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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
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Articles 22 Documents
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Search results for , issue "Vol 7, No 3 (2024)" : 22 Documents clear
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.
ANALYSIS OF CABLE NETWORK READINESS FOR THE IMPLEMENTATION OF ENTERPRISE RESOURCE PLANNING INFORMATION SYSTEMS AT THE FACULTY OF INDUSTRIAL ENGINEERING febriyanto, Akbar; Hediyanto, Umar Yunan Kurnia Septo; Fathinuddin, Muhammad
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.v7i3.8624

Abstract

With the rapid advancement of information and communication technology, organizations increasingly adopt integrated systems to enhance efficiency and productivity. One widely adopted technology is Enterprise Resource Planning (ERP), a comprehensive management system that integrates various business functions, including finance, manufacturing, inventory, and human resources. Implementing an ERP system requires a robust network infrastructure, particularly in terms of quality of service (quality of service). This study aims to evaluate the readiness of the cable network infrastructure across three buildings at the Faculty of Industrial Engineering, Telkom University, to implement an Odoo-based ERP system. The research employs the Network Development Life Cycle (NDLC) methodology, focusing on crucial quality of service parameters such as throughput, delay, jitter, and packet loss. Data were collected through observations, interviews, and network analysis using Wireshark, with tests conducted at different times (low, peak, and intermediate). The results show that the TULT Building, Mangudu Building, and Building B Cacuk networks are generally prepared for ERP implementation. For instance, in the TULT Building, the average throughput without filters at low, peak, and intermediate times was 45.296 Kbps, 50.923 Kbps, and 61.399 Kbps, respectively. Packet loss averaged 0.56%, 0.50%, and 0.65% without filters. Despite jitter values ranging from 103.73 ms to 582.40 ms, below the TIPHON standard, the ERP system remains functional as it is not highly sensitive. The study concludes that the existing network infrastructure is sufficient mainly for the Odoo-based ERP implementation, with recommendations for further improvements to address jitter issues.
QUESTION BANK SECURITY USING RIVEST SHAMIR ADLEMAN ALGORITHM AND ADVANCED ENCRYPTION STANDARD Monica, Taris; Hadiana, Asep Id; Melina, Melina
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.v7i3.8654

Abstract

Data security is essential. Educational question banks at vocational high schools (SMK) contain confidential information that could be misused if not properly secured. This research aims to ensure students question bank data and develop a responsive web platform for Pusdikhubad Cimahi Vocational School by implementing the integration of the Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA) cryptographic algorithms through the encryption and decryption process. AES is a symmetric key cryptography algorithm, while RSA is an encryption algorithm based on using public keys to encrypt the keys required by AES-256. The integration of these two algorithms aims to ensure data confidentiality, prevent manipulation, and facilitate access to exam materials by authorized parties. This research shows that the process of encrypting and decrypting question data using a combination of RSA and AES was successfully carried out on the question bank system. Avalanche Effect testing shows that the RSA and AES 256-bit encryption has an Avalanche Effect level of 49.99%. Apart from that, the system feasibility test using black box testing results shows that the SIFILE system has a percentage level of 100%. It is hoped that the results of this research can serve as a data security system at Pusdikhubad Cimahi Vocational School and other educational institutions to secure the question bank from unauthorized access
CUSTOMER CHURN PREDICTION USING THE RANDOM FOREST ALGORITHM Setiawan, Yosep; Hadiana, Asep Id; Umbara, Fajri Rakhmat
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.v7i3.8711

Abstract

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.
DETECTION OF LIKURAI DANCE MOVEMENT TYPES IN MALAKA REGENCY USING YOLOV8 BASED ON VIDEO Da Costa, Zania Abuk; Rahman, Aviv Yuniar; Putra, Rangga Pahlevi
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.v7i3.8815

Abstract

Indonesia is rich in traditional dances from every region, including the Likurai Dance, originating from East Nusa Tenggara, specifically in Malaka and Belu districts. This dance carries deep symbolic and historical meaning; however, it is currently threatened by lifestyle changes and globalization. Despite this, accurately and in real-time recognizing Likurai Dance movements remains challenging, particularly in detecting the specific dance movements. This research aims to test the effectiveness of detecting three types of Likurai Dance movements using documented digital video. The detection model is the YOLOv8 algorithm, known for detecting objects quickly and accurately. A YOLOv8-based platform is proposed to detect these dance movements precisely. In the testing, the YOLOv8 model demonstrated outstanding performance, achieving a very high mAP of 99.5% for the Wesei Wehali movement, 99.4% for the Be Tae Be Tae movement, and 99.1% for the Tebe Re movement. These results indicate that the model can detect dance movements with exceptional accuracy, precision, and recall rates above 98%. This research concludes that YOLOv8 has excellent potential in detecting traditional dance movements with high accuracy. These findings are significant for preserving and documenting the Likurai Dance and provide an educational means for younger generations to understand better and appreciate traditional cultural values.
IMPLEMENTATION OF MSME CREDIT LOAN DETERMINATION USING MACHINE LEARNING TECHNOLOGY WITH KNN (K-NEAREST NEIGHBORS) ALGORITHM Nawawi, Muchamad Taufik; Suhendar, Agus
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.v7i3.9064

Abstract

This research aims to develop a loan eligibility prediction model for Micro, Small, and Medium Enterprises (MSMEs) using the K-Nearest Neighbors (KNN) algorithm. The dataset utilized includes variables such as the length of business operation, number of workers, assets, and net turnover of MSMEs. The data is split into training and test sets with a 70:30 ratio. The KNN model is trained using the training data to classify loan eligibility based on a specified k value. The model predictions include whether a loan is accepted and the probability associated with each decision. The results indicate that the KNN model achieved an accuracy rate of 83.939% in predicting loan eligibility. Based on the predictions, 929 MSMEs were deemed eligible to receive loans according to the KNN model recommendations, while 170 MSMEs were classified as ineligible. These findings contribute significantly to the development of decision support systems in the banking and finance sectors, particularly in evaluating MSME loan eligibility.
DEVELOPING AN IT INFRASTRUCTURE MODEL FOR ENHANCING DIGITAL LITERACY THROUGH WEB-BASED LEARNING: A COMPREHENSIVE FRAMEWORK Sulianta, Feri; Rumaisa, Fitrah; Puspitarani, Yan; Violina, Sriyani; Rosita, Ai
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.v7i3.8761

Abstract

In today's rapidly evolving educational landscape, there is a growing need to develop an IT infrastructure model that can effectively support web-based learning environments to enhance digital literacy. The proposed model offers a comprehensive framework for educational institutions to integrate digital technologies into their curricula seamlessly. Key elements of the model include essential hardware, user-friendly software, and advanced security measures, each playing a vital role in creating a seamless, secure, and efficient digital learning experience. This study explores the dynamic interactions among these components and their collective influence on fostering a conducive and productive web-based learning environment. By addressing the need for reliable infrastructure, scalable solutions, and robust security protocols, the model provides a holistic approach to improving digital literacy in educational contexts. The research underscores the critical role of a well-structured IT infrastructure in supporting digital education, offering actionable insights and recommendations for implementation. Moreover, it emphasizes that a well-developed IT infrastructure is foundational for the long-term success of web-based learning programs, enabling institutions to meet diverse learner needs, adapt to technological advances, and ensure sustainability in the digital education landscape.
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.

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