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OKTAL : Jurnal Ilmu Komputer dan Sains
Published by CV. Multi Kreasi Media
ISSN : -     EISSN : 28282442     DOI : -
Core Subject : Science, Social, Engineering,
Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences
1. Komputasi Lunak, 2. Sistem Cerdas Terdistribusi, Manajemen Basis Data, dan Pengambilan Informasi, 3. Komputasi evolusioner dan komputasi DNA/seluler/molekuler, 4. Deteksi kesalahan, 5. Sistem Energi Hijau dan Terbarukan, 6. Antarmuka Manusia, 7. Interaksi Manusia-Komputer, 8. Hibrida dan Algoritma Terdistribusi Pemrosesan Informasi Manusia, 9. Komputasi Berkinerja Tinggi, 10. Penyimpanan informasi, 11. Keamanan, integritas, privasi, dan kepercayaan, 12. Pemrosesan Sinyal Gambar dan Ucapan, 13. Sistem Berbasis Pengetahuan, 14. Jaringan Pengetahuan, 15. Multimedia dan Aplikasi, 16. Sistem Kontrol Jaringan, 17. Klasifikasi Pola Pemrosesan Bahasa Alami, 18. Pengenalan dan sintesis ucapan, 19. Kecerdasan Robot, 20. Analisis Kekokohan, 21. Kecerdasan Sosial, 22. Statistic 23. Komputasi grid dan kinerja tinggi, 24. Realitas Virtual dalam Aplikasi Rekayasa, 25. Intelijen Web dan Seluler, 26. Data Besar, 27. Manajemen Informatika, 28. Sistem Informasi, 29. Desain Permainan, 30. Sistem Multimedia, 31. Pemrosesan Gambar, 32. IOT 33. Pemrograman Seluler, 34. Desain Basis Data, 35. Pemrograman Jaringan, 36. Sistem Terdistribusi, 37. Sistem Pendukung Keputusan, 38. Sistem Pakar, 39. Kriptografi, 40. Model dan Simulasi, 41. Jaringan 42. Perhitungan 43. Metematika 44. Kimia 45. Teknik Elektro 46. Robotik 47. Fisika
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,093 Documents
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Literature Review: Klasifikasi Penyakit Paru-paru Menggunakan Metode Decision Tree Angga Rakhmansyah; Perani Rosyani
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

The global increase in lung disease cases presents a serious healthcare challenge requiring early detection systems for optimal treatment. This study examines the implementation of the Decision Tree algorithm in classifying various types of lung diseases based on a comprehensive analysis of recent studies. The methodology employs a Systematic Literature Review (SLR) approach by thoroughly analyzing five selected scientific publications published between 2023-2024. Evaluation results demonstrate that the Decision Tree algorithm shows promising performance in lung condition classification with accuracy ranges from 56.7% to 99.67%. Research findings indicate that Decision Tree algorithm optimization can be achieved through the integration of appropriate data preprocessing techniques and careful feature selection. Based on the analysis conducted, it can be concluded that Decision Tree is a reliable method for lung disease classification, particularly when implemented with optimized parameter configurations and proportional datasets.
Literature Review: Pendekatan K-Nearest Neighbors untuk Klasifikasi Penyakit Kardiovaskular Adinda Sri Wahyuni; Aditya Firsyananda; Nurdiasih; Raden Achmad Ajru Ramadhan
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This research reviews the literature to analyze the use of the K-Nearest Neighbors (KNN) approach in classifying cardiovascular diseases. Rapid technological advancements significantly impact, including in the machine-based heart disease classification systems. The algorithm frequently used in these studies is KNN, supported by Machine Learning. This research employs the Systematic Literature Review method to summarize and analyze various journals examining the use of KNN in heart disease classification, with these journals found through Google Scholar searches. Based on the literature review results, it was found that the KNN algorithm has great potential to be used as an aid in early diagnosis of heart disease. With a very high accuracy rate, this method offers medical personnel the opportunity to use data-driven guidance in making clinical decisions related to patients' cardiovascular risk. More accurate early diagnosis not only facilitates the determination of necessary intervention steps but also plays a crucial role in preventing the development of more serious disease complications.
Literature Review: Penggunaan CNN dalam Klasifikasi Penyakit pada Tanaman Buah Apel Muhamad Choirul Anwar; Januardy Ahda Setia Murad; Ridwan Firdaus Haryono; Saddam Alifio
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study focuses on the classification of diseases in apple plants using deep learning methods, particularly Convolutional Neural Networks (CNN). A primary challenge in agricultural management is the early detection of plant diseases, as failure to identify them promptly can lead to significant losses in yield. In this study, various CNN methods were explored to enhance the accuracy of disease detection and computational efficiency. Data were collected from relevant scientific journals, and a literature review was conducted on five main journals that implemented CNN techniques and hybrid methods. The research findings indicate that data preprocessing techniques, such as data augmentation and image segmentation, play a critical role in improving model performance. Hybrid models that combine CNN with other methods, such as RNN, also showed improvements in accuracy and real-time detection capabilities. In conclusion, the implementation of CNN methods tailored to specific needs, combined with appropriate data preprocessing, can provide effective solutions for the rapid and accurate detection and classification of plant diseases.
Literature Review: Klasifikasi Penyakit Daun Tamanan Kelapa Sawit Menggunakan Convolutional Neural Network Caesar Adhityansyah; Jefi Eliel Tigor Tampubolon; Fransiskus Natalis Eduk; Muhamad Razik
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

The palm oil industry plays an important role in Indonesia's economy, but is vulnerable to foliar diseases that can reduce productivity. PT Agri Palma, an oil palm company in West Kalimantan, faces this challenge, especially in the leaf diseases of Anthracnose, Ganoderma, and Leaf Spot. This study uses Convolutional Neural Network (CNN) to classify leaf diseases through image analysis. The dataset consists of 1,000 leaf images of 224x224 pixel resolution in RGB channel, with 800 images for training and 200 for testing, processed on Google Colab platform. The research aims to develop a CNN-based web application to automatically detect oil palm leaf diseases. Results show that the CNN model achieves 92% accuracy, supporting quick action in disease management and reducing the risk of crop loss.
ANALISIS STATISTIK DAN PROBABILITAS MASA DINAS KEPALA SEKOLAH DAN GURU DI WILAYAH KALIMANTAN MENGGUNAKAN METODE KAJIAN KEPUSTAKAAN Faslih Fauji; Buna Rizal Nur Rohman; Daffa Tri Ananta; Muhamad Fazri Arizki
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 09 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This research analyzes the service periods of school principals and teachers in four provinces in Kalimantan (Central Kalimantan, South Kalimantan, West Kalimantan, and North Kalimantan) to understand patterns and trends in the service periods of teaching staff. Years of service are an important indicator reflecting stability and continuity in the education system. Using a literature review and statistical analysis approach, this research examines the distribution of service periods through calculating frequency, mean value and cumulative frequency, and presents the results in the form of a histogram, polygon and ogive. The results show that the average length of service experience varies by province, from 7.7 to 12.8 years. The conclusions of this research suggest the importance of periodically evaluating service policy to ensure a balance between continuity and innovation in educational leadership, in order to support improving the quality of education in the Kalimantan region.
LITERATURE REVIEW: PENERAPAN GRADIENT BOOSTING UNTUK KLASIFIKASI PENYAKIT DIABETES TIPE 2 Emison Wonda; Mia Septiana Wambrauw; Renaldi Ferrari; Rizka Gifani Napitupulu; Rosita Hermalinda Dwi Febrianti
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Diabetes mellitus type 2 is a metabolic condition with a rising global prevalence. Accurate classification is crucial for proper diagnosis and management. This research reviews the literature on the application of Gradient Boosting algorithms, particularly XGBoost and LightGBM, in classifying type 2 diabetes. The review indicates that Gradient Boosting algorithms have significant potential in improving the accuracy of disease diagnosis and risk prediction. Studies examined demonstrate the ability of these algorithms to handle complex data, achieve high accuracy rates, and address class imbalance issues. Moreover, parameter optimization such as hyperparameter tuning can significantly enhance model performance. This review highlights the benefits and potential of Gradient Boosting algorithms in enhancing healthcare systems through early detection and more effective management of type 2 diabetes.
Klasifikasi Penyakit Tanaman Tebu dengan Pendekatan Support Vector Machine All Iqbar Arifin; Muhammad Alditho Firlata; Aziz Arrasyid; Satria Andikah Putra
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study proposes the use of the Support Vector Machine (SVM) method as an approach to classifying sugarcane plant diseases. This developing research requires an effective method to detect and classify sugarcane plant diseases so that treatment can be carried out appropriately. This study proposes the use of the Support Vector Machine (SVM) method as an approach to classifying sugarcane plant diseases. SVM was chosen because of its high ability to distinguish data from various classes even in complex dimensions, as well as its reliability in handling small datasets with a good level of accuracy. The data in this study were obtained from images of sugarcane leaves that had been classified into several disease categories. These images were then processed through a feature extraction process that included shape, texture, and color as the main parameters. The experimental results showed that the SVM approach could achieve a high level of accuracy in classifying sugarcane plant diseases. These findings indicate that SVM is an effective and efficient method for identifying diseases in sugarcane plants, and has the potential to be applied as a decision support system in sugarcane plantation management.
Klasifikasi Penyakit Menular Dengan Algoritma Machine Learning Berbasis SVM Alessandro; Alfinsa Pratama; Azzani Nurfadia Rizky; Elyananda Subroto
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Infectious diseases pose a serious threat to public health, especially with their rapid spread and the difficulty of detecting early symptoms in some cases. Accurate classification of infectious diseases is essential to support early diagnosis and appropriate treatment. In this research, a machine learning algorithm based on Support Vector Machine (SVM) was used to classify types of infectious diseases. This method was chosen because of its ability to handle complex datasets and produce good classification, especially on data with non-linear patterns. This research uses infectious disease datasets from trusted sources which are processed using the Knowledge Discovery in Databases (KDD) method for extracting relevant features. Several SVM kernels, namely linear, radial basis function (RBF), sigmoid, and polynomial, were evaluated to determine the most optimal kernel in increasing classification accuracy. The aim of this research is to identify the most effective method in predicting infectious diseases, so that it can be applied in decision support systems in the health sector. The research results show that the polynomial kernel provides the highest accuracy compared to other kernels, with an accuracy level reaching 75%. With these results, it is hoped that the SVM-based classification model ca be a solution in identifying and treating infectious diseases more efficiently.
Literatur Review: Penerapan Random Forest untuk Klasifikasi Penyakit Tanaman Padi Anang Muhamad Lutfi; Eko Purwadi; Kamaluddin; Yusuf Ali Hanaan; Perani Rosyani
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Indonesia is an agrarian country where the agricultural sector plays a vital role in the economy. Diseases in rice plants pose a serious threat to farmers as they can significantly reduce the quality and yield of the harvest. Random Forest, one of the machine learning methods, has been implemented in research to effectively classify types of diseases in rice plants. This study reviews various literatures related to the application of the Random Forest method and several other algorithms such as CNN, Decision Tree, and SVM in detecting and identifying rice plant diseases. The review shows that the Random Forest method has high accuracy performance, making it a recommended method for early detection of rice plant diseases. This study is expected to serve as a guide for further research to improve the accuracy and efficiency of rice disease classification methods.
Pendekatan Decision Tree Untuk Klasifikasi Penyakit Pada Tanaman Kopi Altaf Ghani Subekti; Alvin Diaz Setiadi; Muhammad Agung Zikri; Ryandanu Wisnu Pradipta
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Coffee plants are an important commodity in the agricultural sector but are vulnerable to various diseases that can affect productivity and crop quality. To quickly and accurately identify and classify diseases in coffee plants, a technology-based approach is needed to assist farmers in decision-making. This study aims to evaluate the use of the Decision Tree algorithm as a classification method in detecting diseases in coffee plants. Through a Systematic Literature Review (SLR), we collected data from five relevant journals and analyzed the effectiveness of Decision Tree in the disease classification process. The results show that the Decision Tree approach can achieve high accuracy in identifying coffee plant diseases and is easy to implement in the field. This research is expected to provide further insights for the development of decision support systems to help coffee farmers improve plant health and productivity.

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