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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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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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Komparasi Berbagai Metode Klasifikasi Teks Untuk Sentimen Pengguna Gawai Di Usia Dini Meliana, Yovi; Suryono, Ryan Randy
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.v13i5.4439

Abstract

In the context of rapid digital development, the use of gadgets among Indonesian children has become a very important topic to study. This study aims to analyze sentiments related to gadget use by applying classification methods such as Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. To overcome data imbalance, After applying the SMOTE technique, the results of the study revealed that SVM obtained the highest accuracy of 99% with SMOTE, followed by Decision Tree which reached 98% and Naïve Bayes which obtained 94% when SMOTE was applied. In addition, the application of preprocessing techniques such as tokenization, stemming, and filtering contributed to improving data quality. These findings emphasize the importance of choosing the right method in sentiment analysis to understand the impact of gadget use on children's development. This study provides meaningful insights for the development of better policies and practices related to children's digital device use
Perbandingan Berbagai Metode Klasifikasi Teks Untuk Sentimen Kebijakan Makan Gratis Di Indonesia Yuspita, Emi; Suryono, Ryan Randy
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.v13i5.4440

Abstract

The free meal policy is an important initiative to improve the nutrition of children under five and pregnant women and reduce social inequality. This policy supports low-income families by providing free food and milk in schools and Islamic boarding schools. On social media, especially platform X (Twitter), this policy sparked public discussion. This research aims to analyze sentiment regarding the free meal policy using Naive Bayes, SVM, and Decision Tree methods, as well as providing the effectiveness of classification algorithms in understanding public opinion. Of the 5,205 tweets analyzed, there were 4,735 positive tweets and 470 negative tweets. Applying Smote to this analysis provides significant results. SVM achieved 99% accuracy, Decision Tree also showed good performance with 98% accuracy. Meanwhile, Naive Bayes experienced an increase in accuracy of up to 91%, although it was still less than optimal in detecting negative sentiment compared to SVM and Decision Tree.
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
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
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
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
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
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
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
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
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

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