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Helda Yenni, Helda
STMIK Amik Riau
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Analisis Performa Penjualan dan Prediksi Omzet dengan Pendekatan Market Basket Analysis Berbasis Data Analytics Ramadhani, Jilang; Efrizoni, Lusiana; Yenni, Helda; Zoromi, Fransiskus
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4788

Abstract

Pesatnya perkembangan bisnis ritel menuntut strategi pemasaran berbasis data untuk meningkatkan performa penjualan dan omzet. Penelitian ini menggunakan Market Basket Analysis (MBA) dengan algoritma Apriori untuk mengidentifikasi pola pembelian konsumen dan Regresi Linear Sederhana untuk memprediksi omzet berdasarkan jumlah transaksi harian. Data transaksi Alfamart Wingky Mart periode Maret–September 2024 dianalisis guna menemukan hubungan antar produk serta tren penjualan. Hasil MBA menunjukkan kombinasi produk Bimoli, Gula, dan Tepung memiliki support 42.16% dan confidence 99.37%, yang dapat dimanfaatkan untuk strategi pemasaran. Model regresi menghasilkan R² sebesar 35.65%, menunjukkan hubungan antara jumlah transaksi dan omzet, meskipun masih terdapat faktor lain yang berpengaruh. Penelitian ini memberikan wawasan strategis bagi bisnis ritel dalam optimasi tata letak produk, promosi bundling, serta peningkatan omzet berbasis analisis data.
Penerapan Augmented Reality Berbasis Marker Based Dalam Membangun Media Promosi Pada SMK Sulthan Muazzam Syah Putri, Rahmi Apriana; Fatdha, T.Sy Eiva; Haryono, Dwi; Yenni, Helda
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 2 (2025): April 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i2.1926

Abstract

In the digital era, school promotion faces challenges in attracting prospective students. Conventional media such as brochures are less effective for generations who are accustomed to digital technology. This research aims to apply Marker Based Augmented Reality (AR) technology as a promotional media innovation at SMK Sulthan Muazzam Syah. The method used is Multimedia Development Life Cycle (MDLC) with six stages, namely Concept, Design, Material Collecting, Assembly, Testing, and Distribution. Implementation is done using MyWebAR, with system testing through black box testing, distance, light, and compatibility testing. The results show that the system functions optimally, with a System Usability Scale (SUS) score of 85, in the Acceptable category, Grade B, and Excellent rating. Integration with Instagram expands the reach of promotion. The application of Marker Based AR increases the effectiveness of information delivery, attracts the attention of prospective students, and provides a better interactive experience than conventional methods. This technology also serves as an innovative solution for other schools in strengthening promotional appeal in the digital era.
INTEGRASI ALGORITMA K-NEAREST NEIGHBORS DAN DECISION TREE UNTUK MEMPREDIKSI HIPERTENSI Aksha, Muhammad Iqbal Al; Yenni, Helda; Erlinda, Susi; Susanti, Susanti
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2306

Abstract

Hypertension is a prevalent health condition and a major risk factor for cardiovascular diseases. Early detection and management are essential to prevent complications. This study aims to optimize the accuracy and stability of hypertension risk prediction by applying a stacked ensemble technique that combines multiple base classifiers—K-Nearest Neighbors (KNN) and Decision Tree (DT)—with Logistic Regression as the meta-learner. The dataset used was imbalanced, thus requiring class balancing with the Synthetic Minority Over-sampling Technique (SMOTE), along with data preprocessing and scaling. The study applies a quantitative approach to train and evaluate models using Python. Results demonstrate that the stacked ensemble model achieves superior performance compared to individual classifiers, with a maximum accuracy of 74.52%. These findings indicate that the combination of different classifiers through ensemble stacking enhances the reliability and predictive capability of hypertension detection models. The approach offers potential value for improving early diagnosis and supporting clinical decision-making.
Opinion Mining on TikTok Using Bidirectional Long Short-Term Memory for Enhanced Sentiment Analysis and Trend Prediction Muharnisa Haspin, Wafiq; Junadhi, Junadhi; Susanti, Susanti; Yenni, Helda
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8019

Abstract

The widespread use of TikTok has generated a vast number of user reviews, offering a rich dataset for sentiment analysis. This study aims to classify TikTok reviews from the Google Play Store into positive, negative, and neutral categories, a complex task due to the informal and unstructured text. The research seeks to develop a reliable sentiment analysis model using deep learning to understand user perceptions, aiding platform improvements and marketing strategies. We collected 10,000 reviews via web scraping, preprocessed through text cleaning, normalization, tokenization, filtering, and stemming. Sentiment labels were assigned automatically using a lexicon-based approach, showing predominantly positive reviews. Word2Vec transformed text into numerical vectors for feature extraction. The Bidirectional Long Short-Term Memory (Bi-LSTM) model, with Embedding, Bidirectional LSTM, Dropout, and Dense layers, achieved 80% accuracy and an F1-score of 0.78 using a 90:10 train-test split. While effective for positive and negative sentiments, neutral expressions were less accurately detected due to lower recall. Compared to traditional methods like Naive Bayes, Support Vector Machine, and K-Nearest Neighbors, Bi-LSTM offered superior accuracy and better handling of linguistic variability, making it valuable for analyzing social media feedback.
Clustering Junior Schools in Implementing Smart School Using The K-Means in Pekanbaru Nisa, Aida; Anam, M. Khairul; Yenni, Helda; Kudadiri, Parlindungan; Gunadi
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 3 No. 3 (2024)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v3.i3.40

Abstract

The purpose of this research is to determine the readiness of schools in implementing the Smart School system through various stages. One of the concepts of a Smart City involves integrating information and communication technology into the learning process at every school to create Smart Schools. However, not all schools are ready to implement this technology because it requires suitable technology to support the quality of teaching and learning. Another issue is the absence of information systems that can facilitate administrative tasks and the teaching and learning process. The use of the K-Means method is beneficial for clustering schools based on their stages, characteristics, and readiness to implement the Smart School system. This helps identify schools with the highest level of readiness. This research demonstrates that the use of K-Means can identify school readiness based on the established stages related to the Smart School system. It also can pique students' interest in developing and boosting the school's reputation as the best technology-based school.
Optimasi Klasifikasi Tingkat Obesitas Pada Remaja Berdasarkan Pola Hidup Menggunakan SVM Dengan Teknik Smote Setiawan, Andri; Yanti, Rini; Ali, Edwar; Yenni, Helda
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.22-2.2509

Abstract

Obesity is a condition caused by an imbalance between energy intake and expenditure, characterized by excessive fat accumulation in the body. Obesity is influenced by four factors, namely genetics, economics, lack of activity, and diet. The purpose of this study is to analyze the effectiveness of the SMOTE method in improving the accuracy of classification in the Support Vector Machine method and to compare the accuracy of the Support Vector Machine method with the SMOTE and non-SMOTE techniques on adolescent obesity data. The dataset used was obtained from the Kaggle website, which contained 2,111 records. The model evaluation used a confusion matrix with accuracy, precision, recall, and F1-score measurements and used 80:20 data splitting. The results showed that the SVM model using Smote performed well with an accuracy of 88% for Linear SVM, 82% for RBF SVM, and 93% for Polynomial SVM, while the SVM model without Smote achieved an accuracy of 88% for Linear SVM, 79% for RBF SVM, and 91% for Polynomial SVM. The best classification model was then implemented into a Streamlit-based web application to facilitate the process of automatically predicting obesity levels, thereby helping to raise awareness of the potential risks of obesity.
PENGENALAN PLATFORM DIGITAL SEBAGAI MEDIA DAN EVALUASI PEMBELAJARAN SMK BINA PROFESI PEKANBARU Fiti, Triyani Arita; Fatdha, T Sy Eiva; Yenni, Helda; Astarilla, Liya
BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Vol. 2 No. 2 (2022): BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Desember 2022
Publisher : LPPM UNIKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/bhakti_nagori.v2i2.2579

Abstract

Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk menjelaskan pemanfaatan berbagai platform digital sebagai media pembelajaran pada masa pandemi Covid-19 di SMK Bina Profesi Pekanbaru. Kegiatan ini merupakan bagian penting atas adanya perubahan proses kegiatan pembelajaran tatap muka menjadi pembelajaran daring/online dengan bantuan beberapa platform digital yang dikeluarkan Kementerian Pendidikan dan Kebudayaan sebagai media pembelajaran. Teknik analisis data yang digunakan adalah analisis isi (content analysis), yaitu mengkaji informasi utama yang dibahas dalam referensi, mengaitkan dengan setiap topik yang dibahas, kemudian pemetaan konsep dalam bentuk perbandingan dari setiap data perolehan. Hasil kegiatan ini diharapkan memberi kontribusi dalam memudahkan sekolah, guru, dan siswa memanfaatkan beberapa platform digital yang dikeluarkan Kementerian Pendidikan dan Kebudayaan sebagai media pembelajaran selama covid-19, serta diharapkan agar platform digital terus dapat dimanfaatkan dalam proses belajar mengajar baik secara online dan ofline kedepannya.
PELATIHAN DESAIN PRESENTASI UNTUK PENINGKATAN KUALITAS LAPORAN ILMIAH BAGI SISWA SISWI SMK TEKNOLOGI RIAU PEKANBARU Fatdha, T Sy Eiva; Nasution, Torkis; Yenni, Helda; Imardi, Syahrul; Simeru, Arden
BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Vol. 3 No. 1 (2023): BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Juni 2023
Publisher : LPPM UNIKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/bhakti_nagori.v3i1.3050

Abstract

Kegiatan Pengabdian Kepada Masyarakat (PKM) ini adalah salah satu bagian dari Tri Dharma Perguruan Tinggi yang harus dilaksanakan oleh seorang dosen. Pengabdian dilaksanakan sebagai suatu wujud transfer ilmu kepada masyarakat dalam peran dan tanggung jawab intelektual dalam melestarikan dan meningkatkan khasanah budaya. Satu diantara kegiatan yang dilakukan adalah bagaimana mempersiapkan siswa siswi khususnya pada SMK Teknologi Pekanbaru Riau dalam meningkatkan pengetahuan dan mutu dalam pembuatan pelaporan dan desain presentasi pada penulisan ilmiah termasuk dalam pembuatan laporan Praktek Kerja Lapangan (PKL) bagi siswa siswi SMK Teknologi Riau Pekanbaru dengan memanfaatkan pembelajaran berbasis ICT. Hasil kegiatan ini diharapkan memberi kontribusi pada pihak sekolah, guru, dan siswa serta diharapkan hasil pelatihan ini dapat dimanfaatkan dalam peningkatan mutu penulisan karya ilmiah kedepannya pada SMK Teknologi Riau.
Application of the Scrum Method in the Android-based TPQ Learning Application Ramadani, Willy; Fatdha, T Sy Eiva; Yenni, Helda; Haryono, Dwi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.3036

Abstract

RA Alhikmah Kandis has the main material, namely learning TPQ (Al-Qur'an Education Park), learning to recognize, read, and memorize hijaiyah letters, daily prayers, and juz amma. With the development of technology, learning can now be supported by various innovations. One of the innovations is to create a mobile-based application for student learning at RA Alhikmah. The application is based on the design results based on the needs of students, teachers, and parents. Making applications using the Scrum method, applications that are built based on a predetermined time, where if the specified time has reached its end, then the application must be finished, is suitable for the Scrum method because its manufacture is based on a predetermined timeframe. Application testing uses the black box testing method to test whether each feature is running properly so that when used by application users, there are no bugs. This application is intended for raudhatul athfal or kindergarten children who are vulnerable aged 5 to 6 years, and this application can be accessed by accompanied by teachers when at school and parents when at home. The results obtained from the application black box testing can run well in all the features in the application. The author hopes that this application can provide the benefit of giving children a new method of learning to recognize hijaiyah letters, memorizing daily prayers, and juz amma in order to provide children's learning interest so that it is more interesting for their learning interest to get to know the Koran from an early age.
MYCD: Integration of YOLO-CNN and DenseNet for Real-Time Road Damage Detection Based on Field Images Yenni, Helda; Muzawi, Rometdo; Karpen, Karpen; Anam, M. Khairul; Kasaf, Michel; Hadi, Tjut Rizqi Maysyarah; Wahyuni, Dewi Sari
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1040

Abstract

Road damage such as cracks, potholes, and uneven surfaces poses serious risks to transportation safety, logistics efficiency, and maintenance budgeting in Indonesia. Manual inspection is time consuming, labor intensive, and prone to error, motivating the use of reliable computer vision solutions. This study proposes MYCD, a hybrid and mobile ready architecture that combines the fast detection ability of YOLO with the dense feature reuse of DenseNet, enhanced by the Convolutional Block Attention Module (CBAM) for spatial and channel focus and Spatial Pyramid Pooling (SPP) for multi scale context understanding. The system detects and classifies the severity of road damage into minor, moderate, and severe categories using images captured by standard cameras. MYCD was trained and validated on 1,120 field images using an 80/20 split to simulate realistic deployment. Validation achieved 64 percent accuracy, with the highest per class precision of 0.72 for minor damage and mAP@0.5 = 0.677. The confusion matrix showed that most errors occurred in the moderate category because of visual similarity with minor and severe damage. Unlike earlier studies that extended YOLO with heavy backbones such as ResNet or EfficientNet, MYCD focuses on feature propagation (DenseNet), attention precision (CBAM), and multi scale fusion (SPP) optimized for real time operation on standard hardware. Efficiency profiling confirmed its deployability. After compression, the model size is 46.8 MB and it requires 3.7 GFLOPs per inference at 640×640 resolution. On a mid-range Android device (Snapdragon 778G, 8 GB RAM), MYCD runs at 19 frames per second with 1.2 GB peak memory. Compared with YOLOv8 WD (68 MB; 5.2 GFLOPs), MYCD reduces computation by 31 percent while maintaining similar accuracy. Overall, MYCD achieves a practical balance of speed, accuracy, and efficiency, providing a deployable and reproducible framework for real time road damage detection in resource limited settings.
Co-Authors -, Dwi Haryono -, Joko Susilo -, T. Sy. Eiva Fadtha Aksha, Muhammad Iqbal Al Ami Patria Anam, M Khairul Andri Setiawan Arden Simeru Astarilla, Liya Bayu Ramadhanis Bayu Ramadhanis, Bayu BENNY BENNY Benny Benny Dwi Haryono Dwi Haryono Edwar Ali Efrizoni, Lusiana Erlinda, Susi Fatdha, T.Sy Eiva Fauzin, Imam Amin Fiti, Triyani Arita Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gunadi Hadi Asnal, Hadi Hamdani - Herwin Herwin Herwin Herwin, Herwin Imam Amin Fauzin Imardi, Syahrul Irwansyah Sidabutar Isma Dewita Jamaris, Muhamad Joko Susilo - Junadhi Junadhi Junadhi, Junadhi Karpen Khusaeri Andesa Kudadiri, Parlindungan Lathifah Lathifah Leni Apriani Sagita Liya Astarilla M.Ari Ardianto Michel Kasaf muhammad Fuad Muhammad Ridwan Muharnisa Haspin, Wafiq Nanang Susanto Neci Nirwanda Nisa, Aida Nurjayadi Nurjayadi Putri, Rahmi Apriana Rahmiati Rahmiati Ramadani, Willy Ramadhani, Jilang Rini Yanti Rini Yanti, Rini Rizki Basri Rometdo Muzawi, Rometdo Simeru, Arden Susanti Susanti Susanti, Susanti Syahrul Imardi T Sy Eiva Fatdha T Sy Eiva Fatdha, T Sy Eiva T. Sy. Eiva Fatdha Tashid, Tashid Tedy Anwar Tedy Anwar, Tedy Tjut Rizqi Maysyarah Hadi Torkis Nasution Triyani Arita Fiti Unang Rio Wahyuni, Dewi Sari Willy Ramadani Windy Fahrurozi Wirta Agustin Yanti, Rini Yoyon Efendi Yustis Maruhawa zeki Kurniadi