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INDONESIA
JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 998 Documents
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Sistem Pakar Mendiagnosa Penyakit Emfisema Dengan Menggunakan Metode VCIRS Ralis, Ralis
JURNAL RISET KOMPUTER (JURIKOM) Vol. 11 No. 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i2.8489

Abstract

Emfisema adalah penyakit progresif jangka panjang pada paru-paru yang umumnya menyebabkan napas menjadi pendek. Jaringan paru-paru, yang berperan pada bentuk fisik paru-paru dan fungsi pernapasan, pada penderita emfisema sudah rusak. Emfisema merupakan salah satu penyakit yang termasuk dalam kelompok penyakit paru obstruktif kronis (PPOK). Penyakit ini digolongkan sebagai penyakit paru-paru obstruktif  karena kerusakan jaringan paru-paru di sekitar saluran udara yang lebih kecil, bronkiolus. Kerusakan ini akan membuat bentuk fisik paru-paru tidak normal saat kita menghembuskan napas keluar. Bentuk abnormal ini akan mengganggu pertukaran udara kotor dan udara bersih, sehingga oksigen yang masuk dan karbondioksida yang keluar dari aliran darah di paru tidak maksimal Variable-Centered Intelligent Rule System (VCIRS) merupakan perkawinan dari Rule Base System (RBS) dan Ripple Down Rule (RDR). Arsitektur sistem diadaptasi dari RBS dan mengambil keuntungan yang ada dari RDR. Maka dengan hal ini penulis membuat sebuah aplikasi yang dapat Membantu masyarakat yang ingin mengetahui hasil diagnosa penyakit beserta keterangan dan solusi dengan cepat dan mudah.
Penerapan Metode Clustering Dengan Algoritma K-Means Untuk Menganalisa Data Film Lokal (Indonesia) Populer Pakpahan, Risma Rito
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 1 (2025): Februari 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i1.8490

Abstract

Film merupakan media audio visual yang terdiri dari gabungan potongan gambar yang menjadi satu kesatuan dan didalammnya terdapat pesan yang terkandung pada setiap alur ceritanya. Bioskop merupakan tempat pertunjukkan film yang dipertunjukkan dengan lampu yang disorot pada layar lebar. Pemilihan film yang dipertunjukkan merupakan faktor penting yang mempengaruhi tingkat jumlah penonton pada bioskop, akan tetapi pemilihan film yang sesuai dengan minat penonton tidaklah gampang, sehingga masalah ini berdampak pada tidak stabilnya jumlah penonton pada bioskop, bahkan dapat merugikan karena kurangnya penonton pada film yang di pertunjukkan. Solusi yang dapat dilakukan pada permasalah ini adalah melakukan pengelompokkan terhadap data film. Pengelompokkan tersebut dilakukan untuk mengetahui film yang paling banyak penonton dan yang tidak banyak penontonnya. algoritma yang digunakan adalah Algoritma K-Means, dimana algoritma ini mampu melakukan pengelompokkan data berdasarkan karakteristiknya dengan menentukkan nilai klaster, kemudian data diolah pada setiap literasi hingga tidak ada nilai klaster yang berubah. Penerapan algoritma K-Means pada data film lokal (indonesia) berhasil mengelompokkan data film dimana pada data film yang diolah dihasilkan data film lokal yang paling banyak penonton serta data film lokal yang kurang di minati oleh penonton. Sehingga pada penerapan metode K-Means memberikan informasi film yang dapat sering dipertunjukkan pada bioskop jumlah penonton bahkan menjadi lebih banyak.
Implementasi Time Series Forecasting dengan Algoritma LSTM untuk Pemantauan dan Prediksi Produktivitas Kelapa Sawit Berdasarkan Hasil Panen Putra, Ardian Pramana; Harahap, Aninda Muliani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8495

Abstract

Palm oil productivity is a key factor in maintaining the stability and sustainability of Indonesia's agribusiness industry. The fluctuation in yield at PTPN IV Kebun Bah Birung Ulu, which increased from 43,308 tons in 2020 to 44,028 tons in 2022 and then decreased to 34,643 tons in 2024, highlights the need for a more accurate monitoring system. These fluctuations are influenced by weather, fertilizer usage, plant infections, and plant age. Manual record-keeping without digital system support also increases the risk of errors and complicates production monitoring. This study aims to develop a web-based palm oil productivity prediction system using the Long Short-Term Memory (LSTM) algorithm. Five years of daily historical data, including plant age, fertilizer usage, rainfall, infection rates, and harvest results per afdeling, were used as model input. The research process includes data collection, preprocessing with Min-Max normalization, data splitting into 80% training and 20% testing, and training the LSTM model with two LSTM layers, two dropout layers, and one Dense layer. Model evaluation using Root Mean Squared Error (RMSE) shows that the model can predict productivity with good accuracy, with the best RMSE for each target variable achieved at different epochs. The 2025 prediction results indicate a stable or declining trend influenced by plant age, fertilizer application, rainfall, and infection rates. The developed web-based system features real-time monitoring and data visualization, providing a more efficient solution for production management and strategic decision-making in palm oil plantations.
Kombinasi K-Nearest Neighbor dengan K-Means Clustering Klasifikasi Stunting pada Bayi Berbasis Website Aprilsyah, Muhammad; Putri, Raissa Amanda
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8498

Abstract

Stunting is a serious health issue caused by insufficient nutrition over an extended period, especially in young children. This study aims to develop a web-based stunting data classification system using a combination of K-Means Clustering and K-Nearest Neighbors (K-NN) algorithms. The dataset used is sourced from the Health Department of Medan City in 2021-2024, consisting of 683 data entries. The research process includes problem identification, data gathering conducted through observations and interviews, data preprocessing using StandardScaler, and splitting the dataset into 70% training and 30% testing datasets. The K-Means technique is utilized for data segmentation based on z-score values. The clustering results are then used as labels for classification with K-NN. The system implementation shows a classification result with a distribution of 6.9% for mild stunting, 25.8% for moderate stunting, and 67.3% for severe stunting. The results indicate that the combination of K-Means and K-NN produces more accurate classification compared to using a single method. This study is expected to assist the Health Department of Medan City in analyzing stunting data more efficiently and contribute to the future development of stunting classification systems.
Detection of Diseases and Dry Leaves in Corn Plants Using YOLOv8 Putro W, Aditya Dwi; Prihantoro, Cahyo; Paradissa, Queenta; Nurfida A, Wahyu
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8504

Abstract

Corn plants can grow well in areas with hot or tropical temperatures as long as there is adequate rainfall and an adequate irrigation system. Corn is a strategic agricultural commodity that plays an important role in the economy, both on a national and global scale. According to data from the official website satudata.pertanian.go.id, the projection of corn production in Indonesia in the period 2020 to 2024 is estimated to experience a stable annual increase, ranging from 0.94% to 0.97%. However, during its life cycle from seed to seed, every part of the corn is susceptible to a number of diseases that can reduce the quantity and quality of the results. Therefore, the problem of disease is one of the factors that constrains the production and quality of seeds. In this study, detection of types of diseases and pests in corn plants was carried out using YOLOV8 technology as a form of innovation in corn agricultural intelligence. The dataset used in this study consists of four classes of corn leaf images, namely dry spots, blight, rust and healthy plants with a total of 1162 datasets. The dataset was taken at the same time using the POVA Pro5 smartphone. Based on the results of model training and evaluation, it was obtained that with a batch size of 32 and epoch 64, the precision value reached 0.67, recall 0.78, f1 score 0.67, Map0.5 0.701, and Map0.5:0.95 0.295. Meanwhile, with a batch size of 64 and epoch 100, the precision value increased to 0.75, recall 0.79, f1 score 0.75, Map0.5 0.792, and Map0.5:0.95 0.343. These findings indicate that the application of YOLOv8 technology has the potential to provide significant contributions to the development of smart farming systems, especially in efforts to detect early disturbances in corn plants automatically and efficiently. The availability of accurate information on the types of diseases and pests that attack corn plants allows farmers to respond quickly and appropriately, including through the selection of more targeted pesticide use or the application of organic control methods that are appropriate to field conditions.
Sistem Absensi Guru Berbasis Pengenalan Wajah Menggunakan Algoritma YOLOv8 Prayudha, Satria Putra Dharma; Putro, Aditya Dwi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8511

Abstract

This research aims to design and implement a web-based teacher attendance system that utilizes facial recognition technology through the YOLOv8 algorithm as a solution to the conventional paper-based attendance system, which is prone to recording errors, data manipulation, and potential information loss. Data collection was conducted by recording short videos of 24 teachers and staff at SD Negeri 1 Purbalingga Lor under various lighting conditions and viewpoints, which were then converted into an image dataset using the Roboflow platform. The dataset was processed through several preprocessing stages including video-to-image conversion, image resizing, augmentation, and data splitting for training, validation, and testing purposes. The YOLOv8s model was chosen due to its ability to detect faces in real time with high accuracy, as demonstrated by training results showing an mAP of 98.6%, Precision of 97.8%, and Recall of 98.5%. The integration of the model into a backend Flask-based application enables the attendance process to be carried out automatically and in real time, while functional testing using the Black Box Testing method confirms that the face detection feature operates as designed, achieving an accuracy of 93% under optimal lighting conditions. Consequently, this research successfully presents an innovative digital solution that not only enhances the efficiency of attendance administration but also minimizes the risks of data manipulation and recording errors in educational environments.
Sistem Klasifikasi Tingkat Kerusakan Kunci Motor Menggunakan Random Forest dengan Hyperparameter Tuning Yuda, Jalu Wira; Audytra, Hastie; Mahmudah, Nur
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8517

Abstract

Motorcycle key damage is often a problem for users, while the identification process still relies on technicians, which can be time-consuming and subjective. This study develops a classification system for motorcycle key damage levels using the Random Forest method with hyperparameter optimization. The dataset consists of 1,000 samples collected through observation and technician interviews, with data preprocessing using the SMOTE technique to address class imbalance. The model is trained and optimized with Random Forest using GridSearchCV and evaluated based on accuracy, precision, recall, and F1-score. The results show that the optimized Random Forest model achieves an accuracy of 85.5%, an improvement from 82% before tuning, enabling faster and more accurate identification of motorcycle key damage levels. The implementation of this system is expected to improve repair service efficiency and help users take action before the damage worsens.
Deteksi Kualitas Buah Sawo dengan Pendekatan Ekstraksi Fitur GLCM dan Algoritma Support Vector Machine Fidiya, Karisma Risma; Vikri, Muhammad Jauhar; Kartini, Alif Yuanita
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8519

Abstract

The quality of sapodilla fruit is a crucial factor in ensuring product standards and consumer satisfaction. This study aims to detect the quality of sapodilla fruit using the Gray Level Co-occurrence Matrix (GLCM) method for texture feature extraction and Support Vector Machine (SVM) as the classification algorithm. A dataset of sapodilla fruit images was collected and processed using data augmentation techniques to enhance image variation. Extracted features, including contrast, homogeneity, energy, and correlation, were used as input for the SVM model. The model was developed using a train-test split approach and evaluated based on accuracy, precision, recall, and F1-score. Experimental results show that the proposed method successfully classifies sapodilla fruit into three categories—raw, ripe, and damaged—with an accuracy of 85%. This model was implemented in a MATLAB-based Graphical User Interface (GUI), enabling users to automatically classify sapodilla quality easily and efficiently.
Aplikasi Absensi Berbasis Multiplatform Dengan Penerapan Location Based Service Dan Face Recognition Menggunakan Framework Flutter Anggraini, Esa Surya; Alda, Muhamad
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8521

Abstract

Manual attendance systems have many limitations, such as being susceptible to data manipulation, time consuming, and inefficient in managing attendance data. This study aims to build a Multiplatform-based attendance application implementing Location-Based Service (LBS) and Face Recognition technology by utilizing the Flutter framework. This application is designed to improve the accuracy, security, and efficiency of the attendance process at the Nur Adia Education Foundation. LBS technology is used to ensure that the user's location when taking attendance is in a predetermined area, while Face Recognition ensures that the user's face matches the facial data registered in the system. This study utilizes the Research and Development (R&D) method which begins with problem identification, needs analysis, system design, implementation, and application testing. The test results show that the application can validate the location and identity of the user quickly with a good level of accuracy. The Flutter framework enables cross-platform development with a user-friendly interface and optimal performance. This technology integration has successfully replaced the manual attendance method, providing a modern solution to support digital transformation in educational organizations. This research is expected to be the first step in adopting a technology-based system for more efficient and effective operational needs.
Analisis Sentimen Pada Komentar Mengenai Kartu Indonesia Pintar Menggunakan Metode Naïve Bayes Sindy, Dava; Sriani, Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8528

Abstract

The Indonesia Smart Card (Kartu Indonesia Pintar – KIP) is a government program aimed at providing educational assistance to students from underprivileged families. This program seeks to improve access to education and increase learning opportunities for children, enabling them to complete their education up to the secondary or vocational level. In the digital era, public opinion regarding government policies, including KIP, is often expressed through social media platforms such as X. Sentiment analysis is a technique in natural language processing (NLP) used to identify, extract, and classify opinions from text. One of the commonly used algorithms for sentiment analysis is Naïve Bayes, which operates based on Bayes' Theorem with the assumption of feature independence. This algorithm is effective in text classification due to its simplicity and ability to handle large datasets.By utilizing the Naïve Bayes algorithm, sentiment analysis of the KIP program can provide deep insights into public responses. The results of this analysis can assist the government in evaluating policies, understanding public perceptions, and optimizing program implementation to ensure it effectively reaches its intended beneficiaries.

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