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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 92 Documents
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Search results for , issue "Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)" : 92 Documents clear
Algoritma Decision Tree ID3 Bagi Lembaga Pemberi Pinjaman Untuk Menentukan Faktor Yang Mempengaruhi Kelayakan Individu Memperoleh Pinjaman Riza Wirasena, Muhammad; M Rollan Reinaldi; Muhammad Ihsan Jambak
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3790

Abstract

Lembaga pemberi pinjaman menghadapi tantangan dalam mengevaluasi kelayakan suatu individu untuk memperoleh pinjaman. Proses ini memerlukan analisis mendalam terhadap sejumlah faktor yang dapat mempengaruhi keberhasilan pembayaran pinjaman. Oleh karena itu, diperlukan pendekatan yang efisien dan sistematis untuk menentukan faktor-faktor kritis yang memengaruhi kelayakan pemberian pinjaman. Penelitian ini bertujuan untuk memanfaatkan algoritma Decision Tree ID3 sebagai metode analisis yang efektif dalam menentukan faktor-faktor yang paling mempengaruhi kelayakan suatu individu memperoleh pinjaman. Algoritma Decision Tree ID3 digunakan untuk mengidentifikasi dan mengurutkan faktor-faktor yang memiliki dampak signifikan terhadap keberhasilan pembayaran pinjaman. Penggunaan metode ini diharapkan dapat memberikan pemahaman yang lebih baik tentang keputusan pemberian pinjaman. Hasil penelitian menunjukkan bahwa algoritma Decision Tree ID3 mampu mengidentifikasi faktor-faktor utama yang mempengaruhi keputusan pemberian pinjaman. Faktor-faktor tersebut mencakup Cibil score, nilai aset tempat tinggal dan jangka waktu pinjaman. Dengan demikian, lembaga pemberi pinjaman dapat menggunakan hasil analisis ini sebagai panduan dalam meningkatkan proses pengambilan keputusan pemberian pinjaman. mencari nasabah yang telah memiliki beberapa faktor kelayakan tersebut agar nantinya rating mendapatkan nasabah baru yang pasti memperoleh pinjaman dapat lebih tinggi dari sebelumnya.
Distributed Transactions in Cloud Computing: A Review Reliability and Consistency Ferzo, Barwar; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3830

Abstract

The challenges of managing distributed transactions in cloud computing are discussed in this paper. The paper places an emphasis on the critical balance that must be maintained between reliability and consistency in the face of complexities such as hardware failures, network outages, and varying latencies. It sheds light on the delicate balance that must be maintained in order to guarantee that transactions in cloud environments are both reliable and consistent. Cloud environments are prone to hardware glitches and network disruptions. In addition, the paper delves into novel approaches with the objective of cultivating a computing ecosystem that is both resilient and dependable in the face of the ever-changing requirements of cloud computing, also a comparison table is presented for all the literature reviewed.
Analis Sentimen Aplikasi Maskapai Penerbangan Lion Air Menggunakan Metode SVM dan Naïve Bayes Sulistiawati, Risa; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3836

Abstract

Lion Air App is a flight ticket purchase application launched on October 21, 2014. It can be downloaded and used anywhere, anytime. Lion Air App application is available on the Google Play Store and also the Appstore, which aims to facilitate users in the process of purchasing airplane tickets online. online. In several news articles reporting that Lion Air is the world's worst airline. in the world. However, it needs to be realized that the Lion Air application also has many users who give positive, negative and neutral reviews due to several factors. neutral due to the existence of several reviews presented in the Play Store application. This problem was researched for sentiment analysis to get a customer satisfaction rating for the Lion Air application. Lion Air application with the acquisition of 2000 data. In this research, Support Vector Machine (SVM) calculation and Naive Bayes calculation were compared using 80% training ratio and 20% test ratio. In this consideration, 795 positive opinions and 805 negative opinions were used. used, where Support Vector Machine (SVM) with Bigram features became the most superior method with 99.23% precision. method with 99.23% precision, 83.03% recall, 91.75% accuracy, F-1 score of 90.51%.         
Explainable Sentiment Analysis pada Ulasan Aplikasi Shopee Menggunakan Local Interpretable Model-agnostic Explanations Ninda Rizky Nuraeda; Muhaza Liebenlito; Taufik Edy Sutanto
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3870

Abstract

Seiring dengan perkembangan teknologi, pertumbuhan e-commerce mengalami peningkatan secara signifikan. Hadirnya aplikasi Shopee sebagai salah satu platform e-commerce terkemuka telah mendorong pengguna untuk melakukan transaksi belanja secara online. Dalam konteks ini, perhatian terhadap peningkatan kualitas aplikasi menjadi penting, khususnya melalui evaluasi ulasan pengguna dengan menggunakan analisis sentimen. Analisis sentimen umumnya mengadopsi pendekatan machine learning, meskipun transparansi dalam proses analisis menjadi tantangan utama. Penelitian ini mencoba mengatasi tantangan tersebut dengan menerapkan aspek baru dari Artificial Intelligence (AI), yang dikenal sebagai eXplainable Artificial Intelligence (XAI), khususnya pada analisis sentimen yang disebut Explainable Sentiment Analysis. Metode Local Interpretable Model-agnostic Explanations (LIME) digunakan untuk menjelaskan faktor-faktor yang mempengaruhi prediksi model machine learning. Model yang dievaluasi yaitu Logistic Regression, Random Forest, Support Vector Machine, dan Naïve Bayes. Hasil penelitian memberikan wawasan yang berharga tentang alasan di balik prediksi sentimen pada ulasan, sehingga diharapkan dapat meningkatkan pemahaman tentang bagaimana model machine learning membuat prediksi pada data tertentu.
Analisis Cluster Intensitas Data Kebencanaan di Provinsi Sulawesi Tengah Menggunakan Metode K-Means Vigo, Ayub
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3897

Abstract

Central Sulawesi often experiences natural disasters such as earthquakes, floods, and landslides. The Central Sulawesi Regional Disaster Management Agency is in charge of managing, responding to, and providing accurate information about the event. This study aims to categorize the Central Sulawesi region based on annual disaster intensity using the k-means method. Using the K-Means algorithm, each region is grouped according to the characteristics of its disaster events. Utilizing data from the Regional Disaster Management Agency (BPBD) of Central Sulawesi Province, this study focuses on understanding disaster event patterns and intensity clusters. From the results of clustering obtained every year, the most dominant type of flood disaster occurs in several regions of Central Sulawesi, and these areas tend to be included in cluster 2 (high disaster intensity) or 1 (medium disaster intensity) and this study records a downward trend in the number of disaster events over time on a scale of 2020 to 2023 which reaches 66%. The information obtained can facilitate mitigation planning, improving the effectiveness of future disaster response.
Design and Build Applications for Choice of Vocational Schools Using the Simple Additive Weighting Method Sisrayanti; Rizal, Fahmi; Ambiyar
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3907

Abstract

The fact that the problem of incompatibility of majors and educational backgrounds of students is one of the factors in the problems of education in Indonesia. This is of particular concern to the world of education, including vocational education. To enter education, especially at the Vocational High School (SMK) level, students are required to choose a major according to their interests and talents. However, many students are not aware of their abilities. Consequently, the objective of this research is to develop an Android application tailored for Vocational High Schools, employing the Simple Additive Weighting technique. The methodology employed here is the Research and Development (R&D) approach utilizing the 4D development model. The outcome of this investigation yields an application that serves as a valuable tool for both educators and students in making informed decisions regarding their choice of majors in Vocational High Schools
Tata Kelola TI Strategis: Memanfaatkan Kerangka COBIT 2019 untuk Mengurangi Kegagalan Pencadangan Data dalam Operasi Industri Melissa Indah Fianty
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3918

Abstract

The development of information technology today has had a significant impact on various aspects of human life, including in the industrial sector. One example is the integration of information technology in company operations to use it as a supporting tool for sending data and information which is the main reference for organizational management in decision making. To measure the level of capability of the IT system used, companies can use the COBIT 2019 framework, which not only helps in assessing the suitability of IT systems, but also provides recommendations for solutions to problems faced by the company. For example, research results show that problems related to data backup failures have been identified in Domain DSS01, with recommended solutions such as implementing policies related to information security from outsourced employees, establishing internal management processes, setting up timely incident tickets, and implementing recommendations to overcome non-compliance.
A Qualitative Study on the Influencing Factors of E-Government Adoption to Improve Public Trust in Local Government: Case Study of Rokan Hulu Municipality Fadrial, Rudy; Sujianto; Simanjuntak, Harapan Tua Ricky Freddy; Wirman, Welly
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3931

Abstract

Amidst global public trust challenges, e-government emerges as a promising solution to bolster trust. In Indonesia, rural areas face obstacles hindering effective e-government implementation. This paper explores Rokan Hulu Municipality's initiatives, aiming to understand e-government's impact on public trust at the rural/district level, bridging critical knowledge gaps. This study employs a qualitative approach to investigate the factors influencing e-government adoption. Primary data is gathered through interviews with key stakeholders, supplemented by secondary data from organizational documents. Employing open and axial coding, this study organizes findings to the Technology-Organization-Environment framework. Within the technological dimension, obstacles such as infrastructure; integration and interoperability; data security and confidentiality; and service providers, third parties, or vendors emerge as significant barriers. In the organization dimension, culture, organizational capability, budget constraints, human resource quality, perceptions, bureaucracy, and strategy become challenges, with organizational capability and strategy showing mixed impacts due to incomplete initiatives and limited inter-agency coordination. In environment dimension, digital divide, regulatory availability, and public participation become inhibiting factors, while political intervention becomes the driving factor.
Migration Success Strategy and Implementation of Enterprise System: A Case Study on PT XYZ Biller Message Switching and Aggregator Service Merger Work From Home Oristania Wahyu Nabasya
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3939

Abstract

Purpose – The purpose of the research is to determine the strategy for the successful implementation of Biller Message Switching and Aggregator carried out by organizations with multicultural employees during work from home conditions. Methodology – The method used in this research was a summative evaluation with the research subjects of the merger of Bank X, Bank Y, and Bank Z. Research subjects were chosen because of the success of the development team in integrating enterprise systems to support the merger process carried out during the pandemic. Results – The results of the research concluded that the ES implementation strategy during the pandemic required consistency, actively documenting each process, over-communicating, and using the appropriate SDLC method. Originality – This research conducted a thematic analysis on the process of integrating and migrating ES companies that merged during the pandemic. Contribution – This research contributes to organizations that want to integrate and migrate ES during the pandemic. For academics, research can serve as a foundation for future research in developing theories of ES implementation in virtual or pandemic environments.
The Performance Analysis of Graph Neural Network (GNN) and Convolutional Neural Network (CNN) Algorithms for Cyberbullying Detection in Twitter Comments Muhammad Rizki Nurfiqri; Fitriyani
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3940

Abstract

Cyberbullying incidents have surged due to the expansion of social media network and advancements in internet technology, presenting a substantial challenge in online communities. Previous studies employing Support Vector Machine (SVM) techniques have exhibited promising outcomes, achieving a superior accuracy of 71.25%. However, recognizing the dynamic nature of cyberbullying behaviors and the necessity for more robust detection methodologies, this research explores cyberbullying detection on Twitter utilizing Convolutional Neural Network (CNN) and Graph Neural Network (GNN). The selection of CNN and GNN is motivated by the deficiencies observed in prior SVM-based approaches and the capacity of neural network to capture intricate patterns in textual and network data. The GNN consistently outperforms CNN in terms of F1 score, accuracy, precision, and recall. With only 20 epochs, GNN achieves an accuracy of 80.25%, surpassing CNN's 68.43%. Through GNN optimization, its accuracy reaches 89.04% after 100 epochs, underscoring its efficacy in Twitter cyberbullying detection.

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