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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 1,114 Documents
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Perbandingan Nilai Akurasi Analisa Sentiment Pada Kata Kunci Pemilu 2024 I Wayan Suardi
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.v14i2.4777

Abstract

Democracy is a government system that gives equal rights to every citizen in making decisions that can affect their lives. In Indonesia, the democratic system is realized through the General Election (Pemilu) organization, which is held periodically. An election is always discussed in the real and virtual worlds, especially on X social media. Considering the huge number of tweets, manual analysis of public opinion is inefficient. Therefore, technology is needed to analyze and categorize tweets into positive or negative sentiments automatically. This research compared the accuracy value of sentiment analysis on 2024 election data keywords using the NAIVE BAYES CLASSIFIERS and SUPPORT VECTOR MACHINE methods. The data used was 5651 tweets and obtained an accuracy value of 64.59% in the naïve bayes classifiers method and 76.14% in the support vector machine method. It shows that SVM is reliable in the context of sentiment analysis involving complex and diverse data.
Analisis Performa Penjualan dan Prediksi Omzet dengan Pendekatan Market Basket Analysis Berbasis Data Analytics Ramadhani, Jilang; Efrizoni, Lusiana; Yenni, Helda; Zoromi, Fransiskus
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.v14i2.4788

Abstract

Pesatnya perkembangan bisnis ritel menuntut strategi pemasaran berbasis data untuk meningkatkan performa penjualan dan omzet. Penelitian ini menggunakan Market Basket Analysis (MBA) dengan algoritma Apriori untuk mengidentifikasi pola pembelian konsumen dan Regresi Linear Sederhana untuk memprediksi omzet berdasarkan jumlah transaksi harian. Data transaksi Alfamart Wingky Mart periode Maret–September 2024 dianalisis guna menemukan hubungan antar produk serta tren penjualan. Hasil MBA menunjukkan kombinasi produk Bimoli, Gula, dan Tepung memiliki support 42.16% dan confidence 99.37%, yang dapat dimanfaatkan untuk strategi pemasaran. Model regresi menghasilkan R² sebesar 35.65%, menunjukkan hubungan antara jumlah transaksi dan omzet, meskipun masih terdapat faktor lain yang berpengaruh. Penelitian ini memberikan wawasan strategis bagi bisnis ritel dalam optimasi tata letak produk, promosi bundling, serta peningkatan omzet berbasis analisis data.
Navigating the Frontier: Theoretical Frameworks and Technical Approaches to Responsible AI Tiwari, Naresh
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.v14i2.4789

Abstract

This paper provides a comprehensive analysis of responsible AI development, examining both theoretical foundations and practical implementations. It explores core ethical principles including fairness, accountability, transparency, and safety, while also addressing emerging concepts like autonomy, dignity, and solidarity. The research analyzes competing philosophical frameworks—consequentialist, deontological, and virtue ethics—and highlights tensions between universalist and particularist ethical perspectives. The paper documents regional variations in responsible AI approaches across Europe, the United States, and East Asia, noting the concerning underrepresentation of Global South perspectives. Technical advancements in fairness are thoroughly examined, including pre-processing, in-processing, and post-processing techniques, alongside newer fairness-aware deep learning methods involving attention mechanisms and transfer learning. The work further investigates transparency challenges, comparing local and global explainability methods, and addresses the unique interpretability issues posed by foundation models and large language models. Safety and alignment techniques are also explored, including robustness against adversarial attacks, constitutional AI approaches, and various value learning methodologies. The paper concludes by evaluating measurement frameworks and assessment strategies for responsible AI interventions, offering insights into evaluation frameworks, benchmarks, and longitudinal studies needed to advance the field. 
Prediksi Nilai Redaman Jaringan Fiber Optik untuk Menilai Kinerja Jaringan Menggunakan Random Forest Regression Febrianda Putra; Susanti; Herwin; Khusaeri Andesa; Mardainis
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.v14i2.4796

Abstract

The increasing demand for telecommunication services requires reliable fiber optic network infrastructure. However, signal attenuation remains a major challenge in data transmission that leads to service quality degradation. This research aims to predict fiber optic network attenuation values using the Random Forest Regression algorithm by utilizing historical data from PT. Telkom Akses Indonesia. The dataset consists of 1225 New Connection Installation (PSB) data with features including ODP coordinates, customer location, cable length, and attenuation values. Research results show that the model with a 90:10 data ratio provides optimal performance with an R² Score of 0.9832, MSE of 0.0559, RMSE of 0.2363, and MAE of 0.1009. The model successfully explains 98.32% of variation in network attenuation data. Predictions of attenuation values for future periods show a stable trend, enabling proactive assessment of fiber optic network performance so operators can anticipate disruptions before they impact customer service.
Smart Surveillance Systems: Trends, Challenges and Future Directions Moepi, Glen
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.v14i2.4802

Abstract

Smart surveillance systems integrate the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and extensive data processing (big data) to enhance real-time monitoring, automated decision-making, and data analytics across multiple sectors. In security, they improve threat detection through facial recognition and pattern analysis. In power distribution, they enhance grid stability and detect unauthorized electricity usage using predictive analytics and advanced metering infrastructure (AMI). Agriculture benefits from precision farming, optimizing resource use while monitoring crops and livestock. Despite their advantages, these systems face challenges such as high implementation costs, communication limitations, data privacy concerns, and digital security risks (cybersecurity). Urban areas benefit from high-speed networks like fifth-generation wireless technology (5G) and fiber optics, yet costs and cyber vulnerabilities remain issues. In rural regions, limited internet access hinders adoption, necessitating alternatives like satellite technology and Long-Range Wide Area Network (LoRaWAN). Overcoming these challenges will drive the development of scalable, intelligent monitoring solutions, ensuring broader accessibility and efficiency in various industries.
Exploring AutoText Summarization Methods in Turkish: A Literature Review Alipour, Neda; Pourmousa, Hadi; Naserinia, Mohammad
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.v14i2.4803

Abstract

In recent years, the huge volume of textual data has become a challenge, as this challenge is seen in various fields, including scientific articles, legal documents, Internet archives, and even online product reviews. Given the limited data processing capacity of humans, processing large amounts of data is impractical and causes confusion; on the other hand, it requires a lot of effort, which ultimately results in a waste of time. To overcome this problem, the need to implement automated techniques such as automatic text summarization has emerged. Automated text summarization is an automated technique used to create a more condensed version of the original content that provides the same meaning and information. In fact, the generated output should contain important information from the original document. Various techniques for automatic summarization have been proposed in studies. Many studies have been presented on automatic text summarization methods, however, limited papers have contributed to reviewing different techniques of summarization methods in different languages, so this topic is evolving to reach maturity. This study focuses on different automatic text summarization methods in Turkish by reviewing the literature and previous studies, thus analyzing the performance of automatic text summarization methods.
Investigation of Macrobending Losses in Single Mode Optical Fiber Han, War War Moe Myint; Hla, Tin Tin
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.v14i2.4804

Abstract

Microbending losses are initiated by bending the optical fiber and caused escaping the light from the cladding and core. Macrobending losses degrade the signal quality in long-haul data communication. In the proposed system, macrobending losses are measured by different bending diameters of patch cord single-mode fiber G.652 optical fiber cable by using an Optical Time Domain Reflectometer (OTDR) and an optical power meter to identify the bending losses. The investigation of macro-bending losses aims to analyze the signal power loss in single-mode fiber. The proposed system is investigated by measuring the optical power losses at different bending diameters ranging from 200 mm to 80 mm and the number of turns up to 5 turns and comparison of losses variation for wavelengths 1310 nm and 1550 nm, which are affected by macrobending. The results are compared with theoretical calculations and the practical measurements.
Optimization of Deep Learning with FastText for Sentiment Analysis of the SIREKAP 2024 Application Handoko; Junadhi; Triyani Arita Fitri; 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.v14i2.4809

Abstract

This study analyzes public sentiment towards the SIREKAP 2024 application using deep learning. Data was collected from Google Playstore reviews and processed through cleaning, tokenization, and stemming. Word embedding was performed using FastText to capture more accurate word representations, including OOV words. The deep learning models compared were CNN, BiLSTM, and BiGRU. Performance evaluation used accuracy, precision, recall, and F1-score metrics. The results showed that the CNN model with FastText Gensim embedding achieved the highest accuracy of 95.98%, outperforming BiLSTM and BiGRU. This model was more effective in classifying positive and negative sentiments. This study provides insights for developers to improve the performance and public trust in SIREKAP 2024 and opens opportunities for further research with more complex embedding approaches and deep learning models.
Klasifikasi Jenis Peralatan Gym Menggunakan Convolutional Neural Network Andika, Farid; Yunarti, Sry; Baharuddin, Suardi Hi
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.v14i3.4065

Abstract

The use of artificial intelligence, especially Convolutional Neural Networks (CNN), has shown significant progress in image classification and object recognition. This research aims to develop an effective CNN model for automatically classifying gym equipment types, with the potential to improve the operational efficiency of fitness centers. The CNN model was trained using TensorFlow and Keras with the Adam optimizer and categorical cross-entropy loss function for 10 epochs, with data augmentation using ImageDataGenerator. The model evaluation shows satisfactory accuracy with a precision value of 0.9760, recall of 0.9772, and F1-score of 0.9766. The model successfully identified image samples from test data with a high level of confidence. The results of this study show that the use of CNNs in gym equipment classification has great potential to improve the efficiency of equipment recognition and contribute to the development of more advanced fitness technologies.
Pengembangan Tongkat Cerdas Pemandu Tunanetra Menggunakan Arduino Berbasis Sensor Ultrasonik Joni Eka Candra
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.v14i2.4157

Abstract

This research aims to develop a smart cane that can be used as a guide for the blind by utilizing Arduino technology and ultrasonic sensors. This smart cane is designed to help blind people detect and avoid obstacles around them, thereby increasing safety and independence when walking. The method used in this research involves designing and manufacturing hardware and software that are integrated into a smart stick. Ultrasonic sensors are used to detect the distance and presence of objects in front of the user, while the Arduino microcontroller processes the sensor data and provides feedback in the form of vibrations or warning sounds. Trials were carried out on several blind people to evaluate the effectiveness and practicality of this tool. The research results show that this Arduino-based smart stick with an ultrasonic sensor is able to detect objects with good accuracy and provide quite effective warnings to users. The feedback provided allows users to recognize and avoid obstacles more easily, thereby increasing confidence when moving in unfamiliar environments. Thus, it is hoped that this smart cane can be an innovative and practical solution to increase the mobility and independence of blind people. This research also opens up opportunities for further development in assistive technology for people with disabilities.

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