cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Agus Harjoko
Contact Email
ijccs.mipa@ugm.ac.id
Phone
+62274 555133
Journal Mail Official
ijccs.mipa@ugm.ac.id
Editorial Address
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Location
Kab. sleman,
Daerah istimewa yogyakarta
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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 466 Documents
 41   42   43   44   45 
Strategy Selection to Enhance Customer Data Quality using AHP: Case Study of General Insurance Company (PT XYZ) Syafira, Adinda Rizkita; Hidayanto, Achmad Nizar; Nugroho, Widijanto Satyo; Samik-Ibrahim, Rahmat Mustafa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.99263

Abstract

This study examines strategies to enhance customer data quality at PT XYZ, a general insurance company, which is crucial for strategic decision-making and revenue growth. The importance of this topic lies in its potential to significantly improve operational efficiency and customer satisfaction. The Analytical Hierarchy Process (AHP) is utilized to select the optimal strategy from available alternatives. The research begins by establishing criteria and identifying alternative strategies to improve customer data quality. Expert evaluations are conducted based on four criteria: cost, time, security, and availability. Four experts are involved, chosen for their expertise in technology and organizational impact. The experts include a Senior IT Business Intelligence, a Data Analyst, a Unit Head of IT Infrastructure & Security Department, and a Division Head of Digital. The findings highlight that the strategy of Strengthening Collaboration Across Departments, with a value of 0.455, is the most effective. This strategy emphasizes interdepartmental cooperation to enhance data quality. The results underscore the long-term benefits of improved operational efficiency and customer satisfaction despite initial investment challenges, demonstrating the practical application of AHP in organizational settings. 
Offensive Language and Hate Speech Detection using BERT Model Amalia, Fadila Shely; Suyanto, Yohanes
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.99841

Abstract

Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.
The Application of the Rabin-Karp Algorithm with the Synonym Recognition Approach to Detect Plagiarism in Student Assignments Handayani, Irma; Waluyo, Anita Fira
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.100308

Abstract

Kemajuan teknologi yang pesat telah mempermudah segala hal, termasuk dalam bidang pendidikan. Namun, kecanggihan tersebut juga mengakibatkan penyalahgunaan teknologi, terutama dalam hal duplikasi atau plagiarisme. Masalah ini tidak hanya terjadi pada tugas esai tetapi juga pada kode program. Untuk mengatasi hal tersebut, telah dilakukan penelitian untuk mendeteksi plagiarisme pada tugas mahasiswa dengan menggunakan metode Rabin-Karp dan pendekatan Synonym Recognition. Penelitian ini menemukan bahwa tingkat kesamaan terkecil adalah 20%, sedangkan yang terbesar adalah 76%. Penelitian ini bertujuan untuk memberikan solusi yang cepat dan akurat untuk mencegah maraknya aktivitas plagiarisme di bidang akademik.
Analysis of the Implementation of ISO 27001: 2022 and KAMI Index in Enhancing the Information Security Management System in Consulting Firms Apriany, Allisha; Wibowo, Antoni
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.100385

Abstract

Keamanan Informasi Elektronik ini kini sudah menjadi hal yang perlu diperhatikan oleh seluruh perusahaan agar aset penting perusahaan tetap terjaga dan mendapatkan kepercayaan dari pelanggan atau klien. Dalam operasional sehari-hari, banyak aktivitas dan data pribadi yang dikirimkan ke perusahaan untuk melakukan transaksi. Akan tetapi, belum banyak perusahaan yang memiliki kesadaran akan keamanan informasi, yang apabila tidak dilakukan akan merugikan perusahaan. Selain itu, dapat menurunkan nilai kompetitif karena dinilai tidak mampu melindungi data pribadi pelanggan atau klien. Setiap kebocoran data dan pelanggaran keamanan informasi dapat merusak reputasi organisasi [1]. Oleh karena itu, penting untuk memiliki ISMS yang efektif sesuai dengan standar ISO 27001:2022 yang merupakan standar keamanan informasi internasional yang telah diterapkan pada banyak perusahaan di seluruh dunia. ISO 27001:2022, standar internasional untuk manajemen keamanan informasi, memberikan panduan dan persyaratan yang jelas untuk membangun, menerapkan, dan memelihara sistem keamanan informasi yang efektif. Dalam makalah ini, penulis akan menilai tingkat kematangan sistem manajemen keamanan informasi berdasarkan ISO 27001: 2022. Berdasarkan penilaian tersebut, perusahaan masih mampu mencapai standar ISO 27001:2022 dan Index KAMI. Beberapa perbaikan harus dilakukan untuk mencapai tingkat kematangan minimum III+ dari penilaian Index KAMI. Selain itu, berdasarkan ISO/IEC 27001:2022, skor hasil yang diperoleh adalah 39% yang dapat disimpulkan bahwa sebagian besar perusahaan belum menerapkan prosedur apa pun dan beberapa kontrol telah diterapkan. Oleh karena itu, rekomendasi perbaikan diperlukan bagi perusahaan, mulai dari penerapan kebijakan dan prosedur terkait manajemen keamanan informasi.
Feasibility Study of Using Blockchain Technology for Criminal Records in Central Java Wulan, Puspa Ira Dewi Candra; Fauzi, Rofiq; Perdana, Danis Putra; Alfianto, Muhamad Eko; Maharani, Clarissa Monique; Abdi Susila, Yudha Satria
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.100953

Abstract

A criminal record is an official document filed with the police that contains a person's criminal history. In practice, managing criminal record data is not easy, the complex challenge is the risk of data manipulation. Data manipulation is carried out for various purposes, one of which is deleting or changing data for certain purposes. The increase in cyber crime in Indonesia does not rule out the possibility that criminal record data will be hacked. Blockchain technology, through smart contracts, is an innovative solution to overcome the problem of data manipulation. Blockchain as an immutable distributed digital ledger, provides guarantees of data security and integrity. By adopting smart contracts in data collection, it can increase the speed of information access, reduce the risk of manipulation and provide a high level of transparency. A feasibility study regarding the importance of implementing Blockchain technology for storing criminal records needs to be carried out before deciding to realize this technology.  The aim of this research is to analyze how important it is that criminal record data must be secured using Blockchain technology in Central Java.  Qualitative methods were used in this research, data collection was carried out using interview techniques with predetermined sources
Analysis User Satisfaction of XYZ Application with End User Computing Satisfaction Method and Delone and Mclean Giansyah, Qhoiril Aldi; Maita, Idria; Megawati, Megawati; Salisah, Febi Nur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.101004

Abstract

Rapidly developing technology has made internet service providers such as XYZ provide an application which promises convenience for its consumers to make transactions to purchase credit, internet quota, and other services. this application is one of the fairly well-known providers and is currently competing with other providers. this provider targets young people because in terms of its products which are quite competitive with a wide reach and prices that are quite cheap for young people. Of course, customers will feel the convenience of transacting using the official service application. The convenience offered can make business processes more efficient and can be used to improve the quality of their services.This study uses the End User Computing Satisfaction model method and the Delone and Mclean model with 9 research variables (content, accuracy, format, ease of use, timeliness, system quality, information quality, and service quality, and security), The purpose of this study is to assess the effectiveness of the application..Out of the nine variables included in the research results, only two Service Quality and Accuracy have a statistically significant impact. The model provided in this study has a Rsquared score of 0.792, indicating a strong level of customer satisfaction.
Exploring the Relationship between Artificial Intelligence and Business Performance Lutfiani, Ninda; Sembiring, Irwan; Setyawan, Iwan; Setiawan, Adi; Rahardja, Untung; Sulistio, Sulistio
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.86697

Abstract

The integration of Artificial Intelligence (AI) into business operations has garnered significant attention due to its potential impact on business performance. However, the relationship between AI adoption and business performance remains not fully understood. This article comprehensively analyzes this relationship through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption. In this study, we employ SmartPLS as an analytical tool to evaluate the relationships between identified factors and the impact of AI adoption on business performance. Our findings reveal that several factors influence the adoption and implementation of AI, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations. Moreover, AI adoption significantly influences business performance metrics such as productivity, efficiency, revenue, and customer satisfaction. Nonetheless, challenges arising from AI adoption, including shifts in job roles, data privacy, and security concerns, also require substantial attention. In conclusion, successful AI adoption and implementation necessitate careful consideration of organizational, technical, and ethical factors. This research provides valuable insights for business leaders and researchers seeking a deeper understanding of the relationship between Artificial Intelligence and business performance.
Financial Forecast Optimization with Ensemble Models and Error Analysis Hari Purwidiantoro, Moch; Aini, Afifah Nur; Agustin, Tinuk
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.100689

Abstract

 This study proposes an error mitigation model applied to the financial sector in higher education, aiming to improve the prediction accuracy in a linear regression model used to monitor and manage campus finances. By analyzing the error distribution of the original model, an additional model is developed to reduce the impact of errors on identified sensitive areas. These two models are then combined into one ensemble model, which is able to reduce the standard residual error (RSE) by up to 7%. The use of this ensemble model has proven effective in improving the accuracy of the results compared to a single model. A case study using university financial data, including parameters such as operating costs, revenues, and budget allocations, shows that error mitigation can provide significant improvements in campus financial management, especially in terms of budget planning and expenditure prediction. This study opens up opportunities for wider application in the higher education sector that requires more accurate and efficient financial management
Obstacles Detection in Underwater Environment Using ROV Based on Convolutional Neural Network Asri, Purwidi; Widiarti, Yuning; Purwanti, Endang Pudji; Wismawati, Endah; Arifin, M. Firman Tsany
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.101698

Abstract

Pada saat RoV berada dibawah air tidak sedikit obstacle yang dijumpai dan berpengaruh terhadap  kinerja dan keselamatan body ROV itu sendiri. Obyek yang tertangkap kamera ROV seringkali sulit untuk diidentifikasi dan dideteksi karena besarnya noise bawah air. Selain itu, sifat air yang membiaskan cahaya dan tingkat kejernihan air turut berpengaruh terhadap kualitas gambar yang dihasilkan. Untuk membantu dalam mengidentifikasi obyek yang ada di bawah air, maka pada penelitian ini proses identifikasi dilakukan dengan menggunakan Convolutional Neural Networks (CNN). CNN mengekstraksi fitur penting dari gambar melalui beberapa lapisan konvolusi. Setiap lapisan konvolusi menggunakan filter untuk mendeteksi pola seperti tepi, sudut, atau tekstur dari gambar input. Pada tahap akhir, fitur-fitur yang sudah diproses ini dihubungkan ke lapisan fully-connected yang bertindak sebagai pengklasifikasi. CNN kemudian memetakan fitur-fitur tersebut ke dalam kelas-kelas tertentu , misalnya objek seperti botol, tiang kayu, rantai, dan propeller. Dari pengujian secara real-time sistem berhasil menunjukkan performansi yang baik dengan akurasi validasi sebesar 99.25% dan akurasi klasifikasi real-time sebesar 85%. Hasil klasifikasi selanjutnya menentukan pergerakan thruster ROV.
Sentiment Analysis Mobile JKN Reviews Using SMOTE Based LSTM Tamami, Ghufron; Triyanto, Wiwit Agus; Muzid, Syafiul
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.101910

Abstract

The JKN Mobile application plays an important role in providing easy and fast access to health services for JKN-KIS users. However, user reviews indicate dissatisfaction with several aspects of the application, such as login issues and OTP codes, which can affect the overall user experience. Another challenge faced is class imbalance in the review dataset, which can affect the performance of sentiment analysis. This study uses Long Short-Term Memory (LSTM) combined with Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Review data was collected from Google Play Store and Kaggle, then preprocessed including lemmatization, tokenization, and padding. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that LSTM with SMOTE achieved 88% accuracy, 90% precision, 88% recall, and 89% F1-score. SMOTE successfully improved performance in the minority class although there was a slight decrease in accuracy compared to the model without SMOTE. Word cloud visualization reveals positive sentiments regarding the ease of use of the application, while negative sentiments indicate areas that need improvement. This study emphasizes the importance of handling imbalanced datasets to produce more accurate sentiment analysis.

Page 43 of 47 | Total Record : 466

 41   42   43   44   45 

Filter by Year

2006 2025


Reset
Filter By Issues
All Issue Vol 19, No 4 (2025): October Vol 19, No 3 (2025): July Vol 19, No 2 (2025): April Vol 19, No 1 (2025): January Vol 18, No 4 (2024): October Vol 18, No 3 (2024): July Vol 18, No 2 (2024): April Vol 18, No 1 (2024): January Vol 17, No 4 (2023): October Vol 17, No 3 (2023): July Vol 17, No 2 (2023): April Vol 17, No 1 (2023): January Vol 16, No 4 (2022): October Vol 16, No 3 (2022): July Vol 16, No 2 (2022): April Vol 16, No 1 (2022): January Vol 15, No 4 (2021): October Vol 15, No 3 (2021): July Vol 15, No 2 (2021): April Vol 15, No 1 (2021): January Vol 14, No 4 (2020): October Vol 14, No 3 (2020): July Vol 14, No 2 (2020): April Vol 14, No 1 (2020): January Vol 13, No 4 (2019): October Vol 13, No 3 (2019): July Vol 13, No 2 (2019): April Vol 13, No 1 (2019): January Vol 12, No 2 (2018): July Vol 12, No 1 (2018): January Vol 11, No 2 (2017): July Vol 11, No 1 (2017): January Vol 10, No 2 (2016): July Vol 10, No 1 (2016): January Vol 9, No 2 (2015): July Vol 9, No 1 (2015): January Vol 8, No 2 (2014): July Vol 8, No 1 (2014): January Vol 7, No 2 (2013): July Vol 7, No 1 (2013): January Vol 6, No 2 (2012): July Vol 6, No 1 (2012): January Vol 5, No 3 (2011): November Vol 5, No 2 (2011): July Vol 5, No 1 (2011): January Vol 1, No 2 (2007): July Vol 1, No 1 (2006): January More Issue