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Sri Ngudi Wahyuni
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ijcsr@subset.id
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Jl. Gatotkaca, Janti Buana Asri 4 Nomor B7, Jurugentong, Banguntapan, Bantul, Yogyakarta, Indonesia
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INDONESIA
The Indonesian Journal of Computer Science Research
Published by Hemispheres Press
ISSN : -     EISSN : 29639174     DOI : https://doi.org/10.59095/ijcsr
Core Subject : Science,
Computer Science & IT
The Indonesian Journal of Computer Science Research (IJCSR) adalah jurnal yang memuat naskah ilmiah dari peneliti, akademisi, maupun praktisi, berupa hasil penelitian, tinjauan pustaka ( literature review ) dan/atau bentuk karya tulis ilmiah lainnya, yang khusus mengkaji bidang Ilmu Komputer antara lain sebagai berikut : Computational and algorithm Numerical Methods and Algorithms Autonomic Computing Big Data Computer and Network Architecture Cloud Computing Cluster Computing Workflow Design and Practice Data Mining Artificial Intelligence Web-Based Computing Scientific Visualization Computer Graphics Pattern Recognition Virtual Reality Augmented Reality Geometric Modeling Industry 4.0 Bioinformatics Digital Forensic
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 91 Documents
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PENGEMBANGAN SISTEM INFORMASI PENJEMPUTAN SAMPAH MENGGUNAKAN GLOBAL POSITIONING SYSTEM (GPS) BERBASIS ANDROID DI YOGYAKARTA Arya Fito Pramanda
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.53

Abstract

GPS technology is a satellite-based navigation technology with more than 30 satellites orbiting 20,000 km above the earth's surface. GPS is widely used for various purposes, such as directions and determining the location point of a place. In this study using GPS technology to determine the location of garbage pickup. Garbage is a common problem in most countries, including Indonesia. Garbage is a source of disease if left alone. Limited access to landfills makes people only throw garbage in certain places that are not actually landfills. The location of the garbage is unknown to the janitor and if left unchecked, there will be a buildup of garbage. The use of geocoding technology in the Global Positioning System (GPS) can make it easier for people to call cleaners by providing the location of garbage collection sites that were previously unknown to cleaners. To attract people to use this application, there are points earned after collecting garbage. These points can be exchanged into credit, E-Wallet balance, or retail vouchers. With this application, garbage that accumulates in places that were previously difficult to reach by cleaners can be handled faster.
PENGEMBANGAN SISTEM INFORMASI RESERVASI HOMEYKU DI KALIURANG BERBASIS WEBSITE MENGGUNAKAN METODE WATERFALL Arnika Fitria Diah Utami; Muhammad Ridwan; Abdul Khakim; Ervira Diva Grafvera
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.61

Abstract

The aim of this research is to develop a Web-based Homeyku Reservation Information System in Kaliurang using the Waterfall method. This system is designed to provide users with an efficient and easy-to-use reservation experience. The Waterfall method is used in the development of this system, which involves the stages of requirements analysis, system design, implementation, testing and maintenance. Requirements analysis is carried out to identify user requirements and system technical requirements. The system design includes the appearance of the user interface, database structure and business logic. The implementation phase involves building a website-based system using the appropriate technology and programming language. Tests are carried out to ensure the system works properly and according to requirements. The maintenance phase is conducted to periodically monitor the system, address issues, and upgrade features if needed. By implementing the Waterfall method, it is hoped that the Homeyku reservation information system in Kaliurang can provide a satisfying reservation experience for users and make it easier for managers to manage the reservation process.
PENGEMBANGAN VIDEO EDUKASI ANTISIPASI GEMPA BUMI MENGGUNAKAN METODE MOTION GRAPHIC Ghita Octarisa Angelia
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.62

Abstract

Earthquakes are natural phenomena that frequently occur in various parts of the world. To enhance public awareness about the importance of understanding and preparing for earthquakes, the use of educational videos has become a popular approach. In this study, we propose the development of an educational video using the motion graphic technique to improve the effectiveness of the messages conveyed to the audience. This research focuses on the development of an educational video that visually explains the steps for earthquake preparedness using captivating motion graphic techniques. The motion graphic method utilizes animations, moving graphics, and other visual effects to simplify and present complex concepts and information in an engaging manner. The aim is to enable the audience, particularly children and teenagers, to easily comprehend the information and necessary actions during an earthquake. Keywords : Earthquakes, Motion graphic, Education
PERANCANGAN SISTEM INFORMASI MANAJEMEN PERUSAHAAN “GEN-Z” MENGGUNAKAN METODE AGILE BERBASIS WEBSITE Ega Bagus Purnama; Germecca Germecca; Niza Aidha Wardhani; Nurul Azizah
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.66

Abstract

The development of information technology has had a significant impact on modern companies, affecting the way companies manage information and carry out their operations. In the digital era dominated by generation Z, it is important for companies to adopt an effective and efficient management information system to support their business activities.The purpose of this research is to design a website-based management information system for "Gen-Z" companies using Agile methods. The Agile method was chosen for its flexibility and ability to handle rapidly changing needs in a dynamic business environment.This research will involve the stages of needs analysis, system design, development, implementation, and evaluation. The needs analysis will involve a case study and interviews with stakeholders in the "Gen-Z" company to understand specific business requirements. System design will encompass user interface design, database structure, and system architecture. System development will be carried out iteratively using an Agile approach. The development team will work in short sprints, focusing on developing the most important features that provide direct business value. The system will be implemented as a website accessible to internal users of the "Gen-Z" company through a web browser. System evaluation will involve end-user involvement to assess the effectiveness, usability, and performance of the system. In this phase, user feedback will be collected, and necessary system improvements will be implemented. It is expected that the design of the website-based management information system using the Agile method will provide the "Gen-Z" company with a competitive advantage by improving operational efficiency, information accessibility, and responsiveness to market changes. Additionally, this research can also provide insights and recommendations for other companies looking to adopt Agile methods in developing their management information systems.
SISTEM PENUNJANG KEPUTUSAN PEMILIHAN SMARTPHONE BERBASIS WEBSITE DENGAN METODE SIMPLE ADDITIVE WEIGTHING Andika Fakhrizal; Sri Ngudi Wahyuni; Rosyidah Jayanti Vijaya
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.75

Abstract

Along with the development of smartphones in Indonesia, people in various circles are very dependent on the use of smartphones. Especially with the current pandemic making people depend on the use of smartphones. With the various brands, types, and specifications of smartphones, people are confused about which smartphones to choose according to their respective needs. From these problems, a website-based decision support system is needed to help and facilitate people in choosing the right smartphone model according to the criteria. The criteria that will be included in this system are price, brand, RAM, storage, camera, battery screen, and features. The purpose of this thesis is to build a system that can help people find smartphones that match the required criteria. This system uses the Simple Additive Weighting (SAW) method which is used to normalize the weights of the inputted criteria and to determine the highest smartphone value as a recommendation option. The results of this study are the creation of a smartphone selection decision support system with the website-based Simple Additive Weighting method that can provide recommendations for smartphone types according to their respective needs.
PENDEKATAN DEEP LEARNING MENGGUNAKAN METODE LSTM UNTUK PREDIKSI HARGA BITCOIN Hastari Utama
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.77

Abstract

Bitcoin price prediction involves analyzing a variety of factors, including market sentiment, trading volume, economic news, technological developments, and other factors that affect supply and demand. Both technical and fundamental analysis methods can be used to try to predict Bitcoin price movements. In this Bitcoin price prediction using a Deep Learning approach with the chosen method is LSTM. The LSTM (Long Short-Term Memory) method is a popular type of Recurrent Neural Network (RNN) model for predicting the price of Bitcoin and other financial assets. LSTM can solve the problem of price movements that have long-term dependencies, which traditional RNN models cannot handle well. LSTMs have the ability to "remember" information from longer periods of time, thereby recognizing complex patterns and trends in historical data. In this study the prediction period used a dataset from March 1 2016 to November 24 2018. This study used an epoch parameter of 10 with a learning rate of 0.001. In addition, the batch size parameter used is 25 with layers only. The evaluation results of this study resulted in an RMSE of 77.74 and an MAE of 278.33. This shows that the RMSE value is small because the Bitcoin price range is too far.
DETEKSI INDIKASI GANGGUAN KESEHATAN MENTAL BERBASIS TEKS MENGGUNAKAN NLP DENGAN TEKNIK AUGMENTASI EDA Sherly Dian Tiara; Erna Daniati; Arie Nugroho
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.273

Abstract

This study aims to build a classification model for the early screening of mental health disorders from social media text data using the CRISP-DM framework. The primary issue of data imbalance between categories was addressed using the Easy Data Augmentation (EDA) technique. Logistic Regression algorithm and TF-IDF feature extraction were used to classify six categories of mental conditions. Test results showed that the model with EDA experienced a slight decrease in global accuracy to 0.74 (compared to 0.76 without EDA) but successfully increased the Recall for the minority class, Mentalillness, significantly from 0.28 to 0.56. This improvement proves that EDA effectively enriches linguistic variation in limited data. The model has been validated by a psychologist and implemented into a web-based application as an indicative early detection tool, not a clinical medical diagnosis.  
Deteksi Makna Mengenai Kebijakan Tunjangan DPR RI dengan NBC dan Lexicon Eka Fauziah; Erna Daniati; Dwi Harini
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.274

Abstract

Social media serves as a source of information that can be used to gauge public opinion regarding government policies one topic frequently discussed by the public is the policy regarding allowances for members of the Indonesian House of Representatives. The objective of this study is to examine public sentiment regarding these policies using the Naïve Bayes Classifier and Lexicon Sentiment methods. The research approach applied is CRISP-DM (Cross Industry Standard Process for Data Mining), which encompasses the stages of business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data was collected from the social media platform X (Twitter) via scraping and processed through preprocessing steps and TF-IDF weighting. The findings of this study indicate that the Naïve Bayes Classifier method achieved an accuracy of 74%, while the Lexicon Sentiment method helped in understanding the emotional nuances present in the text. The combination of these two methods produces a more comprehensive and relevant sentiment analysis compared to using only one method alone. This study demonstrates that the combination of statistical and lexicon-based approaches is highly useful in analyzing sentiment regarding the opinions of the Indonesian-speaking public.
Implementasi Regresi Logistik untuk Klasifikasi Cyberbullying pada Komentar Instagram Berbahasa Indonesia Pita Penengah; Erna Daniati; M. Najibulloh Muzaki
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.275

Abstract

The rapid growth of social media usage, especially Instagram, has increased user interaction while also raising the occurrence of cyberbullying in the form of insulting, mocking, and offensive comments that may negatively affect victims’ psychological conditions. This study aims to develop a cyberbullying detection model for Indonesian-language Instagram comments using the Logistic Regression algorithm with a Natural Language Processing (NLP) approach. The dataset used consists of 650 comments labeled as cyberbullying and non-cyberbullying. The preprocessing stages include cleaning, case folding, tokenization, stopword removal, and stemming, followed by text transformation into numerical representation using the Bag of Words method with CountVectorizer. The research applies the CRISP-DM methodology consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The evaluation results show that the Logistic Regression model performs well in classifying comments, achieving an accuracy of 83%, precision of 0.83, recall of 0.83, and F1-score of 0.83. These findings indicate that the combination of the Bag of Words method and Logistic Regression algorithm is effective for detecting cyberbullying in Indonesian Instagram comments.
Implementation of the IndoBERT Model on Named Entity Recognition for Entity Identification in the Folklore of the Tale of Wayang Arjuna & Purusara Erna Daniati Erna; Danda Baskoro; Rina Firliana
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.283

Abstract

The development of Natural Language Processing (NLP) has encouraged the use of Named Entity Recognition (NER) technology to extract important information from text data automatically. This research aims to apply the IndoBERT model to recognize named entities in classic Indonesian literary texts, namely Hikayat Wayang Arjuna & Purusara. The research focused on the identification of three categories of entities, namely Person (PER), Location (LOC), and Organization (ORG). The dataset is compiled through an annotation process using the BIO (Beginning, Inside, Outside) scheme, then processed through the preprocessing stage, tokenization using the IndoBERT Tokenizer, and fine-tuning the IndoBERT model. Evaluation was conducted using precision, recall, and F1-score metrics. The results showed that the IndoBERT model obtained a precision score of 56.30%, recall of 60.55%, and an F1-score of 58.35%. Based on the evaluation of each entity category, the Location (LOC) category obtained the best performance with an F1-score of 76.07%, followed by Person (PER) of 64.67%, while Organization (ORG) obtained a score of 44.96%. These results show that IndoBERT is quite effective in recognizing entities in classic literary texts in Indonesian and has the potential to support the process of information extraction, digitization, and preservation of folklore based on NLP technology.

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