cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tri A. Sundara
Contact Email
tri.sundara@stmikindonesia.ac.id
Phone
+628116606456
Journal Mail Official
ijcs@stmikindonesia.ac.id
Editorial Address
Jalan Khatib Sulaiman Dalam 1, Padang, Indonesia
Location
Kota padang,
Sumatera barat
INDONESIA
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
 76   77   78   79   80 
Multi Stage Analisis Sentimen Berbasis Aspek Pada Ulasan Pengguna Aplikasi Dompet Digital Menggunakan Metode Multinomial Naïve Bayes Hikmatul Maulidia Putri
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4197

Abstract

Digital wallet is one of the financial technology that is currently popularly used by Indonesians as a non-cash transaction tool. The more users of digital wallet applications, the number of reviews, comments, and opinions also increases and varies. User reviews are considered very helpful as well as a forum for information because they can assess certain aspects. This study proposes research related to Aspect-based Sentiment Analysis using Multinomial Naïve Bayes to analyze user sentiment towards an aspect, namely service, cost, and security on digital wallet applications and determine the evaluation of system performance using the Multinomial Naïve Bayes algorithm. The data in this study was taken using scraping techniques with keywords from the Google Play Store platform as many as 500 in each aspect. The results of this study show that the 70:30 data division is better than other data division ratios, namely the 80:20, and 90:10 data division ratios, with performance evaluation using accuracy, precision, recall, and f1-score respectively 0.841, 0.844, 0.841, and 0.841.
Optimasi Pemilihan Fitur untuk Prediksi Penyakit Jantung Menggunakan Algoritma Genetika dan Random Forest Gori, Takhamo; Hestiningtyas, Annisa
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4214

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, menekankan urgensi prediksi dini dan manajemen risiko yang efektif. Dalam upaya meningkatkan akurasi prediksi penyakit jantung, penelitian ini mengusulkan pendekatan metode GridSearchCV (GS) dan Genetic Algorithm Feature Selection (GA-FS) pada model Random Forest (RF). Setelah proses seleksi fitur dengan GA-FS, dari sebelas atribut awal dimasukkan, delapan atribut terpilih, yakni Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, ExerciseAngina, dan ST_Slope, sementara atribut Age, MaxHR, dan Oldpeak dieliminasi. Hasil penelitian menunjukkan bahwa model RF yang dioptimalkan dengan GS dan GA-FS (RF-GS-GAFS) mencapai akurasi 91.85%, presisi 95.10%, recall 90.65%, dan F1-Score 92.82%, mengungguli model RF dengan optimasi GS (89.67%) dan RF tanpa optimalisasi (88.04%). Temuan ini memberikan kontribusi positif yang signifikan dalam meningkatkan kinerja model prediksi penyakit jantung melalui optimalisasi parameter dan pemilihan fitur menggunakan algoritma genetik.
Designing an Effective Job Recommender System based on Embedded Machine Learning Models Ayodele, Abiola Olaide; Gbadebo, Adedeji
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4232

Abstract

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.
Data Governance Improvement Strategy for Peer-to-Peer Lending Sharia in Indonesia: Study Case PT ABC Priastomo, Ristyo Yogi; Ruldeviyani, Yova; Gunawan, Adi; Al Haq, Muhammad Hezby; Utami, Aisyah Nurlita
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4233

Abstract

Abstract—Financial Technology (fintech) is a company that supervised by the Indonesian Financial Services Authority (OJK) and fintech associations which has strict regulations. Well-defined data management can support organizations to comply with mandatory regulations. This research was conducted on a sharia peer-to-peer lending fintech in Indonesia with the aim of solving data governance problems in organizations by measure of Data Governance Maturity Level to get recommendations strategies to improve the implementation of data governance in the organization. The measurement was carried out using IBM Data Governance Maturity Model Framework. After validation and finalization of the assessment, the results showed that the average score was 2.47. It's shown that currently at the Managed level. Some domains need to be improved in the future, data value creation, data organizational structure and awareness, data policies and rules and data stewardship.
An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem Tsedura, Nyaradzo Alice; Bhero, Ernest; Chibaya, Colin
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4275

Abstract

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.
Analisis Sentiment Publik Mengenai Neuralink dari Twitter dengan Menerapkan Naïve Bayes: Multinomial, Gaussian, dan Complement Azwan Triyadi; Purnawansyah; Darwis, Herdianti
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4278

Abstract

Elon Musk owns the business Neuralink, which attempts to build brain-machine interfaces. This study categorizes public opinion towards the use of Neuralink goods, including whether people agree (positive), disagree (negative), or feel neither way. Without accessing the Twitter API, the Twint Python Libraries were utilised to retrieve a dataset of 3000 using the keyword “neuralink”. What datasets are included in positive, neutral, or negative categories are designated using RoBERTa. Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) is employed to handle class imbalance. Complement Naive Bayes, achieved accuracy of 81%, followed by Multinomial Naive Bayes, which achieved accuracy of 80%, and Gaussian Naive Bayes, which achieved accuracy of 75%. The model Complement Naïve Bayes was used in this study to attain the maximum accuracy, and accuracy increases when employing SMOTE compared to other Naïve bayes variants.
Rancang Bangun Aplikasi Diagnosa Sexually Transmitted Diseases Menggunakan Algoritma Certainty Factor Mandra; Nouval Trezandy Lapatta; Syaiful Hendra; Syahrullah
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4293

Abstract

This research aims to design and develop an Android application that can be used to diagnose results Sexually Transmitted Diseases using algorithms Certainty Factor. Sexually Transmitted Diseases is a sexually transmitted disease that can cause serious health impacts if not immediately identified and treated appropriately. This application is designed to help users carry out initial diagnoses independently. The method used in developing this application is the Certainty Factor algorithm, which is a rule-based decision support method. This algorithm utilizes knowledge from experts in the medical field and combines it with symptom data provided by users to produce more accurate diagnoses. The app will allow users to input suggested symptoms and generate a diagnosis based on that information. It is hoped that this application will be a useful tool in a self-directed approach to diagnosis Sexually Transmitted Diseases.
Optimasi Pengambilan Keputusan dengan Neural Network: Menuju Era Keputusan Pintar Akram Kemal Dewantara
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4300

Abstract

The ability to make quick and accurate decisions in the midst of a digital age laden with complex data is critical to success in various industries. In this research, a neural network-based reasoning system is developed to enhance the smart decision-making process. We developed a neural network architecture by utilizing the power of deep learning. This enables the processing of historical and real-time data. We also conducted case studies in healthcare, finance, and HR management to test how effective it is. The results show that, compared to conventional approaches, this method has a significant improvement in processing time efficiency and an increase in decision accuracy of up to 15%. These results show that neural networks have the ability to change the decision-making landscape and offer intelligent solutions to real-world problems.
Designing a Presence Information System for Student Mentoring Activities Using the Laravel Framework Misna Asqia
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4311

Abstract

STT NF is a private higher education institution that combines technological knowledge with Islamic values. To achieve this goal, STT NF has a program called student mentoring. Mentoring is led by a mentor and attended by several mentees. The mentoring evaluation process is conducted regularly to assess the progress of each mentee in each group. The evaluation results of each group are sent to BKPK of STT NF. The researcher intends to build an information system for student mentoring attendance using Laravel Framework. The design method used the extreme programming approach. Based on the discussions conducted, it determined that there are five mentoring activities dashboards to be created. There are two known user categories, namely admin and mentor. The program was tested using black box testing with 22 scenarios. Based on the testing, it was concluded that all categories function correctly
Penerapan Algoritma K-Means Clustering dalam Menganalisis Pola Peminjaman Buku di Perpustakaan Sigit, Rapel
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4317

Abstract

Library book borrowing reflects readers' preferences on various topics, providing insights for improving collection management. This study categorizes books into five main categories: old books with few pages, modern books with short borrowing periods, classic books with long borrowing durations, books with many pages and moderate borrowing, and new books with varying borrowing durations. The dataset is analyzed through pre-processing, including feature creation, normalization, and standard scaling. The K-Means algorithm is used to cluster data based on Euclidean distance, with results evaluated using the Davies-Bouldin Index (DBI), where a lower value indicates better clustering quality. The optimal number of clusters is determined using the Elbow Method, showing five clusters as the most effective. Applying K-Means Clustering produces five informative clusters, with a DBI of 0.50 indicating good clustering quality. Scatter plots illustrate cluster distribution based on publication year, number of pages, and borrowing duration, from a dataset of 1323 borrowing records.

Page 78 of 117 | Total Record : 1170

 76   77   78   79   80 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science More Issue