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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) INTECOMS: Journal of Information Technology and Computer Science Jurnal Ilmu Komputer Journal of Applied Data Sciences
Yulanda, Yulanda
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Other

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ANALISIS MENUMBUHKAN MINAT BERWIRAUSAHA PESERTA DIDIK OLEH GURU DI SMK SUPM KALBAR Yulanda, Yulanda; Genjik, Bambang; Basri, Muhammad
Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Vol 13, No 12 (2024): Desember 2024
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jppk.v13i12.70881

Abstract

This research aims to determine teachers' efforts to foster students' interest in entrepreneurship, supporting factors and inhibiting factors to foster students' interest in entrepreneurship. This type of research uses descriptive methods and the approach used by researchers is a qualitative approach. The presence of researchers is the main tool in collecting data and the research participants in this research are entrepreneurship teachers and students. The data collection techniques used in this research were interviews and documentation. The analysis technique in this research uses data reduction, data presentation, and drawing conclusions. The results of this research are the efforts of entrepreneurship subject teachers to foster interest in entrepreneurship in students at the Pontianak SUPM Vocational School, namely motivating students, giving students examples of inspiring and successful stories in entrepreneurship, and practical activities. The supporting factor in fostering students' interest in entrepreneurship at SMK SUPM KALBAR is the entrepreneurial practice activities carried out every semester. Factors inhibiting the obstacles faced in fostering students' interest in entrepreneurship at the SUPM KALBAR Vocational School, namely the presence of students during class hours, students at the Vocational School are required to live in a dormitory so their dormitory is still in the same area as the classroom, sometimes they are too lazy to go to class, participant response Students who are lacking in learning, adequate infrastructure but students who are less enthusiastic about receiving lessons are one of the obstacles in increasing interest in entrepreneurship.
Inovasi Teknologi Dalam Pendidikan Dengan Penerapan Aplikasi Fuzzy Logic Untuk Identifikasi Belajar Di Sekolah Menengah Kejuruan Melyanti, Rika; Yulanda, Yulanda; Fonda, Hendry; Muhardi, Muhardi
INTECOMS: Journal of Information Technology and Computer Science Vol 8 No 1 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i1.14569

Abstract

Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan aplikasi berbasis fuzzy logic untuk mengidentifikasi gaya belajar siswa di SMK Perbankan Riau. Ruang lingkup penelitian mencakup analisis gaya belajar siswa dan penerapan teknologi fuzzy logic dalam proses identifikasi tersebut. Metode yang digunakan meliputi pengembangan aplikasi menggunakan algoritma fuzzy logic dan uji coba pada sampel siswa di SMK Perbankan Riau. Hasil penelitian menunjukkan bahwa aplikasi ini mampu mengidentifikasi gaya belajar siswa dengan tingkat akurasi yang tinggi. Selain itu, aplikasi ini juga membantu guru dalam menyesuaikan metode pengajaran sesuai dengan gaya belajar siswa yang teridentifikasi. Penerapan teknologi ini menunjukkan peningkatan dalam keterlibatan siswa dan pemahaman materi pembelajaran. Simpulan dari penelitian ini menyatakan bahwa penggunaan fuzzy logic dalam identifikasi gaya belajar merupakan inovasi yang efektif dan efisien. Implikasi dari hasil riset ini memberikan kontribusi signifikan bagi pengembangan ilmu pengetahuan di bidang pendidikan, khususnya dalam penerapan teknologi informasi untuk meningkatkan kualitas proses pembelajaran. Penggunaan aplikasi ini juga diharapkan dapat diaplikasikan pada tingkat pendidikan lainnya di masa depan.
SISTEM PAKAR (EXPERT SYSTEM) UNTUK PENGHITUNGAN ANGKA KREDIT DOSEN DENGAN MENGGUNAKAN METODE FORWARD CHAINING DALAM PENGURUSAN JABATAN FUNGSIONAL DOSEN Yulanda, Yulanda
Jurnal Ilmu Komputer Vol 3 No 1 (2014): Jurnal Ilmu Komputer
Publisher : STMIK Hang Tuah Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33060/JIK/2014/Vol3.Iss1.21

Abstract

Sistem pakar merupakan sistem yang menggabungkan antara pengetahuan dan penelusuran data untuk memecahkan suatu masalah yang memerlukan keahlian manusia. Tujuan pengembangan sistem pakar bukan untuk menggantikan peran manusia atau pakar, melainkan untuk mendistribusikan pengetahuan manusia ke dalam bentuk sistem. Representasi pengetahuan yang digunakan pada penelitian ini adalah production rule, dan metode inferensi yang digunakan adalah forward chaining. Pembahasan utama dalam penelitian ini adalah perancangan dan pembuatan sistem pakar dengan menggunakan metode forward chaining untuk menghitung Angka Kredit Dosen dalam pengurusan jabatan Fungsional Dosen.
PENERAPAN DATA MINING MENGGUNAKAN METODE CLUSTERING EVALUASI DATA PENJUALAN PT ASPACINDO KEDATON MOTOR: PENERAPAN DATA MINING MENGGUNAKAN METODE CLUSTERING EVALUASI DATA PENJUALAN PT ASPACINDO KEDATON MOTOR Hartomi, Zupri Henra; Sabna, Eka; Yulanda, Yulanda; Amartha, Mohd; sanjaya, Rifki
Jurnal Ilmu Komputer Vol 11 No 2 (2022): Jurnal Ilmu Komputer
Publisher : STMIK Hang Tuah Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33060/JIK/2022/Vol11.Iss2.275

Abstract

Perkembangan teknologi komunikasi dari waktu kewaktu dirasakan semakin pesat, salah satunya adalah dalam usaha penjualan. PT. Aspacindo Kedaton Motor Pekanbaru merupakan usaha yang bergerak dalam bidang Penjualan sepeda motor. Dalam hal ini penginputan penjualan hanya dijadikan sebagai laporan tanpa ada pengembangan data yang lebih lanjut untuk dijadikan sebuah pengetahuan. Oleh karena itu dibutuhkan Penegeloaan Data Mining dengan motode klustering untuk mengolah data transaksi penjualan, sehingga diproleht sebuah keputusan yang dapat digunakan untuk menganalisis data penjualan. Tujuan utama dari metode clustering adalah pengelompokan sejumlah data/obyek ke dalam cluster, dimana cluster tersebut akan berisi data yang sama dengan groupnya masing-masing. Manfaatnya mempermudah analisis data yang besar dan membantu memberikan informasi data penjualan. Hasil dari penelitian ini diperoleh perbandingan daerah mana menghasilkan banyak penjualan yaitu kluster 1 pada daerah Tenayan Raya, kluster 2 pada daerah Limapuluh. Dan kluster 3 pada daerah Payung sekaki. Dari pola yang di peroleh di harapkan dapat memberi pengetahuan untuk PT. Aspacindo Kedaton Motor Pekanbaru sebagai pendukung untuk mengambil kebijakan.
Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas Devis, Yesica; Muhamadiah, Muhamadiah; Yulanda, Yulanda; Irawan, Yuda; Wahyuni, Refni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.898

Abstract

Dengue fever is an endemic disease that poses a serious threat to public health in tropical regions such as Indonesia. Efforts to control this disease require a data-based approach that is able to accurately predict the level of risk so that interventions can be targeted. This study aims to develop a predictive model of DHF risk using ensemble stacking method optimized with Optuna algorithm and integrated into an interactive dashboard based on Streamlit. The dataset used includes environmental, climate, and socio-demographic indicators from 2015 to 2024 with a total of 1,440 data entries. The preprocessing process includes normalization with Standard Scaler, feature selection using LASSO, and label data balancing with the SMOTE method. Model validation was performed using 10-Fold Cross Validation to ensure model generalization to new data. The stacking model is built with three basic algorithms, namely SVM, KNN, and Random Forest, which are combined using Logistic Regression as a meta-learner. The evaluation results show that the model is able to achieve an average accuracy of 97.57%, with high precision, recall, and f1-score values in all three prediction classes (low, medium, high). The ROC-AUC for each class also showed near-perfect performance. The implementation of the model in the Streamlit dashboard allows non-technical users such as health center or health office staff to perform regional risk prediction and obtain data-driven intervention recommendations automatically. This research not only contributes to the development of predictive technology, but also strengthens evidence-based health promotion practices in urban areas. Further research is recommended to integrate IoT-based real-time data and expand the scope of application areas.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Kurniawan, Bambang; Wahyuni, Refni; Yulanda, Yulanda; Irawan, Yuda; Habib Yuhandri, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Co-Authors Amartha, Mohd Bambang Genjik Bambang Kurniawan Devis, Yesica Fonda, Hendry Habib Yuhandri, Muhammad Melyanti, Rika Muhamadiah, Muhamadiah Muhammad Basri Muhardi Muhardi Refni Wahyuni Sabna, Eka sanjaya, Rifki Yuda Irawan Zupri Henra Hartomi