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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 287 Documents
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DETECTION OF THE SIZE OF PLASTIC MINERAL WATER BOTTLE WASTE USING THE YOLOV5 METHOD Karyanto, Dony Dwi; Indra, Jamaludin; Pratama, Adi Rizky; Rohana, Tatang
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.v7i2.8535

Abstract

The use of plastic bottles for various needs is increasingly massive, especially in consumption needs such as mineral water bottles. The use of plastic bottles is used to reduce costs and be effective in maintaining the quality of mineral water, but its impact can affect natural conditions if not managed properly. Plastic bottle waste if left buried in the ground will have difficulty expanding, which can cause environmental pollution. Therefore, we can take advantage of technology to sort plastic bottle waste using a camera based on the size of plastic bottles. Differentiating the size of bottles aims to distinguish the economic value when exchanged at the waste bank. This technology utilizes object detection and recognition functions such as the YOLO (You Only Look Once) method. YOLO is a detection method that is a development of the CNN (Convolutional Neural Network) algorithm. By using YOLOv5, we can detect objects in the form of plastic bottle waste of various different sizes. To maximize object detection according to size, data annotation is done by creating a Bounding Box on each dataset according to its size. The test was carried out with several different distance configurations including 40cm, 80cm and 1m. Detection results using YOLOv5 produce up to 84% accuracy in real-time.
COMPARISON OF ROBUSTNESS TEST RESULTS OF THE EYE ASPECT RATIO METHOD AND IRIS-SCLERA PATTERN ANALYSIS TO DETECT DROWSINESS WHILE DRIVING Aditia, Risky; Sriani, Sriani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.9239

Abstract

Traffic accidents caused by driver drowsiness are a leading factor in fatal road incidents. This study introduces a computer vision-based drowsiness detection system utilizing two methods: Eye Aspect Ratio (EAR) and Iris-Sclera Pattern Analysis (ISPA). The EAR method measures the eye aspect ratio to determine whether the eyes are open or closed. This involves calculating the vertical distance between specific landmark points on the eyelids and comparing it to the horizontal distance between points on the eye. A decrease in this ratio serves as an early indicator of drowsiness. The ISPA method employs symmetry analysis between the iris and sclera. This approach relies on the visual pattern formed when the eyes are open, where the sclera appears symmetrically distributed around the iris. During this process, eye images are processed to extract iris and sclera features, which are then analyzed for symmetry to detect signs of drowsiness. The study evaluates the reliability of both methods under varying conditions, such as changes in lighting, viewing distances, head movements, and the use of eyeglasses. The results show that the EAR method achieved an accuracy of 83.33% in distance testing, indicating its effectiveness in stable lighting environments. In contrast, the ISPA method achieved an accuracy of 59.25% under low and variable lighting conditions and proved more reliable for detecting the eyes of users wearing glasses.
ANALYSIS OF FUZZY C-MEANS IN PERSONALITY CLUSTERING BASED ON THE OCEAN MODEL Pamput, Jessicha; Dillah, Salsa; Muthmainnah, Aindri; Surianto, Dewi Fatmarani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8369

Abstract

Personality is the pattern of an individual's behavior in daily life, reflected in their thoughts, feelings, and actions. The Big Five Personality Traits Model, known as OCEAN, helps to understand the complexity of human personality through five main traits. The identification and classification of personality, particularly among students, impacts academic performance, personal development, anxiety levels, and risky behaviors. Collaboration between educators, mental health professionals, and career advisors is crucial to creating an educational environment that supports students' holistic development. The Fuzzy C-Means (FCM) method is used to identify students' personalities with adequate accuracy. This study adopts the OCEAN model with FCM to efficiently identify and classify students' personalities. Data were obtained from 142 respondents, resulting in 27% of respondents being classified in cluster 1, 21% in cluster 2, 18% in cluster 3, 16% in cluster 4, and 18% in cluster 5. This study has important implications for students, educators, and educational institutions to understand that learning patterns, social interactions, and decision-making processes can be influenced by an individual's personality.
PRIMARY QUERY ANALYSIS ON SQL DATABASE RESTRUCTURING IN GEOGRAPHIC INFORMATION SYSTEMS Ilyas, Ridwan; Witanti, Wina; Syarafina, Fildzah
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.v7i2.8565

Abstract

Database restructuring is a crucial process aimed at enhancing data management and access efficiency by modifying the existing data structure. This research focuses on improving a Geographic Information System (GIS) for taxation by migrating and restructuring an inefficient and redundant database. The study conducts a comparative performance evaluation of the old and restructured databases using benchmarking tests with varying numbers of threads and ramp-ups. The results reveal a significant increase in average throughput (24.60%) following the restructuring, indicating a substantial improvement in the database's data processing capacity. However, there is also an average increase in response time (21.65%), suggesting a trade-off between enhanced throughput and slower response times. This increase in response time indicates that while the system can handle more data, it requires more time to process each query. Overall, the restructured database demonstrates enhanced performance and efficiency, though further optimization is necessary to achieve consistent throughput across different workloads and to mitigate the increased response times
CLASSIFICATION OF BONE FRACTURES IN THE WRIST AND HAND USING DENSENET AND XCEPTION Nusantara, Michelle Swastika Bianglala; Wonohadidjojo, Daniel Martomanggolo
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9201

Abstract

This study aims to apply Convolutional Neural Network (CNN) using DenseNet and Xception to classify fracture in the wrist and hand bones, while utilizing transfer learning to enhance model's performance. Accurate diagnosis and successful treatment of bone fractures depend on early identification, which lowers the likelihood of long-term issues such avascular necrosis or non-union. The research utilized data from two publicly available musculoskeletal radiography datasets and employed deep learning techniques with the Keras framework. DenseNet was selected for wrist image analysis due to its dense connectivity, which preserves information from previous layers, while Xception was chosen for hand bone image analysis because of its ability to identify complex patterns using depthwise separable convolutions. Transfer learning was implemented to accelerate training and improve accuracy. The DenseNet model achieved a test accuracy of 97.5% for wrist classification, while the Xception model reached 92% accuracy for hand bone classification. By tailoring CNN architectures to specific radiographic images and employing transfer learning, this study demonstrates significant potential for improving diagnostic precision in clinical situations. Furthermore, the findings can support medical personnel in detecting bone fractures more efficiently and accurately, ultimately expediting clinical decision-making and improving patient care.
SPERM ABNORMALITY CLASSIFICATION USING MULTI-PURPOSE IMAGE EMBEDDING AND CLASSICAL MACHINE LEARNING Adinugroho, Sigit; Sari, Yuita Arum; Kurniawan, Wijaya; Arwan, Achmad
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8938

Abstract

Since sperm cells have big impact for human welfare in terms of reproduction, there are many studies have been done. In this case, we are attracted to enrich the method in determining the morphological properties of them using machine learning. Most study about it is done using 2-steps action that are feature extraction which is continued by classification. In our work, we aimed to lower the complexity by using image embedding as a general-purpose feature extractor that requires no training. For feature extraction using image, it is found that RGB has better performance compared to grayscale if we want to use Support Vector Machine (SVM). Meanwhile, when a comparation is done between SVM, random forest, Multi-Layer Perceptron (MLP), Naïve Bayes, and k-Nearest Neighbour (kNN) for classification process, MLP shows the best performance among them which is around 85%. Moreover, our proposed method has low complexity indicated by the training time around one and a quarter minute s for the most accurate method, compared to hours of training time in similar methods.
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.
TRANSFORMER WITH LAGGED FEATURES FOR HANDLING LONG-TERM DATA DEPENDENCY IN TIME SERIES FORECASTING Verianto, Eko; Shimbun, Annisa Fikria
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.9247

Abstract

Data with long-term dependencies plays an important role in time series forecasting. However, studying data with long-term dependencies in time series data presents challenges for most algorithms. While some algorithms can forecast time series data, not all can model data with long-term dependencies effectively. The algorithm typically used for forecasting data with long-term dependencies is Long Short-Term Memory (LSTM), but LSTM can still face vanishing gradient issues, making it difficult to identify long-term dependencies in very long datasets. Another algorithm used for forecasting long-term time series data is the transformer. However, this algorithm has not yet shown better performance compared to simple linear models. The goal of this research is to develop an effective solution for forecasting time series data with long-term dependencies. The approach proposed in this research is the transformer with lagged features and also using time series cross-validation techniques. The results of this study show the performance of the transformer model in MAPE per fold on the BBCA stock dataset with a lag=5 and fold=5 configuration as follows: 0.0390, 0.0329, 0.0207, 0.0554, 0.0423. On the USD/IDR exchange rate dataset, the results are 0.0273, 0.0431, 0.0498, 0.0236, 0.237. The results of each fold are inconsistent and show unstable performance, indicating that the transformer with lagged features and using time series cross-validation techniques has not yet been able to provide its best performance in long-term time series forecasting.
DEVELOPING ENTERPRISE ARCHITECTURE FOR BPRACO SMEs DIGITAL TRANSFORMATION BY USING TOGAF 10 Safitri, Shintya Rahma; Mulyana, Rahmat; Fajrillah, Asti Amalia Nur
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8629

Abstract

Organizations must embrace emerging technology through Digital Transformation (DT) to remain competitive in the digital revolution era. While previous research has highlighted the critical role of DT strategy and architecture in driving DT success in large banks, these insights have not been thoroughly tested in small-scale banks. Small and medium-sized enterprise (SME) banks like BPR often encounter significant challenges in the DT journey, including limited infrastructure, reliance on outdated and poorly integrated systems, and slow technology adoption. These barriers hinder their ability to support the DT initiatives necessary for thriving in the digital age. This study aims to develop an enterprise architecture blueprint tailored to support DT in BPRACo, an SME-scale bank. The research follows a five-stage Design Science Research (DSR) methodology, encompassing problem explication, requirement specification, design and development, demonstration, and evaluation. Data were collected through semi-structured interviews, validated through document triangulation, and analyzed using the TOGAF 10 framework, covering phases from preliminary planning to migration. The resulting blueprint was integrated into BPRACo's DT Strategy for 2024-2026. This research enhances the understanding of enterprise architecture's role in DT within the context of SME banks. It offers practical guidance for BPRACo and similar institutions to implement prioritized enterprise architecture artifacts, facilitating a successful DT journey.

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