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Helda Yenni, Helda
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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.
Pelatihan Teknologi Internet of Things Dan Mobile untuk Meningkatkan Kompetensi bagi Guru pada SMK Sulthan Muazzam Syah Pekanbaru Imardi, Syahrul; Tashid, Tashid; Yenni, Helda; Efendi, Yoyon; Muzawi, Rometdo
Community Education Engagement Journal Vol. 6 No. 1 (2024): October
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/ceej.v6i1.19134

Abstract

The weak ability of teachers in the field of network expertise, especially technology-based, also has an impact on the Competency Test (UKK) of school students. The implementation of Internet of Things (IoT) and Mobile Technology training to improve teacher competence at SMK Sulthan Muazzam Syah (SMS) was attended by 18 teachers from various schools. In addition to SMS SMK teachers, there are also SMK Perbankan teachers, SMK Plus Terpadu, SMK Ekatama, SMKS Global, SMK MM Masmur, SMK Taruna Masmur and SMK Pharmacy Ekasari Pekanbaru in the field of Computer Network Engineering (TKJ). This activity lasts for 3 days starting from the delivery of material followed by practicum. For evaluation using pre-test and post-test techniques. The material is delivered starting from the introduction of the Internet of Things (IoT), IOT practicum, introduction to mobile technology, mobile technology practicum and evaluation of devotion in the form of handover of minutes of handover. The result is an increase in knowledge and skills to be applied in schools to students. In order to improve the quality of graduates and schools in the fields of IOT and mobile.
Model Klasifikasi Tingkat Kerusakan Jalan Berbasis Algoritma Random Forest di Kabupaten Kuantan Singingi: A Classification Model for Road Damage Severity Using the Random Forest Algorithm in Kuantan Singingi Regency Sapitri, Riska Mela; Yenni, Helda; Rahmaddeni, Rahmaddeni; Agustin, Agustin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2765

Abstract

Kerusakan jalan merupakan salah satu permasalahan infrastruktur yang dapat menghambat mobilitas masyarakat serta mempengaruhi efektivitas transportasi. Penilaian kondisi jalan yang masih dilakukan secara manual membutuhkan waktu yang cukup lama dan berpotensi menghasilkan penilaian yang kurang konsisten. Penelitian ini bertujuan untuk membangun model klasifikasi tingkat kerusakan jalan menggunakan algoritma Random Forest pada data infrastruktur jalan Kabupaten Kuantan Singingi. Data yang digunakan berasal dari Dinas Pekerjaan Umum dan Penataan Ruang (PUPR) Kabupaten Kuantan Singingi sebanyak 1.240 data dengan 13 variabel. Tahapan penelitian meliputi preprocessing data, data cleaning, pembagian data dengan rasio 80:20, seleksi fitur menggunakan feature importance, serta pembangunan model klasifikasi Random Forest. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan kondisi jalan ke dalam empat kategori, yaitu baik, sedang, rusak ringan, dan rusak berat dengan nilai accuracy sebesar 83%, precision sebesar 78%, recall sebesar 83%, dan F1-score sebesar 80%. Hasil tersebut menunjukkan bahwa tujuan penelitian berhasil dicapai, yaitu menghasilkan model klasifikasi yang mampu melakukan penilaian kondisi jalan secara otomatis dengan performa yang baik. Model yang dihasilkan diharapkan dapat membantu pemerintah daerah dalam melakukan penilaian kondisi jalan secara lebih objektif, cepat, dan efisien sebagai dasar dalam perencanaan pemeliharaan infrastruktur jalan
Application of the Scrum Method in the Android-based TPQ Learning Application Willy Ramadani; T Sy Eiva Fatdha; Helda Yenni; Dwi Haryono
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.
PENGENALAN PLATFORM DIGITAL SEBAGAI MEDIA DAN EVALUASI PEMBELAJARAN SMK BINA PROFESI PEKANBARU Triyani Arita Fiti; T Sy Eiva Fatdha; Helda Yenni; Liya Astarilla
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.
Co-Authors -, Dwi Haryono -, Joko Susilo -, T. Sy. Eiva Fadtha Agustin Agustin Aksha, Muhammad Iqbal Al Ami Patria Anam, M Khairul Andri Setiawan Arden Simeru 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 Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gunadi Hadi Asnal, Hadi Hamdani - Herwin Herwin Herwin Herwin, Herwin Imam Amin Fauzin Imardi, Syahrul Isma Dewita Jamaris, Muhamad Joko Susilo - Junadhi Junadhi Junadhi, Junadhi Khusaeri Andesa Kudadiri, Parlindungan Lathifah Lathifah Leni Apriani Sagita Liya Astarilla M.Ari Ardianto muhammad Fuad Muhammad Ridwan Muharnisa Haspin, Wafiq Nanang Susanto Neci Nirwanda Nisa, Aida Nurjayadi Nurjayadi Putri, Rahmi Apriana Rahmaddeni Rahmaddeni Rahmiati Rahmiati Ramadhani, Jilang Rini Yanti Rini Yanti, Rini Rizki Basri Rometdo Muzawi Sapitri, Riska Mela Susanti Susanti Susanti, Susanti Syahrul Imardi T Sy Eiva Fatdha T. Sy. Eiva Fatdha Tashid, Tashid Tedy Anwar Tedy Anwar, Tedy Torkis Nasution Triyani Arita Fiti Unang Rio Willy Ramadani Wirta Agustin Yanti, Rini Yoyon Efendi Yustis Maruhawa zeki Kurniadi