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Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science,
Computer Science & IT
Khazanah Informatika: Jurnal Ilmiah Komputer dan Informatika, an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : -
Articles 8 Documents
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Search results for , issue "Vol. 7 No. 2 October 2021" : 8 Documents clear
Analysis of the Causal Relationship of Body Image Factors in Patients with Cancer Vita Ari Fatmawati; Christantie Effendy; Ridho Rahmadi
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.14287

Abstract

Patients with cancer can potentially experience the negative impacts of treatment. Physical conditions due to illness and therapy can affect the patient's body image. This study aims to find a causal model among body image factors of patients with cancer using the S3C-Latent Method. The measurement of body image of patients with cancer used the BIS questionnaire. One hundred and ninety-nine patients with cancer participated in this study. The results showed the existence of causal relationships between behavior to cognitive factors and duration of illness with reliability scores of 0.8 and 0.6, respectively; from gender to affective factors, illness duration, behavior, and cognitive factors with reliability scores of 0.6, 0.8, 0.65, and 1, respectively. There are also causal relationships from age to affective factors, duration of illness, and cognitive factors with reliability scores of 0.8, 0.7, and 0.9, respectively. The results also showed that affective factors are associated with behavior, cognitive factors, and duration of illness, with reliability scores of 1, 1, and 0.9, respectively. The results showed further the association of cognitive factors and illness duration with a reliability score of 1. We expect that the estimated causal model will serve as a scientific reference for medical experts in developing a better intervention such as treatment.
Implementation of the Fisher-Yates Shuffle Algorithm in Exam-Problem Randomization on M-Learning Applications Chandra Kirana; Benny Wijaya; Abdul Holil
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.11761

Abstract

Many schools are currently using conventional approaches in learning material deliveries and examination methods. Conventional examination processes referred to here are the provision of question sheets in paper form. They have several drawbacks, such as students cheating and a waste of paper printing costs. To overcome these problems, we propose an online examination system. The online system leaves students to work on a different question set from other students. The feature is made possible by applying a randomization algorithm. There are several algorithms for scrambling questions, one of which is the Fisher-Yates Shuffle algorithm. This study aims to ease schools in the implementation of quality exams that may find out the level of student understanding of study materials and reduce the risk of cheating. The research product works on Android smartphones, which may be attractive to students and schools. The product allows schools to hold quality exams and reduce paper costs.
Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms Redy Indrawan; Siti Saadah; Prasti Eko Yunanto
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.14629

Abstract

Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.
Word Cloud of UKSW Lecturer Research Competence Based on Google Scholar Data Suryasatriya Trihandaru; Hanna Arini Parhusip; Bambang Susanto; Carolina Febe Ronicha Putri
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.13123

Abstract

There is a need in the Universitas Kristen Satya Wacana (UKSW) to identify the research competence of their faculties at a study program and University level. To accomplish this requirement, we need to automate the analysis of research output and publications quickly. Research articles are scattered in many publisher systems and journals which may be reputable, unreputable, accredited, and unaccredited. We devised a computer code to quickly and efficiently retrieve publication titles recorded in Google Scholar using a machine learning algorithm. The result display is in the form of a word cloud so that dominant and frequent words will be prominent in the visualization. In determining scientific terms to display, we used a modified version of the word cloud Python module and unmodified Term Frequency - Inverse Document Frequency (TF-IDF) library. The algorithm was tested on publication titles of our study program in UKSW and confirmed directly. The system features the ability to produce a word cloud visualization for an individual faculty, for faculties in a study program, or in the University as a whole. We have not differentiated publication sources, whether they are reputable or unreputable, which might affect the accuracy of competence identification.
DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema Cynthia Hayat
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.13455

Abstract

Acute pulmonary edema (EPA) is a condition of emergency respiratory distress that results from the sudden and rapid build-up of fluid into the lungs. Rapid screening of EPA patients is necessary so that radiologists can make the prognosis as early as possible. In addition, reliance on the expert's knowledge of reasoning also hinders the diagnostic process. This research proceeded by developing an architectural model for machine learning systems with a deep learning approach. With the concept of representative learning, the denseNet-CNN algorithm connects each layer to another utilizing a feed-forward. The data used is Image CXR-14 that is specifically labeled pulmonary edema pathology. Each CXR-14 image is 1024 × 1024 in size with a value of 8 bits grayscale. The architectural model development consists of several stages: the preparation stage, data resampling, data training, and data testing. Optimizer parameters used are Adam's optimizer, a learning rate of 0.0001, weight decay = 1e-5, and the loss used is binary cross-entropy. The resulting mean of AUROC analysis showed that the sensitivity value of the 10% dataset was 71.493%, and the specificity value of 10.011% was obtained at the second hold of the k-fold cross-validation method after holdout validation so that the resulting model was valid. The detection system developed from the denseNet-CNN model is to expectedly help radiologists identify abnormalities in CXR images quickly, precisely, and consistently. The development of the denseNet CNN model is in the form of a heatmap visualization by localizing the features one is watching. With localization in the heat map form, pathological abnormalities detection of PEA is easier to do and recognize.
Load Balancing Server and Homomorphic Encryption in Internet of Things Muhammad Hafiz Amrullah; Favian Dewanta; Sussi Sussi
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.13607

Abstract

User demand for Internet of Things (IoT) services is ever increasing. The growing user demand can lead to an escalation of server workloads and faces the threat of theft of critical data. Consequently, a system is necessary to balance the server load and is protected with data encryption. In this study, we designed a system to share server workloads using load balancing methods. The load balancing technique uses open-source web server software. The system is equipped with data security using a homomorphic encryption algorithm from AES on the sender's side. The system embeds in an IoT telemedicine apparatus. During testing, we analyze the error requests that arrive at each server for the HTTP GET and POST methods. We also evaluate the speed of data encryption and decryption. The results showed that server load balancing reduces the number of error requests for the GET method by 97%. Meanwhile, the number of error requests for the POST method decreases by 66.75%. Observations reveal that the average homomorphic encryption speed, computation time, and decryption time are 15.66 ms, 764.18 µs, and 362.49 µs, respectively.
Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm Annisa Annisa; Salsa Khairina
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.12939

Abstract

Selecting a good location is an essential task in many location-based applications. Intuitively, a place is better than another if there are many good facilities around it. The most popular location selection platform today is Google Maps. Unfortunately, Google Maps has not provided the location selection based on the number of surrounding facilities. Assume a situation when a college student wants to let a house near his campus. Besides the distance from the campus, the student certainly will consider amenities surrounding it, such as food courts, supermarkets, health clinics, and places of worship. The rent house will become a better choice if there are more of these facilities around. Skyline query is a well-known method to select interesting desirable objects. We applied the Sort Filter Skyline (SFS) Algorithm on Google Maps to get a small number of attractive locations based on the number of nearby facilities. This study has succeeded in developing a web-based application that facilitates Google Maps users to search for places based on the figure of surrounding facilities. The time required to do a location search using SFS in Google Maps will increase with the number of surrounding facility types considered by the user.
Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm I Gusti Agung Premananda; Ahmad Muklason
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.12879

Abstract

University timetabling is an issue that has received more attention in the field of operations research. Course scheduling is the process of arranging time slots and room for a class by paying attention to existing limitations. This problem is an NP-Hard problem, which means the computation time to find a solution increases exponentially with the size of the problem. Solutions to problems of this kind generally use a heuristic approach, which tries to find a sufficiently good (not necessarily optimal) solution in a reasonable time. We go through two stages in solving the timetabling problem. The first stage is to schedule all classes without breaking any predefined rules. The second stage optimizes the timetable generated in the first stage. This study attempts to solve the class timetabling problem issued in a competition called the 2019 International Timetabling Competition (ITC 2019). In the first stage, we use the Iterative Forward Search (IFS) algorithm to eliminate timetable candidates and to generate a schedule. In the second stage, we employ the Great Deluge algorithm with a hyper-heuristic approach to optimize the solution produced in the first stage. We have tested the method using 30 datasets by taking 1,000,000 iterations on each dataset. The result is an application that does schedule elimination and uses the IFS algorithm to produce a schedule that does not violate any of the hard constraints on 30 ITC 2019 datasets. The implementation of the Great Deluge algorithm optimizes existing schedules with an average penalty reduction of 42%.

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