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INDONESIA
Indonesian Journal of Information System
Published by Universitas Atma Jaya Yogyakarta
ISSN : 26230119     EISSN : 26232308     DOI : -
Core Subject : Science,
Computer Science & IT
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Articles 6 Documents
 1 
Search results for , issue "Vol. 5 No. 1 (2022): August 2022" : 6 Documents clear
Trojan Detection System Using Machine Learning Approach Mohd Faizal Ab Razak; M. Izham Jaya; Zahian Ismail; Ahmad Firdaus
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5673

Abstract

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers in order to steal their data. As a result, Trojan horse detection is essential to identify the Trojan and limit Trojan attacks. In this study, we proposed a Trojan detection system that employed machine learning algorithms to detect Trojan horses within the system. A public dataset of Trojan horses that contain 2001 samples comprises of 1041 Trojan horses and 960 of benign is used to train the machine learning classification. In this paper, the Trojan detection system is trained using four types of classifiers which are Random Forest, J48, Decision Table and Naïve Bayes. WEKA is used for the execution of the classification process and performance analysis. The results indicated that the detection system trained with the Random Forest and Decision Table algorithms obtained the maximum level of accuracy.
Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia Rendra Gustriansyah; Juhaini Alie; Ahmad Sanmorino; Rudi Heriansyah; Megat Norulazmi Megat Mohamed Noor
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5682

Abstract

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities from the Indonesian government based on inflation and poverty rates in 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, and Divisive analysis (Diana) are utilized and compared to achieve that purpose. Clustering 90 regencies/cities in Indonesia produced five optimal clusters. Furthermore, the clustering results were validated using the Silhouette width (SW) and Dunn index (DI). The results showed that the k-means method produced the most compact cluster. Hence, this study's results can be utilized as a reference for the government in determining the steps and priorities of economic policy in Indonesia.
Translator of Indonesian Sign Language Video using Convolutional Neural Network with Transfer Learning Sesilia Shania; Mohammad Farid Naufal; Vincentius Riandaru Prasetyo; Mohd Sanusi Bin Azmi
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5865

Abstract

Sign language is a language used to communicate by utilizing gestures and facial expressions. This study focuses on classification of Bahasa Isyarat Indonesia (BISINDO). There are still many people who have difficulty communicating with the deaf people. This study builds video-based translator system using Convolutional Neural Network (CNN) with transfer learning which is commonly used in computer vision especially in image classification. Transfer learning used in this study are a MobileNetV2, ResNet50V2, and Xception. This study uses 11 different commonly used vocabularies in BISINDO. Predictions will be made in real-time scenario using a webcam. In addition, the system given good results in the experiment with an interaction approach between one pair of deaf and normal people. From all the experiments, it was found that the Xception architectures has the best F1 Score of 98.5%.
System Dynamics Modeling of Multi-Channel Supply Chain System: A Hypothetical LPG Distribution with Disloyal Household Customers Petrus Setya Murdapa; Putu Dana Karningsih; I Nyoman Pujawan
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.6025

Abstract

The availability of LPG in the community is a sensitive issue. If there is a shortage, it has the potential to cause chaos and inflation. The final consumers, namely households and micro business stalls, will generally be disloyal. They are not tied to one particular store to get the LPG they need. Since the number of tanks in circulation will remain constant, the analysis of LPG availability can be focused on the number of tanks. Continuous simulation method with system dynamics is generally used in system growth analysis. In this paper the method is utilized to model the discrete movement of tanks circulating in the community which in this case is represented in a much smaller system, consisting of filling stations, agents, and stores. The modeling results provide a sufficient picture of how the availability of the tank can be optimized appropriately.
Implementation of Group-Based Human Movement Model in Opportunistic Network Vittalis Ayu; Bambang Soelistijanto
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.6164

Abstract

As an instance of a distributed computing system, opportunistic networks facilitate message dissemination in a store-carry-forward manner. In this setting, the mobile devices are communicating in opportunistic contacts as they move across the network areas. However, the movement of these mobile devices is exclusively reliant on the mobility of their human owner, thereby limiting the likelihood of contact. The current state of the art typically simulates human movement based on randomness, which is unsuitable for representing how people move in groups. Therefore, this paper proposes an implementation of a group-based human mobility model to simulate device-to-device communication in opportunistic networks. In this model, individuals are able to move as a set within a group and have the ability to join and leave the group dynamically We built the model in BonnMotion and subsequently implemented it in an opportunistic environment simulator, ONE Simulator. To evaluate the proposed model, we compared them to the random-based model as a benchmark. Subsequently, we assess the impact of the movement model on two major areas of network performance: message delivery performance and resource utilization, such as nodes’ energy consumption. We are concerned about these aspects since the mobile agents have limited resources yet are expected to achieve a high rate of message delivery as well. The simulation results show that our model outperformed the random-based model in terms of the number of successfully delivered messages and average delay. However, the number of message replications and the energy consumption is fairly higher than those of the benchmarks.
BarkDroid: Android Malware Detection Using Bark Frequency Cepstral Coefficients Paul Tarwireyi; Alfredo Terzoli; Matthew O. Adigun
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.6266

Abstract

Since their inaugural releases in 2007, Google’s Android and Apple’s iOS have grown to dominate the mobile OS market share. Currently, they jointly possess over 99% of the global market share with Android being the leading mobile Operating System of choice worldwide, controlling close to 70% of the market share. Mobile devices have enabled the exponential growth of a plethora of mobile applications that play key roles in enabling many use cases that are pivotal in our daily lives. On the other hand, access to a large pool of potential end users is available to both legitimate and nefarious applications, thus making mobile devices a burgeoning target of malicious applications. Current malware detection solutions rely on tedious, time-consuming, knowledge-based, and manual processes to identify malware. This paper presents BarkDroid, a novel Android malware detection technique that uses the low-level Bark Frequency Cepstral Coefficients audio features to detect malware. The results obtained outperform results obtained using other features on the same datasets. BarkDroid achieved 97.9% accuracy, 98.5% precision, an F1 score of 98.6%, and shorter execution times.

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