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Jurnal Ilmu Komputer dan Informasi

JIKI
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Published by Universitas Indonesia

ISSN : 20887051 EISSN : 25029274 DOI : 10.21609

Core Subject : Science,

Computer Science & IT Library & Information Science

Jurnal Ilmu Komputer dan Informasi is a scientific journal in computer science and information containing the scientific literature on studies of pure and applied research in computer science and information and public review of the development of theory, method and applied sciences related to the subject. Jurnal Ilmu Komputer dan Informasi is published by Faculty of Computer Science Universitas Indonesia. Editors invite researchers, practitioners, and students to write scientific developments in fields related to computer science and information. Jurnal Ilmu Komputer dan Informasi is issued 2 (two) times a year in February and June. This journal contains research articles and scientific studies. It can be obtained directly through the Library of the Faculty of Computer Science Universitas Indonesia.

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Articles 247 Documents

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Towards Erlang-based ABS Microservices Framework for Software Product Line Development Adrika Novrialdi; Daya Adianto; Aulia Rosyida; Priambudi Lintang Bagaskara; Ade Azurat
Jurnal Ilmu Komputer dan Informasi Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i2.1065

The current widely used software system can be categorised as a large or very large decentralised control system with various requirements and continuous interchangeable elements. This characteristic leads to a need to control the variability to manage such systems. Software Product Line Engineering (SPLE) is one of the approaches that can manage the variability by developing sets of products. However, there is a need for support tools for development with software product line engineering. One language that supports the SPLE process is Abstract Behavioral Specification (ABS). Some SPLE research has used ABS to create frameworks that support the SPLE process. ABS Microservices is one research that utilises ABS to create a web framework that supports the SPLE process. This framework uses ABS to generate Java-based applications. The research interest in the web application is driven by the fact that it is one of the software types widely used by organisations and serves as the primary support of their business. Microservices are highly interoperable, thus enabling researchers to integrate different technology from other research. However, there is a need for renewal to the ABS Microservices framework. There is a need for more variants of SPLE-enabled frameworks that use more programming language as a specific programming language has its strength and weakness. Deprecation of the Java backend of the ABS opens a new exploration of another web framework that uses other ABS backend languages. We present the ABS microservices web framework based on Erlang OTP. We choose Erlang because it promises more efficient resource usage and the Erlang backend is one of the ABS backends with the most available features. This research aims to create an entry point for ABS Microservices to support more language. This research shows that the Erlang variant of ABS Microservices has less resource usage than the Java variant. Hence, this promises more options to develop product lines using ABS Microservices.

UML Transformation to Java-based Software Product Lines Falah Prasetyo Waluyo; Maya Retno Ayu Setyautami; Ade Azurat
Jurnal Ilmu Komputer dan Informasi Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i2.1070

Software product line engineering (SPLE) is an emerging approach that enables variability management in software development. SPLE offers tremendous benefits, but lack of tool support becomes a barrier in the adoption of SPLE. Variability modules for Java (VMJ) is an implementation approach that is defined based on the variability modules (VM) concept to support SPLE. VMJ combines Java modules system and design patterns that are commonly used by software developers. VMJ is accompanied by a UML profile, called UML-VM profile, which extends UML notation to model variability in the UML diagram. UML-VM diagram is used to model the problem domain, and VMJ is used in the domain implementation. In this research, we design a model transformation from Unified Modeling Language (UML) diagram into VMJ. The transformation rules are defined based on the UML-VM profile and implemented in the Eclipse Acceleo model to text transformation. As a result, a UML diagram can be transformed automatically into Java-based software product lines. The transformation tool is evaluated using a case study by comparing the generated code and the actual implementation.

Improving Recognition of SIBI Gesture by Combining Skeleton and Hand Shape Features Erdefi Rakun; Noer FP Setyono
Jurnal Ilmu Komputer dan Informasi Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i2.1014

SIBI (Sign System for Indonesian Language) is an official sign language system used in school for hearing impairment students in Indonesia. This work uses the skeleton and hand shape features to classify SIBI gestures. In order to improve the performance of the gesture classification system, we tried to fuse the features in several different ways. The accuracy results achieved by the feature fusion methods are, in descending order of accuracy: 88.016%, when using sequence-feature-vector concatenation, 85.448% when using Conneau feature vector concatenation, 83.723% when using feature-vector concatenation, and 49.618% when using simple feature concatenation. The sequence-feature-vector concatenation techniques yield noticeably better results than those achieved using single features (82.849% with skeleton feature only, 55.530% for the hand shape feature only). The experiment results show that the combined features of the whole gesture sequence can better distinguish one gesture from another in SIBI than the combined features of each gesture frame. In addition to finding the best feature combination technique, this study also found the most suitable Recurrent Neural Network (RNN) model for recognizing SIBI. The models tested are 1-layer, 2-layer LSTM, and GRU. The experimental results show that the 2-layer bidirectional LSTM has the best performance.

Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features Rahmad Mahendra; Hadi Syah Putra; Douglas Raevan Faisal; Fadzil Rizki
Jurnal Ilmu Komputer dan Informasi Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i2.1079

The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%.

Improved mask RCNN and cosine similarity using RGBD segmentation for Occlusion handling in Multi Object Tracking Siti Hadiyan Pratiwi; Putri Shaniya; Grafika Jati; Wisnu Jatmiko
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1073

In this study, additional depth images were used to enrich the information in each image pixel. Segmentation, by its nature capable to process image up to pixel level. So, it can detect up to the smallest part of the object, even when it’s overlapped with another object. By using segmentation, the main goal is to be able to maintain the tracking process longer when the object starts to be occluded until it is severely occluded right before it is completely disappeared. Object tracking based on object detection was developed by modifying the Mask R-CNN architecture to process RGBD images. The detection results feature extracted using HOG, and each of them got compared to the target objects. The comparison was using cosine similarity calculation, and the maximum value of the detected object would update the target object for the next frame. The evaluation of the model was using mAP calculation. Mask R-CNN RGBD late fusion had a higher value by 5% than Mask R-CNN RGB. It was 68,234% and 63,668%, respectively. Meanwhile, the tracking evaluation uses the traditional method of calculating the id switching during the tracking process. Out of 295 frames, the original Mask R-CNN method had ten switching ID times. On the other hand, the proposed method Mask R-CNN RGBD had much better tracking results with switching ids close to 0. Keywords—Occlusion, RGBD, Mask R-CNN, Late fusion, Cosine similarity

LexID: The Metadata and Semantic Knowledge Graph Construction of Indonesian Legal Document Nur Siti Muninggar; Adila Alfa Krisnadhi
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1096

The Legal Fiction principle stipulates that the government needs to ensure the public availability of all of their legal documents. Unfortunately, the text-based search services they provide cannot return satisfactory answers in retrieval scenarios requiring proper representation of relationships between various legal documents. A key problem here is the lack of explicit representation of such relationships behind the employed retrieval engines. We aim to address this problem by proposing LexID knowledge graph (KG) that provides an explicit knowledge representation for Indonesian legal domain usable for such retrieval purposes. The KG contains both legal metadata information and semantic content of the legal clauses of the legal document's articles, modeled using formal vocabulary from the LexID ontology also presented in this paper. The KG is constructed from thousands of Indonesian legal documents. Since the procedure of writing a legal document regulated by the government is clear and detailed, we use a rule-based approach to construct our KG. At the end, we describe several use cases of the KG to address different retrieval needs. In Addition, we evaluated the quality of our KG by measuring its ability to answer questions and got that LexID can answer questions with the macro average F1 score is about 0.91.

Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal Yuni Arti; Aniati Murni Arymurthy
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1100

Face spoofing can provide inaccurate face verification results in the face recognition system. Deep learning has been widely used to solve face spoofing problems. In face spoofing detection, it is unnecessary to use the entire network layer to represent the difference between real and spoof features. This study detects face spoofing by cutting the Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results showed that face spoofing detection has a good performance on the RGB and fusion models. Both models have better performance than the depth model because RGB modal can represent the difference between real and spoof features, and RGB modal dominate the fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset.

Poetry Generation for Indonesian Pantun: Comparison Between SeqGAN and GPT-2 Emmanuella Anggi Siallagan; Ika Alfina
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1113

Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of messages. Each ending-line word in pantun forms an ABAB rhyme pattern. In this work, we compare the performance of Sequence Generative Adversarial Nets (SeqGAN) and Generative Pre-trained Transformer 2 (GPT-2) in automatically generating Indonesian pantun. We also created the first publicly available Indonesian pantun dataset that consists of 7.8K pantun. We evaluated how well each model produced pantun by its lexical richness and its formedness. We introduced the evaluation of pantun with two aspects: structure and rhyme. GPT-2 performs better with a margin of 29.40% than SeqGAN in forming the structure, 35.20% better in making rhyming patterns, and 0.04 difference in giving richer vocabulary to its generated pantun.

Embedded Deep Learning System for Classification of Car Make and Model Ari Wibisono; Hanif Arief Wisesa; Satria Bagus Wicaksono; Puteri Khatya Fahira
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1118

Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.

Design and Development of EcoSense: Android-Based Incentivized Environmental Campaign App Deddy Romnan Rumapea; Kenrick Tandrian; Rivano Ardiyan Taufiq Kurniawan; Mirsa Salsabila; Darren Ngoh; Kenji Marwies; Bukhori Muhammad Aqid; Ade Azurat
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 2 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i2.1144

This research paper presents the design and development of EcoSense, an Android-based incentivized environmental campaign app, using the Design Science Research Methodology (DSRM). The DSRM approach involved six steps: problem identification, objective definition, design and development, demonstration, and evaluation. The problem of insufficient participation in environmental activities in Indonesia was identified using the Five Whys method, and user requirements were collected through an online survey. Various design artifacts were created to ensure the app is user-friendly, and the app was evaluated based on user engagement and participation. The results suggested that the app has the potential to effectively engage users in environmental campaigns, as evidenced by the relatively high retention and conversion rates. The study has some limitations, such as a small sample size and limited evaluation metrics, but future research can address these limitations and expand on the findings.

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Nurul Fazriah

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Contact Name Nurul Fazriah

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Journal Mail Official jiki@cs.ui.ac.id

Editorial Address "Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

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Contact Name
Nurul Fazriah

Contact Email
jiki@cs.ui.ac.id

Phone
+62217863419

Journal Mail Official
jiki@cs.ui.ac.id

Editorial Address
"Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

Location
Kota depok,

Jawa barat

INDONESIA