Productivity aims to increase the effectiveness and efficiency of business processes that occur in the UMKM. The expected results of this community service program are the achievement of the goal of implementing digital transformation in Rundan Ali Sejahtera UMKM for management and increased productivity. The digital transformation carried out is: (i) Process digitization. (ii) Digital collaboration. (iii) Improving digital skills. The management system created is very helpful for members to record finances. In addition, the system can also be used to book places and times for fishing. This activity also introduced the E-Fishery platform and Instagram social media. While Instagram social media is used as a promotional media for fishing ponds. An automatic fish feeder was also made, named WFish Feeder. The improvement of digital skills by members of POKDAKAN Rundan Ali Sejahtera averaged between 20% - 50%. Questions related to the use of digital devices in daily work and confidence in using online applications increased by around 25%. Questions related to internet access for cultivation information and the ability to advertise products online have increased by about 30%. Skills in creating digital content and using simple financial applications have increased by about 25%.
Application of Large Language Model for New Student Admission Chatbot Anwar, Rafidan; Pratiwi, Heny; Wahyuni, W
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.379

Abstract

This study aims to develop a chatbot system based on a Large Language Model (LLM) that provides information related to new student admission in higher education. The system utilizes the SentenceTransformer model to generate embeddings of question and answer texts, as well as FAISS for vector-based search. Additionally, LLAMA is used to generate context-based answers, allowing the chatbot to provide more dynamic and relevant responses. System evaluation is conducted using ROUGE-1, ROUGE-2, and ROUGE-L metrics. The evaluation results show an average ROUGE-1 Precision of 54.89%, ROUGE-2 Precision of 47.37%, and ROUGE-L Precision of 52.72%. The Recall scores for ROUGE-1, ROUGE-2, and ROUGE-L are 89.43%, 74.08%, and 82.91%, respectively
The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression Pratiwi, Heny; Muhammad Ibnu Sa’ad; Wahyuni, Wahyuni; Syamsuddin Mallala
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6112

Abstract

Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model.
Pengembangan Sistem Deteksi Hand Gesture untuk Mempermudah Menghafal Sandi Morse dengan Metode KNN Wahyuni, Wahyuni; Pitrasacha Adytia; Adha Trisna Lidya
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2202

Abstract

Sandi morse adalah teknik komunikasi unik yang masih digunakan dalam berbagai konteks, seperti komunikasi darurat dan amatir radio. Pengendali frekuensi radio di Indonesia sering menghadapi kesulitan dalam menghafal sandi morse. Media pembelajaran sandi morse saat ini masih terbatas pada titik dan garis yang sulit untuk dihafalkan. Penelitian ini mengembangkan sistem deteksi hand gesture menggunakan metode K-Nearest Neighbors (KNN) untuk mempermudah penghafalan sandi morse. Sistem ini memanfaatkan gerakan tangan seperti mengepal dan membuka telapak tangan, untuk mewakili kombinasi titik dan garis dalam sandi morse, dengan harapan membuat proses belajar lebih intuitif dan interaktif. Implementasi sistem dilakukan dengan menggunakan webcam, algoritma Mediapipe, library OpenCV, dan aplikasi Unity. Kemudian model dievaluasi performanya dan serta antarmukanya diuji degan blackbox. Sistem deteksi hand gesture berhasil mengidentifikasi huruf abjad berdasarkan gerakan tangan dengan akurasi minimal 60%. Pengujian lebih lanjut menggunakan KNN dengan nilai K-1, menunjukkan rata-rata akurasi sebesar 81%. Sehinga sistem efektif dalam mendeteksi gerakan tangan untuk mempermudah penghafalan sandi morse. Secara keseluruhan, dengan akurasi rata-rata 81%, sistem deteksi hand gesture ini menunjukkan potensi besar dalam meningkatkan pembelajaran sandi morse secara efektif dan menarik. Kendala utama dalam penelitian ini adalah terbatasnya data partisipan, yang mengakibatkan variasi dalam gerakan tangan dan potensi tumpang tindih antara kelas gerakan. Penelitian ini membutuhkan lebih banyak data untuk meningkatkan akurasi dan mengurangi kesalahan dalam deteksi gerakan. Sehingga, pada penelitian selanjutnya diharapkan peneliti memperbanyak dataset yang digunakan pada deteksi gerakan tangan untuk sandi morse.
Pengembangan Chatbot Berbasis AI untuk Mendukung Pelayanan Perpustakaan Muhammad Ahsanu Qaulan; Wahyuni; Pitrasacha Adytia
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2283

Abstract

Penelitian ini mengembangkan chatbot berbasis kecerdasan buatan (AI) untuk mendukung layanan informasi perpustakaan di STMIK Widya Cipta Dharma. Pengembangan chatbot dilakukan dengan pendekatan CRISP-DM dan teknologi LLM (Llama3.2) yang diintegrasikan melalui metode Retrieval-Augmented Generation. Dataset yang digunakan terdiri dari 11 pasangan pertanyaan-jawaban, kemudian dilakukan proses preprocessing, embedding vektor, dan pencarian dokumen menggunakan FAISS. Evaluasi dilakukan menggunakan metrik BERTScore untuk mengukur kesamaan semantik antara jawaban chatbot dan referensi, dengan hasil rata-rata precision sebesar 0.6513, recall sebesar 0.7924, dan F1-Score sebesar 0.7124. Nilai tersebut menunjukkan bahwa chatbot memiliki kemampuan semantik yang baik dalam menjawab pertanyaan umum terkait layanan perpustakaan, meskipun masih memerlukan pengembangan lebih lanjut untuk meningkatkan akurasi pada pertanyaan yang kompleks.
Analisis Sentimen Pelanggan Kopi Kenangan pada Media Sosial Instagram Menggunakan Metode Lexicon Based Riani Sela; Wahyuni; Ivan Haristyawan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp198-207

Abstract

This study aims to analyze customer sentiment towards Kopi Kenangan on Instagram using a lexicon-based method. The rapid growth of Kopi Kenangan as a local coffee brand in Indonesia has made Instagram a key platform for building brand image and interacting with customers. The main problem faced is the large volume of unstructured comment data that cannot be processed manually, so an efficient and systematic automated approach is needed. This study uses a case study methodology and quantitative descriptive techniques. The object of research in this study is the official Kopi Kenangan Instagram account, and the dataset used to conduct this study comes from that account. The dataset that can be used to test this research will be created by developing processing stages. The Lexicon-Based method is used in this research approach. The purpose of the dictionary-based Lexicon-Based method is to determine the weight of sentences in the dataset to identify sentiment class labels. The data used in this study comes from 1689 records that have been preprocessed to produce 1438 patent records by removing empty comment records and verifying duplicates up to a certain threshold. The next step is to identify comments based on their sentiment: 25 negative, 1347 neutral, and 317 positive. The results show that negative sentiment reached 1,5%, neutral 79.8%, and positive 18.8%. Based on this presentation, the majority of the text examined was consumer responses to Kopi Kenangan's Instagram posts, and most of them were negative. This indicates that the Lexicon-Based model developed is capable of classifying sentiment with good accuracy.
Comparative Performance Analysis of YOLOv12 and RF-DETR in Face Detection David Hendrawan; Wahyuni; Pitrasacha Adytia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1561

Abstract

Face detection in dense and occluded environments remains a significant challenge in computer vision. This study compares the CNN-based YOLOv12 and the Transformer-based RF-DETR to determine the optimal balance between accuracy and latency for resource-constrained edge computing. Using the WIDER FACE dataset and an NVIDIA T4 GPU, multiple model variants were evaluated. Due to GPU memory constraints during training of the RF-DETR Medium variant, a standardized batch size of 8 was implemented across all models. To ensure methodological rigor, quantitative metrics (precision, recall, F1-score, mAP) were strictly assessed on the validation set. Concurrently, a 100-image subset of the test set was used exclusively for inference efficiency benchmarking, completely separate from detection evaluation. Results indicate YOLOv12X achieved superior overall detection performance (F1-score: 0.764, mAP@50:95: 0.440), significantly outperforming RF-DETR Medium. For real-time applications, YOLOv12M demonstrated the highest efficiency (36.17 FPS vs. 23.32 FPS). Qualitatively, YOLOv12 maintained high sensitivity in crowded scenes, whereas RF-DETR provided stable small-scale face detection despite its lower recall. Overall, under these constrained-hardware conditions, YOLOv12 appears to be a highly viable solution for surveillance systems, while RF-DETR offers a stable alternative for small-object detection when computational overhead and training budgets are less restrictive.
Sentiment Analysis of Grab Driver Motorbike User Reviews as a Basis for Service Improvement Recommendations Mario Benediktus Weruin; Ita Arfyanti; Wahyuni Wahyuni
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2837

Abstract

The development of information technology has driven the growth of application-based transportation services, one of which is Grab Motor, which is widely used by the public to meet their daily mobility needs. Service quality is an important factor influencing user satisfaction levels. One source of information that can be utilized to evaluate service quality is user reviews available on digital platforms. This study aims to analyze the sentiment of Grab Motor user reviews as a basis for developing service improvement recommendations. The data used are 200 user reviews obtained from the Google Play Store. The applied method is Lexicon-Based because it is able to identify sentiment polarity without requiring training data. The research stages include data collection, preprocessing consisting of cleansing, case folding, tokenization, normalization, and stopword removal, then continued with the assignment of sentiment scores based on the lexicon dictionary to classify reviews into positive, negative, and neutral categories. The results show that 120 reviews (60%) include positive sentiment, 50 reviews (25%) negative sentiment, and 30 reviews (15%) neutral sentiment. Model evaluation using a confusion matrix yielded an Accuracy of 89%, a Precision of 89%, a Recall of 91%, and an F1-score of 90%. The research findings indicate that the majority of users responded positively to Grab Motor's services, particularly regarding the app's ease of use and speed of service. Meanwhile, negative sentiment was still found regarding fares, pickup delays, order cancellations, and app technical issues. The results of this study are expected to serve as evaluation material and a basis for decision-making in efforts to improve service quality and Grab Motor user satisfaction.
Penerapan Algoritma Naïve Bayes Dalam Analisis sentiment Masyarakat Terhadap STMIK Widya Cipta Dharma Helmelya Putri Jelita; Muhammad Ibnu Sa'ad; Wahyuni
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2029

Abstract

This study applies the Naïve Bayes algorithm to analyze public sentiment toward STMIK Widya Cipta Dharma using Google Maps reviews as the primary data source. The research aims to classify community perceptions into three categories: positive, neutral, and negative. The methodology follows the CRISP-DM framework, incorporating stages such as data preprocessing (text cleaning, stopword removal, and stemming), TF-IDF for feature extraction, and SMOTE to address class imbalance. Sentiment labels were derived from a combination of review ratings (1–5 stars) and textual content. Results indicate that Naïve Bayes achieved 91% accuracy in classifying the majority (positive) class but struggled with minority classes (neutral and negative), yielding 0% precision and recall for these categories. After applying SMOTE, recall for the negative class improved to 100%, although overall accuracy dropped to 38%, reflecting a trade-off between balanced class recognition and model performance. The study highlights the algorithm's effectiveness in handling large-scale text data but underscores challenges in managing imbalanced datasets. These findings provide actionable insights for STMIK Widya Cipta Dharma to enhance service quality and institutional image by leveraging public feedback. Future research could explore hybrid algorithms or advanced preprocessing techniques to optimize sentiment analysis accuracy across all classes.
Penerapan Algoritma Logistic Regression dalam Deteksi Komentar Promosi Judi Online pada YouTube Alyudani; Wahyuni; Rizky Zakariyya Rasyad
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/1mjskk23

Abstract

The growing popularity of video-based social media platforms such as YouTube has significantly increased user interaction through comment sections. However, this high level of activity has also led to the misuse of comment sections to promote online gambling through various writing patterns intended to disguise specific keywords. This study aims to detect online gambling promotional comments on YouTube using a text mining approach. The framework applied in this study consists of several stages, including text preprocessing, keyword-based feature engineering, TF-IDF feature extraction using character n-grams, data balancing through the Synthetic Minority Over-sampling Technique (SMOTE), and classification using the Logistic Regression algorithm. The dataset used in this study consists of 11,972 YouTube comments obtained from a YouTube video comment section and manually labeled into two classes: normal comments and online gambling promotional comments. The evaluation results show that the model achieved a precision of 1.000, recall of 0.912, F1-score of 0.954, and overall accuracy of 0.999 on the test data. These findings indicate that the combination of keyword-based features, character n-gram TF-IDF, SMOTE, and Logistic Regression can effectively detect online gambling promotional comments, particularly in minimizing the misclassification of normal comments as promotional gambling content.
Co-Authors Adha Trisna Lidya Aexsa Alfyano Alyudani Andayani Andayani Anwar, Rafidan David Hendrawan Eka Arriyanti Ekawati, Hanifah Haristyawan, Ivan Helmelya Putri Jelita Heny Pratiwi Hidayatul Muttaqin Ita Arfyanti Ita Arfyanti Ivan Haristyawan Jajang Nurjaman Jhoanne Fredricka Jundro Daud Hasiolan Kelik Sussolaikah Mario Benediktus Weruin Muhammad Ahsanu Qaulan Muhammad Akmal Amrullah Ali Muhammad Fahmi Muhammad Ibnu Sa'ad Muhammad Ibnu Saad Saad Nila Ratna Sari Pitrasacha Adytia Pitrasacha Adytia Reza Andrea Riani Sela Rizky Zakariyya Rasyad Rizky Zakariyya Rasyad Siti Lailiyah Syamsuddin Mallala Theo Buana Pongsumarre Yudha Satria Yunita Yunita Yunita Yunita