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All Journal Jurnal Statistika dan Matematika (Statmat) JOURNAL OF ICT APLICATIONS AND SYSTEM RJOCS (Riau Journal of Computer Science)
Fauzi Erwis
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Other

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PERSEPSI RESPONDEN (DOSEN/MAHASISWA/LAINNYA) MENGENAI HUBUNGAN KINERJA DOSEN DAN EFEKTIFITAS PENDIDIKAN TEKNOLOGI INFORMASI TERHADAP DAYA SAING LULUSAN Agung Setiawan; Fauzi Erwis; Ambiyar Ambiyar; Nurhasan Syah; Syahril Syahril
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 4, No 1 (2022)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v4i1.16052

Abstract

Perguruan tinggi hakekatnya sebagai wadah untuk memberikan ilmu pengetahuan kepada mahasiswa dengan harapan menghasilkan lulusan yang berkompetensi dibidangnya. Peran seorang dosen dapat menentukan lulusan yang berkompetensi dan dapat mengikuti perkembangan teknologi terkini. Selain peran seorang dosen, kemampuan dosen memberikan ilmu kepada mahasiswa sangat penting dibutuhkan mahasiswa untuk memahami dan menguasai ilmu pengetahuan. Penelitian ini menganalisis hubungan antara kinerja dosen (X1) dan efektitas pendidikan teknologi informasi (X2) dengan daya saing lulusan (Y). Hubungan antara X1, X2 dan X3 adalah rendah, sedang dan kuat, ini dapat dilihat dari uji korelasi antara X1 dan X2 sebesar (r) 0,269 atau rendah, hubungan antara X1 dengan Y sebesar (r) 0,547 atau sedang, hubungan antara X2 dengan Y sebesar (r) 0,695 atau kuat dan hubungan antara X1 dan X2 dengan Y sebesar (R) 0,745 atau kuat. Sehingga terjadi hubungan antara X1, X2 dan Y terjadi hubungan ssatu dengan lainnya, baik secara sendirisendiri maupun secara bersama-sama.
Akurasi Citra Image Penyakit Daun Kentang berdasarkan Citra Sehat, Citra Early Blight, dan Citra Late Blight Menggunakan Convolutional Neural Network (CNN) Rohima; Hariyen Ulfa; Yuliani; Hafiz Maulana; Fimawahib, Luth; Fauzi Erwis
RJOCS (Riau Journal of Computer Science) Vol. 10 No. 2 (2024): RJOCS (Riau Journal of Computer Science)
Publisher : Fakultas Ilmu Komputer, Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjocs.v10i2.2862

Abstract

Kentang merupakan salah satu produk hasil pertanian yang memiliki kandungan tepung tertinggi keempat setelah jagung, gandum, dan padi. Pengelolaan pertanian kentang menghadapi berbagai permasalahan, salah satunya adalah penyakit pada daun kentang yang dapat menyebabkan hasil produksi yang buruk hingga gagal panen jika tidak ditangani dengan tepat. Dua penyakit yang sering ditemui pada daun kentang adalah early blight dan late blight, yang masing-masing memiliki gejala serta penanganan berbeda. Proses klasifikasi penyakit yang lambat dapat menyebabkan tambahan biaya untuk perawatan. Penelitian ini memanfaatkan algoritma deep learning, yaitu Convolutional Neural Network (CNN), untuk klasifikasi citra daun kentang. Metode CNN menggunakan proses konvolusi di mana citra dipecah menjadi gambar-gambar yang lebih kecil dengan konvolusi yang sama. Hasil dari gambar-gambar kecil tersebut kemudian dimasukkan ke dalam array baru yang digunakan untuk prediksi. Data yang digunakan dalam penelitian ini berjumlah 5400 citra, terbagi menjadi tiga kelas: citra sehat, citra early blight, dan citra late blight. Hasil pengujian menunjukkan akurasi tertinggi pada data validasi sebesar 99% dengan waktu komputasi per epoch sekitar 2,5 detik. Dengan demikian, dapat disimpulkan bahwa algoritma deep learning Convolutional Neural Network (CNN) mampu melakukan proses klasifikasi penyakit pada citra daun kentang dengan sangat baik.
Classification of Capsicum Varieties Using Color Analysis with Convolutional Neural Network Azzahra, Tantia; Riski Rahmadan; Fernanda Abi Maulana; Ismi Asmita; Efendi Rahayu; Fauzi Erwis
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.394

Abstract

Paprika (Capsicum annuum L.) is a high-value horticultural commodity widely consumed for its nutritional content and vibrant color variations. In the agricultural industry, classifying paprika varieties based on color is crucial for ensuring product quality and optimizing sorting processes. This study developed an automated classification system for three main paprika varieties—red, green, and yellow—using the Convolutional Neural Network (CNN) method. The dataset consisted of 1,820 images sourced from Kaggle, with data split into 60% for training and 40% for validation. Preprocessing steps included resizing images, normalizing pixel values to the range [0,1], and data augmentation techniques such as rotation, flipping, and brightness adjustments to enhance dataset diversity and reduce the risk of overfitting. The CNN model was designed with key layers, including convolutional, pooling, and fully connected layers, optimized using the Adam algorithm and categorical cross-entropy loss function. The training results showed an accuracy of 99.9% on the training data and 92% on the testing data, with an average processing time of 64 seconds per image and a maximum of 78 seconds, demonstrating the model's efficiency for real-time applications. The k-fold cross-validation technique was also employed to ensure the model's generalization ability to new data. This study demonstrated that CNN is an effective method for classifying paprika varieties based on color analysis, offering an accurate, fast, and scalable solution for automating sorting and grading processes in the agricultural sector, reducing human errors, and improving operational efficiency.
Visualization of Sales Danalyser Data in Dashboard Form Google Data Studio Agung Surya Maulana; yanto, Budi Yanto; Fauzi Erwis
Journal of ICT Applications System Vol 2 No 2 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i2.270

Abstract

In the business sector, the use of information technology is used as a means of supporting company performance. Data visualization is the answer to simplifying complex data into a graphical format so that it is easier to understand the business. Managing sales data is an important process that must be carried out by companies. With good data management, companies get more value. This added value includes information supporting decision making, in order to increase the efficiency and effectiveness of company operations. This research uses data on sales of goods obtained from the internet, namely 69 data. The research was carried out with the help of Google Data Studio tools for creating dashboards. The results obtained are that there are several elements that help make it easier to read information, namely scorecard elements, Pie Chart elements, bar chart elements, geographic diagram elements, and table elements. The Scorecard element displays total income, average price of goods, total items sold, and total buyers. In the Pie Chart, the product insight is displayed in percent, the items that sell best are P010. The bar chart element displays total sales for each month, the highest sales were in August 2021. The geographic diagram element displays the distribution of sales to various countries. The table element displays insight information on price cuts or discounts.
Co-Authors Agung Setiawan Agung Surya Maulana Ambiyar, Ambiyar Azzahra, Tantia Efendi Rahayu Fernanda Abi Maulana Hafiz Maulana Hariyen Ulfa Ismi Asmita Luth Fimawahib Riski Rahmadan Rohima Syahril Syahril yanto, Budi Yanto YULIANI