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International Journal of Computer and Information System (IJCIS)
Published by Institut Teknologi Bisnis AAS Indonesia
ISSN : -     EISSN : 27459659     DOI : https://doi.org/10.29040/ijcis
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
The aim of this journal is to publish quality articles dedicated to all aspects of the latest outstanding developments in the field of informatics engineering. Its scope encompasses the applications of (but are not limited to) : 1. Artificial Intelligence 2. Software Engineering 3. System Design Methodology 4. Data mining and Big Data 5. Human and Computer Interaction 6. Mobile Computing 7. Soft Computing 8. Animation 9. Multimedia and Image Processing 10. Parallel/Distributed Computing 11. Machine Learning 12. Computational Lingustics 13. Data Comunication 14. Networking
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 188 Documents
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Sequential Modeling of News Headlines and Descriptions for Multi-Class Classification Pradana, Musthofa Galih; Saputro, Pujo Hari; Wijaya, Dhina Puspasari
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.229

Abstract

Automatic classification of news content plays a vital role in organizing and filtering data for various applications such as news recommendation systems and media monitoring. This study investigates the use of Recurrent Neural Networks (RNN) and sequential modeling for multi-class classification of news data. A dataset consisting of 12,000 news sentences, categorized into four distinct classes politics, economy, sports, and technology was utilized for training and evaluation. The research focuses on comparing the performance of RNN models without optimization techniques and RNNs enhanced through optimizer implementation and sequence modeling. The baseline RNN model, trained without any optimizer or sequence enhancements, achieved a classification accuracy of 89%. By incorporating optimizer functions and leveraging sequential dependencies in both news headlines and descriptions, the proposed model demonstrated a 1% improvement, achieving an overall accuracy of 90%. These findings indicate that even a slight enhancement in modeling temporal dependencies and optimization can result in measurable gains in multi-class classification performance. The sequential combination of news headlines and descriptions is shown to be an effective strategy for capturing contextual features that improve the model’s predictive accuracy. This research contributes to the field of natural language processing by highlighting the effectiveness of sequential modeling and optimization in neural network-based text classification systems.
Performance Comparison K-Nearest Neighbors and Random Forest on Predicting The Performance New Polimedia Student Admissions Riyono, Dwi; Mawardi, Cholid
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.237

Abstract

New student admissions are at the forefront of the school's operational process. the success of each college's input stems from this. Polimedia always conducts new student admissions every year with various strategies used. Polimedia has 23 study programmes that can enable it to move in the creative industry that can be utilised by the community. in this study, a strategy using a prediction algorithm is used to be able to see the possible opportunities that occur if implemented in the coming year. with a dataset of 3738 data received by new students, an analysis will be carried out on prospective students who have re-registered or who have not re-registered. The classification model with 2 classes will be used. by conducting a data analysis process using exploratory data analysis (EDA) and also performing data cleansing so that the data modelling process runs well. The method used uses the main model of K-Nearest Neighbors by comparing with other machine learning models such as decision tree and random forest. It is expected that this research can produce high accuracy values 86.90% with powerful machine learning model comparisons. This research is also expected to be a reference for other studies that also conduct performance testing processes with machine learning models using various objects.
Revolutionizing English Learning with AI: Insights from ChatGPT and Google Gemini Fitria, Tira Nur
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.219

Abstract

This research describes the use of both Google Gemini and ChatGPT in English language learning. This research is descriptive qualitative. The analysis shows that both ChatGPT and Gemini provide valuable insights into AI's role in English language learning, though their approaches differ. ChatGPT focuses on practical applications like personalized feedback, chatbots, and analytics tools, emphasizing text generation and teacher workload reduction. Google Gemini, with its multimodal capabilities, highlights interactive learning systems, automation, and accessibility improvements in education. Both models stress personalization, interactivity, and teaching efficiency but with different emphases—ChatGPT on student analysis, Gemini on immersive experiences, and data-driven teaching. While both tools offer great potential, the choice between them depends on whether we prioritize text-based tasks (ChatGPT) or multimedia content (Google Gemini). Choosing between ChatGPT and Google Gemini depends on our specific needs. If we require text generation, such as creating articles or engaging in text-based conversations, ChatGPT is a better fit due to its focus on producing natural, relevant text. However, if you need to handle multiple types of data like text, images, audio, and video, Google Gemini's multimodal capabilities make it more versatile for multimedia tasks. While ChatGPT is ideal for text-based applications and integrates easily through APIs, Gemini is more suited for users within the Google ecosystem. ChatGPT is known for generating high-quality text, while Gemini provides good quality across various formats, but its effectiveness varies with context. Ultimately, the best choice depends on whether your focus is on text or multimedia tasks.
Integrating Instagram Analytics into Information Systems: Enhancing Pastry Sales Through Seasonal and Non-Festive Engagement Insights Weandri, Aliffa Ratumahesa; Wandy, Wandy; Saptaji, Kushendarsyah
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.228

Abstract

This study investigated the integration of Instagram analytics into the information systems used by MSMEs in the pastry industry, with a focus on engagement trends. Using a descriptive quantitative approach, the research identifies a benchmark engagement rate of 16.5%, calculated from the midpoint between peak festive engagement and regular-day averages. Findings show a sharp increase in engagement leading up to Eid, exceeding the benchmark, followed by a noticeable decline post-event. In contrast, regular day engagement remains consistently below the benchmark. These patterns highlight the impact of cultural events on consumer behavior and the difficulty of sustaining interest afterward. The study concludes that integrating social media data into enterprise systems enables MSMEs to optimize marketing strategies and time promotions more effectively and respond better to seasonal trends, offering a practical model for data-driven decision-making in small businesses.
Techno-Economic Feasibility Analysis of a Small-Scale Waste-to-Energy Power Plant as a Supporting Electricity Source: A Case Study on Sabira Island Harahap, Saskia Saraswati; Garniwa, Iwa
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores the techno-economic feasibility of establishing a small-scale waste-to-energy (WTE) power plant using anaerobic digestion technology on Sabira Island, one of the outermost islands of Jakarta, Indonesia. As an isolated area with limited energy access and increasing organic waste generation—estimated at around 1 to 1.2 tons per day—Sabira presents both an environmental challenge and a renewable energy opportunity. Through the conversion of organic waste into biogas, which can then be used to generate electricity, this project seeks to address waste management issues while contributing to sustainable energy production in remote regions. A comprehensive techno-economic analysis was conducted, incorporating factors such as capital and operational costs, biogas yield potential, energy conversion efficiency, and local electricity pricing. Two different electricity selling price scenarios were evaluated to determine financial viability. The results show that under the first pricing scheme, the project fails to meet the minimum return expectations, whereas the second scenario demonstrates acceptable economic performance, suggesting that the project can be considered feasible if more favorable electricity tariffs are adopted. The study concludes that successful implementation of such a WTE system would depend not only on technical and economic parameters but also on supportive policy frameworks, appropriate pricing mechanisms, and access to clean energy financing. The findings offer valuable insights for policymakers and stakeholders aiming to promote decentralized renewable energy solutions in Indonesia’s remote islands.
5G's Role in the Data Communication Revolution Efendi, Tino Feri
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.225

Abstract

The 5G technology is an important milestone in the evolution of data communications by offering higher transmission speeds, lower latency, and greater network capacity. The implementation of 5G contributes to the improvement of data communication efficiency in various sectors, including industry, health, and transportation. This research aims to analyze the impact of 5G technology on data communication speed and reliability as well as the challenges in its implementation. The method used in this research is a literature study that refers to various academic sources related to the development of 5G and its application in data communication. The results show that 5G technology has great potential in improving network performance, although it still faces challenges in terms of infrastructure and security. Therefore, a proper strategy is needed in the implementation of 5G to maximize its benefits in data communication.
Comparative Evaluation of Classification Algorithms for the Diagnosis of Polycystic Ovary Syndrome Wulandari, Sri
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.233

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder that affects women's reproductive and metabolic health. Early detection is essential to prevent long-term complications. This study aims to analyze and compare the performance of four machine learning classification algorithms, namely Naive Bayes, K-Nearest Neighbor (KNN), Decision Tree, and Support Vector Machine (SVM), in assisting the diagnosis of PCOS based on clinical data. The dataset used contains 1000 patient data with five main features: age, body mass index (BMI), menstrual irregularities, testosterone levels, and antral follicle count. The data were divided using stratified sampling (80:20) and validated using the k-fold cross-validation technique (k=5). Model evaluation used accuracy, precision, recall, F1-score, and AUC metrics. The results showed that Decision Tree had the best performance (100% accuracy, AUC 0.997), followed by SVM (97% accuracy) and KNN (96%). Naive Bayes had the lowest accuracy (72%) and produced many false positives. Although Decision Tree is superior, there is a risk of overfitting, while SVM and KNN show more stable performance. This study confirms that classification algorithms, especially SVM and KNN, are effective for PCOS diagnosis based on clinical data. The practical implication of this finding is the development of accurate and efficient clinical decision support systems to improve women's healthcare.
Impact of the Advanced Metering Infrastructure (AMI) Program on Non-Technical Losses at Distribution System Sinurat, David Daniel Christianto; Husnayain, Faiz
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The implementation of Advanced Metering Infrastructure (AMI) is a strategic initiative by PT PLN (Persero) UP3 Bandengan to support digital transformation and enhance efficiency in the electricity distribution system, particularly in reducing non-technical Losses. This study aims to evaluate the impact of AMI implementation on improving meter reading accuracy, managing customer arrears, and detecting electricity misuse. The deployed AMI system includes 142,083 single-phase and 10,278 three-phase smart meters, with connectivity rates to the Meter Data Management System (MDMS) reaching 99.36% and 99.60%, respectively. A total of 1,190 Data Concentrator Units (DCUs) have been installed to enable automatic meter reading, achieving a success rate of 96.65%. Remote disconnection and reconnection processes via MDMS recorded success rates of 97.14% and 98.65%, respectively, while anomaly current detection—used as an indicator of energy misuse—achieved an effectiveness rate of 23.87%. The findings show that AMI implementation resulted in operational cost savings of IDR 245,792,276 in manual meter reading and IDR 7,807,696,000 in arrears-related disconnections. These results highlight the significant contribution of AMI in promoting a transparent, efficient, and reliable electricity distribution system.
Safety Risk Analysis of Cliff Landslide Handling Using The Hirarc (Hazard Identification, Risk Assessment, And Risk Control) to Reduce The Frequency of Mine Operational Incidents at PT Semen Gresik Syaifuddin, M.; Primasanti, Yunita; Indriastiningsih, Erna
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.226

Abstract

Work safety is a crucial aspect of the Cement industry, especially in high-risk jobs such as handling cliff landslides. This study aims identify potential hazards, assess risks, and control them using the HIRARC (Hazard Identification, Risk Assessment, and Risk Control) method to reduce the frequency of operational incidents at PT Semen Gresik. The research employs field observations, interviews with workers, and data analysis using the HIRARC approach. Findings indicate that before implementing this method, several tasks were categorized as high risk and extreme risk. After risk control measures were applied, the level of risk was reduced to moderate risk and low risk. This study recommends changing the slope design from single slope to multi slope, which has been proven to increase the Safety Factor (SF) to 1.742. Additionally, improving work safety management is suggested through the promotion of Occupational Safety and Health (K3) and the installation of Extenso Meters to monitor ground movement.
Data Security Applications for School by Using Android Sipahutar, Lahmudin; Handayani, Nursyah
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.232

Abstract

School is an agency that operates in the field of education. The school uses Microsoft Office applications to support the school system regarding the grades of students' lessons. The teacher will input student grades in the form of a report card or report on student grades in the form of numbers, while students will see the grades that have been input by the teacher in the report card or report on student grades. However, the value data in the student's report card or report has not undergone an encryption process, or in other words, it is still in plaintext form. This will certainly make it easier for unauthorized parties to read and manipulate student grade data if the grade data is still in plaintext. Therefore, some literature states that the way to solve this problem is by applying cryptography. To maintain data security and avoid data leaks, various methods can be used, one of which is by using cryptography. Cryptography is the process of hiding or coding information so that only the person a message was intended for can read it. The art of cryptography has been used to code messages for thousands of years and continues to be used in bank cards, computer passwords, and ecommerce.

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