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All Journal Jurnal Pendidikan Informatika dan Sains Jurnal Khatulistiwa Informatika JMM (Jurnal Masyarakat Mandiri) VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Indonesian Journal of Data and Science Jurnal Computer Science and Information Technology (CoSciTech) SmartComp Jurnal Medika: Medika
Siregar, Alda Cendekia
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Klasifikasi Penyakit Daun Tanaman Timun Berbasis Convolutional Neural Network (CNN) Yanto, Maryogi; Siregar, Alda Cendekia; Abdullah, Asrul
Computer Science and Information Technology Vol 6 No 2 (2025): 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.v6i2.9982

Abstract

Penyakit daun pada tanaman mentimun merupakan salah satu tantangan utama dalam meningkatkan hasil panen, terutama di Kalimantan Barat. Identifikasi penyakit secara manual seringkali tidak akurat dan memakan waktu. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis untuk penyakit daun mentimun berbasis Convolutional Neural Network (CNN) menggunakan arsitektur VGG-16. Dataset terdiri dari 2.000 citra daun mentimun yang dikategorikan ke dalam lima kelas: Bercak Daun Bakteri, Penyakit Bulai Berbulu, Daun Sehat, Penyakit Mosaik, dan Penyakit Bulai Tepung. Metode yang diterapkan meliputi praproses (pengubahan ukuran, augmentasi, normalisasi), pelatihan model, pengujian, dan evaluasi menggunakan metrik akurasi, presisi, recall, dan skor F1. Model mencapai akurasi 88% pada data pelatihan, 84% pada data validasi, dan 81,50% pada data pengujian. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi berbasis web menggunakan Streamlit untuk memfasilitasi klasifikasi interaktif. Hasilnya menunjukkan bahwa Jaringan Saraf Konvolusional (CNN) efektif dalam mengklasifikasikan penyakit daun mentimun secara otomatis dan dapat diterapkan sebagai solusi teknologi di bidang pertanian.
Prediction of Diabetes Mellitus Using the Case-Based Reasoning Method Rahimah, Auliyya; Siregar, Alda Cendekia; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 6 No 3 (2025): 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.v6i3.10266

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected and treated early. According to data from WHO and the Indonesian Ministry of Health, the prevalence of DM continues to rise each year, highlighting the need for a diagnostic support system that is both fast and accurate. This study aims to develop an expert system capable of predicting Diabetes Mellitus using the Case Based Reasoning (CBR) method. CBR is applied because it solves new problems by comparing them to previous cases based on the similarity of symptoms. The system incorporates 20 symptoms classified into two types of DM: type 1 and type 2. The prediction process follows the four main stages of CBR: retrieve, reuse, revise, and retain. Test results show that the system can predict the disease with an accuracy rate of over 90%, and user feedback through Blackbox Testing and User Acceptance Testing (UAT) confirms its usability. This expert system is expected to serve as an initial consultation tool to help users obtain early information related to potential DM quickly, easily, and efficiently.
Implementasi Ant Colony Optimization Untuk Rute Terpendek Pada Pengiriman Barang J&T Cahyo, Rahandya; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): 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.v6i3.10467

Abstract

Parcel delivery is a logistics service that requires speed and efficiency, especially in determining delivery routes. The choice of this topic is based on the problem faced by J&T delivery in Kubu Raya, particularly Desa Kapur, where long travel distances often result in inefficiency. This study applies the Ant Colony Optimization (ACO) algorithm to identify the shortest route for parcel delivery. ACO mimics the behavior of ants in finding optimal paths based on pheromone intensity. Location data were obtained using coordinates from the Google Maps API and modeled into a weighted graph, where nodes represent delivery points and edges represent distances. The optimization process was carried out by simulating the movement of ant agents to evaluate alternative routes, followed by pheromone updates on the more efficient paths. The results indicate that ACO successfully generated more efficient delivery routes compared to conventional methods, achieving a distance reduction of 28.29%, equivalent to approximately 10.68 km saved. This efficiency contributes to reduced travel time and operational costs. The optimized routes were also visualized through an interactive map using Leaflet.js to facilitate analysis and interpretation. Therefore, ACO is proven to be effective in optimizing delivery routes and has strong potential for real-world application in courier services.
Identifikasi Penyakit Daun Cabai Menggunakan Arsitektur DenseNet169 Setiarini, Putri Rizka; Oktariadi, Barry Ceasar; Siregar, Alda Cendekia
Computer Science and Information Technology Vol 6 No 3 (2025): 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.v6i3.10631

Abstract

Chili is a high-value agricultural commodity in Indonesia, but its production is often hindered by leaf diseases such as spots, curling, and yellowing. Early identification of these diseases is crucial to prevent significant yield losses. This study aims to develop an automated system for identifying chili leaf diseases using the DenseNet169 Deep Learning architecture, implemented via a web-based platform. The methodology includes data collection from Roboflow.com (3,610 images of chili leaves across four classes: spots, curling, yellowing, and healthy), data preprocessing, augmentation, model training, and evaluation. The results demonstrate that the DenseNet169 model achieves an accuracy of 98%, with consistent precision, recall, and *F1-score* values for each class. The model is integrated into a Flask-based web application, allowing users to upload images of chili leaves for disease prediction and treatment recommendations. This system is expected to assist farmers in early disease detection, thereby improving cultivation efficiency and reducing crop failure risks.
Co-Authors Aris Wahyu Murdiyanto Asrul Abdullah azwar, agim aljanata Cahyo, Rahandya Dian Hafidh Zulfikar Istikoma Istikoma Menur Wahyu Pangestika, Menur Wahyu Nasim, Muhammad Nesa, Fathul Hakim Octariadi, Barry Ceasar Oktariadi, Barry Ceasar Poetro, Bagus Satrio Waluyo Rachmat Wahid Saleh Insani Rahimah, Auliyya Ramdhania, Nur Annisa Setiarini, Putri Rizka Sonjoruri Budiani Trisakti Sucipto Sucipto Sucipto Yanto, Maryogi