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Tri A. Sundara
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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 1,170 Documents
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Prediksi Mahasiswa Berpotensi Non-Aktif Menggunakan Algoritma Decision Tree Classifier Rahim, Abdul; Pareza Alam Jusia
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3692

Abstract

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.
A Systematic Review of Risk Management Tools and Techniques in Software Projects Fadhli Luthfiansyah; Prasetyo, Aji; Raharjo, Teguh
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3694

Abstract

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.
Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce Priatna, Wowon
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3698

Abstract

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
Beyond the Classroom: The Potential of Project-Based Learning and YouTube in Fostering Learning Engagement and Creativity in Digital Learning Putri, Elviza Yeni; Oktarina, Rahmi; Sidiqi, Adam Rasyid; Saputra, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3699

Abstract

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.
Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model Hasan, Dathar; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3700

Abstract

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.
Klasifikasi Gambar Burung Konservasi di Wilayah Papua Barat Menggunakan Transfer Learning Muh. Falach Achsan Yusuf
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3702

Abstract

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%.
Sistem Penyiraman dan Pemupukan Otomatis pada Tanaman Pinang Menggunakan Metode Fuzzy Mamdani Bayti Widya Rezky; Nirmala, Irma; Sari, Kartika
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3703

Abstract

Tanaman pinang dapat tumbuh pada kelembapan tanah sekitar 60 - 80%, dengan pH tanah antara 4 – 8, dan suhu optimum 20 - 32℃. Tanaman pinang membutuhkan pasokan air dan pupuk dengan kadar yang sesuai dan pemberiannya dilakukan secara terjadwal untuk mendapatkan pertumbuhan yang optimal. Maka dari itu, dikembangkan sebuah sistem yang dapat mengambil keputusan durasi penyiraman dan pemupukan otomatis yang bekerja secara terjadwal. Penelitian ini menggunakan logika fuzzy mamdani yang diintegrasikan dengan Arduino Uno sebagai pengendali utama sistem. Sensor DHT11, capacitive soil moisture sensor, dan sensor pH tanah dijadikan parameter masukan fuzzy. Pengujian sistem dilakukan dengan jadwal penyiraman setiap hari pada pukul 08.00 dan 16.00, pemupukan jadwalkan setiap seminggu sekali pada pukul 10.00 menggunakan Real Time Clock (RTC). Sistem inferensi fuzzy yang dirancang telah berhasil mengatasi permasalahan yang ada. Hasil pengujian basis aturan fuzzy pada proses penyiraman didapatkan nilai akurasi sebesar 93% dan proses pemupukan diperoleh nilai akurasi sebesar 86%.
Usability Evaluation and Solution Design Improvement of OCTO Friends as Banking Referral Application Utami, Aisyah Nurlita; Santoso, Harry Budi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3704

Abstract

OCTO Friends application was developed to allow customers to directly participate in banking product referral program, has not met expectations and targets. Only 10.6% of the 39,105 members actively provided referrals, and business conversion rate was only 10% of the target. This study aims to evaluate the application's usability and generate recommendations for interface design improvement. A mixed methods research approach was used, including quantitative research through user needs questionnaires and System Usability Scale (SUS), along with qualitative research through in-depth interviews and usability testing. Data collection through questionnaires involved 104 respondents, resulting in a list of user needs and SUS score of 66. The results showed that OCTO Friends is requires some improvements. In-depth interviews and usability testing for six main functions led to 46 interface design improvements, especially improvements in referral data input, follow-up referral, and incentive information functions as these are primary functions of referral application.
Facial Expression Recognition Based on Deep Learning: A Review Jajan, Khalid Ibrahim Khalaf; Abdulazeez, Prof. Dr. Eng. Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3705

Abstract

This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.
Credit Card Fraud Detection using KNN, Random Forest and Logistic Regression Algorithms : A Comparative Analysis Ashqi Saeed, Vaman; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3707

Abstract

Because credit cards are utilized so frequently, fraud appears to be a significant concern in the credit card industry. It is challenging to quantify the effects of misrepresentation. Globally, credit card fraud has cost institutions and consumers billions of dollars. Despite the existence of numerous anti-fraud mechanisms, fraudsters continue to seek out novel methods and strategies to commit fraud. An additional challenge in the estimation of credit card fraud loss is that the magnitude of unreported or undetected forgeries cannot be determined, only losses associated with those frauds that have been detected can be measured. Implementing effective fraud detection algorithms through the utilization of machine-learning techniques is crucial in order to mitigate these losses and provide support to fraud investigators. This paper presents a machine learning-based method for the detection of credit card fraud. Three methodologies are implemented on the raw and pre-processed data. Python is used to implement the work. By comparing the accuracy-based performance evaluations of k-nearest neighbor and logistic regression with Random Forest, it is determined that the former exhibits superior performance.

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