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Yeka Hendriyani
Universitas Negeri Padang
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Rancang Bangun Kamus Digital Komputer Mobile dengan Algoritma Knuth Morris Pratt dan Fitur Text-to-Speech Fahlevi, Muhammad Farel; Darni, Resmi; Hendriyani, Yeka; Sriwahyuni, Titi
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.26856

Abstract

Kamus digital merupakan solusi efektif untuk mengatasi keterbatasan kamus cetak, khususnya dalam hal aksesibilitas dan kecepatan pencarian. Penelitian ini bertujuan untuk mengembangkan kamus digital komputer berbasis mobile dengan penerapan algoritma Knuth-Morris-Pratt sebagai optimasi pencarian, serta integrasi Fitur Text-to-Speech untuk mendukung pelafalan istilah secara real-time. Pengembangan dilakukan menggunakan model prototyping dengan penerapan React Native sebagai antarmuka pengguna, Node.js sebagai pengelola backend, dan MySQL sebagai basis data. Hasil pengujian menunjukkan bahwa algoritma Knuth-Morris-Pratt mampu memberikan waktu respons pencarian rata-rata antara 0,0048 hingga 0,1539 milidetik untuk kata kunci dengan panjang 3 hingga 15 karakter. Fitur Text-to-Speech berhasil menghasilkan pelafalan istilah secara langsung dengan kualitas suara yang jelas dan respons cepat. Uji praktikalitas dan efektivitas menunjukkan skor masing-masing sebesar 89 persen dan 91 persen, yang mengindikasikan aplikasi sangat praktis dan efektif. Dengan demikian, aplikasi TechVocab dinilai layak menjadi media referensi istilah komputer dengan performa tinggi dan antarmuka yang intuitif.
Analisis Sentimen Ulasan Pengguna Aplikasi Bibit Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbors (KNN) Azmi, Arafil; Hendriyani, Yeka; Dewi, Ika Parma; Budayawan, Khairi
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.27464

Abstract

Perkembangan aplikasi investasi digital seperti Bibit menuntut pemahaman mendalam terhadap persepsi pengguna. Penelitian ini menganalisis sentimen ulasan pengguna aplikasi Bibit di Google Play Store menggunakan algoritma Naïve Bayes dan K-Nearest Neighbors (KNN). Sebanyak 2.586 ulasan dikumpulkan, kemudian diproses melalui pelabelan data, praproses teks, pemberian bobot menggunakan TF-IDF, dan klasifikasi dengan rasio data latih-uji 60:40, 70:30, 80:20, dan 90:10. Hasil penelitian menunjukkan bahwa sentimen positif mendominasi dengan persentase 74,2%, sedangkan sentimen negatif sebesar 25,8%. Naïve Bayes unggul dengan akurasi tertinggi 89,70% pada rasio 90:10, dengan presisi dan recall yang seimbang serta stabilitas yang lebih baik dibandingkan KNN yang mencapai akurasi tertinggi 88,84%, tetapi fluktuatif. Temuan ini merekomendasikan Naïve Bayes sebagai algoritma yang konsisten untuk analisis sentimen ulasan aplikasi investasi. Hasil penelitian ini dapat menjadi referensi berbasis data bagi calon investor dalam pengambilan keputusan.
Penerapan Pipeline ETL Apache Airflow Menggunakan Algoritma Collaborative Filtering untuk Pemberian Rekomendasi Gastrodiplomasi Indonesia Ramadhan, Heri; Hendriyani, Yeka; Sriwahyuni, Titi; Resmidarni, Resmidarni
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.27485

Abstract

Gastrodiplomasi, sebagai strategi diplomasi budaya melalui kuliner, memerlukan pendekatan berbasis data untuk meningkatkan efektivitasnya. Penelitian ini mengembangkan sistem rekomendasi berbasis Collaborative Filtering dan pipeline ETL (Extract, Transform, Load) menggunakan Apache Airflow untuk mengintegrasikan data survei rempah (2021-2024), restoran internasional, dan distribusi bumbu. Data diolah dengan algoritma user-based untuk merekomendasikan kombinasi bumbu, menu, serta strategi promosi restoran Indonesia di pasar global. Hasil evaluasi menunjukkan akurasi tinggi dengan Nilai MAE 0,058-0,088, yang menegaskan kemampuan sistem dalam memprediksi preferensi pasar. Analisis hasil divisualisasikan melalui dashboard Tableau interaktif, memudahkan pemantauan tren distribusi rempah dan preferensi kuliner secara real-time. Penelitian ini membuktikan efektivitas integrasi workflow ETL terotomatisasi dengan analitik machine learning untuk mendukung diplomasi berbasis data. Sistem yang dikembangkan tidak hanya menyediakan rekomendasi strategis bagi promosi kuliner Indonesia, tetapi juga menawarkan kerangka kerja yang dapat diadaptasi untuk skala lebih luas. Ke depannya, pengembangan model hybrid dan integrasi data real-time dapat meningkatkan presisi rekomendasi serta responsivitas kebijakan. Temuan ini menjadi langkah awal dalam membangun praktik gastrodiplomasi Indonesia yang lebih terukur dan adaptif terhadap dinamika pasar global.
Penerapan Data Mining Untuk Klasifikasi Calon Siswa Penerima Program Indonesia Pintar (PIP) Menggunakan Algoritma Naive Bayes Suciko, Adelina; Hendriyani, Yeka; Budayawan, Khairi; Syafrijon, Syafrijon
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.27930

Abstract

Program Indonesia Pintar (PIP) merupakan bantuan pendidikan yang ditujuakan bagi siswa dari keluarga kurang mampu. Namun, proses penentuan kelayakan penerima di sekolah masih dilakukan secara manual sehingga rawan ketidaktepatan sasaran. Penelitian ini bertujuan untuk membangun model klasifikasi kelayakan penerima PIP menggunakan algoritma Naive Bayes. Data yang digunakan mencakup 379 siswa dengan atribut penghasilan ayah, penghasilan ibu, jumlah saudara kandung, penerima KIP, penerima KPS serta status layak PIP. Tahapan klasifikasi dilakukan menggunakan perangkat lunak Orange dan hasilnya divisualisasikan melalui Tableau. Model dievaluasi dengan metrik akurasi, precision, recall dan AUC. Hasil menunjukkan bahwa model memiliki akurasi 85,9%, precision 86,3%, recall 85,9% dan AUC 0,973. Visualisasi membantu memperjelas distribusi dan kelayakan PIP. Model ini dapat mendukung keputusan yang lebih objektif dalam penyaluran bantuan PIP.
Implementasi Model Yolov8 untuk Deteksi Jenis Sampah Organik dan Anorganik Berbasis Android Ridha, Muhammad Rasyid; Syafrijon, Syafrijon; Hendriyani, Yeka; Hadi, Ahmaddul
Abdimas Indonesian Journal Vol. 5 No. 1 (2025)
Publisher : Civiliza Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/aij.v5i1.655

Abstract

The mismanagement of waste poses serious environmental and public health issues in Indonesia, exacerbated by the increasing volume of waste due to population growth. To address this problem, this research develops a mobile application based on Flutter, utilizing YOLOv8 object detection technology to classify organic and inorganic waste. The application aims to simplify household waste sorting, raise public awareness, and support better and more sustainable waste management. The research methodology involves using a dataset of waste images trained with the YOLOv8 algorithm via google colab. The dataset is divided into training (70%), testing (20%), and validation (10%) portions. The model training process is conducted over 25 and 50 epochs, showing improved accuracy with more epochs. At the 50th epoch, the model achieved a precision of 0.81 and a recall of 0.61, demonstrating good performance in detecting and classifying waste. The implementation of this application is expected to facilitate waste sorting, reduce environmental pollution, and improve public health. Recommendations for further development include enhancing detection accuracy, expanding the range of detectable waste types, and optimizing application performance to ensure a better user experience.
The Effect of Teaching Factory, Industrial Work Practices and Vocational Competence Through Self-Efficacy on Work Readiness of Vocational High School Students Hatta, Mega Amelia Prihatini; Rizal, Fahmi; Rahmiati, Rahmiati; Hendriyani, Yeka
JETL (Journal of Education, Teaching and Learning) Vol 10 (2025): Special Issue
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26737/jetl.v10i1.7095

Abstract

This study aims to examine the effect of Teaching Factory (TeFa), Industrial Work Practices (Prakerin), and vocational competence on students' work readiness, with self-efficacy as a mediator. The method used is Systematic Literature Review (SLR), which collects and synthesizes various studies related to learners' work readiness, focusing on variables that play a role in vocational education. The findings show that Teaching Factory and Prakerin have an important role in improving learners' technical and non-technical skills, as well as bridging the gap between academic learning and industry needs. Self-efficacy was found to be a factor that strengthens learners' confidence and performance in the industrial environment. This study concludes that holistic development of technical competencies, work-based experiences, and strengthening self-efficacy can improve learners' readiness to face the challenges of the world of work. Further research needs to focus on the long-term impact of these programs as well as the role of teachers in facilitating industry-based learning
Deteksi Anomali menggunakan Isolation Forest pada Permintaan Kebutuhan Farmasi Pasien di Rumah Sakit Mitra Sejati Medan Novaldi, Farhan; Syafrijon, Syafrijon; Hendriyani, Yeka; Anwar, Muhammad
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.30808

Abstract

Rumah Sakit Mitra Sejati Medan menghadapi tantangan dalam mengelola volume permintaan farmasi yang tinggi, menyebabkan proses verifikasi manual menjadi tidak efisien dan berisiko. Penelitian ini bertujuan merancang dan mengimplementasikan sistem deteksi anomali untuk meningkatkan efektivitas pengelolaan permintaan. Metode yang digunakan adalah algoritma Isolation Forest dengan menerapkan metodologi Cross-Industry Standard Process for Data Mining. Data historis permintaan obat, barang medis habis pakai, dan alat kesehatan diolah menggunakan Python untuk melatih model secara kontekstual. Hasil penelitian menunjukkan dari 2.167.942 transaksi, model berhasil mengidentifikasi 13.503 (0,62%) permintaan sebagai anomali statistik. Sistem yang dikembangkan melalui aplikasi web ini terbukti berhasil menjadi alat bantu keputusan berbasis data untuk meningkatkan efisiensi operasional, akurasi stok, dan memberikan peringatan dini.
Grouping of Student Achievement Based on Student Names in Class VII of SMPN 28 Sarolangun Using the K-Means Clustering Method Chintia Ningsih, Nur; Novaliendry, Dony; Hendriyani, Yeka; Asmara, Delvi
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.967

Abstract

Although the selection of outstanding students is important to provide awards and recognition for student achievement, the methods currently used by schools are not optimal. The process often takes a long time and requires a lot of manpower to collect and process student data, which can ultimately disrupt daily school operations. This study aims to identify outstanding students in class VII at SMP 28 Sarolangun using the clustering method with the K-Means algorithm. This type of research is quantitative research. The method used in this study is K-Means Clustering , with the determination of the optimal number of clusters using the Elbow Method. The results of the study obtained a grouping of students into four clusters, including Cluster 1 with 10 students (15.2%), Cluster 2 with 16 students (24.2%), Cluster 3 with 25 students (37.9%) and Cluster 4 with 15 students (22.7%). From the resulting Elbow graph, the elbow point is seen at the value of K = 4, which indicates that four clusters are the most effective and efficient number to separate student data into meaningful groups .
Digital Literacy Integration Model and Creative Thinking for Enhancing Vocational Student Competencies in the Society 5.0 Era Suriyani, Cici; Hendriyani, Yeka; Rizal, Fahmi; Huda, Asrul
JETL (Journal of Education, Teaching and Learning) Vol 10 (2025): Special Issue
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26737/jetl.v10i2.7775

Abstract

The vocational education sector faces significant challenges in preparing graduates who meet industry demands in the digital era. Data from SMKN 1 Padang shows that the achievement of vocational competencies is still not optimal with aspects of knowledge 65%, skills 70%, occupational safety and health 70%, and work tests 70%. This study aims to analyze the influence of Digital Literacy, Learning Styles, and Critical Thinking on Student Competencies of SMKN 1 Padang. This study uses a quantitative approach with an explanatory survey design. The sample consisted of 81 active students of SMKN 1 Padang selected using a purposive sampling technique. Data were collected through a structured questionnaire with a Likert scale of 1-5 and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) with SmartPLS. The research model showed excellent psychometric quality with all constructs having adequate validity and reliability (Cronbach's Alpha > 0.925, AVE > 0.597). Structural model evaluation revealed that Digital Literacy (β = 0.275, p = 0.008), Learning Styles (β = 0.266, p = 0.050), and Critical Thinking (β = 0.411, p = 0.004) had a positive and significant effect on Student Competencies. The model explained 82.3% of the variance in Student Competencies (R² = 0.823). The results of the study confirmed the importance of integrating Digital Literacy, appropriate Learning Styles, and the development of Critical Thinking in improving Student Competencies in Vocational High Schools. These findings provide important implications for the development of holistic learning strategies in vocational education.
BankCare: A Mobile Complaint Management System for the Banking Sector Using TOPSIS-Based Prioritization Muadzah, Kayla Nahda; Samala, Agariadne Dwinggo; Hendriyani, Yeka; Sriwahyuni, Titi
Journal of Hypermedia & Technology-Enhanced Learning Vol. 3 No. 3 (2025): Journal of Hypermedia & Technology-Enhanced Learning—Next Horizon
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.201

Abstract

Efficient complaint management is critical for maintaining service quality in the banking sector. This study presents BankCare, a cross-platform mobile application developed using the Flutter framework, designed to streamline the handling of customer complaints. The application enables users to submit, edit, and monitor complaints, while administrators can manage, respond to, and prioritize them through a dedicated dashboard. A key feature of BankCare is the integration of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which allows complaints to be automatically prioritized based on urgency level and submission time, supporting objective and data-driven decision-making. Additional features include real-time notifications, user-friendly interfaces, and role-based access control for both users and administrators. Comprehensive testing confirmed the effectiveness, reliability, and usability of core functionalities, including complaint submission, admin responses, and priority-based sorting. User feedback further indicated high levels of satisfaction and ease of interaction. By combining mobile technology with a decision-support algorithm, BankCare offers an intelligent, responsive, and structured solution for optimizing complaint handling in the banking sector.
Co-Authors Afifah Rizki, Putri Agariadne Dwinggo Samala Agung Gemilang Agung Pranata Ahmaddul Hadi Ahmaddul Hadi, Ahmaddul Akbar Ilahi Akbar Ilahi Almasri Almasri Ambiyar, Ambiyar Amelia Zahra Anni Faridah Annisa Saputri Ardiyanti, Sri Arifin, Ari Syaiful Rahman Arita Arita Arita, Arita Ariyadi Armi Asmar Yulastri Asmara, Delvi Asrul Huda Aswardi Aswardi Aulia Putri, Rahilla Azmi, Arafil Bayu Ramadhani Fajri Bobby Rachman Budayawan, Khairi Budayawan, Khairi Cheng-Hong Yang Cheng-Hong Yang, Cheng-Hong Chintia Ningsih, Nur Dedy Irfan Dekry, Muhammad Reviza Delvi Asmara Deny Kurniadi Desi Susanti Dessy Hardeyenti Dhanil, Muhammad Dina Febrianti Dini Sumanti Dony Novaliendry Dony Novaliendy Edi Prasetio Effendi, Hastria Elfi Tasrif Emelsy, Nalurry Fadhilah Fadhilah Fadhli Ranuharja Fahlevi, Muhammad Farel Fahmi Rizal Fajri, Bayu Ramadhani Fanny Oktavia Farizi, Habib Al Fatni Mufit Fauzan Jamza Fauzana Azizah Firdaus Firdaus Firza, Fahira Utami Fitri, Yolanda Idha Ganefri . Geovanee Farell Geovanne Farell Giatman, M Hadi Kurnia Saputra Hadi, Ahmadul Hafilah Hamimi Hafilah Hamimi Hafilah Hamimi, Hafilah Hamdani . Hamimi, Hafilah Hansi Effendi Hardeyenti, Dessy Hasan Maksum Hasnita, Sri Hastria Effendi Hatta, Mega Amelia Prihatini Hayati Rahmatika Hendra Hidayat Henny Yustisia Huda, Asrul Huda, Asrul Huda, Yasdinul Igor Novid Ika Parma Dewi Insan, Khoirul Irma Delianti , Vera Irma Delianti, Vera Islapirna Islapirna Jayadi, Ridho Dimas Tri Prasetyo Jayandra Jayandra Jefry Herianto Juanda, Yeni Marita Jusmardi Karmila Sari, Nanda Kasman Rukun Kasman Rukun Khairi Budayawan Khairy, Mubarakh Hayatna Kurniadi, Denny La Ode Alwin Syahputra Putra Laila Fajri Lathifah, Ulfi Latifah Annisa Lativa Mursyida Lativa Mursyida Legiman Slamet Lise Asnur Lusi Tridarni M. Fikri Mariani Mariani Mariani Mariani Masrizal Masrizal Maulana Putra, Risky Mawarni, Junia Melri Deswina Mhd Arya Dhaifullah Muadzah, Kayla Nahda Muhammad Adri Muhammad Anwar Muhammad Anwar Muhammad Decky Andani Muhammad Rasyid Ridha Mukhaiyar, Riki Muskhir, Mukhlidi Mutiara Pertiwi Nabila Azzahra Shammi Nasution, Marsinah Dewi Feiyska Nizwardi Jalinus Nobi Albion Ziqkra Novaldi, Farhan Novaliendy, Dony Nurindah Dwiyani Nursyafti, Yolana Parma Dewi, Ika Pepi Resmanti Ponimin Purwanto, Wawan Putra, La Ode Alwin Syahputra Putra, Meiyaldi Eka Putri Khairunisak Putri, Alfiola Eka Putri, Dessy Hardeyenti Rahmatika, Hayati Rahmi Ramadhani Rahmi, Salsabila Tri Rahmiati Rahmiati Rakhel Cakra Sandika Ramadhan, Heri Resmi Darni Resmidarni, Resmidarni Reza Aurora Rizkayeni Marta Rizki Kurniawan, Rizki Rizky Ananda Ronaldo Ronaldo Saari, Erni Marlina SABRINA, ELSA Sandika, Rakhel Cakra Santuni, Nofrizal Silvia Gina Rahayu Alvi Sri Ardiyanti Sri Muliani Sianipar Suciko, Adelina Sukmawati, Murni Suriyani, Cici Syafrijon , Syafrijon Syafrijon Syafrijon Syafrijon, Syafrijon Ta’ali Ta’ali Titin Sriwahyuni Vera Indriani Vera Irma Delianti Vera Irma Delianti Wakhinuddin Waskito Waskito Yang, Cheng-Hong Yolanda Idha Fitri Yones, Indra Yuda Putra Utama Yudhi Diputra Yuliarnis, Sri Kurnia Yusmerita Yusmerita Zulhendra Zulhendra Zulhendra Zulhendra Zulhijra Rahma Dia Zulmi Arifah