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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 Citra Medis Penyakit Pneumonia dengan Convolutional Neural Network Noufal Maulana; Muhammad Fauzan Rusby Kholiq; Muhammad Dabit H. A.; Geraldo Sabila F.
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

Pneumonia is a dangerous respiratory infection with a fairly high mortality rate, especially in countries with limited medical resources. Examination of chest X-ray images is usually the main method for diagnosing this disease, but the process can be time consuming and requires special expertise. In this study, the Convolutional Neural Network (CNN) method was applied to help classify chest X-ray images into "pneumonia" and "normal" categories. By using CNN, this model is able to recognize complex visual patterns in images and produce predictions with a high level of accuracy. The test results show that the CNN model can be relied upon to assist in the automatic diagnosis of pneumonia, providing opportunities for application on a wider scale.
Kondisi Tenaga Pendidik di Indonesia Timur: Keterkaitan Jumlah Guru Menurut Kelompok Umur di Maluku, Maluku Utara, Bengkulu, dan Papua Muhammad Rifaldi Akbar; Gentra Ramadhan; Haykal Aiman Rayyan; Pandu Erlangga
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 journal focuses on the condition of the teaching workforce in Eastern Indonesia, particularly in the provinces of Maluku, North Maluku, Bengkulu and Papua. Using a quantitative approach, we analyze the relationship between the number of teachers and principals by age group, as well as the challenges faced in the supply of educators. The results show an imbalance between the number of teachers and principals, with the majority of teachers over 50 years old and less than 30% of teachers under 30 years old. In addition, remote areas have difficulty in attracting qualified educators. To address these issues, we recommend providing incentives for teachers who teach in remote areas, continuous training programs, collaboration between the government and educational institutions, and improved access to educational resources. With these measures, it is hoped that the quality of education in Eastern Indonesia can be significantly improved.
Literatur Review: Klasifikasi Penyakit Parasit dengan Algoritma Decision Tree dan K-Nearest Neighbors (KNN) Muhammad Mabdail Hidayat; Kakan Kandarsyah; Randy Rizkiani; Fendi Indra Pradana
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 literature review discusses the classification of parasitic diseases using Decision Tree and K-Nearest Neighbors (KNN) algorithms. Parasitic diseases, which are commonly found in tropical areas, require accurate diagnosis to prevent their spread and improve the effectiveness of treatment. In recent decades, Decision Tree and KNN algorithms have been widely used in medical data classification, especially for disease diagnosis. This study aims to evaluate the effectiveness of these two algorithms in parasitic disease classification based on a recent literature review. The literature review method was carried out by collecting and analyzing five related articles in the last five years. The results show that both algorithms have their own advantages and disadvantages; KNN excels in accuracy on large datasets while Decision Tree provides easier interpretation of results. The main challenges in using these two algorithms involve parameter selection and data sensitivity. Further recommendations in this study include the use of ensemble techniques to combine the advantages of both algorithms.
Literature Review: Klasifikasi Penyakit Daun Dengan Deep Learning Pada Tanaman Kacang Ade Ahmad Mirza; Ivan Afriza; Muhamad Rizky Fadillah; Muhammad Julyanto Sarwinata; 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

This research discusses the implementation of deep learning for leaf disease classification in peanut plants, focusing on Convolutional Neural Network (CNN), Modified K-Nearest Neighbor (MKNN), and Multiclass Support Vector Machine (SVM) models. The main objective is to evaluate the accuracy and efficiency of the models in automatically detecting leaf disease types to support smart agricultural practices. Using a dataset of infected peanut leaf images, the proposed CNN model achieved an accuracy of 95%, superior to the MKNN method which obtained an accuracy of 89% and SVM of 87%. These results demonstrate the potential of CNNs in fast and accurate plant disease classification, while highlighting the need for specific datasets to improve performance in real environments. This study provides guidance for further development in the application of deep learning in agriculture, particularly in peanut plant disease detection systems.
LITERATUR REVIEW: ANALISIS KLASIFIKASI PENYAKIT TANAMAN KEDELAI MENGGUNAKAN DECISION TREE Akhmad Alvi Sahri; Aly Furqaan Abdurrafi; Calvin Maulana Ersa; Fitra Rio Ramadan; 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

This study aims to conduct a comprehensive review of the application of the decision tree algorithm in classifying diseases in soybean plants. The decision tree algorithm was chosen for its ability to produce interpretable models and its efficiency in handling large and complex datasets. Through an in-depth analysis of existing studies, this research is expected to provide further insight into the use of decision trees in agriculture, particularly in diagnosing diseases in soybean plants. Additionally, this review will explore recent research trends, challenges faced, and potential future developments in this field.
Klasifikasi Penyakit Ginjal Kronis Menggunakan Algoritma Naïve Bayes: Literature Review Bagus Taufik Hidayat; Dani
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

Chronic Kidney Disease (CKD) is a significant global health issue that requires early detection to prevent serious complications. In the field of healthcare, the Naïve Bayes algorithm has shown potential as an effective method for classifying medical data, including CKD, due to its simplicity yet accuracy in handling data with independent variables. This study aims to conduct a literature review on the application of the Naïve Bayes algorithm in CKD classification, focusing on the accuracy, efficiency, and reliability of the resulting models. The research analyzes various previous studies, including data preprocessing techniques, important features used, and performance model evaluations based on parameters such as accuracy, precision, and recall. The review findings indicate that the Naïve Bayes algorithm offers competitive accuracy for classifying CKD compared to other methods, especially on datasets with a limited number of features. The conclusion of this review highlights the importance of optimal data management and the selection of relevant features to improve the performance of the Naïve Bayes algorithm. This study is expected to provide guidance for future researchers in developing early detection systems for CKD based on machine learning.
Klasifikasi Penyakit Bercak Daun Pada Tanaman Gandum Menggunakan Metode Convolutional Neural Network Raihan Salman Al Parisy; Damars Alfi Syahri; Reyvalqy; Chairina Fachrunnida
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Wheat leaf diseases such as yellow rust and powdery mildew are very harmful to wheat yields worldwide. It is important to detect these diseases as early as possible so that losses can be minimized. In this work, we have used lightweight convolutional neural networks (CNNs) and Transformer-based methods to detect wheat leaf diseases under complex environmental conditions. In the first study, we tried several lightweight CNN models, such as MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2. These models were trained using different learning methods and achieved the highest accuracy of 98.65% using MnasNet and a fine-tuned learning rate. The second study focused on detecting yellow rust with UNET Segmentation and Swin Transformer classification methods. They achieved 95.8% accuracy in the field without manual intervention. These studies created a complete pipeline, including finding and delimiting wheat leaves from a complex background. They used YOLOv8 to quickly find leaves, then performed Segmentation and classification. The results showed that the combination of Segmentation, lightweight CNN, and Transformer techniques can handle leaf disease detection in nature with different backgrounds. This system has high accuracy and good efficiency for use in the field. This method can help the development of smart agricultural applications by accelerating and facilitating automatic detection of wheat leaf diseases. Using technologies such as Convolutional neural networks, Transformers, and Segmentation to overcome complex backgrounds.
JUMLAH KEPALA SEKOLAH DAN GURU DI SEKOLAH SWASTA MENURUT KELOMPOK UMUR PROVINSI BANTEN, SULAWESI UTARA, KALIMANTAN UTARA, DAN PAPUA SELATAN TAHUN 2023/2024 Mutia Salsa Rizkyta; Nanda Zahira Shofa; Rizma Fauziah
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study discusses the distribution of the number of principals and teachers in private schools in Banten, North Sulaesi, North Kalimantan, and South Papua Provinces by age group. This research uses quantitative methods to analyze data from 2023/2024. The aim is to provide comprehensive information about the demographic gap among educators and to plan more effective education policies
Jumlah Kepala Sekolah Dan Guru Menurut Kelompok Umur Provinsi Jambi, Kalimantan Barat, Kalimantan Tengah, Sulawesi Tenggara Tahun 2023/2024 Achmad Raihan; Mifdzal Azriel; Muhammad Lutfi Syabriyan; Muhammad Rofi Baihaqi
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study analyzed the age distribution of principals and teachers in Jambi, West Kalimantan, Central Kalimantan, and Southeast Sulawesi in the 2023/2024 school year to understand the need for renewal of the teaching force. Data were obtained from the Ministry of Education, Culture, Research, and Technology, and analyzed using histograms, frequency polygons, and ogives. The results show an age imbalance in the teaching force, especially in remote areas, which risks a shortage of teachers in the near future. Recommendations include recruitment of young teachers, continuous training and strategic planning to ensure the continuity and quality of education.
Literatur Review: Klasifikasi Penyakit Parkinson Menggunakan Algoritma Decision Tree Rikha Lutfiati; Yudha Dirgantara; Fitri Anggraeni; Siti Ayu Nurfadilah; Perani Rosyani
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

Parkinson's disease is one of the neurodegenerative disorders that arises due to various risk factors, such as age, gender, and other contributing factors. Therefore, early detection of Parkinson's disease is crucial to prevent the condition from worsening. To develop an automated detection system for Parkinson's disease, a medical record dataset is required, consisting of frequency and amplitude data from the voice waves of several subjects. One of the main challenges in detecting Parkinson's disease is effectively analyzing this data. Additionally, a system that can quickly and automatically analyze clinical data is necessary. In response to this need, we propose the development of an automated system using the decision tree method to detect Parkinson's disease. This method can improve the system's performance in diagnosing whether an individual is affected by Parkinson's disease or not. The results of our proposed method show an accuracy of 90%, which is superior by 8%, 10%, 14.5%, and 20% compared to Naïve Bayes, SVM, K- NN, and other Decision Tree methods.

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