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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 1,170 Documents
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Analisis Sentimen Ulasan Pengguna pada Aplikasi Cryptocurrency: Evaluasi Dampak Skenario Pembagian Dataset Menggunakan Multinomial Naive Bayes Ramaputra, Chrisdion Andrew; Al Faroby, Mohammad Hamim Zajuli; Lidiawaty, Berlian Rahmy
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4263

Abstract

The surge in cryptocurrency investors in Indonesia, reaching 18.83 million by January 2024, signifies an expanding interest in this market. This research conducts a sentiment analysis of user reviews on Indodax and Tokocrypto, the premier cryptocurrency trading platforms in Indonesia. Utilizing the Multinomial Naive Bayes method, the study examines the influence of various dataset split scenarios and random states on the model's performance. The findings reveal substantial variability in the model's accuracy based on different random states and test sizes. Notably, the Positive sentiment label consistently shows high-performance metrics, while the Neutral label underperforms. These insights are invaluable for developers aiming to improve user experience and for investors seeking to make informed decisions. This research underscores the significance of sentiment analysis in understanding user interactions and enhancing the credibility of cryptocurrency investment platforms.
Question Similarity Detection in Indonesian Language Consumer Health Forums with Feature-based Binary Classification Approach Irianti, Eka Putri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4264

Abstract

Two questions are considered similar if the same response can be given to both. Due to the increase in users of consumer health forums, a growing number of similar questions are not being adequately answered. Identifying duplicate questions in online medical Question Answering (QA) forums offers several advantages for users and medical professionals. Therefore, it is crucial for online medical QA forums to identify similar questions to provide relevant and useful answers. This study examines a feature-based binary classification method for detecting similar questions in the Indonesian consumer health domain. The results indicate that the feature-based classification approach using the CatBoost model yields the best performance. The research also explores techniques to address class imbalance in the dataset, finding that imbalanced learning technique such as ADASYN and SMOTE results in improved classification performance. This study also analyzes discriminative features for identifying semantic similarity between question pairs, concluding that a combination of distance, medical, and encoding features produce the best results.
Analisis Pengaruh Gratifikasi Sosial Media TikTok terhadap Transactive Memory System (TMS) dalam Adopsi Informasi Kesehatan Obesitas Cahyaningtyas, Astri; Hidayanto, Achmad Nizar
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4269

Abstract

Obesity has become a global issue with increasing prevalence across various age groups, including children and adults. Obesity increases the risk of non-communicable diseases such as diabetes, heart disease, and cancer. Accurate health information is crucial for the prevention and management of obesity in Indonesia. However, the dissemination of inaccurate information on social media platforms like TikTok poses significant challenges. This study analyzes the motivation behind the adoption of health information via TikTok by integrating the Uses and Gratification Theory (UGT) and the Transactive Memory System (TMS). Data were collected from 255 respondents through an online questionnaire. The data were analyzed using SmartPLS with PLS-SEM. The results of the study indicate that gratifications such as social interaction and personalization have a significant influence on all dimensions of TMS. Furthermore, all three dimensions of TMS significantly impact information adoption. These findings provide insights into the utilization of TikTok for health education related to obesity in Indonesia.
Assessment on Water Resource Management for Sedawgyi Dam: A WEAP Analysis Approach Htoo, Thet Zin; Yin Yin Htwe; Cho Cho Thin Kyi
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.v13i5.4272

Abstract

This study employs the Water Evaluation and Planning (WEAP) system to analyze water resource management for the Sedawgyi Dam in Myanmar. The research integrates hydrological data, demand projections, and infrastructure information to simulate current and future water scenarios. Key steps include model setup, calibration, scenario development, and analysis. The results indicate that Mandalay City's highest domestic water demands are in Chanmyatharzi and Aungmyaetharzan townships, with agricultural water demands peaking in Mattaya township. The reference scenario highlights a significant domestic water supply shortfall, meeting only 10% of the demand, while agricultural demands are nearly fully met. The study underscores the urgent need for improved water management strategies to address rising unmet water demands, particularly for domestic use, to ensure sustainable water resource allocation in the region.
Evaluation of Stream Flow and Water Demand due to Climate Change in the Katha Basin Using Water Evaluation and Planning (WEAP) Model Tun, Win Lwin; Cho Cho Thin Kyi; Yin Yin Htwe
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.v13i5.4273

Abstract

This study addresses the critical issue of assessing climate change impacts on streamflow and water demand in the Katha basin using the WEAP hydrological simulation model with the Soil Moisture (SM) method. The Katha basin, situated within Ayeyarwaddy River basin, faces variability in stream flow due to changing climatic conditions, impacting water availability for agriculture and domestic use. Through WEAP modeling, calibrated and validated against observed data from 2000-2012, the study projects an increase in annual flows under future scenarios (SSP245 and SSP585). Results indicate potential decreases in monsoonal flows, affecting water availability for agriculture and domestic use, necessitating adaptive management strategies. Model performance assessed by Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) shows satisfactory agreement with observed data. The study underscores the urgency of integrated water resource management to sustainably address water demands amid climate variability, highlighting implications for agricultural productivity and water security in the region.
Perbandingan Random Search dan Algoritma Genetika dalam Penyetelan Hyperparameter XGBoost pada Retail Sales Forecasting Tiastama, Sheren Afryan; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4285

Abstract

Sales is part of the important factor that influences a company in determining two things, namely profits and losses on the company. The right strategy to determine the amount of sales can be done through forecasting. Therefore, sales forecasting requires the right technique to produce accurate results. Machine learning has been proven to help overcome sales forecasting, one of which is XGBoost. However, XGBoost has many hyperparameters that affect its performance, it requires a hyperparameter setting method to produce an optimal hyperparameter. Random searches and genetic algorithms are optimized methods to find the optimal hyperparameter on XGBoost. The two methods of optimization were compared in this study with the measurement of RMSE performance in doing retail sales forecasting on the sales data of the retail company Rossmann Store which comes from the Kaggle site. The research obtained random search results superior to the genetic algorithm with RMSE values on the training process and the testing process are 0.123 and 0.122. Meanwhile, the RMSE values generated by genetic algorithms in the training and testing process are 0.333 and 0.332.
Penentuan Kriteria Vendor Teknologi Informasi Strategis Menggunakan Metode Fuzzy Analytical Hierarchy Process: Studi Kasus PT ABC Ardyani, Silvia Ayu; Shihab, Muhammad Rifki
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.v13i5.4288

Abstract

PT ABC is a national scale financial company whose services have spread throughout Indonesia and several countries in the world. Apart from that, PT ABC also encourages digitalization in improving its digital services. In improving its digital services, PT ABC involves all internal resources and IT vendors. During 2023, there will be 189 IT vendors collaborating with PT ABC. With a large number of IT vendors, it is necessary to group IT vendors to make it easier for companies to determine future strategies. Currently it is felt that determining the criteria for strategic IT vendors is no longer appropriate to the company’s conditions, so updated are needed. Determination of the criteria and subcriteria for this research was carried out through focus group discussion with 3 respondents. The results of the FGD stated 3 criteria and 2 subcriteria that could be used in this research. These criteria and subcriteria were then weighted using the fuzzy analytical hierarchy process (FAHP) method which was then tested on 25 IT vendors randomly.
Project Management Maturity Level and PMBOK 7th Recommendations: Case Study of an IT Service Provider Company Egie, Maria Clara
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4290

Abstract

As a provider of consulting and solutions in the ICT field, PT XYZ has the responsibility to complete projects within budget, on time, and meeting all specified scopes. However, projects from 2022-2023 have experienced delays in completion ranging from 1-6 months from the initial planning. This has resulted in the projects being considered unsuccessful due to these delays. Based on the analysis, there is a need to analyze the project management maturity level in PT XYZ's CRM division. This research aims to measure the project management maturity level and provide recommendations to improve it to the desired level. The maturity level measurement will use the Kerzner Project Management Maturity Model (KPM3). Through the measurement results, it was found that PT XYZ's CRM division has not yet passed the first level. Recommendations have been provided based on KPM3 to progress to the next level. Knowledge area-specific recommendations have also been given based on the 7th edition of PMBOK.
Application of Fuzzy Logic Mamdani in IoT-Based Air Quality Monitoring Systems Richi Andrianto; Nopi Purnomo; Yuda Irawan
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.v13i5.4291

Abstract

This study aims to develop and implement an air quality monitoring system using Internet of Things (IoT) technology and Mamdani fuzzy logic. The system integrates sensors to detect PM2.5, PM10, and CO concentrations. Real-time data is processed using fuzzy logic to generate an easily understandable Indeks Standar Pencemar Udara (ISPU). Testing showed 95% accuracy in ISPU measurement, 2-second response time, and 99.5% uptime over 30 days. The Mamdani fuzzy logic effectively handles uncertain data, providing accurate air quality interpretations. The system classifies air quality into different ISPU categories (Good, Medium, Unhealthy, Very Unhealthy, Dangerous) in real-time. The study concludes that integrating IoT and fuzzy logic yields a high-performing, reliable air quality monitoring tool, significantly aiding pollution mitigation and public health. Further research is recommended to enhance algorithms and integrate additional technologies for improved functionality and accuracy.
Penerapan Oversampling Pada Klasifikasi Ujaran Kebencian Menggunakan Bidirectional Encoder Representations from Transformers Syahwaluddin, Risal; Alita, Debby
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4295

Abstract

The problem of class imbalance is a common challenge in classification model building, especially in the context of hate speech. This study evaluates the effectiveness of the SMOTE oversampling technique in improving the performance of hate speech classification models using BERT. The dataset used has significant class imbalance, with the largest number of samples in the Hate class, followed by Offensive, and Neither. Two experiments were conducted: one without using SMOTE and one with SMOTE applied. Results showed that the application of SMOTE improved the overall model accuracy from 85% to 88%. Precision for the Offensive minority class increased from 0.33 to 0.45, although recall decreased from 0.45 to 0.28. In the Neither class, the F1-score increased, indicating an improvement in the balance between precision and recall. Performance on the majority Hate class remained stable, indicating that SMOTE did not interfere with the model's performance on the already dominant class. Overall, the application of SMOTE provides significant benefits in handling class imbalance, especially in improving precision for minority classes, resulting in a more accurate classification model.

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