The Indonesian Journal of Computer Science
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.
Articles
1,127 Documents
<![CDATA[Issues and Strategies of Agile Methodology Adoption in Remote Working Environment: A Systematic Literature Review ]]>
<![CDATA[Chairina Marsya]]>;
<![CDATA[Raharjo, Teguh]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3677
<![CDATA[Agile and remote work are two topics that are currently popular in the business world, especially in the information technology industry. Both have been implemented in several companies, but Covid-19 has made it more widely used. Even though remote work offers many conveniences such as saving transport time and flexibility of place, it contrasts with Agile which requires intense collaboration and communication. Some of the obstacles found in previous research were that the team had fewer opportunities for communication, a lot of time was spent on meetings, so it was easy to get distracted when working remotely. This study uses a Systematic Literature Review to answer what are the obstacles and strategies in adopting agile in remote work systems. This research found that, there are five majors factors that effected while implement remote agile; coordination, response to change, leadership, facilitating condition, and policies & guidelines.]]>
<![CDATA[Enhancing IT Hybrid Projects in The Directorate General Of Customs And Excise of Indonesia: An Improvement Process ]]>
<![CDATA[Nugraha, Tito Febrian]]>;
<![CDATA[Raharjo, Teguh]]>;
<![CDATA[Wibowo, Wahyu Setiawan]]>;
<![CDATA[Syahnuddin, Bob Hardian]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3680
<![CDATA[The Directorate General of Customs and Excise (DJBC) confronts mounting challenges due to the expanding international trade. To address this issue, an optimal software solution is required for efficient service provision and supervision of transactions. However, the completion of IS projects faces significant delays, with only 25% reaching completion despite the immediate need for a reliable system. These delays stem from various problems encountered during project execution. This research employs the Kerzner Project Management Maturity Model to assess the IS project management maturity level at DJBC and categorise the identified problems. Subsequently, solutions extracted from PMBOK 7 are mapped to address these issues. Findings indicate an average maturity score of 390, falling short of the target of 600 required for level 1 maturity. Additionally, 13 problems have been identified and linked to solutions within the seven standards/domains of PMBOK 7. The research presents two strategic goals: improving project management processes and enhancing client satisfaction, assessed using seven measurement indicators. This study offers valuable insights for DJBC to address project management challenges, enhance maturity levels, and achieve desired outcomes.]]>
<![CDATA[Analisis Proses Bisnis Pada Pendaftaran Pelanggan Pemasangan PDAM Kota Baturaja Dengan Metode BPI (Business Process Improvement) ]]>
<![CDATA[Saputri, Sonia Dwi]]>;
<![CDATA[Putra, Pacu]]>;
<![CDATA[Oktadini, Nabila Rizky]]>;
<![CDATA[Sevtiyuni, Putri Eka]]>;
<![CDATA[Meiriza, Allsela]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i2.3681
<![CDATA[Notasi Pemodelan Proses Bisnis (BPMN) adalah standar pemodelan proses bisnis yang menawarkan diagram proses bisnis dengan representasi grafis dari proses bisnis. BPMN memberi organisasi representasi grafis untuk komunikasi standar. BPMN bertujuan untuk mendukung manajemen proses bisnis bagi pengguna teknis dan bisnis dengan menyediakan notasi intuitif kepada pengguna bisnis yang dapat mengekspresikan proses semantik yang kompleks. Tujuan dalam penelitian adalah untuk mendeskripsikan proses bisnis registrasi pelanggan pada fasilitas PDAM Kota Baturaja dengan menggunakan model Business Process Modeling Notation (BPMN), menganalisis proses bisnis pada pendaftaran pelanggan pada fasilitas PDAM Kota Baturaja dengan menggunakan metode Proses Bisnis Peningkatan (BPI). Dan merekomendasikan proses bisnis pendaftaan pelanggan PDAM Kota Baturaja dengan metode Proses Bisnis Peningkatan (BPI) yang efektif. Manfaat penelitian bagi perusahaan adalah meningkatkan kualitas kinerja untuk mencapai tujuannya tanpa mengalami kerugian. Metode dalam penelitian adalah Proses Bisnis Peningkatan (BPI), yang merupakan metode untuk menganalisis dan memperbaiki proses bisnis bantuan Bizagi Modeler untuk analisis simulasi proses bisnis dan tools yang memperbaiki proses bisnis lama agar proses bisnis menjadi lebih efektif dan efisien. Data perama kali dikumpulkan melalui obsevasi berbicara dengan beberapa karyawan dan memunculkan ide-ide agar proses bisnis ini lebih efektif dan efisien. Hasil penelitian menunjukkan bahwa pada penelitian ini terdapat beberapa kendala dalam proses bisnis pendaftaran pelanggan pemasangan pdam baik dari segi ketersediaan personel, proses pengerjaan, dan tugas perusahaan. Oleh karena itu, proses bisnis harus diperbaiki sesuai dengan rekomendasi yang disampaikan.]]>
<![CDATA[Optimizing E-Commerce in Indonesia: Ensemble Learning for Predicting Potential Buyers ]]>
<![CDATA[Insani, Faiz Nur Fitrah]]>;
<![CDATA[Denny]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3690
<![CDATA[In the competitive Indonesian e-commerce sector, data-driven decisionmaking is crucial for success. This study addresses the challenge faced by a leading e-commerce company, where despite a 134% increase in promotional expenses, active user transactions remained low. Focusing on predicting potential buyers to optimize promotional spending, the research evaluates various ensemble learning methods, including Random Forest, XGBoost, and LightGBM algorithms. Through extensive testing, all three models demonstrated high precision in identifying potential buyers. Remarkably, XGBoost achieved an exceptional precision score of 89.5%. Further enhancement through a soft voting strategy combining XGBoost and LightGBM resulted in the highest precision rate of 89.8%, suggesting a promising approach for targeted marketing and improved promotional strategies in the e-commerce industry]]>
<![CDATA[Prediksi Mahasiswa Berpotensi Non-Aktif Menggunakan Algoritma Decision Tree Classifier ]]>
<![CDATA[Rahim, Abdul]]>;
<![CDATA[Pareza Alam Jusia]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3692
<![CDATA[Dengan pertumbuhan jumlah mahasiswa yang semakin dinamis, kebutuhan untuk menerapkan strategi preventif guna meningkatkan tingkat retensi mahasiswa menjadi semakin penting. Penelitian ini bertujuan untuk mengembangkan model yang dapat digunakan untuk mendeteksi mahasiswa yang berpotensi status akademiknya menjadi non-aktif menggunakan algoritma Decision Tree Classifier di lingkungan Universitas Dinamika Bangsa. Data yang digunakan dalam penelitian ini mencakup beragam variabel seperti data pribadi mahasiswa, nilai akademik dan informasi demografis lainnya. Proses pemodelan menggunakan Decision Tree Classifier dilakukan dengan memanfaatkan data historis mahasiswa untuk melatih model dalam mengklasifikasikan mahasiswa yang berpotensi non-aktif. Selanjutnya, model ini diuji coba pada data mahasiswa baru untuk menguji tingkat akurasi dan efektivitasnya. Hasil penelitian ini menunjukkan bahwa algoritma Decision Tree Classifier mampu memberikan kontribusi yang signifikan dalam prediksi mahasiswa yang berpotensi non-aktif dengan tingkat akurasi 95.63% dengan variabel yang paling berpengaruh adalah indeks prestasi semester 3, indeks prestasi semester 2 dan umur saat diterima.]]>
<![CDATA[A Systematic Review of Risk Management Tools and Techniques in Software Projects ]]>
<![CDATA[Fadhli Luthfiansyah]]>;
<![CDATA[Prasetyo, Aji]]>;
<![CDATA[Raharjo, Teguh]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3694
<![CDATA[The crafting of software is a continual procedure, and the success of each step of that process is contingent on effective management. Despite this, numerous organizations need help developing e-service systems, frequently dealing with budget constraints and tight deadlines. The lack of focus on risk management in software projects is likely to blame for these failures. Management of risks is crucial to ensuring the success and efficacy of software development projects, as it assists in identifying areas of vulnerability and provides valuable insights into the project's most important aspects. This study identifies and analyzes tools and techniques to support software development projects’ risk management activity. A systematic literature review (SLR) methodology was employed to collect and evaluate relevant research articles. The findings highlight various risk management tools and techniques, including brainstorming, root cause analysis, risk probability assessment, artificial intelligence, and risk response planning. These tools and techniques contribute to identifying, analyzing, planning, and controlling risks in software projects. The research provides insights into the state of the art in risk management. It complements previous studies by offering practical guidance on software development project risk management tools and techniques.]]>
<![CDATA[Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce ]]>
<![CDATA[Priatna, Wowon]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i2.3698
<![CDATA[Tujuan dari penelitian ini adalah untuk mengatasi masalah pengklasifikasian dan prediksi data yang tidak seimbang terkait dengan kondisi transaksi E-Commerce. Menjamurnya transaksi e-commerce menimbulkan potensi permasalahan: penipuan dalam pembelian e-commerce. Kasus penipuan e-niaga terus meningkat setiap tahun sejak tahun 1993. Menurut survei tahun 2013, untuk setiap $100 transaksi e-niaga, terdapat kerugian sebesar 5,65 sen akibat penipuan. Mendeteksi penipuan merupakan pendekatan yang efektif untuk meminimalkan terjadinya aktivitas penipuan dalam transaksi e-commerce. Pembelajaran menjadi metode yang semakin dapat diandalkan untuk memprediksi keadaan. Tidak adanya keseimbangan antara data yang curang dan tidak curang mengakibatkan klasifikasi menjadi bias. Algoritma SMOTE diperlukan untuk mencapai keseimbangan data. Selanjutnya peristiwa transaksi akan diklasifikasikan menggunakan algoritma Support Vector Machine, K-Nearest Neighbor, Naive Bayes, dan C45, dengan mempertimbangkan hasil penyeimbangan data. Di antara algoritma SVM, KNN, dan C45, metode Naive Bayes menunjukkan nilai akurasi tertinggi. Oleh karena itu, disarankan untuk menggunakan teknik ini untuk tujuan mengidentifikasi kondisi e-commerce]]>
<![CDATA[Beyond the Classroom: The Potential of Project-Based Learning and YouTube in Fostering Learning Engagement and Creativity in Digital Learning ]]>
<![CDATA[Putri, Elviza Yeni]]>;
<![CDATA[Oktarina, Rahmi]]>;
<![CDATA[Sidiqi, Adam Rasyid]]>;
<![CDATA[Saputra, Indra]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3699
<![CDATA[The background of this research is the low of student learning engagement and creativity. The purpose of this study is to analyze the potential of project-based learning and YouTube in enhancing learning engagement and creativity. The method used in this research is a semi-systematic literature review. The results of the study describe that project-based learning and the YouTube platform have the potential to increase learning engagement and creativity. Some potential aspects of project-based learning integrated with YouTube include relevant and interesting learning content, real project learning experiences, interactive and responsive platforms, flexibility and adaptability, online collaboration features, experimental activity opportunities, and constructive feedback. The findings of this research can be considered by educators when designing learning experiences aimed at improving student learning engagement and creativity. Further research recommendations include the need for experimental implementation of project-based learning integrated with YouTube and measuring the impact of treatment in increasing learning engagement and creativity.]]>
<![CDATA[Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model ]]>
<![CDATA[Hasan, Dathar]]>;
<![CDATA[Abdulazeez, Adnan Mohsin]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3700
<![CDATA[As a result of technological advancements, a variety of medical diagnostic systems have grown rapidly to support the healthcare sectors. Over the past years, there has been considerable interest in utilizing deep learning algorithms for the proactive diagnosis of multiple diseases. In most cases, Coronavirus (COVID-19) and tuberculosis (TB) are diagnosed through the examination of pulmonary X-rays. Deep learning algorithms can identify tuberculosis with an almost medical-grade level of consistency by extracting the lung regions in the X-ray images. The probability of tuberculosis detection is increased when classification algorithms are applied to segmented lungs rather than the entire X-ray. The main focus of this paper is to execute lung segmentation from X-ray images using the deeplabv3plus CNN-based semantic segmentation model. In other CNN architectures, the feature resolution diminishes as the network becomes deeper due to the use of sequential convolutions with pooling or striding within the down-sampling stage. To tackle this drawback, deeplabv3plus incorporates "Atrous Convolution" in addition to modifying the pooling and convolutional striding components of the backbone. The experimental results were: an accuracy of 97.42%, a Jaccard index of 93.49%, and a dice coefficient of 96.63%. We also conduct an extensive comparison between the deeplabv3plus segmentation model and other benchmark segmentation architectures. The results prove the ability of the deeplabv3plus model to achieve precise lung segmentation from X-ray images.]]>
<![CDATA[Klasifikasi Gambar Burung Konservasi di Wilayah Papua Barat Menggunakan Transfer Learning ]]>
<![CDATA[Muh. Falach Achsan Yusuf]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i1.3702
<![CDATA[Birds play an important role in maintaining the ecosystem. However, human activities such as poaching have caused some bird species to become endangered. This is rooted in the lack of public understanding of conservation bird species. The purpose of this research is to build an effective machine learning model that classifies conservation and non-conservation birds based on images, so that it is expected to improve the public's understanding of conservation and non-conservation bird species. In this study, Big Transfer (BiT), DenseNet121, and VGG16 are used as the basis for building the model. The dataset used consists of ten species that have been annotated into two classes, namely conservation and non-conservation. The model achieved the best performance with the highest accuracy obtained by the Big Transfer (BiT) model at 96.53%. The built DenseNet121 and VGG16 models have lower accuracy of 92.36% and 81.94%.]]>
