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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
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Search results for , issue "Vol 7, No 2 (2024)" : 26 Documents clear
DESIGN OF MICROSLEEP DETECTION SYSTEM IN 32-BIT MICROCONTROLLER-BASED MOTORISTS WITH RANDOM FOREST METHOD Maqdis, Syiva Awaliyah; Adiwilaga, Anugrah; Munawir, Munawir
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.v7i2.8539

Abstract

The number of motorcycle accidents has increased rapidly every year. Many occur due to drowsiness or fatigue because motorists force themselves to keep driving. The state of fatigue while driving is also known as microsleep. To overcome this problem, we propose a design of a prototype system that can be installed on the helmet of a motorized user so that the driver is more alert when driving a vehicle. This system utilizes machine learning technology with the Random Forest algorithm with two prediction results: prediction 1, which means the motorcyclist is tired, or prediction 0, which means the motorcyclist is in a normal state, embedded in the ESP32 microcontroller, and a tilt sensor that can detect signs of drowsiness in motorists. This system design will use the MPU6050 sensor to measure changes in the angle of the motorcyclist's head. The microcontroller will process the data obtained to identify head changes that indicate the possibility of drowsiness. If it occurs, the buzzer will beep as a warning to warn the driver to take a short break. The test results in drowsiness conditions with an angle of 10°–30° resulted in 100% accuracy, and normal conditions only at an angle of 0°–6° resulted in 100% accuracy. The result of the developed system is expected to reduce the number of accidents caused by drowsiness
WEBSITE QUALITY ANALYSIS OF PT. ORIGINAL ISOAE SOLUSINE BY USING THE WEBQUAL 4.0 METHOD Fadly, Raihan Abi; Ryansyah, Muhamad; Taufik, Andi
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.v7i2.8265

Abstract

Customer satisfaction is an important benchmark for the company. PT. Asli Isoae Solusine wants to improve the quality of its website exists so that customers are increasingly satisfied, but the company has not carried out an assessmen on customer satisfaction on the company profile page. The purpose of This research is to measure the level of superior quality of the company's website PT Profile Original Isoae Solusine based on user perceptions of this research using the webqual 4.0 method which has 3 main variables Usability (X1), Information Quality (X2) and Service Interaction (X3) to determine the effect quality of use, influence of interaction quality, and influence of information quality on a website. Using questionnaires as a data collection technique, Questionnaires were distributed to PT employee staff. Asli Isoae Solusine via social media. The overall quality of the https://isoae.id website is based on the R² value contributed 58% to user satisfaction. Service interaction makes a significant contribution to user satisfaction of 0.112 based on the output regression coefficients table. Usability and quality information has an influence but is not significant on user satisfaction of 0.044 and 0.011 based on the output regression coefficients table. This is possible occurs when users believe that the usability of the site and the quality of the information are not significant or only occasionally used by visitors.
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
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.
COMPARISON OF DECISION TREE AND NAÏVE BAYES ALGORITHMS IN PREDICTING STUDENT GRADUATION AT YPK JUNIOR HIGH SCHOOL, NABIRE REGENCY Yuliawan, Kristia; Murib, Stevanus
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.v7i2.8506

Abstract

This study aims to compare the accuracy of the Decision Tree C4.5 and Naive Bayes algorithms in predicting student graduation at YPK Immanuel Nabire Junior High School, Central Papua. Student data from the 2022 and 2023 school years were used as training data, whereas student data for the 2024 school year were used as testing data. Data collection methods included field studies, interviews with schools, and literature studies. The implementation of the algorithm is carried out using the Orange software, which simplifies the process of data visualization and analysis. Both algorithms are applied to data processed through stages of cleaning and normalization to ensure the quality and relevance of the data used. The results show that the Decision Tree C4.5 algorithm has a prediction accuracy of 90.91%, while the Naive Bayes algorithm has an accuracy of 63.64%. The C4.5 Decision Tree algorithm is superior in predicting student graduation compared to Naive Bayes, which means that the C4.5 Decision Tree is more effective in identifying students who are likely to pass or not pass. The implementation of the C4.5 Decision Tree algorithm also helps schools make better decisions to support students who require additional attention. The study concluded that the Decision Tree C4.5 algorithm is recommended for use in predicting student graduation because it provides higher accuracy. The results of this research can be used by schools to improve the efficiency of the graduation prediction process and develop more effective and efficient learning programs. Using the right algorithms, schools can be more proactive in identifying students who need additional support, which can reduce academic failure rates and improve the overall quality of education
OPTIMIZING HADITH CLASSIFICATION WITH NEURAL NETWORKS: A STUDY ON BUKHARI AND MUSLIM TEXTS Rasenda, Rasenda; Fabrianto, Luky; Faizah, Novianti Madhona
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.v7i2.8732

Abstract

The Bukhari and Muslim hadith collections encompass a total of 7008 hadith sentences, but it is not immediately clear which of these hadiths fall into the categories of prohibitions or orders. To enhance understanding and accessibility for readers, this study focuses on classifying these hadiths through a systematic process. The classification involves several key stages: Text Pre-processing, pre-processing the raw text data to clean and normalize (Stemming, Stopword Removal and Tokenization), Word vector features are extracted to capture the semantic relationships and contextual meanings of the words, then processed into a neural network model based on a multilayer perceptron (MLP) architecture (Model Architecture, Training and Optimization). The approach leverages the strength of neural networks, particularly through the use of multiple layers and feature extraction via word vectors, which significantly contributes to the accuracy of the classification process. The results of the study is very good, with a high accuracy rate of 97.72% achieved by employing a model with two layers and 256 neurons

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