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 1,127 Documents
 79   80   81   82   83 
Modul Elektronik Berbasis Project Based Learning (PjBL) pada Pembelajaran Manajemen Proyek Widyastuti, Rini
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4444

Abstract

The research started with the problem of project management learning, where learning uses modules that do not involve active students and independent learning, namely there are no electronic modules that attract students, the material is difficult to understand, problems are not carefully analyzed, so that at the end of the lecture there are still difficulties in managing projects. The aim of this research is to produce a valid and practical PjBL-based electronic module. The research method uses research and development by applying the 4D model, namely define, design, develop, and disseminate. The validity sheet and practicality questionnaire are the instruments of this research. The data analysis technique is descriptive analysis with a percentage of validity and practicality tests. Validity analysis through design and material experts, as well as practicality was carried out by 17 PTIK students. The research results showed that the electronic module was declared valid by the validator at 81% from the aspects of language, presentation, writing, content and programming. The results of the practicality test with practical criteria are 80% seen from the aspects of language, benefits, use and appearance. So that electronic modules can be used according to students' learning needs.
Metode Support Vector Machine Untuk Analisis Sentimen Aplikasi Threads di Google Play Store Triully Prasetyo, Dimas; Atiqah Meutia Hilda
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4446

Abstract

This research started with user interest in the latest Twitter app and the Google Play Store's Threads app.The primary goal was to apply the Support Vector Machine (SVM) technique to analyze user evaluations for sentiment, both positive and negative.The collected data went through a preprocessing process that included cleaning, casefolding, tokenizing, stop word removal, stemming, and filtering. After going through preprocessing data as many as 1000 comments were implemented into the Support Vector Machine method showing 54.1% positive sentiment and 45.9% negative sentiment. The accuracy value of 81.19% and the confusion matrix results from the values of the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative) variables are also displayed by the testing. Based on the accuracy value acquired, it can be inferred that the Threads application receives positive feedback and is likely to be improved to yield even better outcomes.
Simulation of Voltage and Frequency Stabilization in PV-Based Microgrids Using Virtual Synchronous Generator Control Zaw Tway Oo
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4447

Abstract

In recent years, distributed generation technologies and micro-grids have garnered increasing attention, particularly with the rapid growth of renewable energy sources (RESs) integrated via power electronic converters. As the penetration of RESs in modern power systems rises, voltage and frequency support, traditionally provided by synchronous generators, can be effectively managed by Virtual Synchronous Generator (VSG) control. This paper presents the modeling and analysis of voltage and frequency stability enhancement in a PV-based micro-grid system using VSG control. The micro-grid system under study operates at 400V, 50Hz, and is integrated with a 0.4kV power distribution line. Mathematical models of the VSG control strategy are developed in MATLAB/Simulink. Simulation results demonstrate that VSG control significantly improves the voltage and frequency stability of the PV-based micro-grid system.
Penerapan Metode Rapid Application Development (RAD) pada Sistem E-Career Politeknik LP3I Kampus Cirebon Berbasis Aplikasi (Startup) Aris Riyanto; Ade Johar Maturidi; Khair, Rizaldy
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4450

Abstract

In today's digital era, the Rapid Application Development (RAD) method has shown great potential across various sectors, including education and career development. However, its application in electronic career systems, or E-Careers, remains relatively uncommon. This creates an urgency to conduct research on how the Rapid Application Development (RAD) method can be applied in E-Career systems to enhance efficiency, transparency, and security in the career search process for students. This study aims to apply the Rapid Application Development (RAD) method in the development of a mobile-based E-Career system at Politeknik LP3I Cirebon Campus. One sector that can benefit from the Rapid Application Development (RAD) method is the electronic career system or E-Career. This system aims to facilitate career searches for students in a more efficient and transparent manner. However, a challenge in implementing this system is ensuring that the data shared between different parties is secure and unalterable. The primary goal is to create a system that is not only efficient but also secure and transparent. Thus, students can search for careers more efficiently and transparently, while companies can more easily find the right candidates. Additionally, this research also aims to identify and address challenges in integrating the Rapid Application Development (RAD) method with mobile applications.
Analisis Sentimen Ulasan Penumpang Maskapai Penerbangan Indonesia Menggunakan Support Vector Machine , Naive Bayes, dan Random Forest Daryanti; Tri Widodo
The Indonesian Journal of Computer Science Vol. 13 No. 5 (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.v13i5.4452

Abstract

Maskapai penerbangan merupakan perusahaan yang menyediakan layanan penerbangan untuk penumpang dan barang. Di Indonesia, terdapat dua jenis penerbangan, yaitu Low-Cost Carrier (LCC) dan full service. Penelitian ini menganalisis sentimen terhadap maskapai penerbangan Indonesia dengan menggunakan data ulasan dari X (Twitter) dan TripAdvisor, yang terdiri dari 6.469 ulasan. Tujuan utama penelitian adalah membandingkan kinerja tiga algoritma machine learning: Support Vector Machine (SVM) dengan kernel RBF, Naive Bayes, dan Random Forest. Model dievaluasi berdasarkan metrik akurasi, presisi, recall, dan F1-Score. Hasil menunjukkan bahwa Random Forest memberikan kinerja terbaik dengan akurasi 91%, presisi 91%, recall 91%, dan F1-Score 90%. Sementara itu, SVM dengan kernel RBF mencapai akurasi 89%, dan Naive Bayes memperoleh akurasi 79%. Dengan demikian, Random Forest terbukti lebih efektif dalam analisis sentimen pengguna maskapai penerbangan di Indonesia.
a PREDIKSI SISWA PUTUS SEKOLAH DAN KEBERHASILAN AKADEMIK MENGGUNAKAN MACHINE LEARNING: Prediksi Siswa Putus Sekolah dan Keberhasilan Akademik Fitriana, Siti; riniyanty; laila, rahma; pratama, septiano anggun; lamasitudju, chairunnisa ar
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4453

Abstract

In 2020-2023. The issue of high school dropouts at SMA Negeri 2 Sigi increased, causing impacts such as declining school accreditation, a decrease in the number of students, and operational aid. This research aims to build an early prediction system for student dropouts using Machine Learning (ML). In this study, data from 200 students were used. With 16 students labeled as dropout. The results showed a model accuracy of 0.942 and an area under the curve (AUC) of 0.948. the factors most influencing student droppout are average grades, meeting targets, and father’s education leve.
Penerapan Algoritma Dijkstra Untuk Menentukan Rute Terpendek Dalam Distribusi Darah Di Palang Merah Indonesia Kota Palu Berbasis Mobile Dival Maulana, Muhammad; Hendra, Andi; Yudhaswana, Yuri; Anshori, Yusuf; Ar. Lamasitudju, Chairunnisa
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4454

Abstract

Efficient blood distribution is crucial for the Indonesian Red Cross (PMI) to save lives. This research develops a mobile-based blood distribution system that utilizes Dijkstra's Algorithm to determine the shortest delivery routes. The system is specifically designed for PMI in Palu City, assisting drivers in finding optimal paths and monitoring blood stock availability in hospitals in real-time. A prototyping method was employed for development, while the Google Maps API enables accurate route visualization. Research results indicate that Dijkstra's Algorithm reduces blood distribution time by 15-20% compared to the previously used manual methods. Additionally, this system facilitates better management of blood stocks and increases distribution speed. Blackbox testing ensures that all features function according to specifications. This research contributes to enhancing blood distribution efficiency at PMI, with the hope of minimizing the risk of blood shortages. Future research is recommended to further develop the system on a larger scale to address more complex distribution challenges.
PENGEMBANGAN APLIKASI BERBASIS WEB DENGAN FRAMEWORK CODEIGNITER UNTUK PREDIKSI STATUS KEGAWATAN PASIEN TUBERCULOSIS MENGGUNAKAN ALGORITMA NBC DI KOTA PADANG Nurul Abdillah; Alfita Dewi
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4455

Abstract

Tuberculosis (TBC) masih menjadi masalah kesehatan serius di Indonesia, termasuk Kota Padang. Penanganan tepat sangat penting, terutama dalam memprediksi status kegawatan pasien TBC agar intervensi medis bisa lebih cepat dan tepat. Saat ini, sistem di rumah sakit masih memiliki keterbatasan dalam mengklasifikasikan status kegawatan pasien TBC, yang bisa mengakibatkan keterlambatan penanganan. Penelitian ini bertujuan mengembangkan aplikasi web menggunakan framework CodeIgniter dengan algoritma Naïve Bayes Classifier (NBC) untuk memprediksi status kegawatan pasien TBC di Kota Padang. Data rekam medis yang digunakan berasal dari RSUP Dr. M. Djamil dan RSUD Dr. Rasidin, terdiri dari 227 data pasien TBC periode April hingga Juni 2024, dengan 13 atribut, termasuk jenis kelamin, usia, jenis batuk, sesak napas, nyeri dada, dan status kegawatan sebagai atribut kelas. Hasilnya menunjukkan bahwa algoritma NBC memiliki akurasi 71,81% dalam memprediksi status kegawatan pasien. Aplikasi ini diharapkan membantu tenaga kesehatan dalam pengambilan keputusan dengan menyediakan prediksi yang mendukung rencana penanganan pasien
Klasifikasi Genre Music Menggunakan Hybrid Resnet18-Bidirectional LSTM dengan Mekanisme Atensi dan Fitur MFCC dan Mel-Spectogram Dimas Elang Setyoko
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4456

Abstract

Incorrect Genre Classification is still often found. One of the causes is the selection of inappropriate features. This has an impact on the ability of the classifier model because some methods with a machine learning approach are highly dependent on the features used. Utilization of several features, especially spectral features, can improve the performance of the classifier model. On the other hand, methods with a deep learning approach such as CNN and RNN have been proven to outperform machine learning-based methods. This study proposes a hybrid Resnet18-BiLSTM model with the addition of the Convolutional Block Attention Module (CBAM) attention mechanism to improve the accuracy of music genre classification. Moreover, this study also combines two spectral features, namely mel-spectrogram and MFCC. The results of the experiment using the GTZAN dataset showed that the combination of mel-spectrogram and MFCC and the addition of the CBAM attention mechanism were able to classify music genres with an accuracy rate of 95.60% in validation and 95.30% in testing.
Analisis Kepuasan Pengguna Aplikasi DANA Menggunakan Metode CSI dan E-Servqual Febriansyah, Yudhi; Syaifullah; Ahsyar, Tengku Khairil; Muhammad Jazman
The Indonesian Journal of Computer Science Vol. 13 No. 6 (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.v13i6.4457

Abstract

Kemajuan teknologi informasi saat ini telah mempermudah berbagai aspek kehidupan, termasuk transaksi non-tunai yang semakin populer. Teknologi ini berperan besar dalam meningkatkan jumlah konsumen serta mempengaruhi kebutuhan dan gaya hidup masyarakat di era ekonomi digital. Penelitian ini bertujuan untuk mengukur tingkat kepuasan pengguna dan kualitas layanan aplikasi DANA sebagai alat pembayaran digital di kalangan UMKM di Kota Pekanbaru, dengan menggunakan lima dimensi e-Servqual. Metode penelitian melibatkan penyebaran kuesioner kepada 30 responden untuk mengevaluasi aspek Customer Satisfaction Index (CSI) dan e-Servqual. Hasil penelitian menunjukkan bahwa tingkat kepuasan pengguna berada dalam kategori "puas" dengan skor CSI sebesar 72,21%. Namun, terdapat kesenjangan antara harapan dan persepsi pengguna, terutama pada dimensi Responsiveness, yang menunjukkan kualitas layanan terendah. Kesimpulannya, meskipun pengguna secara umum merasa puas, peningkatan pada dimensi Responsiveness diperlukan untuk memperkuat kepuasan dan loyalitas pengguna aplikasi DANA di masa mendatang.

Page 81 of 113 | Total Record : 1127

 79   80   81   82   83 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue 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 (IJCS) Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS) Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS) Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS) Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS) 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