cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
FIRMAN TEMPOLA
Contact Email
firma.tempola@unkhair.ac.id
Phone
-
Journal Mail Official
if_jiko@unkhair.ac.id
Editorial Address
-
Location
Kota ternate,
Maluku utara
INDONESIA
Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 26 Documents
 1   2   3 
Search results for , issue "Vol 7, No 2 (2024)" : 26 Documents clear
IMPLEMENTATION OF THE COMPLEX PROPORTIONAL ASSESSMENT METHOD IN DETERMINING THE PLACE OF INDUSTRIAL WORK PRACTICE Aulansari, Suwinda; Irawan, Muhammad Dedi
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8393

Abstract

Industrial Work Practices are a form of directly applying the knowledge gained in the classroom to the industrial world or the world of work. However, in its implementation, problems exist, such as workloads that must be by majors and conditions. This research uses the COPRAS method to build a decision support system to determine the place of industrial work practice at Prama Artha Private Vocational School. This method focuses on resolving each alternative's relative weight and utility and performing complex calculations proportionally. This study used 10 alternative data for internship places as samples and five criteria: student expertise, company division, distance, number of students, and type of company. The results of this study are that the PLN UP3 Pematang Siantar alternative is ranked first with a final calculation result of 100, the USI Pematang Siantar alternative is ranked second with a final calculation result of 96.29 and continued with the Tunas Bangsa STIKOM alternative with a final calculation value of 93.01. This COPRAS ranking system is based on the weights and values given to each criterion so that objectivity and accuracy are guaranteed in determining the place of internship based on the needs and abilities of students. Based on the results of black box testing of the system, it can be concluded that the system as a whole can run according to functionality and is ready for use
APPLICATION OF SUPPORT VECTOR MACHINE ALGORITHM FOR STUDENTS' FINAL ASSIGNMENT STRESS CLASSIFICATION Wicaksono, Pandu; Sriani, Sriani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8618

Abstract

In the context of higher education, the final assignment represents the last step in a student's academic journey, a period where students are particularly susceptible to stress. Implementing machine learning techniques, such as the Support Vector Machine (SVM) method, presents a promising approach for early classification of students' stress levels and offers tailored stress management recommendations. This study adopts a quantitative research approach, aimed at classifying student stress levels using the SVM algorithm known for its high prediction accuracy. The research methodology encompasses stages like data collection, preprocessing, classification, results analysis, and accuracy evaluation. In this research, 80% of the dataset is allocated for training, while the remaining 20% is reserved for testing. The study finds that the most effective SVM kernel function is the Radial Basis Function (RBF) with a γ parameter value of 1, which, when applied using RapidMiner, achieves an accuracy of 93.33%. This research is anticipated to make a significant contribution to the development of early stress detection systems for students and offer valuable insights into leveraging machine learning technology for mental health applications. The findings demonstrate that the SVM method with the RBF kernel provides highly accurate classification results, making it a useful tool for effectively identifying student stress level
FORECASTING SALES USING SARIMA MODELS AT THE SINAR PAGI BUILDING MATERIALS STORE Aminullah, Ahmad Adiib; Idhom, Mohammad; Saputra, Wahyu Syaifullah Jauharis
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8266

Abstract

Sinar Pagi Building Materials Store faces the challenge of maintaining optimal stock levels of goods to avoid excess and understock, which affects customer satisfaction and operational efficiency. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast sales in the store. Leveraging its ability to model seasonal patterns on historical sales data, various SARIMA models were analyzed and compared using the Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE). The dataset is divided by a 95:5 ratio into training and testing sets for robust evaluation. The results show that the SARIMA model with SARIMA notation (p,d,q)(P,D,Q  has the best model value of (1,0,0) . This model is the most suitable model based on the lowest AIC value of 1245 and the lowest RMSE of 7,95 compared to other SARIMA models after model identification using the model looping test. For other models such as model (1,0,1)  and (0,0,1) , the AIC and RMSE values are greater, namely model (1,0,1)  with AIC 1246 and RMSE of 8,05, while model (0,0,1)  gets an AIC of 1252 and an AIC of 8,15 .The lower the AIC value, the better the model and the lower the RMSE value, the better the model. This shows a superior balance between model complexity and prediction accuracy. The model manages to capture seasonal patterns in sales data, providing a pretty good prediction framework. This study shows that the SARIMA (1,0,0)  model is effective in the accuracy of the sales forecasting process so that Sinar Pagi Building Materials Store can make more reliable sales predictions, which can help in inventory planning and marketing strategies
IMPLEMENTATION OF THE FUZZY TIME SERIES METHOD FOR FORECASTING BLOOD NEEDS IN THE INDONESIAN RED CROSS (PMI) MEDAN Harahap, Rina Syafiddini; R, Rakhmat Kurniawan
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8614

Abstract

The primary issue faced by PMI (Indonesian Red Cross) about blood requirements is often associated with insufficient blood supplies to satisfy the demand of patients, particularly during emergencies or significant catastrophes such as natural calamities. Hence, it is essential to use appropriate methodologies to forecast blood requirements accurately and determine the quantity of blood bags required in the future. When forecasting calculations using fuzzy time series, the interval length is established at the start of the calculation procedure. The duration of the gap significantly affects the establishment of fuzzy associations, which in turn affects the difference in forecast computation outcomes. The investigation reveals that Group AB has the lowest Root Mean Square Error (RMSE) value of 136.90, indicating that your model demonstrates superior accuracy in predicting blood group AB compared to other blood groups. The RMSE score for Group O is 819.5, which suggests that your model's accuracy in predicting blood group O is lower compared to other blood groups
CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Fitriani, Lely Mustikasari Mahardhika; Litanianda, Yovi; Cobantoro, Adi Fajaryanto
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.

Page 3 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 8, No 3 (2025) Vol 8, No 2 (2025) Vol 8, No 1 (2025) Vol 7, No 3 (2024) Vol 7, No 2 (2024) Vol 7, No 1 (2024) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer) Vol 6, No 1 (2023) Vol 5, No 3 (2022) Vol 5, No 2 (2022) Vol 5, No 1 (2022) Vol 4, No 3 (2021) Vol 4, No 2 (2021) Vol 4, No 1 (2021) Vol 3, No 3 (2020) Vol 3, No 2 (2020) Vol 3, No 1 (2020) Vol 2, No 2 (2019) Vol 2, No 1 (2019) Vol 1, No 2 (2018) Vol 1, No 1 (2018) Vol 1, No 2 (2017): Jurnal Informatika dan Komputer (JIKO) Ternate Vol 1, No 1 (2017): Jurnal Informatika dan Komputer (JIKO) Ternate More Issue