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All Journal Jurnal Riset Informatika bit-Tech Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Media Computer Science Jurnal Pengabdian Masyarakat Bangsa
Sanjaya, Imam
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Penerapan Algoritma K-Means Clustering pada Sentimen Pengunjung Desa Wisata Hanjeli Arul, Sahrul Ismail Usman; Sanjaya, Imam
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6955

Abstract

Berwisata merupakan suatu kegiatan yang sudah menjadi kebiasaan yang dilakukan masyarakat, semakin berkembangnya informasi GEO wisata maka berpengaruh terhadap perkembangan masyarakat ekonomi lokal. GEO wisata desa hanjeli seharusnya sudah ada pada tahap maju karena sudah 10 tahun dari 2013. Analisis sentimen objek wisata desa hanjeli berguna untuk mengetahui pandangan pengunjung yang datang ke Desa Wisata Hanjeli dan Kabupaten Sukabumi. Dengan menggunakan perhitungan berbasis python untuk mengetahui sentimen dari pengunjung yang datang pada tahun 2022 dan euclidean distance untuk mengetahui jumlah kemungkinan pengunjung yang datang kembali. Berdasarkan hasil penelitian dengan membagikan angket pada pengunjung yang datang pada tahun 2022 melalui whatsapp dan email jumlah data yang terkumpul ada 143 serta setelah melalui perhitungan pertama(python) sentimen pengunjung dinyatakan positif dan hasil perhitungan kedua euclidean distance kemungkinan berkunjung kembali 22% dari pengunjung yang hadir.
Implementation of Convolutional Neural Network for Soil Type Category Detection in a Web-Based Plant Recommendation System Sanjaya, Imam; Wahyuni, Yulinar Sri; Parwati, Lusiana Sani
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8637

Abstract

The growth of the agricultural sector in Indonesia is highly dependent on soil fertility, as soil is an important factor in the agricultural sector. However, conventional identification of soil types often takes a long time and requires high costs. To overcome this problem, this research develops a soil classification system using an optimized Convolutional Neural Network (CNN) model to improve soil classification accuracy. The results of this classification become the basis for a Content-Based Filtering (CBF) based recommendation system, in order to provide suggestions for crop types that are suitable for soil types. This research was conducted through several main stages, namely soil image data collection, data preprocessing, CNN model training and CBF-based recommendation system implementation. The CNN model is used to recognize soil texture and color patterns, while CBF is used to match soil characteristics with suitable plant species. System evaluation is conducted using confusion matrix to assess the accuracy of the classification model as well as the effectiveness of the recommendation system. The soil type classification process using CNN with MobileNetV2 architecture achieved an accuracy rate of 96%. This result shows that the architecture is effective in recognizing soil types precisely and can be used to provide appropriate crop recommendations. Thus, this system has the potential to support increased agricultural productivity, both on a small and large scale.
Perancangan UI/UX untuk Website Talent Hub di PT Nusa Putra Resources Alawiyyah, Siti Alfiyyatuz Zakiyyah; Sanjaya, Imam
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 5 (2025): Juli
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i5.2556

Abstract

Pengabdian kepada masyarakat melalui penerapan keahlian desain UI/UX memberikan kontribusi nyata dalam mendukung digitalisasi perusahaan. Kegiatan ini dilakukan di PT Nusa Putra Resources dalam rangka mendukung pengembangan website Talent Hub sebagai platform perekrutan kerja. Pengabdian melibatkan proses perancangan UI/UX mulai dari pemahaman prinsip desain, identifikasi kebutuhan pengguna, pembuatan wireframe, hingga implementasi prototipe menggunakan Figma. Hasil kegiatan ini menunjukkan peningkatan kualitas desain digital serta pemahaman penulis dalam merancang antarmuka yang berorientasi pada pengguna.
LONG BEAN LEAF DISEASE IDENTIFICATION SYSTEM BASED ON MOBILE USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD Muhamad Fadiah Nurjaman; Purnama Insany, Gina; Sanjaya, Imam
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.373

Abstract

Long beans (Vigna unguiculata subsp. sesquipedalis), have high nutritional value, besides long beans also have a significant role in the economy of farmers in Indonesia. However, the productivity of this plant is often hampered by various diseases that attack the leaves, which can result in a decrease in the quantity and quality of the harvest. This study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves. The dataset used consists of 2,316 images, divided into training data (80%), validation (15%), and testing (5%). The ResNet-50 model, which consists of 50 layers, applies the transfer learning technique by not training the first 35 layers using a specific dataset, but utilizing weights from ImageNet. Training for 100 epochs produces high accuracy, namely 98.3% for training data, 98.4% for validation data, and 98.7% for testing data. Evaluation using Confusion Matrix, Precision, Recal and F1 Score shows very good performance without prediction errors. The final result of this research is a mobile-based software system that can diagnose diseases quickly and accurately, which can help farmers take appropriate action, and support sustainable agriculture in Indonesia.
Application Of Vision Transformer For Identifying Indonesian Herbal Plants Based On Visual Images Sanjaya, Imam; Lelita, Tiara; Yustiana, Indra
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8896

Abstract

Indonesia has vast biodiversity, including herbal plants that have been used for generations as traditional medicinal ingredients. However, the many types of herbal plants that have similar shapes, colors, and textures often make it difficult for people to identify them accurately. To overcome this challenge, this research develops a visual image-based herbal plant identification system using the Vision Transformer (ViT) model, an artificial intelligence approach that is able to understand visual patterns more effectively than conventional methods. This research went through several stages, including the collection of herbal plant image datasets from public platforms, data preprocessing and image dimension adjustment, and training of the ViT model. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure optimal performance. The results show that the ViT model is able to identify herbal plants with an accuracy of 92% and consistent performance of other evaluation metrics. This system is also implemented into the web, thus helping users in recognizing herbal plants quickly and accurately
Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification Perhan, Akbar Ilham; Yustiana, Indra; Sanjaya, Imam
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2755

Abstract

The distribution of social assistance in Indonesia often faces challenges in accuracy, where individuals who are financially capable still receive aid, while those truly in need are excluded. To address this issue, this study applies a Machine Learning approach using the C4.5 Decision Tree algorithm to classify the eligibility of recipients in Bojonggenteng Village. This algorithm was chosen because it is easy to interpret, performs well, and is suitable for categorical data. The main objective of the study is to develop a classification model that enhances the objectivity and accuracy in determining aid recipients, ensuring that assistance is directed to those who truly need it. The research process involves several stages, including problem identification, literature review, data collection, preprocessing, classification, and model evaluation. A total of 904 records from the 2023 BPNT and PBI-JK programs were obtained in collaboration with the local village authorities. The classification process was conducted using RapidMiner, which allows for visual data processing and model building without requiring programming. The model evaluation was carried out using a confusion matrix, yielding an accuracy of 98.90%, precision of 100%, recall of 97.60%, and an AUC score of 0.988. These results indicate that the C4.5 algorithm is effective for prediction tasks and can be a valuable tool in supporting fair and data-driven decision-making in social assistance programs. This study concludes that the application of Machine Learning in this context improves the fairness and transparency of aid distribution and recommends future research to involve larger datasets for broader implementation.
Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience Sanjaya, Imam; Sujjada, Alun; Pratama, Yudistira
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2833

Abstract

This study addresses a critical challenge in digital novel platforms: the difficulty of delivering personalized and accurate recommendations due to limited user interaction data. This limitation often leads to irrelevant or generic suggestions, which can diminish user engagement and hinder content discovery. The significance of solving this issue lies in enhancing user experience by ensuring that readers are presented with novels that truly align with their interests, even in the absence of extensive behavioral data. To overcome this problem, the study proposes an innovative hybrid recommendation system that integrates Content-Based Filtering (CBF) with the Random Forest algorithm. The system generates personalized recommendations by analyzing novel attributes such as title, genre, score, and popularity. The methodology involves extracting features from textual data using Term Frequency-Inverse Document Frequency (TF-IDF), followed by the calculation of cosine similarity to assess title relevance. These similarity scores are then combined with popularity predictions derived from the Random Forest model to produce final recommendations that reflect both content similarity and statistical relevance. The proposed system demonstrates strong performance, achieving an accuracy of 94.0%, precision of 81.4%, recall of 80.3%, and an F1-score of 80.8%. These results underscore the system’s capability to deliver accurate and diverse suggestions. By enhancing personalization and addressing the limitations of conventional CBF systems, this hybrid approach offers practical value for digital novel platforms. It serves as an effective tool for improving content discovery, increasing reader satisfaction, and supporting user retention in content-rich environments.
Co-Authors Alawiyyah, Siti Alfiyyatuz Zakiyyah Alun Sujjada Arul, Sahrul Ismail Usman Lelita, Tiara Muhamad Fadiah Nurjaman Parwati, Lusiana Sani Perhan, Akbar Ilham Pratama, Yudistira Purnama Insany, Gina Wahyuni, Yulinar Sri Yustiana, Indra