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All Journal Jurnal Teknologi dan Manajemen Informatika Sistemasi: Jurnal Sistem Informasi Journal on Information Systems, Technology of Information and Communications (JoISTIC) INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science bit-Tech Jurnal Informasi dan Teknologi Idealis : Indonesia Journal Information System Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Abdi Putra Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Alun Sujjada
Universitas Nusa Putra
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Public Health

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Journal : bit-Tech

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Application of YOLOv8 Model for Early Detection of Diseases in Bean Leaves Yustiana, Indra; Sujjada, Alun; Tirawati
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.v8i1.2514

Abstract

Bean plant is one of the high economic value horticultural commodities widely cultivated in Indonesia. However, its productivity declines due to pest attacks and leaf diseases. Farmers' limitations in accurately identifying disease types also pose obstacles in early mitigation efforts. Therefore, technology-based solutions capable of quickly and accurately detecting plant diseases are needed. This research aims to develop and evaluate the performance of a leaf disease detection model for bean plants using the You Only Look Once version 8 (YOLOv8) algorithm with a transfer learning approach. The dataset used consists of 1,037 images of bean leaves, classified into three categories: angular leaf spots, leaf rust, and healthy leaves. Data were obtained from two sources, namely field documentation in Sindang Village, Sukabumi Regency, and an open repository on GitHub. The dataset was divided into training data (70%), validation (20%), and testing (10%). The model was trained using the YOLOv8s architecture for 30 epochs and achieved a detection accuracy of 85%. Performance evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The results of this study are expected to be an initial contribution to the application of artificial intelligence in agriculture, particularly in helping farmers efficiently detect leaf diseases in beans to improve productivity and quality of harvest.
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 Aden Rahmat Ramdani Alpariji, Salman Alvira Fauziah Rahmah Alvira Fauziah Rahmah Anggun Fergina Anggun Fergina Dwi Sartika Simatupang Erlinda Juniar Esa Nurfaizah Fadilah, Muhammad Sahrul Fadillah Alviqih falentino sembiring Febriansyah Febriansyah Fergina, Anggun Haryadi, Habbyan Lazuard Hasanah, Nurul Islam Ihsan Nurhuda Indra Yustiana Insany, Gina Purnama Irpanudin Ivano Kumaran Jelita Asian Kamdan Khadijah Kibtiyah Kharisma, Ivana Lucia Lidena, Salwa Dwi Mansur Ponimat Mayang Gunawan Mega Putri Utami Mohamad Salman Farizi Muchtar Ali Setyo Yudono Muhamad Dani Meinanda Muhamad Ridwan Nullah Muhammad Ikhsan Thohir Noer, Silvia Panji Pratama Pratama, Yudistira Putra, Wahyu Irwan Reka Reni Andian Reni Nur Anggraeni Rizal Fadli Rizaldi, Fazrin Muhammad Rizki Maulana Sanjaya, Imam Sebayang, Alvin Christoper Sepriyadi, Adi Simatupang, Dwi Sartika Somantri Somantri Somantri, Somantri Sulaeman, Asep Surahman Tirawati Usnul Latipah Vemi Januarita Putri Wilianti, Refi Yunita Ramanda Yustiana, Indra Zaenal Alamsyah Zulia nur permatasari