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Contact Name
Tri A. Sundara
Contact Email
tri.sundara@stmikindonesia.ac.id
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+628116606456
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ijcs@stmikindonesia.ac.id
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Jalan Khatib Sulaiman Dalam 1, Padang, Indonesia
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Kota padang,
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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 1,114 Documents
 101   102   103   104   105 
Pengembangan Sistem Sensor berbasis Tekanan Udara untuk Deteksi Kontak Kaki Robot Dwi Prasetyo, Wahyu Agung; Darmawan, Adytia; Dewanto, Raden Sanggar; Alfathdyanto, Khairurizal
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.v14i2.4757

Abstract

Legged robot is preferred choice for travesing uneven terrain. Robot leg can be positioned dynamically to achieve better locmotion. Detection of the leg contact point became more of essential part for the unpredictable course. The common method by deploying resistive force sensor provides a binary condition of whether the leg has touches surface. This paper explores the possibility of implementing air pressure sensor on a sensor system to provide more information at robot leg contact point. Air pressure sensor can provide a more wide and continuous range of value that fluctuates along the contact rate of the leg. Verification of the study uses single leg part of dog-type quardruped. The sensor testing gave the output value with average error of 1,3%. The pressure sensor provides readings at around ± 40ms with maximum readable pressure of 1,5 kPa.
Heart Failure Disease Classification Using Random Forest Algorithm with Grid Search Cross Validation Technique Septia, Rapindra; Junadhi; Susi Erlinda; Wirta Agustin
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.v14i2.4765

Abstract

Heart failure is one of the leading causes of death worldwide and requires early detection to reduce the risk of serious complications. However, the imbalance in medical data poses a challenge in developing accurate prediction models. This study developed a heart failure classification model using the Random Forest algorithm, optimized with Grid Search Cross Validation to find the best combination of hyperparameters. The dataset consisted of 300 observations with 12 medical features and 1 target feature. Data preprocessing included outlier removal using the Interquartile Range (IQR) and Winsorize methods. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, resulting in a more balanced training data distribution. The dataset was split into 80% training and 20% testing data using stratified sampling to maintain class proportions. The model was evaluated using accuracy, precision, recall, and F1-score metrics, with results showing 90% accuracy, 0.94 precision for class 0, 0.80 precision for class 1, 0.91 recall for class 0, and 0.86 recall for class 1. The model was implemented in a Streamlit-based application, allowing users to input health parameters and receive interactive predictions. This study demonstrates that optimizing the Random Forest algorithm with Grid Search Cross Validation can improve heart failure classification performance, providing a practical solution for supporting heart failure classification. Keywords: Heart Failure Classification, Random Forest, Hyperparameter Optimization, SMOTE, Model Evaluation.
Analisis Kepuasan Pengguna pada Aplikasi Spotify di Kota Palembang dengan Menggunakan Metode Usability Tri Wulandari, Dinda; M. Rudi Sanjaya; Dedy Kurniawan; Endang Lestari Ruskan
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.v14i2.4778

Abstract

Usability level is one of the methods that can affect the comfort of mobile application users, and evaluation is very important to do. Spotify, as an app for listening to music and podcasts, has users from different parts of the world. The evaluation is carried out with the aim of improving the user experience, so that the application can evolve and be easier to use. The method applied in this evaluation is usability testing. The results obtained show that the Spotify application is of good quality, effective, efficient, and able to provide satisfaction to users, although there is still room for improvement to meet user expectations even better.
Evaluasi Performa Metode Exponential Smoothing pada Data Runtun Waktu Hierarkis Alkadrie, Syarifah Syila; Wijaya, Madona Yunita; Fitriyati, Nina
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.v14i2.4783

Abstract

Penelitian ini bertujuan untuk menyalakan metode Simple Exponential Smoothing (SES), Double Exponential Smoothing (Metode Holt), dan Triple Exponential Smoothing (Holt-Winters) dalam memperkirakan jumlah wisatawan di Australia dari tahun 1998 sampai dengan tahun 2016. Data yang digunakan memiliki struktur hierarki dengan empat tingkat: Australia, negara bagian, kawasan, dan tujuan kunjungan. Pendekatan bottom-up diterapkan untuk menghasilkan ramalan pada tingkat hierarki teratas dengan menggabungkan ramalan dari tingkat terendah. Evaluasi dilakukan dengan menggunakan metrik Symmetric Mean Absolute Percentage Error (SMAPE) pada setiap tingkat hierarki dan cakrawala peramalan. Hasil penelitian menunjukkan bahwa Metode Holt berkinerja terbaik pada tingkat Australia (SMAPE 3,26%–9,28%) dan tingkat negara bagian (6,96%–12,29%). Sementara itu, Holt-Winters mencapai kinerja terbaik pada tingkat wilayah (16,57%–21,43%) dan tingkat tujuan kunjungan (43,98%–47,63%). Penelitian ini menyoroti efektivitas Exponential Smoothing dalam menangkap pola dan tren musiman dalam hierarki data dan pentingnya pendekatan bottom-up dalam menghasilkan prakiraan yang konsisten di semua tingkat hierarkis.
Perbandingan Akurasi SVM & Naive Bayes Pada Analisis Sentimen Data Berita Online Terhadap COKLIT Pemilu 2024 Mawikere, Sarah Nikensia
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.v14i2.4797

Abstract

Berita online adalah salah satu media masa yang saat ini sering digunakan untuk mengetahui berita terkini tentang pencocokkan dan penelitian (COKLIT) Pemilu. Oleh karena itu, pentingnya melakukan analisis sentimen pada berita online terkait coklit pemilu 2024 karena menjadi salah satu bentuk partisipasi masyarakat dalam menanggapi bagaimana tahapan coklit yang dilakukan, serta dapat mendukung Bawaslu dalam mengawasi pemilihan umum yang akan berlangsung. Pada penelitian ini penulis akan melakukan perbandingan hasil analisis sentimen pada berita online tentang tahapan coklit menggunakan metode Support Vector Mechine (SVM) dan metode Naive Bayes agar dapat mengklasifikasikan opini dan sentimen yang diperoleh dalam beberapa kategori, seperti positif, dan negative. Penelitian ini dilakukan sebagai bentuk partisipasi masyarakat dalam mengawasi jalannya pemilihan umum di Indonesia tahun 2024. Dengan menggunakan confusssion matrix, hasil akurasi score yang didapatkan berdasarkan klasifikasi menggunakan SVM adalah 63.33%. Sedangkan hasil akurasi score berdasarkan klasifikasi menggunakan Naïve Bayes lebih besar yaitu 60.0%.
Nonlinear Analysis of Seismic Vulnerability in Mass Irregular Reinforced Concrete Buildings Chawe, AeintMyet; Nang Su Le'. Mya Thwin
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.v14i2.4798

Abstract

With Myanmar's growing population, buildings adapt through architectural and functional irregularities. However, such irregular structures are more susceptible to earthquake damage than regular ones. This study develops a vulnerability index for a 143-ft, 12-story reinforced concrete condominium building with mass irregularities in Mandalay, designed per ASCE/MNBC seismic codes. Mass irregularity is considered at three locations in the same condominium reinforced concrete building: the lower, middle, and upper thirds. By evaluating these three locations, the study investigates how the position of mass irregularity influences a building's seismic vulnerability in pushover (nonlinear) analysis. Guidelines provided by the HAZUS-MH MR4 technical manual have been used to develop fragility curves. Based on the study analyzing the structural vulnerability of irregular frame buildings by plotting fragility curves and determining a vulnerability index based on plastic hinge formation, the VI values of bottom mass, middle mass, and top mass building were 0.71, 0.00, and 0.94 respectively. It appears that the maximum vulnerability index value is observed in the top mass irregular building. Among all irregular buildings, the top mass irregular building was found to be more vulnerable, and the middle mass irregular building was found to perform better than others.
Identification of characteristics for RC Low Pass, High Pass, Band Pass and Band Stop Filters Maung Aye; Tin Tin Hla
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.v14i2.4799

Abstract

In signal processing, filters are essential components used to modify the frequency content of signals, enabling the separation of desired components from unwanted noise or interference. Among the most commonly used filters in electronic systems are RC (Resistor-Capacitor) filters, which include low-pass, high-pass, band-pass, and band-stop filters. These filters are simple to implement and widely utilized for tasks such as noise reduction, signal shaping, and frequency selection. The performance of these filters varies significantly with desired frequency, and understanding these variations is crucial for their optimal application. The designed parameters and values are calculated and the gain magnitude and phase response and bode response is simulated with MATLAB.
Generative Artificial Intelligence in Agile Product Management: Optimizing Task Coordination and Team Efficiency in Software Development Anissa Putri Widodo, Della; Voutama, Apriade Voutama
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.v14i3.4800

Abstract

The inclusion of Artificial Intelligence (AI) into Agile Product Management is revolutionizing the process of efficient software development in terms of improved coordination of tasks and team productivity optimization. Though Agile processes have gained much momentum with their iterative approach, issues like ineffective backlog management, inefficient resource allocation, and lengthy sprints continue to persist with human errors and long-time product development that could be mitigated with the use of Generative AI. This study discusses how AI-driven automation can resolve such inefficiencies in the form of a case study of the MSIB Batch 7 2024 PINTURA project, where lead times in software development were cut from 6-12 months to just under 2 months.
Initial Design and Development of Business Process Architecture at Universitas Negeri Manado Library Dalle, Arisa
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.v14i2.4807

Abstract

Analyzing, reengineering, improvement, and business process architecture in defining and managing various business processes in an organization. The problem that occurs is the comparison of the business processes carried out and the comparison of business procedures that have been set in the SOP (standard operating procedures). So that it affects the efficiency in carrying out an activity or activity. In calculating the similarity between the two business processes by calculating the similarity of business process models using the jaccard method. The purpose of this research is BPMN modeling that allows continuous improvement, better service development, and adjustment to changes in the library environment. From the results of the equation calculation using jaccard, the average is 0.862222222 with a standard deviation of 0.172639638. So that shows the more similar between the two business process models because the average value is close to one.
Analisis Sentimen dan Ujaran Kebencian Pemberitaan Online Tentang IKN Menggunakan Algortima K-NN Tumimomor, Tirsa
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.v14i2.4810

Abstract

Online news about Ibu Kota Nusantara (IKN) has sparked diverse public reactions, particularly regarding the capital relocation, a highly sensitive topic. The spread of information shapes public perception, especially when news contains hate speech, which can damage IKN’s reputation. This study applies sentiment analysis to online news about IKN using the K-Nearest Neighbor (KNN) algorithm. Data were gathered from Google News (595 articles) and YouTube (398 videos) and classified into four categories: positive, negative, neutral, and hate speech. The results show that Google News achieved 100% accuracy, while YouTube data reached 88.19% at K=3. These findings suggest that Google News articles are easier to classify with KNN compared to YouTube content, highlighting differences in text structure and characteristics between platforms.

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