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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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Multiclass Email Classification by Using Ensemble Bagging and Ensemble Voting Ali Helmut; Danang Triantoro Murdiansyah
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6394

Abstract

Email is a common communication technology in modern life. The more emails we receive, the more difficult and time consuming it is to sort them out. One solution to overcome this problem is to create a system using machine learning to sort emails. Each method of machine learning and data sampling result in different performance. Ensemble learning is a method of combining several learning models into one model to get better performance. In this study we tried to create a multiclass email classification system by combining learning models, data sampling, and several data classes to obtain the effect of Ensemble Bagging and Ensemble Voting methods on the performance of the macro average f1 score, and compare it with non-ensemble models. The results of this study show that the sensitivity of Naïve Bayes to imbalance data is helped by the Ensemble Bagging and Ensemble Voting method with ∆P (delta performance) of range 0.0001 – 0.0018. Logistic Regression has performance with Ensemble Bagging and Ensemble Voting by ∆P of range 0.0001-0.00015. Decision Tree has lowest performance compared to others with ∆P of -0.01
Exploring the Effectiveness of Deep Learning in Analyzing Review Sentiment mariyanto totox; Hilman F Pardede
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6372

Abstract

This study aimed to analyze sentiment in office product reviews by using word embedding with three neural network modeling approaches: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Office product review data is taken from Amazon's reviews of office products covering a wide range of sentiments. Word embedding converts text into a numerical vector representation for neural network processing. Experimental comparison of this model reveals that CNN achieves the highest accuracy, 77.99%. The CNN model effectively extracts significant features from review text, improving sentiment classification performance. Although the LSTM and GRU models show satisfactory results, they do not match CNN performance. These findings demonstrate the effectiveness of word embedding and neural networks for sentiment analysis in office product reviews. This provides valuable insights for companies to improve their products based on user feedback from online reviews. Additionally, this research serves as a foundation for further advances in sentiment analysis across a wide range of other products and services
HARNESSING THE POWER OF PROTOTYPING METHOD FOR ENGAGING RESPONSIVE WEB PROFILES Ananto Tri Sasongko; Sunita Dasman
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6399

Abstract

This research discusses the implementation of the prototyping method in enhancing institutional visibility through the development of responsive web profiles. Institutional visibility is key to achieving success and sustainable growth in today's digital era. Developing a responsive web profile increases visibility by providing optimal user experience across various devices and screen resolutions effectively. The prototyping method was the primary approach in developing this responsive web profile. It allows developers to create initial models that can be tested and evaluated before the full development of the web profile. The research explains the steps in developing a responsive web profile using prototyping. The results show that this method offers an efficient and practical approach to creating a responsive web profile, ensuring user satisfaction, and meeting the increasing expectations of users. Therefore, institutions should consider applying this prototyping method to strengthen their visibility through innovative, responsive web profiles. Responsive web profiles enable institutions to reach and engage their target audience through different devices, enhancing user engagement and providing consistent user experiences. The result shows that prototyping enhances institutional web profiles, improves user experience, and effectively increases visibility with high satisfaction, with an average of 83.2.
DATA MINING IMPLEMENTATION FOR DETECTION OF ANOMALIES IN NETWORK TRAFFIC PACKETS USING OUTLIER DETECTION APPROACH Kurnia Setiawan; Arief Wibowo
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6092

Abstract

The large number of data packet records of network traffic can be used to evaluate the quality of a network as well as to analyze the occurrence of anomalies in the network, both related to network security and network performance. Based on the data obtained, the occurrence of anomalies in computer networks can not be detected specifically on which traffic packets. Meanwhile, to monitor network traffic packets manually will require a lot of time and resources, making it difficult to detect potential anomaly events more specifically. This study analyzes network packet traffic data to see records that include anomalies with an outlier detection approach, using the Isolation Forest algorithm to detect outliers on network traffic packet data, with the result that minority data are of the outliers type of 1,643 records (4.86%), while inliers are 32,098 records (95.13%). Then check and filter the expert attributes that contain expert information. The outlier detection results were classified using 5 algorithms as comparison, namely Random Forest Classifier, Support Vector Machine, Decision Tree Classifier, K-Nearest Neighbor, and Bernoulli Naive Bayes. The Random Forest algorithm has the highest score for accuracy, macro average precision, and macro average f1-score, namely 0.9962067330488383; 0.78; and 0.82. The classification model can be used to classify samples with labels "inliers", "outliers", "Error", and "warning outliers". There are labels that have scores for precision, recall, and f1-scrore that are not too high, namely the labels “error” (0.50; 1.00; and 0.67) and “warning outlier” (0.64; 0 .70; 0.67). The resulting classification model is used for prototype development that facilitates the process of investigating potential network traffic packet anomalies more specifically.
THE IMPLEMENTATION OF NETWORK SERVER SECURITY SYSTEM USING HONEYPOT Faldi Faldi; Dinamita Romadoni; Muhammad T Sumadi
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6385

Abstract

Network Server security is an important aspect of ensuring the integrity and availability of information systems. This research aims to implement network Server security at Muhammadiyah University of East Kalimantan using Honeypot technology. Honeypots are used to attract the attention of attacks and monitor suspicious activities on the network. The research method used is NDLC (Network Development Life Cycle), which includes the design and implementation of Honeypots, as well as the collection and analysis of detected attack data. The research results show that by using three attack techniques, namely Slowloris attack with a Honeypot processing time of 2 seconds and Snort processing time of 180 seconds, GoldenEye attack with a Honeypot processing time of 2 seconds and Snort processing time of 180 seconds, and LOIC tools with a Snort processing time of 180 seconds. Honeypots cannot identify DDoS attacks because they focus more on attacks such as penetration attempts or other suspicious activities.
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.
DETERMINING THE OPTIMAL ROUTE OF BULOG RICE DISTRIBUTION USING SEQUENTIAL INSERTION AND NEAREST NEIGHBOUR METHOD Safitri, Yayang; Rakhmawati, Fibri; Aprillia, Rima
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.v7i1.7443

Abstract

Rice consumption in Indonesia is the 4th highest in the world, with an annual average of 35.3 million tonnes. Apart from that, domestic rice production is relatively low, so finding an optimal distribution pattern to meet Indonesia's food consumption needs is necessary. This research aims to find the best route for rice distribution using sequential insertion and nearest-neighbor methods. The aim is to compare the two methods to determine which is suitable for distributing rice. Apart from that, comparisons are made to find the distribution route with the shortest distance and fastest time. The research results show that the optimal travel distance using the sequential insertion method is t = 1, namely 519 Km with a travel time of 779 minutes. Meanwhile, the optimal travel distance using the nearest neighbor method at r = 1 is 506 km with a travel time of 535 minutes. Thus, this problem's nearest neighbor method performs better than sequential insertion.
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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