Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
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.
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<![CDATA[Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning ]]>
<![CDATA[Rahman Indra Kesuma]]>;
<![CDATA[Rivaldo Fernandes]]>;
<![CDATA[Martin Clinton Tosima Manullang]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15205
<![CDATA[A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing health protocols and adapting a new normal lifestyle. Implementation of the health protocol creates new problems, especially in the health check at the main entrance. The officers in charge of measuring body temperature are at risk of getting infected by COVID. Such a measurement is prone to errors. This study proposed a solution to build an automatic gate system that worked based on the new normal health protocol. The system utilizes the MLX90614 contactless temperature sensor to probe body temperature. It applies deep learning implementing the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture as a determinant of the conditions of wearing face masks. The system is equipped with an IoT-based remote controller to control the gate. Experimental results prove that the system works well. Temperature measurement takes a response time of 20 seconds for each user with 99% accuracy for the sensor and masks classification model.]]>
<![CDATA[Application of the UTAUT Model for Acceptance Analysis of COBIT Implementation in E-Learning Management with Microsoft Teams on Distance Learning in Batam City ]]>
<![CDATA[Suwarno Suwarno]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15311
<![CDATA[Since March 2020, due to the COVID-19 pandemic and in line with Merdeka Belajar - Kampus Merdeka, higher education institutions have conducted distance learning in asynchronous and synchronous modes, such as video meetings using Microsoft Teams and provide e-learning. In order to reach the goals and strategies of the higher education institutions, universities implement several control objectives within the COBIT 5 framework, so they can use and manage resources efficiently, provide the best education for students. This study aims to analyze the acceptance level of the COBIT implementation in higher education institutions by using the UTAUT model in E-Learning management, the use of Microsoft Teams and distance learning. This study uses a quantitative approach with a causal explanatory research design. Dissemination of the survey was conducted by simple random sampling at 6 (six) universities in Batam City. This study reveals that E-Learning management, the use of Microsoft Teams, and the application of distance learning together have a significant influence on the implementation of COBIT with an acceptance index of 85.5%, which refers to the satisfying category.]]>
<![CDATA[Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis ]]>
<![CDATA[Rindu Hafil Muhammadi]]>;
<![CDATA[Tri Ginanjar Laksana]]>;
<![CDATA[Amalia Beladinna Arifa]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15213
<![CDATA[Data from the Ministry of Civil Works and Public Housing (Kementrian PUPR) in 2019 shows that around 81 million millennials do not own houses. Government Regulation Number 25 of 2020 on the Implementation of Public Housing Savings, commonly called PP 25 Tapera 2020, is one of the government's efforts to ensure that Indonesian people can afford houses. Tapera is a deposit of workers for house financing, which is refundable after the term expires. Immediately after enaction, there were many public responses regarding the ordinance. We investigate public sentiments commenting on the regulation and use Support Vector Machine (SVM) in the study since it has a good level of accuracy. It also requires labels and training data. To speed up labeling, we use the lexicon-based method. The issue in the lexicon-based lies in the dictionary component as the most significant factor. Therefore, it is possible to update the dictionary automatically by combining lexicon-based and SVM. The SVM approach can contribute to lexicon-based, and lexicon-based can help label datasets on SVM to produce good accuracy. The research begins with collecting data from Twitter, preprocessing raw and unstructured data into ready-to-use data, labeling the data with lexicon-based, weighting with TF-IDF, processing using SVM, and evaluating algorithm performance model with a confusion matrix. The results showed that the combination of lexicon-based and SVM worked well. Lexicon-based managed to label 519 tweet data. SVM managed to get an accuracy value of 81.73% with the RBF kernel function. Another test with a Sigmoid kernel attains the highest precision at 78.68%. The RBF kernel has the highest recall result with a value of 81.73%. Then, the F1-score for both the RBF kernel and Sigmoid is 79.60%.]]>
<![CDATA[Convolutional Neural Network and Support Vector Machine in Classification of Flower Images ]]>
<![CDATA[Ari Peryanto]]>;
<![CDATA[Anton Yudhana]]>;
<![CDATA[Rusydi Umar]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15531
<![CDATA[Flowers are among the raw materials in many industries including the pharmaceuticals and cosmetics. Manual classification of flowers requires expert judgment of a botanist and can be time consuming and inconsistent. The ability to classify flowers using computers and technology is the right solution to solve this problem. There are two algorithms that are popular in image classification, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM). CNN is one of deep neural network classification algorithms while SVM is one of machine learning algorithm. This research was an effort to determine the best performer of the two methods in flower image classification. Our observation suggests that CNN outperform SVM in flower image classification. CNN gives an accuracy of 91.6%, precision of 91.6%, recall of 91.6% and F1 Score of 91.6%.]]>
<![CDATA[Design Thinking Method to Develop a Digital Evidence Handling Management Application ]]>
<![CDATA[Erika Ramadhani]]>;
<![CDATA[Amrullah Sidiq]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.12760
<![CDATA[Handling digital evidence in forensics is a very crucial task. Incorrect handling can cause the evidence to become invalid as proof of a crime in court. The procedure of handling digital evidence, starting from its collection, usage, and storage, affects its acceptability in the judicial process. Therefore, a digital evidence management system becomes imperative for police researchers and investigators. This study aims at designing such a system using the design thinking method, which goes through five stages: empathy, definition, idea, prototype, and test. The result of the study is a web-based system prototype. The prototype user testing attains a system usability scale (SUS) value of 60. The SUS value means that the prototype is in the category of marginal low and indicates that the prototype does not meet the feasibility and needs improvement.]]>
<![CDATA[Blind People Stick Tracking Using Android Smartphone and GPS Technology ]]>
<![CDATA[Rian Adi Chandra]]>;
<![CDATA[Umi Fadlillah]]>;
<![CDATA[Prasetyo Wibowo]]>;
<![CDATA[Faizal Tegar Nanda Saputra]]>;
<![CDATA[Reyhan Radditya Sulasyono]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15264
<![CDATA[Blindness is a term to describe conditions of people who have visual impairments. When a visually impaired do an activity outside, he usually needs a stick to help them move. This study aims to develop a stick tracking that enable a family member to find the location of the blind when they are outside their home and can help the blind to travel. GPS (Global Positioning System) technology allows the stick to get a signal for its location coordinates. When a family member wants to get the location of the blind, he can send a text message with the keyword TRACKER to the mobile phone number of the stick. A GSM (Global System for Mobile Communication) module will send a reply containing the global coordinate, which Google Maps can visualize. In addition, the blind can actively send an emergency help signal to families if they have difficulty finding their way home. An emergency push button is available on the stick, which, if pressed, will send the coordinates to the family's phone number in the form of a short text message. During travelling, blind people can identify obstacles in front of them thanks to an ultrasonic sensor system on the stick. The sensor can detect an object in the range of 100 cm. If the sensor detects an object less than 100 cm, a buzzer will emit an edible sound for the blind. Observations show that the developed stick works well with an average error on the GPS module at a level of 11.89 meters. It also shows a fluctuating percentage of ultrasonic sensor errors depending on the distance of objects.]]>
<![CDATA[Divorce Fact Detection Based on Internet User Behavior Using Hybrid Systems with Combination of Apriori Algorithm and K-Means Method ]]>
<![CDATA[Sofika Enggari]]>;
<![CDATA[Sarjon Defit]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.14036
<![CDATA[An ideal couple should sustain the family's ark till the end of their life without divorce. This study aims at seeking the association between divorce and internet behavior of searching negative keywords. The study observes four keywords, which are porn, sex, gay, and lesbian. We collected keyword usage data from google trend reports and obtained divorce court figures from the Religion Court of Padang. We used the apriori algorithm to reveal the association between divorce and internet behavior observing individual keyword searches and in groups. We used the K-Means algorithm in classifying negative word searches and divorce trial numbers from a group of existing data. We also investigate the combination of the apriori algorithm and the K-Means method to detect divorce facts and the behavior of internet users. The combined method has been successful in revealing the positive association between divorce facts and the behavior of internet users.]]>
<![CDATA[Academic Information System Assessment of AKRB Yogyakarta Using UTAUT ]]>
<![CDATA[Lukman Reza]]>;
<![CDATA[Sunardi Sunardi]]>;
<![CDATA[Herman Herman]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15533
<![CDATA[Implementation of Academic Information System (AIS) at the Radya Binatama Communication Academy (AKRB) had some problems for some users. That problems are known from interviews with several AIS users. This caused a delay in data exchange with other divisions. This study aims to assess the implementation of AIS to the AKRB by using the Unified Theory of Acceptance and Use of Technology (UTAUT) method. UTAUT has four main constructs that affect user acceptance namely performance expectations, effort expectations, social influences, and facilitating conditions. The data were obtained from distributing questionnaires to all AIS users as many as 40 respondents. Then the data is processed using Structural Equation Modeling (SEM) techniques with the help of SmartPLS. The results of the analysis show that only construct facilitating conditions is valid with a t-statistic value of 2.733. While the other three constructs have values that are in the range of invalid values between -1.96 to 1.96 with the values of each construct being 1.891, 0.050, 1.440. It can be concluded that the application of SIA in AKRB has not been well received by all AIS users. Therefore, it is necessary to conduct an evaluation that represents the other three constructs.]]>
<![CDATA[Information System on Mapping and Geolocation of COVID-19 in the City of Sukabumi ]]>
<![CDATA[Asril Adi Sunarto]]>;
<![CDATA[Yuli Noviawan]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.13727
<![CDATA[Coronavirus Disease (COVID-19) has made Indonesia's health condition critical. Therefore, the President of the Republic of Indonesia disclosed Presidential Decree No. 7 in 2020 regarding the Task Force for the Acceleration of Coronavirus Disease 2019 (COVID-19) Handling. The decree relates to Act No. 14 in 2008 regarding Public Information Disclosure, Presidential Regulation No. 95 in 2018 concerning Electronic-Based Government Systems, and Presidential Instruction No. 3 in 2003 concerning National Policies and Strategies for E-Government Development. The decree demands information system development, similar to https://covid19.go .id, which describes COVID-19 nationwide. The site explains what COVID-19 and data of the COVID-19 outspread with geolocation and digital map, which may attract public attention. The presidential instruction forces local governments to build an information system, which is in line with the site by the central government. This paper describes the development of the system using a spiral model. It involves a variety of free and open-source software such as CodeIgniter, Mapbox, Morris Chart, MySQL, and WordPress. The site has been operational, and it attracts 150 visitors a day with 200 visits per day. As of January 6, 2021, the website has recorded 89,852 views.]]>
<![CDATA[Detection of Highway Lane using Color Filtering and Line Determination ]]>
<![CDATA[Iwan Muhammad Erwin]]>;
<![CDATA[Dicky Rianto Prajitno]]>;
<![CDATA[Esa Prakasa]]>
<![CDATA[ Khazanah Informatika Vol. 8 No. 1 April 2022 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v8i1.15854
<![CDATA[Traffic accidents are generally caused by human error as a driver. The main cause is that the vehicle shifts away from the driving lane without the driver realizing it. Usually, because the driver is sleepy or drunk. Therefore, it is necessary to have a system that functions to assist the driver's navigation to stay on the correct driving path, such as a driver assistance system (DAS). In this system, the driving lane detector is the main part. This system serves to assist the driver's navigation to stay on the correct driving path. Vehicles are installed with cameras to record video towards the road ahead. Computers are also installed for image processing, identifying left and right road lines, and forming ego-lane. This paper offers an image processing-based method for recognizing driving lanes and presenting visualizations in real-time. This method has been tested using a data set, that video driving on Indonesian highway on the Cipali and Palikanci sections using dashboard camera. The test results obtained an accuracy of 99.25%.]]>
