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Jurnal Computer Science and Information Technology (CoSciTech)
Published by Universitas Muhammadiyah Riau
ISSN : 2723567X     EISSN : 27235661     DOI : https://doi.org/10.37859/coscitech
Core Subject : Science,
Computer Science & IT
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 358 Documents
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Deteksi Spam Email Multibahasa: Menggunakan Cross-Lingual Transfer Learning Mahalisa, Galih; Alfah, Rina; Sanjaya, Hendra
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.v6i3.10107

Abstract

Targeting the challenge of text classification in Indonesian, which often faces a scarcity of adequate labeled data, this research adapts the pre-trained language model BERT-base-multilingual-cased, which was trained on a large multilingual corpus. The strategy involves two stages: first, the model is fine-tuned on a rich English-language spam dataset, and second, the trained model is then further fine-tuned using a much smaller Indonesian-language dataset. Quantitative evaluation results show that the model achieved very good and consistent performance in both languages. On the English dataset, the model reached an Accuracy of 0.9738 and an F1-score of 0.9436. More significantly, on the Indonesian dataset, the model achieved an Accuracy of 0.9492 with an F1-score of 0.9494. The comparable performance between the two languages, despite the Indonesian dataset being much smaller, proves that the semantic knowledge acquired from the source language (English) can be efficiently transferred for the same classification task in the target language (Indonesian). This research provides a strong demonstration of how transfer learning can bridge the data resource gap and has important implications for the development of NLP applications in the context of low-resource languages
Klasifikasi Buah dan Sayuran Multi-Label Menggunakan CNN: Mengatasi Class Imbalance dengan Focal Loss Syafarina, Gita Ayu; Purnomo, Indu Indah; Hasbi, Muhammad
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.v6i3.10116

Abstract

Investigates the effectiveness of Focal Loss as a solution to the problem of class imbalance in multi-label fruit and vegetable classification tasks. Using a ResNet50-based Convolutional Neural Network (CNN) architecture, two models were trained and evaluated: one using Focal Loss and another using Binary Cross-Entropy (BCE) Loss as a baseline. To address the availability of multi-label datasets, a synthetic multi-label dataset was created by combining images from existing single-label datasets. Experimental results show that the model trained with Focal Loss achieved an accuracy of 0.9390 and an F1-score of 0.9863, outperforming the BCE Loss model which only reached an accuracy of 0.8850 and an F1-score of 0.9718. The comparative analysis indicates that Focal Loss, with its ability to focus the training process on difficult examples, effectively addresses class imbalance and produces superior performance. This study concludes that Focal Loss is an effective tool for multi-label classification tasks and highlights the existing limitations, including the synthetic nature of the dataset and the limited training duration, which underscore the need for further research
Implementasi logika fuzzy mamdani dan simple additive weighting (saw) pada sistem pakar berbasis web untuk deteksi dini gangguan neurologis Harits, Muhammad Harits Firdaus; Thohir, Muhammad Ikhsan; Sujjada, Alun
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.v6i3.10130

Abstract

Neurological disorders such as low back pain, vertigo, ischemic stroke, epilepsy, and peripheral neuropathy affect the central and peripheral nervous systems and have the potential to reduce quality of life and be fatal if not detected early. In Indonesia, the high prevalence is not balanced with access to early diagnosis due to limited medical personnel, costs, and waiting times. This study developed a web-based expert system for early detection of five neurological disorders using the Mamdani Fuzzy Method for inference and Simple Additive Weighting (SAW) for symptom ranking. The diagnosis process includes fuzzification, rule evaluation, aggregation, centroid defuzzification, and SAW calculation. The system was tested through black box testing and accuracy evaluation using MAE, RMSE, and F1 Score. The results showed an MAE value of 2.8%, RMSE 2.83%, and F1 Score 0.75, which proves the system is accurate, consistent with manual calculations, and easy to use. With a user-friendly interface, this system has the potential to be a pre-diagnosis tool that increases public awareness and supports medical personnel in decision-making.
Studi Literatur : Perencanaan Arsitektur Fisik Server Big Data Manajemen Global Tapak Suci Putera Muhammadiyah: Literature Study: Physical Architecture Planning of Big Data Server Global Management of Tapak Suci Putera Muhammadiyah. Rian, Rahmad Al; Syaifullah, Rony
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.v6i3.10187

Abstract

The Indonesian Martial Arts School Tapak Suci Putera Muhammadiyah has been established in 22 countries. In its cadre formation process, Tapak Suci has 15 levels ranging from elementary students to great warriors, where each level will be achieved through a level promotion exam process. In terms of membership age, Tapak Suci has the youngest members at the elementary school level. In the process of building achievements, Tapak Suci regularly holds regional championship events organized by regional leaders, provincial levels organized by regional leaders, national and international levels organized by central leaders. In addition, Tapak Suci members also actively participate in pencak silat events organized by IPSI, Persilat and other pencak silat events. In the process of organizational management, Tapak Suci has a training branch management, regional leaders, regional leaders, regional representatives (overseas) and central leaders. This article is the result of a literature study to determine the server needed by Tapak Suci to carry out global management of its big data so that the development of Tapak Suci's human resources can be monitored in real-time.
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 Deep Learning Untuk Klasifikasi Penyakit Pada Daun Kelapa Sawit Menggunakan Arsitektur MobileNetV2 Arianda, Habil Putra; Hadiwandra, T. Yudi
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.v6i3.10306

Abstract

Accurate and efficient identification of diseases in oil palm leaves is a crucial challenge in maintaining plantation productivity and preventing significant crop losses. Limited access to experts and slow detection in the field are often obstacles. This study aims to develop a palm oil leaf disease classification model using a deep learning approach based on Convolutional Neural Network (CNN) with MobileNetV2 architecture. This model utilizes a transfer learning strategy from pre-trained ImageNet weights and is optimized through a two-phase training strategy on a dataset consisting of 1200 augmented oil palm leaf images, covering four classes, namely Healthy Sample, Fusarium Wilt, Parlatoria Blanchardi, and Rachis Blight. Model testing results show an accuracy of 85% on separate test data. The MobileNetV2 architecture was chosen for its lightweight characteristics, making this model efficient and highly suitable for implementation on mobile devices to assist in rapid disease identification in the field and support decision-making by farmers.
Rancang Bangun Aplikasi Point Of Sales Berbasis Web Dengan Arsitektur MVC Menggunakan Framework Laravel Di PT Palokoto Agro Industri As'ari, Azi; Hadiwandra, T. Yudi
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.v6i3.10308

Abstract

PT Palokoto Agro Industri still relies on Microsoft Excel for warehouse record-keeping, which is ineffective for managing large-scale data, prone to errors, and lacks security. The manual stock update process increases workload and reduces data accuracy. Furthermore, the absence of real-time access limits managers in monitoring warehouse activities and making timely decisions. To address these issues, this study developed a web-based Point Of Sales (POS) application. The application was built using the Model-View-Controller (MVC) architecture and the Laravel framework, equipped with features that align with warehouse recording standards, such as managerial access, automatic calculation of incoming and outgoing goods, and fast report generation. This research applied the Research and Development (R&D) method with a prototyping approach. The application was evaluated using the ISO/IEC 25010 standard, and the results showed that it fulfilled all aspects of software quality, including functional suitability, reliability, usability, performance efficiency, maintainability, portability, compatibility, and security. Therefore, the developed application meets the required quality criteria and can serve as a structured solution for warehouse record-keeping at PT Palokoto Agro Industri.
Peningkatan Kualitas Citra Hilal Berdasarkan Kontras Menggunakan Metode Histogram Equalization, AHE, dan CLAHE Suprayitno, Ady; Murinto; Kartika Firdausy
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.v6i3.10376

Abstract

The determination of the beginning of the Hijri month is often aided by digital imaging technology, but the quality of the crescent images produced often faces the challenge of very low contrast. The faint light of the crescent is difficult to distinguish from the still bright background of the evening sky, exacerbated by atmospheric conditions and camera sensor noise that reduce visual quality. To improve the image, many still perform manual contrast enhancement. On the other hand, the selection of contrast enhancement methods is often without a measurable basis. This study aims to conduct a comparative performance evaluation between three contrast enhancement methods: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE). The goal is to identify the most suitable technique for improving the quality of crescent images, the specific application of which has not been widely explored. A total of 30 crescent images were tested through a quantitative evaluation approach using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The results show that CLAHE provides the best performance with the lowest average MSE (89.97) and the highest PSNR (30.92 dB), demonstrating the best ability to balance contrast enhancement and distortion reduction. In contrast, the HE and AHE methods produce high MSE and low PSNR values, indicating significant visual distortion. Thus, CLAHE is recommended as the most reliable method for improving the quality of crescent images based on contrast in digital technology-based observation systems. For further research, it is recommended to explore the automatic determination of CLAHE parameters and the use of additional evaluation metrics such as SSIM (Structural Similarity Index Measure).
Designing a Cashier Website for Warkop Disini Aja Using the Laravel Framework with UAT Testing and Usability Testing Checklist Rahman, Taufik; Rahmanaufal, Fatta Rahmanaufal
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.v6i3.10392

Abstract

Advances in information technology have a significant influence on various sectors, including Micro, Small, and Medium Enterprises (MSMEs). However, most MSMEs still have not adopted digital systems in their operational activities. One example is Warkop Diini Aja, which until now still applies manual recording to the cashier transaction process. The use of the conventional system poses various obstacles, such as delays in making reports, potential for recording errors, and difficulties in monitoring sales history. Based on these problems, this research aims to design a web-based cashier system that is able to replace manual methods to be more efficient and structured. System development is carried out using the Laravel framework with a waterfall software development model, which includes the stages of needs analysis, design, implementation, testing, and maintenance. Research data was obtained through direct observation of operational processes, interviews with business owners, and literature review to strengthen the theoretical basis. The developed system has two main roles, namely admin and cashier, which are equipped with menu management features, sales transactions, export reports in digital format, and transaction history monitoring. Based on the results of the User Acceptance Testing (UAT) test, all system functions are declared to run according to user needs. In addition, the results of the Usability testing Checklist show a user satisfaction rate of 95%, which is classified as very feasible. Thus, this website-based cashier system has been proven to be able to improve operational efficiency, make it easier to record transactions, strengthen financial report transparency, and support more modern and digital business management.
Waktu Respons Transmisi Data Dalam Impelentasi Algoritma A-Star Pada Sistem Pengambilan Tempat Sampah IoT Berbasis Telegram Sabuna, Sasridarti Tari Sejahtera; Marisa Midyanti, Dwi; Kasliono, Kasliono
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.v6i3.10393

Abstract

Effective waste management requires an efficient monitoring system and collection route determination. One of the main challenges is the delay in delivering information that is not yet integrated into an automated schedule. To address this, this research designs an IoT-based waste bin monitoring and collection route determination system using the A-Star algorithm, as this algorithm can efficiently determine routes by considering the distance between nodes as well as the waste volume conditions. The system is equipped with a website that displays the waste volume conditions at each node, with the resulting routes sent to officers via the Telegram platform. The focus of this research is to analyze the system's performance, specifically the data transmission response time, which is defined as the time span starting from when the sensor detects the waste volume, data is sent by the ESP32 to the server, the server processes the algorithm, until the route information or notification is received by the user. The results show that the average processing time of the A-Star algorithm on the server is 0.0135 s, the average data transmission delay is 1.2659 s or 1265.9 ms, which is categorized as POOR based on the TIPHON standard, and the average system update time is 6.38473 s.

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