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Agus Harjoko
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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 476 Documents
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A Mamdani FIS to Monitor Programmer Performance on GitHub Purba, Susi Eva Maria; Wardoyo, Retantyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.88575

Abstract

A collaborative activity used to accomplish shared objectives is teamwork. It is essential to know how unequal contributions can inhibit team members' chances to give their all in achieving these objectives. It will be necessary to manage resources in this joint approach. Monitoring each team member’s performance in one technique to do this. In previous research, performance measurement was designed using Prometer with several parameters, utilizing the crisp set at each stage. This study developed the method by adding variables and utilizing fuzzy logic, which can consider the membership value for each value involved. The membership value considered for each variable is expected to provide a significant assessment of each team working on developing software projects using the GitHub platform. The results will be monitored based on the involvement of each collaborator in project work through the data recorded in the pull requests, issues, commits, additions code, and deletion code variables. The results obtained by utilizing the variables and several rules that have been designed with the Mamdani implication function are then compared with the observations obtained by the Project Manager so that an accuracy value of 86.67% is accepted for the use of inclusive and exclusive rules (operand AND).
Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis Hendra, Hendra; Hermawan, Aditiya; Edy, Edy
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.89075

Abstract

 The world of online commerce is becoming increasingly competitive, and to succeed in this field, it is not enough to showcase products to potential buyers. It is crucial to offer various products and keep product recommendations up-to-date, especially for customers who buy multiple items. To address this challenge, an intelligent system is needed that can automatically generate trending product recommendations based on sales data. In this research, the Market Basket Analysis (MBA) method analyzes consumer transaction data and identifies products often purchased together. The apriori algorithm is applied to generate association rules, and the Lift Ratio parameter is used to evaluate the strength of these rules. This research is implemented on an e-commerce website, and the generated association rules will be applied to provide automatic product recommendations based on recent sales trends. The results show that the automatic product recommendation system developed for the e-commerce website significantly helps users enhance their online shopping experience. Using the Lift Ratio parameter in validating association rules provides strong evidence of the relevance and accuracy of the generated product recommendations, which can increase customer satisfaction and sales potential.
Modeling OTP Delivery Notification Status through a Causality Bayesian Network Asriny, Novendri Isra; Dewa, Chandra Kusuma; Luthfi, Ahmat
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90030

Abstract

Digital money is the fundamental driving factor behind today's modern economy. Credit/debit cards, e-wallets, and other contactless payment options are widely available nowadays. This also raises the security risk associated with passwords in online transactions. One-time passwords (OTPs) are another option for mitigating this. A one-time password (OTP) serves as an additional password authentication or validation technique for each user authentication session. Failures in transmitting OTP passwords through SMS can arise owing to operator network faults or technological concerns.To minimize the risk value that arises in online transactions, it is necessary to evaluate the causality of the OTP SMS sending transaction status category by determining the main factors for successful OTP SMS sending and identifying the causes of failure when sending OTP SMS using the Bayesian Network method. According to data analysis, online transactions occur more frequently in the morning, with status summaries such as no delay, unknown status, and others. Furthermore, there is causality with at least three variables in the principal status summary, including no delay, uncertain summary, long delay, normal, likely operator issues, abnormal, and more. With a high accuracy rate of around 90% in forecasting the likelihood of recurrence.
Maintaining Query Performance through Table Rebuilding & Archiving Andriyani, Widyastuti; Pujianto, Pujianto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90062

Abstract

Despite the system previously utilizing optimal query configurations and database settings, the transaction table in the database, which is undergoing significant numerical increases and notable queries and updates on each line, has seen a drop in query speeds simultaneous with data growth. This situation arises due to an increase in disk space in the database tablespace, which results from block fragmentation. At times, database engines do not detect this problem, thereby overlooking it in the database recommendation engine. Lacking an understanding of the fundamental issue, database engineers need analysis and strategies to maintain the query speed of the transaction table in the relational database
Multivariat Predict Sales Data Using the Recurrent Neural Network (RNN) Method Ardriani, Ni Nengah Dita; Yastawil, Jamiin Al Yastawil; Erawati, Kadek Nonik; Yudi Antara, I Gede Made; Santiago, Gede Agus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90165

Abstract

Sales is an activity or business selling a product or service. In this study, I took a case study on Kaggle. Sales problems at the company cause inventory to be very high or vice versa, causing a loss of sales because there are no items to sell. Inventory that is too high results in increased costs due to existing resources being inefficient. In the opposite condition, it will cause a product vacancy in the market. Using the Recurrent Neural Network (RNN) Algorithm, this study predicts sales. The data used is sales data in 2020 with the parameter Number of sales per day in the last four months. The results obtained through testing several training scenarios and testing the implementation of the algorithm, in this case, is the highest accuracy value of 96.92% in the network architecture of three input neuron layers, three hidden layer neurons, one output, division of training, and test data 70: 30, learning value rate of 0.9 and a maximum of 9000000 epochs
Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests Kumoro, Rabbani Nur; Fattima, Audrey Shafira; Susatyo, William Hilmy; Fudholi, Dzikri Rahadian
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90202

Abstract

Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.
Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction Rizky, Muhammad Hevny; Faisal, Mohammad Reza; Budiman, Irwan; Kartini, Dwi; Abadi, Friska
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90437

Abstract

The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms
Anomaly Detection of Hospital Claim Using Support Vector Regression Hapsari, Luthfia Nurma; Rokhman, Nur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.91857

Abstract

BPJS Kesehatan plays a crucial role in providing affordable access to healthcare services and reducing individual financial burdens. However, deficit issues can disrupt the sustainability of the program, making anomaly detection highly important to conduct. Previous research on unsupervised anomaly detection in BPJS Kesehatan revealed a limitation with Simple Linear Regression (SLR), which only accommodates linear relationships among independent variables and the target variable of BPJS Kesehatan claim values. Minister of Health Regulation No. 52 of 2016 identified eight influential non-linear independent variables, leading to the proposal of Support Vector Regression (SVR) to address SLR's shortcomings.Research findings demonstrate SVR's superior anomaly detection performance over SLR. Interestingly, the SVR model excels in anomaly detection but lacks in prediction. Optimal tuning of SVR hyperparameters (C=9, epsilon=90, gamma=0.009, residual anomaly definition > 0.5*RMSE for both datasets) yields impressive metrics: Accuracy=0.97, Precision=0.84, Recall=0.97, and F1-Score=0.90. The anomaly detection results are expected to greatly support the sustainability of the BPJS Kesehatan program in Indonesia.
HOSPITAL MANAGEMENT INFORMATION SYSTEM EVALUATION AT GRHA PERMATA IBU DEPOK Yanti, Layli Hardi; Umniati, Naeli
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.91958

Abstract

The GRHA Permata Ibu Hospital in Depok has been implementing the Hospital Management Information System (HMIS) since 2013 to support all hospital service processes. An evaluation of the HMIS is necessary to understand the actual state of the information system implementation. The objective is to examine and assess the HMIS at GRHA Permata Ibu Hospital to achieve results that are comparable using specific benchmarks. The goal is to obtain performance outcomes that support better, effective, and efficient services, and to identify the system's current condition for further action planning to improve its performance. The research follows a quantitative method with an online survey approach using Google Forms. The HOT-Fit evaluation model is used to assess the readiness level for utilizing an information system, focusing on the crucial components of Human, Organization, Technology, and Net Benefits. The study's results reveal that out of the 13 developed hypotheses, 6 hypotheses were accepted, while 7 hypotheses were rejected. Therefore, the research proves that not all proposed hypotheses are empirically supported. Based on the test results, several recommendations are provided to enhance the success rate of the HMIS implementation at GRHA Permata Ibu Hospital in Depok.
DEVELOPMENTS AND TRENDS IN CYBERSECURITY AGAINST HUMAN FACTORS AND TIME PRESSURE USING BIBLIOMETRIC ANALYSIS Saputri, Aprilia Mayang; Syaifullah, Syaifullah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.92636

Abstract

Memahami keamanan siber sangat penting di era digital saat ini, dan penelitian telah dilakukan untuk memahami faktor-faktor yang mempengaruhi keberhasilan atau kegagalannya. Faktor manusia berperan penting dalam keamanan siber, dan lebih dari 95% serangan yang berhasil disebabkan oleh kesalahan manusia. Tekanan waktu adalah faktor lain yang tidak boleh diabaikan, karena organisasi sering kali menghadapi tekanan waktu yang tinggi dalam lingkungan bisnis yang kompetitif dan dinamis. Penelitian mengenai faktor manusia dalam keamanan siber menunjukkan bahwa faktor manusia masih menjadi perhatian utama dibandingkan dengan teknologi. Penelitian ini bertujuan untuk menganalisis perkembangan dan tren keamanan siber mengenai faktor manusia dan tekanan waktu dari tahun 2014 hingga 2023 menggunakan Analisis Bibliometrik dari software R studio. Metodologi penelitian meliputi perencanaan, identifikasi kata kunci, pencarian data Scopus, dan pembatasan pencarian pada "semua bidang" untuk memperoleh data yang sesuai dengan tema penelitian. Penelitian dibatasi sebanyak 110 jurnal yang diambil dari database Scopus. Kesimpulannya, memahami faktor manusia dan tekanan waktu dalam keamanan siber sangat penting bagi organisasi untuk meningkatkan langkah-langkah keamanan siber mereka. Dengan menganalisis perkembangan dan tren faktor-faktor ini, para peneliti dapat lebih memahami masa depan keamanan siber dan mengambil keputusan yang tepat untuk melindungi informasi dan infrastruktur penting. 

Page 40 of 48 | Total Record : 476

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