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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 1,114 Documents
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Klasifikasi User Review pada Aplikasi Online Travel Booking Menggunakan Multinomial Naïve Bayes Pratama, Mohammad Yoga; Cahyo Crysdian
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): 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.v14i3.4199

Abstract

Perkembangan teknologi yang pesat telah membawa perubahan dalam berbagai aspek kehidupan, termasuk dalam sektor pariwisata. Aplikasi tiket travel online seperti Traveloka, pegipegi dan Tiket.com merupakan aplikasi travel yang sangat populer di Indonesia. penelitian ini bertujuan untuk mengukur performa dari metode Multinomial Naïve Bayes dalam mengklasifikasikan ulasan pengguna aplikasi tersebut menjadi kelas “satisfied” dan “unhappy”. Dataset berjumlah 1339 ulasan pengguna yang diambil dari Google Play Store. Uji coba dilakukan dengan tiga skenario rasio pembagian dataset (7:3, 8:2, 9:1) dan dievaluasi menggunakan confusion matrix dan K-Fold Cross Validation. Hasil uji coba menunjukkan skenario pembagian data 9:1 menghasilkan akurasi model tertinggi sebesar 81.34% dengan precision 81.47%, recall 81.44% dan F1-Score 81.34%. Analisa kata menggunakan TF-IDF menunjukkan bahwa kata-kata seperti “good”, “nice” dan “nice” mendominasi pada kelas “satisfied”, sedangkan kata seperti “price”, “cant”, dan “app” merupakan 3 kata yang paling mendominasi pada kelas “unhappy”. Dapat disimpulkan bahwa metode Multinomial Naïve Bayes memiliki performa yang baik untuk klasifikasi ulasan pengguna aplikasi travel online, dan semakin banyak dataset yang digunakan makan semakin bagus pula model yang dihasilkan.
Analisis Prediktif untuk Mendeteksi Penipuan E-Commerce Menggunakan Algoritma Pembelajaran Mesin Achmad Achsarul Karim
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4202

Abstract

The rapid development of e-commerce provides new challenges faced by society, one of them is fraudulent e-commerce transactions. Losses caused by e-commerce fraud globally are expected to exceed 48 billion USD by 2023. The use of advanced technology such as machine learning can be a solution in an effort to detect and prevent e-commerce fraud. This research aims to evaluate several machine learning algorithms, such as deep learning, naive bayes, logistic regression, decision tree, and neural network, to detect e-commerce fraud. The dataset used consists of 1,472,952 transactions. This research consists of several stages, namely: data retrieval, weighting, feature selection, normalization, data sharing and data analysis. At the analysis stage, the algorithms were compared using a confusion matrix consisting of sensitivity, precision, accuracy, and F1 Score. The results show that each algorithm used gets a very high test value with a percentage of more than 90%.
Penerapan Algoritma Decision Tree untuk Prediksi Tekanan Udara di Stasiun Meteorologi Cengkareng Fariyani; Djuniadi; Sunarno; Iqbal
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4220

Abstract

Meteorology is a science that studies the movement of wind, clouds and water vapor with a main focus on weather prediction. Air pressure is one of the important meteorological parameters that can provide an initial indication of weather changes. Air pressure is used as a reference for taking off and landing aircraft at the airport. The variables needed to predict air pressure include air pressure, wind direction, temperature, wind speed and air humidity. The aim of this research is to predict air pressure at the Cengkareng Meteorological Station using the Decision Tree Algorithm and RapidMiner software. The data in this research comes from BMKGSoft for the period 2019 – 2023. BMKGSoft is used to track data sent from the station to the database server. The research results show that the prediction of air pressure at the Cengkareng Meteorological Station using the decision tree algorithm is very good with RMSE values: 0.208 +/- 0.000, MAE: 0.046 +/- 0.203, squared error: 0.043 +/- 2.967 and squared correlation: 0.987.
Prediction Of Alzheimer Patients with Machine Learning Algorithms Priyono, Eko
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): 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.v14i3.4364

Abstract

Alzheimer's disease is a neurological illness that impacts mental and emotional functions functions, has become a global concern due to its increasing prevalence. While age is the primary risk factor, other factors such as the APOE ε4 gene, family history, and brain injury also play a role. To date, there is no effective treatment for Alzheimer's, making early detection crucial. This study aims to explore early detection methods for Alzheimer's using machine learning algorithms, including transformer techniques. The results indicate that the Random Forest algorithm with Transformer methods achieved the highest accuracy of 98.9%. These findings are expected to contribute to the development of more accurate and efficient early detection strategies and improve the management of developing Alzheimer's later on.
Pengembangan Game Android Pada Anak Menggunakan Pendekatan User Centered Design Dan Evaluasi Usability Think Aloud Alvico, Alvico; Kurniawan, Dedy; Meiriza, Allsela; Syahbani, Muhammad Husni; Firnando, Ricy
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): 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.v14i3.4396

Abstract

Technology, especially handheld devices, has become an integral part of modern life. The use of handheld devices among children aged 12-15 years reaches 99.61%. Despite the potential to cause dependency, these devices can be utilized positively, for example through learning with educational games. One of them is a titungan game that aims to increase user motivation and skills. However, the development of this game must also consider user needs. This research applies the User Centered Design method to improve the experience and comfort of playing, and the Think Aloud method as an evaluation. This study involved 8 participants consisting of children with an age range of 10-14 years. The results showed that the developed application has met the needs of users, with only two problems identified from 64 total evaluation scenarios with a percentage of 96.87% using the Think Aloud method.
Deteksi Ekspresi Wajah Pada Scene Film Menggunakan Residual Masking Network Bhanu Sri Nugraha; Fahma Inti Ilmawati; Dhani Ariatmanto; Lukman
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4422

Abstract

In a film, there are various things that must be paid attention to, one of which is the actor's expression in deepening his role. This is what can make the audience immersed in the storyline of the film and can provide value for the accuracy of deepening the role for the people who watch it. With the popularity of deep learning, especially CNN (Convolutional Neural Network) can automatically extract and learn for a good facial expression recognition system. In this experiment, we use Residual Masking Network (RNM). Building on this understanding, we evaluate this dataset with standard image classification models to analyse the feasibility of using facial expressions in determining the appropriateness of emotional content in an actor's role in a film. The accuracy results in this study were 99% for detecting angry expressions.
Eksplorasi Kebutuhan Pelanggan dari Produk Backlog untuk Pengembangan Produk Berkelanjutan Permatasari, Indah; Santana, Ade Raka
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4498

Abstract

In the digital era, sustainable software development faces challenges in meeting dynamic user needs while ensuring data management security and efficiency. Effective methods are needed to create an adaptive product backlog. This study uses Focus Group Discussion (FGD) involving five experts to identify customer needs in sustainable products. The research aims to formulate a backlog that encompasses multi-tenant security, API integration, and efficient data management. FGD results indicate that tenant security and CI/CD implementation are essential for continuous updates, while performance statistics are necessary for data-driven decision-making. This study recommends using the AHP method to establish numeric priorities and the Delphi method to achieve expert consensus. These results support the development of a backlog that is more responsive to market changes and needs.
PENGGUNAAN MIXED METHOD USABILITY TESTING (EYE TRACKING METHOD DAN COGNITIVE WALKTHROUGH (STUDI KASUS: WEBSITE JURUSAN SISTEM INFORMASI FAKULTAS ILMU KOMPUTER UNIVERSITAS SRIWIJAYA): Case Study: Website of The Department of Information Systems, Faculty of Computer Science, Sriwijaya University Putra, Pacu; Oktadini, Nabila Rizky; Hardiyanti, Dinna Yunika; Larasati, Salsabila; Putri, Nyayu Dwi Tarisa
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): 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.v14i2.4516

Abstract

Universitas Sriwijaya is one of the state universities in Indonesia. Universitas Sriwijaya has 10 faculties, one of which is the Faculty of Computer Science. Information Systems is one of the departments in the Faculty of Computer Science, Sriwijaya University. Based on the observation results, the Department of Information Systems has just updated the appearance of its website. To determine the level of usability of the website, this research uses two methods, namely eye-tracking method and cognitive walkthrough. The activities that are the research material in this research include searching for lecturers' schedules, downloading final project guidelines and searching for course codes. As a result of the cognitive walkthrough method, the activity of searching for lecturers' timetables has the lowest success rate of 0%, followed by the activity of searching for course codes with 40% and the activity of downloading final project guidelines with 80%. In addition, the research continued using the eye-tracking method to identify areas of confusion for the respondents and to understand the emotional level of the respondents when carrying out these activities. It can be seen that the average respondent is still confused or unfocused when working on the pre-defined activities.
The Effects of Data Sampling and Feature Selection on Public Service Satisfaction Using an Ensemble Classifier Algorithm Priatna, Wowon
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): 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.v14i3.4533

Abstract

Customer satisfaction is an important factor that determines quality. User satisfaction analysis can identify the service quality and measure quality through an evaluation process to improve services. This research aims to measure the performance of services provided by the village government. Villages and sub-districts offer services based on the community's specific needs. Nevertheless, by delivering impeccable service, it is possible to satisfy the community without causing physical or material harm. An essential requirement is the development of a service user classification methodology to enhance service quality, efficiently address service user grievances, detect recurring trends, and promptly offer feedback to enhance the offerings of products and services. Machine learning approaches can be used to quantify public service satisfaction in the analytical process. Machine learning is an algorithmic approach used to assess and prioritize satisfaction with public services offered by service providers. The main approach for machine learning is an ensemble classifier. The data was analyzed using Excel; then, the data was processed first to create a classification model. At the preprocessing stage, the data is grouped to obtain labels/targets to be processed based on algorithmic classification. The classification uses the Classifier aggregation algorithm. Type improvements using optimization features using the Particle Swarm Optimization (PSO) sampling algorithm and random subsampling techniques. This research produced an accuracy value before adding sampling techniques and a PSO accuracy value of 92.68. After adding sampling techniques and PSO optimization, an accuracy value of 100% was obtained
Penerapan K-Means untuk Pengelompokan Hasil Belajar Informatika Rahman, Taufik; Ahmad Sahroni, Abdul
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4556

Abstract

This study was conducted at SMK Kesehatan Prima Indonesia, which integrates informatics subjects into its curriculum, although the main focus is on developing students' health competencies. The main challenge faced is the management of informatics learning outcomes, especially in grouping students based on their understanding and achievements, which has been done manually and inefficiently. This study aims to identify groups of student learning outcomes using the K-Means Clustering method and describe the differences between the groups formed. This study is an exploratory study with a quantitative approach. The results of the clustering analysis on grade 10 students showed the formation of three groups: high score groups (24 students), medium score groups (51 students), and low score groups (42 students). In addition, learning interest and use of IT devices were shown to have a significant influence on informatics learning outcomes. These findings confirm that the application of the K-Means algorithm can improve the effectiveness of teaching strategies by grouping students in a more structured manner based on their learning outcomes.    

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