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IJISCS (International Journal of Information System and Computer Science)

ijiscs
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Published by Institut Bakti Nusantara

ISSN : 25980793 EISSN : 2598246X DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

The IJISCS (International Journal of Information System and Computer Science) is a publication for researchers and developers to share ideas and results of software engineering and technologies. These journal publish some types of papers such as research papers reporting original research results, technology trend surveys reviewing an area of research in software engineering and technologies, survey articles surveying a broad area in software engineering and technologies. The scope covers all areas of software engineering methods and practices, object-oriented systems, rapid prototyping, software reuse, cleanroom software engineering, stepwise refinement/enhancement, ambiguity in software development, impact of CASE on software development life cycle, knowledge engineering methods and practices, formal methods of specification, deductive database systems,logic programming, reverse engineering in software design, expert systems, knowledge-based systems, distributed knowledge-based systems, knowledge representations, knowledge-based systems in language translation & processing, software and knowledge-ware maintenance, Software Specification and Modeling, Embedded and Real-time Software (ERTS), and applications in various domains of interest.

Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi

Articles 121 Documents

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A HYBRID ARIMA-MLP ALGORITHM USING ARIMA AND MLP TO IMPROVE ESTIMATION MODEL PERFORMANCE IN SOLAR RADIATION SENSOR DATA Syahab, Alfin Syarifuddin; Hermawan, Arief; Avianto, Donny
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1617

Ground-based solar radiation measurements help solar energy projects and applications. Various models have been developed to estimate solar radiation. Then, several additional models were created using improved machine learning. Currently, estimating solar radiation with the help of hybrid models is more efficient. In this research, the basic concepts of modeling procedures for hybrid between the Autoregressive Integrated Moving Average (ARIMA) and the Multilayer Perceptron (MLP) are used to improve the performance of the ARIMA and MLP models in estimating solar radiation data from a pyranometer sensor installed on the automatic weather station (AWS) at Stasiun Klimatologi Daerah Istimewa Yogyakarta. The test results of the estimation model based on the coefficient of determination (R2) value and root mean square error (RMSE) show that the ARIMA model can provide a high coefficient of determination value in each data splitting scenario. The MLP estimation model shows a coefficient of determination value that is lower than the ARIMA model. On the other hand, MLP is able to improve the RMSE value in the ARIMA model in 70:30 and 90:10 splitting data. Furthermore, the ARIMA-MLP hybrid estimation model is able to improve the RMSE value of the ARIMA and MLP models even though the coefficient of determination value is not as good as the ARIMA model. This research shows that the ARIMA-MLP hybrid model is able to contribute to increasing the accuracy value in RMSE compared to the ARIMA and MLP models in estimating solar radiation sensor data.

IMPLEMENTATION OF CMMI IN MEASURING THE PERFORMANCE OF THE WASTE BANK INFORMATION SYSTEM DEVELOPMENT PROJECT IN KELURAHAN DURI KEPA JAKARTA BARAT Fajriah, Riri; Meiyanti, Ruci
IJISCS (International Journal of Information System and Computer Science) Vol 8, No 2 (2024): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v8i2.1727

The goal of CMMI (Capability Maturity Model Integration) adoption in project performance measurement is to enhance and optimize the Waste Bank Information System project's development process. Through the adoption of the CMMI's best practices and principles, the project team can increase the productivity and efficiency of project implementation. With improved risk identification and management, the project is less likely to encounter difficulties or fail. This is made possible by CMMI. The GAP Analysis assessment stage of the research process is used to determine how well Duri Kepa Village has followed the CMMI process. Next, the project's goals and scope are established. Finally, the CMMI process is mapped to the stages of implementation and process modifications in accordance with CMMI practices, as well as the process of performance monitoring and measurement. The aim of this study is to improve the project completion predictability for the Waste Bank Information System design and implementation in Duri Kepa Village. The project team will be better equipped to plan, coordinate, and manage project resources in order to promote project success by putting CMMI standards into practice.

EXPERIMENTAL STUDY ON THE USE OF SHARP INFRARED SENSOR FOR DISTANCE DETECTION IN PARKING ASSISTANCE APPLICATIONS IN AUTOMOTIVE SYSTEMS Paramasatya, Johanes Dimas; Saputri, Fahmy Rinanda
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1593

In this study, experiments were conducted on the implementation of the Sharp Infrared Sensor, specifically the Sharp 0A4ISK model, for distance detection in parking assistance applications within automotive systems. The increasing complexity of modern vehicles and the need for enhanced parking safety have prompted the exploration of innovative sensor technologies. In this research, we focused on the application of the Sharp Infrared Sensor to improve parking assistance systems. The study involved calibration, testing, and performance evaluation of the sensor on a laboratory scale. The methodology included the design of electronic component circuits and program development. Subsequently, testing of the sensor's ability to accurately detect distances between a miniature vehicle and a wall was performed. The research results indicate that the Sharp Infrared Sensor offers the ability to detect distance, thus holding the potential for integration into automotive systems.

K-NEAREST NEIGHBOR ALGORITHM AS A PREDICTION METHOD BEST SELLING ELECTRONIC PRODUCTS Martin, Afrizal; Bowo, Ari; Dwifandi, Eviliana Putri
IJISCS (International Journal of Information System and Computer Science) Vol 8, No 3 (2024): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v8i3.1779

The use of electric vehicles is now supported by a braking Development increasingly advanced technology rapid bring impact on various sectors, including sector trade. One of the affected sectors impact is trading product electronics. Products electronic be one of the most popular products among the public Because need will increasingly advanced technology increased. MK Store is having trouble in predict sale product electronic best seller, then from That done study optimization planning provision stock product electronics at MK Store with use prediction sale product electronic best seller. With implementing prediction models sale product electronic best seller at MK Store using data mining methods for optimize planning supplies stock product electronics at MK Store with use prediction sale product electronic best seller, and implementing a prediction model sale product electronic best seller at MK Store developed use K-Nearest Neighbor method in provision product electronics. Using The K-Nearest Neighbor algorithm is believed to can give very accurate results with count use distance neighbor nearest , and use variable 10 variables For predicted . Using the Orange tool because can make it easier in operate with use between drag-and-drop face create channel work, data analysis and own diverse widgets components that include data processing, modeling predictive, visualization and analysis text. The result of research conducted use the K-Nearest Neighbor algorithm produces mark with correlation positive (excellent), and the results prediction product electronic best seller at MK Store namely Magicom/Rice Cooker. With existence study This expected for study next, recommended expand range with develop criteria new and considering use method alternative.

DATA MINING TO PREDICT INVENTORY AT DAPUR PINTAR STORE USING MULTIPLE LINEAR REGRESSION METHODS Syafitri, Yuli; Sari, Putri Mayang; Rustam, Rustam; Supriyanto, Supriyanto
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1511

Dapur pintar store is a shop that sells household furniture. However, there are several obstacles faced by Dapur Pintar Store, such as the shop still having difficulty predicting future inventory, and the absence of the application of data mining in predicting inventory. This is interesting to solve in order to make it easier for the store to determine inventory predictions at Dapur Pintar Store. Utilizing data mining, especially using linear regression, can provide valuable insight for predicting sales of household products at Smart Kitchen Stores. Identify and collect relevant data for sales analysis. This includes historical sales data, customer demographic data, promotions or discounts applied, and other factors that may influence household product sales. Perform data preprocessing to clean incomplete or inaccurate data. In addition, perform data transformations such as normalization to ensure consistency and accuracy in analysis. Apply a linear regression model to understand the linear relationship between the independent variable (for example, time, promotion, or demographics) and the dependent variable (household product sales). This model can be used to make predictions based on historical patterns. Use historical data to train a linear regression model. The training process involves adjusting the model parameters to fit the training data, so that it can provide accurate predictions. Validate the model using data that was not used in training to ensure that the model can provide good predictions on new data. This helps avoid overfitting and ensures generalization of the model. In making inventory predictions for goods, we will use the Multiple Linear Regression method. Software that will be used to support data processing is RapidMiner.

SENTIMENT ANALYSIS OF THE GOPAY APPLICATION USING THE NAIVE BAYES METHOD BASED ON USER REVIEWS Gumanti, Miswan; Aprianto, Rudi; Zahra, Fista Anisa; Muslihudin, Muhamad
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1865

The development of financial technology, particularly digital wallet applications, has made significant progress in Indonesia. One of the most widely used applications is GoPay, which offers various conveniences in conducting financial transactions. However, despite GoPay 's many advantages, user responses to this application are not always positive. Some users provide negative comments reflecting their less than satisfactory experiences. In this context, this study aims to analyze sentiment from user reviews of the GoPay application using the Naive Bayes method, which is known to be effective in text classification. This method was chosen because of its ability to classify data well, even in large and diverse datasets. This study involved data collection from platforms Using Kaggle, 1,000 reviews were randomly selected for further analysis. The analysis revealed that positive comments predominated among the reviews. This indicates that the majority of users had a positive experience with the GoPay app, although there were also a number of negative reviews worth noting.

SINGLE-LABEL LEARNING STYLE CLASSIFICATION USING MACHINE LEARNING WITH GRIDSEARCH-BASED HYPERPARAMETER TUNING ON LMS BEHAVIORAL DATA Lestari, Uning; Salam, Sazilah; Choo, Yun Huoy
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1876

The rapid growth of online learning environments has increased the importance of Learning Management Systems (LMS) as a rich source of behavioral data for learning analytics. One learner characteristic that strongly influences learning effectiveness is learning style; however, traditional questionnaire based identification approaches suffer from subjectivity, limited scalability, and static representation. To address these limitations, this study proposes a machine learning-based approach for automatic learning style classification using LMS behavioral data grounded in the Felder–Silverman Learning Style Model (FSLSM). This study utilizes LMS activity log data collected from Universitas Siber Asia over three academic years (2022–2024). The dataset consists of 5,633 student interaction records with 72 raw behavioral attributes, which were preprocessed, aggregated, and transformed into 12 representative behavioral features reflecting students’ interactions with learning materials, assessments, discussions, multimedia resources, and navigation patterns. A rule-based FSLSM mapping mechanism was applied to generate 16 learning style profiles, which were treated as targets in a single-label classification setting. Support Vector Machine (SVM) and Gradient Boosting (GB) classifiers were implemented and optimized using feature selection and GridSearch-based hyperparameter tuning. The dataset was divided into 75% training data and 25% testing data using a stratified split to preserve class distribution. Experimental results show that Gradient Boosting consistently outperforms SVM across all evaluation metrics. The GB model achieved an accuracy of 0.84 and a macro F1-score of 0.79, demonstrating strong generalization capability and robustness to class imbalance. In contrast, SVM exhibited lower and less stable performance, particularly on minority learning style classes. These findings confirm that ensemble-based methods such as Gradient Boosting are more effective for LMS-based single-label learning style classification and support the feasibility of automatic FSLSM-based learning style detection for data-driven adaptive learning systems.

DEVELOPMENT OF AN EYE-CONTROLLED MOBILE ROBOT SYSTEM USING EOG SIGNALS Triloka, Joko; Fauzi, Adi Ahmad
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1859

The development of an eye-controlled mobile robot system using Electrooculography (EOG) signals is presented in this study. The proposed system enables robot motion control through eye movement detection, providing an alternative interaction method for individuals with limited physical mobility. The EOG sensor captures eye movement potentials, which are processed by a microcontroller to generate motion commands. A threshold-based detection algorithm was implemented to classify eye movements into four directional commands: left, right, forward, and backward. The system was tested to evaluate movement accuracy and response time. Experimental results show that the proposed system achieved an average directional detection accuracy of 88.3% and an average response time of 218 milliseconds, indicating reliable and real-time performance. The findings demonstrate that EOG-based control provides a feasible and responsive approach for human–robot interaction. Future improvements may involve noise filtering techniques and machine learning models to enhance signal stability and classification precision.

DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS Oluwagbemi, Johnson Bisi; Akinbo, Racheal Shade; Mesioye, Ayobami Emmanuel
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1857

Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.

ANALYSIS OF HUMAN RESOURCE EMPOWERMENT STRATEGY BASED ON ANALYTICAL HIERARCHY PROCESS (AHP) FOR LOCAL CREATIVE ECONOMY DEVELOPMENT Bangsawan, Laksamana; Rohmah, Aida; Susanto, Ferry
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1874

This study examines effective human resource (HR) empowerment strategies to improve the local creative economy. The urgency of this research is based on the fact that the creative economy, despite being a driver of economic growth, has not reached its optimal potential due to the low capacity of local HR. This research is important to identify effective HR empowerment strategies to increase the productivity, innovation, and competitiveness of creative economy actors at the local level. The results are expected to serve as a reference for the government and stakeholders in formulating HR-based policies. The purpose of this study is to analyze the inhibiting and supporting factors of HR empowerment in the local creative economy sector, formulate strategies that are appropriate to the characteristics and needs of creative economy actors, and provide policy recommendations to increase this sector's contribution to local development. The method used is a qualitative approach with literature studies, in-depth interviews, and Focus Group Discussions (FGDs). SWOT analysis will be used to evaluate strengths, weaknesses, opportunities, and challenges, while AHP (Analytic Hierarchy Process) analysis will be used to prioritize strategies based on weighted criteria. The results of this study indicate a significant relationship between HR empowerment strategies and the improvement of the local creative economy. Through the analysis, the most effective strategy was identified as a combination of increasing technology access and business mentoring. The formulated empowerment model is applicable and evidence-based, providing policy recommendations for the government and stakeholders. Thus, this research contributes to the development.

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Journal Mail Official ijiscs@ftikomibn.ac.id

Editorial Address Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

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Contact Name
Muhamad Muslihudin

Contact Email
ijiscs@ftikomibn.ac.id

Phone
+6272922240

Journal Mail Official
ijiscs@ftikomibn.ac.id

Editorial Address
Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location
Kab. pringsewu,

Lampung

INDONESIA