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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 241 Documents
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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
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
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.
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
APPLICATION OF THE K-MEANS AND DECISION TREE ALGORITHMS IN DETERMINING STUDENT ACHIEVEMENT Jevintya, Nandya Rifki; Darussalam, Ucuk; Abdullah, Syahid
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7580

Abstract

Various factors influence student achievement, both internal and external; this makes it difficult for some teachers to detect every student in class. This research aims to determine student achievement in class among students at the SDS Kartika X-6 school. Data comes from SDS Kartika X-6, an elementary school owned by the Indonesian Army. By knowing the factors that influence the determinants of student learning achievement, steps can be taken to improve student learning achievement at SDS Kartika x-6. The methods used in this research are the K-Means algorithm and Decision Tree. This method will be chosen to determine student learning achievement. The process begins by determining clusters using the K-Means algorithm; then a classification process is carried out using a Decision Tree. The number of datasets in this research is 28, and the criteria are gender, mathematics grades, English, natural sciences, religion, class performance, and school achievement. The implementation results show that academic grades, class achievements, and school achievements play a role in determining student achievement for SDS Kartika X-6 students. Meanwhile, 3 clusters were formed: Fairly Good, Good, and Very Good. In the testing stage using the Decision Tree method, prediction accuracy was 71%, with an error of 29.
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
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
IMPLEMENTATION MULTIMEDIA DEVELOPMENT LIFE CYCLE IN INTERACTIVE MULTIMEDIA DESIGN FOR TRADITIONAL INDONESIAN MUSIC INSTRUMENTS INTRODUCTION Zuhdi, Aditya Imam; Mustafidah, Zahrotul; Nur Alam, Muhammad Risqi; Irawan, Safira Anggraini
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7640

Abstract

This research aims to address the lack of interactive multimedia-based educational media in introducing traditional Indonesian musical instruments to the public, especially children. The issue arises from the fact that the diversity of traditional musical instruments in Indonesia has not been presented attractively in an interactive media format. Therefore, this study utilizes the Multimedia Development Life Cycle (MDLC) method as a guide in designing and developing interactive multimedia. The MDLC stages, namely Concept, Design, Material Collecting, Assembly, Testing, and Distribution, are implemented to ensure that each step of the system development is well-organized. The results of alpha testing indicate that all features of the interactive multimedia work well. Beta testing, involving 36 respondents, yields a rating of 4.52 out of 5, demonstrating that this interactive multimedia is excellent and suitable for use as a learning media for traditional Indonesian musical instruments. This research addresses the gaps in the presentation of educational information by providing an interesting and effective media, especially in the context of traditional musical instruments. Thus, it is expected that this interactive multimedia can enrich the knowledge of the public, especially children, about the cultural wealth of traditional Indonesian musical instruments.
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.

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