The Indonesian Journal of Computer Science
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
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<![CDATA[Analisis Faktor Perilaku Penggunaan Sistem Pendaftaran Online Berdasarkan Unified Theory of Accepptance and Use of Technology (UTAUT) ]]>
<![CDATA[Suradi, Asy Syfa]]>;
<![CDATA[Firdawati]]>;
<![CDATA[Pujani, Vera]]>;
<![CDATA[Fiandra, Yudha]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3177
<![CDATA[Penelitian ini bertujuan mengevaluasi sikap penerimaan masyarakat terhadap penggunaan pendaftaran online di RS Aisyiyah Pariaman menggunakan model Unified Theory of Accepptance and Use of Technology (UTAUT). Penelitian ini merupakan penelitian observasional analitik dengan pendekatan cross sectional yang menjelaskan hubungan variabel independen (performance expectancy, effort expectancy, social influence dan facilitating conditions), variabel moderasi (usia dan pengalaman) terhadap variabel dependen (use behavior dan behavioral intention). Populasi adalah pasien poliklinik rawat jalan menggunakan pendaftaran online. Sampel yang diteliti sebesar 243 sampel dengan menggunakan teknik purposive sampling dalam pengambilan sampel. Metode analisis data deskriptif menggunakan SPSS 26 dan analisis Structural Equation Modelling (SEM) menggunakan piranti LISREL 8.8. Berdasarkan hasil penelitian didapatkan bahwa performance expectancy, effort expectancy, social influence berhubungan signifikan terhadap behavior intention dan facilitating conditions, behaviaoral Intention berhubungan signifikan terhadap use behavior, sedangkan variabel moderasi usia dan pengalaman tidak berhubungan signifikan dalam memoderasi variabel independen terhadap variabel dependen.]]>
<![CDATA[Anti-spoofing methods in face recognition ]]>
<![CDATA[Bezas, Konstantinos]]>;
<![CDATA[Foteini Filippidou]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3198
<![CDATA[Biometric data are personal data that result from specialized processing techniques and are associated with physical, biological, or behavioral characteristics of a natural person that allow for or confirm their unquestionable identification. These characteristics or identifiers are permanent and unique. This paper refers to the biometric characteristics used by systems, their mode of operation, and the categories they are distinguished in. The types of attacks that they may be subjected to are then analyzed, along with the anti-spoofing methods proposed in some studies specifically for systems that use the face as a biometric feature. Finally, numerical data is presented regarding the scientific interest that the topic of anti-spoofing methods in biometric systems has shown in the last decade.]]>
<![CDATA[Analisis Sentimen Terhadap Topik Kenaikan Harga Bahan Bakar Minyak (BBM) pada Media Sosial Twitter ]]>
<![CDATA[Tiara Danirmala]]>;
<![CDATA[Nugroho, Yusuf Sulistyo]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3199
<![CDATA[Media sosial Twitter menjadi salah satu media yang banyak digunakan masyarakat Indonesia untuk membagikan informasi, pendapat, ataupun sekedar berdiskusi tentang topik yang sedang tren dan beredar luas, misalnya tentang kenaikan harga bahan bakar minyak (BBM). Penelitian ini bertujuan untuk menganalisis sentimen masyarakat tentang kenaikan harga BBM di media sosial Twitter . Penelitian ini dilakukan dengan cara menganalisis sentimen tweet yang diunggah di Twitter untuk mengetahui polaritasnya dengan menerapkan klasifikasi Naïve Bayes. Selain analisis sentimen, data tweet juga dicari diskusi topik yang paling banyak dibahas terkait dengan kenaikan harga BBM dengan menggunakan metodeAlokasi Dirichlet Laten (LDA ). Hasil penelitian menunjukkan bahwa sebanyak 53.3% tweet memiliki polaritas positif, 31.2% tweet berpolaritas negatif, dan 15.5% tweet dinyatakan netral. Hasil evaluasi klasifikasi dengan Naive Bayes diperoleh nilai akurasi 60% . Sedangkan topik yang banyak dibicarakan masyarakat di Twitter secara umum menyatakan penolakannya terhadap kenaikan harga BBM. Hal ini menunjukkan bahwa masyarakat menolak kebijakan tersebut namun dinyatakan dengan komentar secara positif.]]>
<![CDATA[Pengaruh Sistem Informasi Terhadap Proses Penciptaan Ilmu Pengetahuan Pada Mahasiswa Fakultas Ilmu Komputer Universitas Sriwijaya ]]>
<![CDATA[Aritonang, Cindy Nadira Elfarisa]]>;
<![CDATA[Jambak, Muhammad Ihsan]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3200
<![CDATA[Penciptaan pengetahuan merupakan suatu proses yang dilakukan oleh individu untuk menciptakan dan mendapatkan ide-ide kreatif oleh organisasi. Namun, terjadinya proses penciptaan pengetahuan dalam diri masing-masing individu tentunya berbeda berdasarkan faktor-faktor yang mempengaruhi proses penciptaan pengetahuan tersebut. Teknologi merupakan faktor pendukung paling penting dari produktivitas proses penciptaan pengetahuan di berbagai kalangan dan sistem informasi diperlukan sebagai media dalam proses penciptaan pengetahuan. Theory of Planned Behavior dan teori Budaya Organisasi merupakan teori yang mendasari konstruk penelitian ini. Penelitian ini menggunakan metode kuantitatif. Populasi dalam penelitian ini berjumlah 2.666 dan jumlah sampel sebanyak 348 yang diperoleh melalui metode stratified random sampling. Dalam menganalisis data dalam penelitian ini menggunakan teknik Structural Equation Model (SEM-PLS) dan menggunakan software SmartPLS. Hasil yang diperolah adalah proses penciptaan pengetahuan dipengaruh oleh Sikap Individu, Budaya Organisasi dan Sistem Informasi. Sedangkan Sistem Informasi tidak dapat mempengaruhi hubungan antara Sikap Individu dan Budaya Organisasi dengan Proses Penciptaan Pengetahuan.]]>
<![CDATA[Perspektif Penerima Pengetahuan untuk Penciptaan Pengetahuan Mahasiswa Fakultas Ilmu Komputer Universitas Sriwijaya ]]>
<![CDATA[Chantika, Trievanni]]>;
<![CDATA[Ihsan Jambak, Muhammad]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3201
<![CDATA[Dalam proses pembelajaran, penciptaan pengetahuan terjadi ketika seorang individu dapat menerima pengetahuan dari individu lain dan dapat menciptakan pengetahuan yang baru. Namun, proses tersebut terkadang tidak berjalan dengan baik, maka dibutuhkan suatu sistem informasi yang sesuai dengan kebutuhan pengguna dengan mengetahui perspektif penerima pengetahuan dalam proses pembelajaran untuk penciptaan pengetahuan. Teori yang mendasari konstruk penelitian ini adalah Model SECI (Sosialisasi, Eksternalisasi, Kombinasi, Internalisasi). Penelitian ini menggunakan metode kuantitatif. Populasi dalam penelitian ini berjumlah 2.666 dan jumlah sampel 348 yang diperoleh menggunakan metode Stratified Random Sampling. Dalam menganalisis data, menggunakan teknik Rasch Model dan software Winstep. Hasil yang diperoleh adalah proses sosialisasi dan kombinasi merupakan faktor yang mempengaruhi penerima pengetahuan karena cenderung lebih mudah dilakukan dan banyak disetujui untuk diterapkan dalam proses pembelajaran.]]>
<![CDATA[The Multi-Layer Perceptron Neural Network Implementation as Train Type Classification ]]>
<![CDATA[Cahyo, Anton Cahyo Saputro]]>;
<![CDATA[sudarsono, amang sudarsono]]>;
<![CDATA[Yuliana, Mike Yuliana]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3204
<![CDATA[The purpose of train detection systems is to check that related track section is clear of vehicles before a train may be authorized to pass through a railroad. The detection of the train is important task for ensuring the safety of train traffic. Multi-layer Perceptron classifier, which consists of feedforward neural networks constructed of multiple layers of interconnected artificial neurons, proved to be effective for trainset class classification in this study. Using Raspberry Pi and IMU sensor BNO055, dynamic response of any train type interaction can be handled by windowing and Real Fast Fourier Transform (RFFT). Dense layer with 5 neurons, using the ReLu activation function, and specifying the input shape as (6= 3-axis accelerometer in X, Y, and Z directions, and 3 axis directions from gyroscope). The classification process in this implementation, which consist of three classes of train types, has been completed with accuracy above 92,7%.]]>
<![CDATA[Semantic Information Search with Automatic Ontology Creation in Regulations National Standards for Higher Education in Indonesia ]]>
<![CDATA[Hidayah, Nadila Wirdatul]]>;
<![CDATA[Ali Ridho Barakbah]]>;
<![CDATA[Iwan Syarif]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3207
<![CDATA[In Indonesia, there are around ten types of legal products that contain higher education regulations. With a large number of articles, more effort is needed when users search for links between one article and another. Based on these problems, it is necessary to have an automatic article representation search system using an automatic ontology. Ontology refers to the hierarchical structure of entities and their relationships. In this paper, the results of the development of an information retrieval system with an automated ontology will be explained. This system describes a process begins with receiving input of higher education regulatory files which are used as data samples Permendikbud No 3 of 2020. Then split the data into articles, paragraphs and contents which are then formed ontologies by building 3 detection functions (Definitive Creation, Compound Creation, and Reference Detection). System output has an accuracy of search results reaching an accuracy of 92.5%.]]>
<![CDATA[Using Quality Measures During the Software Development Process: Case Study of Cameroonian Software Industry ]]>
<![CDATA[Oumarou, Hayatou]]>;
<![CDATA[Moulla, Donatien Koulla]]>;
<![CDATA[Kolyang, kolyang]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3208
<![CDATA[Many studies on software quality use a variety of techniques and tools to assess quality in IT organizations. However, it is still difficult to ensure the proper use of measures to guarantee software quality. Cameroon, like many developing countries, faces a number of challenges in its software industry including limited market size, poor infrastructure, and lack of software engineering best practices. This study evaluates the software quality measurement practices in Cameroon and identifies potential areas of improvement. This study conducted a questionnaire survey of 30 companies by identifying five main categories and nine research questions. 57% of the companies surveyed consider that the impact of the measures on the success of the project is significant, and the measurement findings are, by large, accessible to executives as well as to the staff concerned. Furthermore, the adoption of a measurement tool can improve the monitoring and management of software projects.]]>
<![CDATA[Studi Perbandingan Kombinasi GMI, HSV, KNN, dan CNN pada Klasifikasi Daun Herbal ]]>
<![CDATA[Alfitriana Riska]]>;
<![CDATA[Purnawansyah]]>;
<![CDATA[Darwis, Herdianti]]>;
<![CDATA[Astuti, Wistiani]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3210
<![CDATA[Tumbuhan herbal memiliki banyak variasi yang dapat dikenali melalui ciri uniknya secara visual. Namun, cara ini sulit diterapkan pada tumbuhan yang memiliki ciri hampir sama. Penelitian ini membandingkan kinerja metode K-Nearest Neighbour (KNN) dan Convolutional Neural Network (CNN) dalam klasifikasi fitur daun herbal yang diekstraksi dengan menggunakan Geometric Moment Invariant (GMI) dan Hue Saturation Value (HSV). Dataset yang digunakan adalah dataset citra daun katuk (Sauropus androgynus) dan daun kelor (Moringa oleifera) dengan skenario citra terang dan citra gelap. Pembagian data untuk tiap skenario adalah 80% untuk training dan 20% untuk testing. Metode KNN diuji menggunakan nilai dan evaluasi kinerja KNN dan CNN meliputi accuracy, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa CNN tanpa ekstraksi fitur dan CNN dengan kombinasi ekstraksi fitur HSV memperoleh performa terbaik dengan rata-rata nilai precision, recall, f1-score dan accuracy sebesar 98% untuk skenario gelap maupun terang.]]>
<![CDATA[Feature Selection in Naïve Bayes for Predicting ICU Needs of COVID-19 Patients ]]>
<![CDATA[Taslim, Taslim Malano]]>;
<![CDATA[fajrizal]]>;
<![CDATA[Handayani, Susi]]>;
<![CDATA[Toresa, Dafwen]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3211
<![CDATA[COVID-19 is a global pandemic that requires a coordinated global response in all healthcare and national healthcare systems. Identifying patients at high risk of contracting the COVID-19 virus is crucial to increasing awareness before patients become further infected by the virus, which can cause severe respiratory illnesses requiring specialized care in intensive care units (ICUs). This study aims to predict the need for ICUs in patients infected with the COVID-19 virus. The predicted ICU requirements serve as a reference for hospitals to meet the ICU needs of COVID-19 patients. The prediction of ICU requirements for COVID-19 patients is performed using the Naïve Bayes algorithm, and particle swarm optimization (PSO) used to obtain the best accuracy values from Naïve Bayes. In the initial testing, Naïve Bayes without feature selection resulted in an accuracy rate of 74.75%. Testing Naïve Bayes+PSO by increasing the number of PSO generations shows that as the number of generations in PSO increases, the accuracy rate also increases. Testing Naïve Bayes+PSO with 3000 generations and a population size of 20 shows an increase in the accuracy rate to 80.95%. Testing Naïve Bayes+PSO by increasing the population size to 40 with 1000 generations for each population size shows an increase in the accuracy rate to 80.70%.]]>
