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Journal : RJOCS (Riau Journal of Computer Science)

IDENTIFICATION AND DIAGNOSIS EXPERT SYSTEM DESIGN FOR OIL PLANT DISEASE USING FORWARD CHAINING Iskandar; Lubis, Adyanata; Prasiwinigrum, Elyandri; Maulana, Sabda
RJOCS (Riau Journal of Computer Science) Vol. 7 No. 2 (2021): 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.v7i2.1835

Abstract

This research was conducted to create an expert system expert system that can identify and diagnose major diseases of oil palm trees with chaining forward method. The system is designed to analyze a disease that can strike at the nursery stage, the plants in the field, both at thestage of immature plantations (TBM) and crop yield (TM). The result of this research is a learning system to provide knowledge regarding the disease of oil crops by utilizing a computer.
Analisa Visualisasi Data Penjualan dan Tingkat Kepuasan Penjualan Menggunakan Platform Lookerstudio Arfandi, Zirhan; Yanto, Budi; Sabri, Khairul; Aini, Yulfita; Lubis, Adyanata
RJOCS (Riau Journal of Computer Science) Vol. 10 No. 1 (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.v10i1.2402

Abstract

Data management in projects is an important activity in a company because over time the company develops more and more versatile data it has. Growing and highly complex business and supply of goods on a large scale makes data processing difficult. In the current situation, data processing starting from exporting, filtering data, analyzing and visualizing data is still done using Excel files which takes quite a long time, so that management decision making is still not optimal. . The purpose of this research is to provide users with important information and data in real time to speed up the decision-making process. Therefore, the data must be analyzed using the exploratory data analysis (EDA) method. EDA is carried out starting from understanding business objects, with revenue/sales as one of the metrics used to see the company's performance profile and the correlation of other variables. target knife The results of this study indicate that monthly sales comparisons, sales comparisons for each product and composition have the lowest sales generation and customer satisfaction, so that they can be used as material for management evaluation and EDA results can be seen in data visualization applications
Implementasi Deep Learning dengan Convolutional Neural Network untuk Pendeteksian Hama pada Sawi Hijau Menggunakan Google Colab Ulfi, Meitra; Nurliani; Nurafidah, Annisa; Saudah; Lubis, Adyanata
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.v10i2.2854

Abstract

Penelitian ini mengkaji penerapan metode Convolutional Neural Network (CNN) untuk mengidentifikasi penyakit hama pada daun sawi hijau berdasarkan gambar berwarna, dengan tujuan utama mengembangkan model yang mampu mendeteksi berbagai jenis penyakit hama dengan akurasi tinggi guna membantu petani dalam mengelola penyakit pada tanaman sawi hijau secara lebih efektif. Google Colab digunakan sebagai platform pemrosesan karena menyediakan lingkungan komputasi yang kuat dengan akses gratis ke GPU, sehingga mempercepat pelatihan model. Dataset yang digunakan dalam penelitian ini diperoleh dari platform Kaggle, yang menyediakan 100 gambar sampel untuk pelatihan dan 50 gambar untuk validasi yang terbagi dalam dua kelas: sehat dan terinfeksi hama. Validasi dilakukan untuk menguji kemampuan model dalam memprediksi data baru yang belum pernah dilihat sebelumnya, dan model CNN dibangun menggunakan berbagai pustaka seperti TensorFlow, Keras, NumPy, Pandas, Matplotlib, dan scikit-learn. Hasil penelitian menunjukkan bahwa model CNN yang dikembangkan mampu mencapai akurasi sebesar 99% pada pengujian menggunakan 10 epoch. Dengan hasil ini, diharapkan sistem yang diusulkan dapat digunakan sebagai alat bantu yang efektif bagi petani dalam mengidentifikasi penyakit hama pada daun sawi hijau, sehingga dapat meningkatkan hasil dan kualitas produksi tanaman sawi hijau
Co-Authors Acep Solihin agung setiawan Agung Setiawan AGUNG SETIAWAN Agung Setiawan Agung Setiawan Ahmad Akhyar Aini, Yulfita Akhyar, Ahmad Alexius Ulan Bani Alvin, Muhammad Amelia Chandra, Detri Andri Febriansyah Angriamilleni Angriamilleni Anik Supriani Arfandi, Zirhan Arief Hidayat Afendi, Arief Hidayat Asih Ria Ningsih Asmiati B. Herawan Hayadi Basorudin Basorudin Bayu Kusuma Bela Salsabila Budi Yanto Budi Yanto, Budi Chandra, Detri Amelia Cossy Maychandra Delima, Rika Detri Amelia Candra Detri Amelia Chandra EFENDI Ego Oktafanda Elyandri Prasiwinigrum, Elyandri Elyandri Prasiwiningrum Erliyen Nofrianda Erna Armita, NST Fadzilatul Mutmainah Fauzi Erwis Fifto Nugroho Firman Santosa Gina Sonia Amelya Gustari, Rinda Handayani, Meli Hasrijal Hasrijal Hendrisman Hera Deswita Hommy Dorthy Ellyany Sinaga Irwan Hidayat Isdaryanto Iskandar ISKANDAR Jihan Jufri Jufri Jufri Jufri Jufri Junadhi, Junadhi Karmelia, Mila Karmi Karmi Khardianti Alviani Ishak Maulana, Sabda Miftahul Jannah Miftahul Jannah Mila Karmelia Nasution, Yuli Asnita Novica Irawati Nur Aisyah Nur Azizah Nur Azizah Nurafidah, Annisa Nurhidayati Sholihah Nurliani Pariang Sonang Siregar Prasiwiningrum, Elyandri Rani Rasna, Rasna Reski, Seri Mulia Rika Delima Rina Wati Rinda Gustari Rita Arianti Sabri, Khairul Safinaz Sahira Sahira, Safinaz Saiful Anwar Salsabila, Bela Sarjon Defit Sasnita Riyani Saudah Septi Nadia Putri Seri Mulia Reski Sri Mures Walef Sriwahyudi Sulis Wulandari Suryadi, Dikky Tofikin, Tofikin Torkis Nasution Ulfi, Meitra W Panjaitan, MM Lanny Wahyudi, Sri Wulandari, Sulis Yuda Irawan Yuhasnil Yuli Asnita Nasution