Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Jurnal Saintekom adalah singkatan dari Sains, Teknologi, Komputer dan Manajemen, merupakan jurnal ilmiah yang berfungsi sebagai media mengkomunikasikan ide, gagasan dan pemikiran seputar kajian aktual tentang sains, teknologi, komputer dan manajemen antarkademisi dan peneliti.
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<![CDATA[Game Edukasi Pengenalan Hewan Endemik Pulau Kalimantan Berbasis Android Menggunakan Construct 2 ]]>
<![CDATA[Irwan, Gt]]>;
<![CDATA[Rusdiana, Lili]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.409
<![CDATA[The purpose of this research is to make an Android-based educational game to introduce endemic animals to the island of Borneo using Construct 2, the results of which can be useful for the community to increase knowledge about endemic animals on the island of Borneo. This educational game has a game of arranging animal name letters and multiple choice questions which are expected to hone the user's memory of endemic animals on the island of Borneo. The method used in this research is the Multimedia Development Life Cycle (MDLC) method are concept, design, material collecting, assembly, testing, and distribution. In this study, Black Box Testing was carried out and the result was that the game system ran well and its functionality could work smoothly. Based on the results of the questionnaire which was filled in by 30 respondents, the Endemic Animals of Kalimantan game was in the interval of strongly agreeing with the questionnaire and application category with a percentage of 85.42%, which means that the Endemic Animals of Kalimantan game is in demand by the public.]]>
<![CDATA[Penerapan Web Service dalam Mengintegrasikan IoT dengan Platform Investasi Berbasis Website dan Mobile Android ]]>
<![CDATA[Amrulloh, Arif]]>;
<![CDATA[Saputra, Wahyu Andi]]>;
<![CDATA[Arini, Ratih Windu]]>;
<![CDATA[Pane, Sayyid Yakan Khomsi]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.595
<![CDATA[The industrial revolution 4.0 which is occurring rapidly has resulted in changes in various fields, IoT is a technology that is currently widely used to automate processes in everyday life. In this research, IoT was used to monitor the development of goat livestock in Dermaji village. The problem with IoT development is that when you want to display data or results obtained, IoT does not work well if the information displayed is less interactive and difficult for end users to understand, so a solution needs to be created to display the results obtained by developing website and mobile-based applications. The aim of this research is to integrate an IoT platform with Android mobile-based websites and applications by implementing a RESTful web service API. Web and mobile application development uses the RAD method which consists of several stages, namely the design stage of system requirements, system design and implementation into a programming language. In integrating IoT with websites and mobile, RESTful web service technology is applied. The result of this research is an IoT platform that is integrated with website and Android mobile based applications.]]>
<![CDATA[Klasifikasi Predikat Kelulusan Mahasiswa Menggunakan Algoritma C4.5 ]]>
<![CDATA[Fitriyanti, Vina]]>;
<![CDATA[Testiana, Gusmelia]]>;
<![CDATA[Eri Gunawan, Catur]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.626
<![CDATA[Determination of student graduation predicate based on GPA and additional requirements of timely study period for honors predicate. Currently, there is no in-depth classification to identify graduation predicate, so understanding of the patterns that influence the results is still limited. Accumulation of student graduation data can be used to find new information. This study aims to produce a decision tree classification model using the C4.5 algorithm and evaluate its accuracy in classifying student graduation predicates at UIN Raden Fatah Palembang. The data division technique used is k-fold cross validation to divide the data into training and testing data. The k value used is k = 3 in the first data test, this is based on previous tests with several k values, where k = 3 produces higher accuracy than the others. The rules formed are 242 and the attribute that influences student graduation predicate is GPA. The accuracy of the application of the C4.5 algorithm in classifying student graduation predicates is 83.31% which is included in the Good Classification category.]]>
<![CDATA[Implementasi Deteksi Objek Real-Time Sebagai Media Edukasi dengan Algoritma YOLOv8 pada Objek Sampah ]]>
<![CDATA[Ramdan, Adam]]>;
<![CDATA[Asriyanik, Asriyanik]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.638
<![CDATA[Waste is one of the complex global issues and is one of the points of the SDGs indicators related to municipal waste, food waste, hazardous waste and recycling systems that cannot be resolved. According to data from SIPSN in 2022, waste generation in Indonesia will reach 35,289,535.55 tons/year while about 47.32% of waste handling is done, which is 16,697,790.76 tons/year. The National Research and Innovation Agency said that currently there are still few Indonesians who have the awareness to start sorting waste from their own homes. As many as 80% of Indonesians do not sort their waste. To overcome these problems, everyone needs to make changes early on by making waste management a habit so as to change people's skeptical attitude towards waste management. Thus, research was conducted to identify the type of waste using the YOLOv8 algorithm, with a dataset of 17,617 data which was then analyzed by creating a yolov8 model. The best accuracy results were obtained by using the yolov8l variant as well as 16 batch sizes and the SGD optimizer with a learning rate value of 0.001 as a parameter. The model training process was then evaluated using confusion matrix with a percentage reaching 86.5%.]]>
<![CDATA[Kerangka Kerja Inovasi Digital untuk Organisasi Skala Kecil: Integrasi Teori dan Praktik ]]>
<![CDATA[Marcel, Marcel]]>;
<![CDATA[Pratama, Dimas W.]]>;
<![CDATA[Aotearoa, Garpepi H.]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.641
<![CDATA[This research proposes a framework designed to support digital innovation in small-scale organizations often constrained by limited resources and knowledge. This framework is built based on theoretical knowledge, which comes from literature reviews, and practical knowledge, which comes from focus group discussions involving several experts. The framework includes nine key processes: Leadership Commitment and Support; Culture of Innovation and Risk Tolerance; Cross-Functional Collaboration and Diversity; Agile and Flexible Process; Integration and Utilization of Digital Technology; Knowledge Sharing and Continuous Learning; Allocation of Resources for Innovation; External Engagement and Open Innovation; Performance Evaluation and Feedback. These processes were developed to be interrelated and form a comprehensive approach to fostering an innovative environment. In addition, this framework also integrates the ADKAR, a change management model, to facilitate the practical application of this framework in organizations. The existence of limited resources and knowledge in small-scale organizations demands particular strategies in adopting the framework, including more strategic resource allocation, intensive training for skill development, and collaboration with external institutions to support technology and innovation needs.]]>
<![CDATA[Evaluasi Heuristik E-Learning Institut Agama Hindu Negeri Tampung Penyang Palangka Raya ]]>
<![CDATA[Purnawati, Ni Wayan]]>;
<![CDATA[Alit Arsana, I Nyoman]]>;
<![CDATA[Janardana Febrian Agus, I Putu]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.648
<![CDATA[Institute of Hindu Religion (IAHN) Tampung Penyang Palangka Raya has e-learning in an e-campus with complex features. Many e-learning features become ineffective, and make users unable to maximize their use. Therefore, a heuristic evaluation approach from Nielsen's is needed to find out the extent of usability in the e-learning. The heuristic evaluation method is a method used to find usability problems of a product using 10 heuristic evaluation principles. In this research, the assessment was carried out by 4 expert usability evaluators. The calculation of usability heuristic evaluation using severity ratings scale has been done. The results of the assessment based on heuristic evaluation on the IAHN-TP Palangka Raya e-campus application resulted in 24 problem findings. The highest value of the average severity rating (SR) is on the principle of error prevention and as much as 92% of the largest percentage is on the principle of visibility of system status. This research also provides recommendations for design improvements ranging from adding functions, changing appearance, and layout according to the results of the heuristic evaluation. These recommendations can be used as a consideration for the IAHN-TP campus management in improving the application.]]>
<![CDATA[Klasifikasi Kualitas dan Kematangan Pisang Cavendish Menggunakan Convolutional Neural Network ]]>
<![CDATA[Hastungkoro, Arya Widya]]>;
<![CDATA[Putro Wicaksono, Aditya Dwi]]>;
<![CDATA[Diah Rosita, Yesy]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.686
<![CDATA[This research aims to develop a classification model using Convolutional Neural Networks (CNN) to determine the ripeness and quality of Cavendish bananas. The model classifies bananas into four categories: good quality unripe (MHBS), poor quality unripe (MHBK), good quality ripe (MGBS), and poor quality ripe (MGBK), using a total of 1,000 images. In this study, the classification process of the ripeness and quality of Cavendish bananas was carried out based on automatic feature extraction using CNN,after which an evaluation was carried out using a confusion matrix to assess model performance. The research developed 36 models with variations in parameters such as the number of epochs, batch size, and dataset split. The analysis results indicate that the number of epochs significantly affects the model's accuracy, with an increase in the number of epochs leading to higher accuracy. However, the dataset split scenario and batch size do not have a significant impact on the model's overall accuracy. Evaluation shows that the highest accuracy of 95% was achieved by the model with a 90:10 dataset split, a batch size of 16, and 20 epochs.]]>
<![CDATA[Penggunaan Decision Tree dalam Penentuan Faktor yang Mempengaruhi Status Gizi Buruk Balita di Kelurahan Tamamaung ]]>
<![CDATA[Permata Hati, Tithania Indah]]>;
<![CDATA[Rahman, Rahman]]>;
<![CDATA[Rizaldy, Adhy]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.737
<![CDATA[This study aims to evaluate the use of Decision Tree algorithms in determining the nutritional status of children based on Posyandu activity reports. Malnutrition poses serious risks for developing children, including weakened immune systems, long-term developmental delays, and high mortality rates. By applying the Decision Tree algorithm to classify the nutritional status of toddlers, this research seeks to identify nutritional status, which can then be addressed by health centers (Puskesmas). Using attributes such as weight (W), age (A), and height (H), aligned with child anthropometric indices, the Decision Tree method will be utilized to determine the factors influencing nutritional status in toddlers. The application of this method will facilitate the identification of at-risk toddlers, enabling timely prevention and intervention. Testing through k-fold cross-validation yielded an accuracy of 79.43%, a recall of 53.1%, and a precision of 76.6%. The results indicate that, out of 350 data points, the most significant factor affecting children's nutritional status is weight.]]>
<![CDATA[Evaluasi Model Deep Learning pada Pola Dataset Biomedis ]]>
<![CDATA[Gunawan, Gunawan]]>;
<![CDATA[Wibowo, Septian Ari]]>;
<![CDATA[Andriani, Wresti]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.738
<![CDATA[This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.]]>
<![CDATA[Analisis Kualitas Sistem Informasi Layanan pada PT. Inti Jasa Kreatif Menggunakan Metode Webqual 4.0 ]]>
<![CDATA[Putri, Nuryani Mawar]]>;
<![CDATA[Achyani, Yuni Eka]]>
<![CDATA[ Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[STMIK Palangkaraya ]]>
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DOI: 10.33020/saintekom.v14i2.739
<![CDATA[PT Inti Jasa Kreatif is an Event Organizer company that relies heavily on the quality of information systems to maintain smooth operations, efficiency, and client satisfaction. This company uses websites to convey business information. To improve the quality of design and user interaction, PT Inti Jasa Kreatif uses the WebQual 4.0 method in analyzing website quality. This study will analyze the quality of the service information system www.intijasakreatif.co.id with a focus on the aspects of usability, informativeness, and trustworthiness, measuring client needs and satisfaction with services using the Webqual 4.0 method. The Webqual 4.0 method is a method developed to measure the quality of web-based services with the data used being the respondents of the questionnaire that has been distributed. The data collection method uses primary data in the form of questionnaires distributed to 102 respondents. Based on data processing, it can be seen that in the validity test of variable X1 (Usability Quality) the results show that variable X1 correlates with the potential quality of website usability that respondents find it easy to use the website application. From the results above, the author concludes that variable X1 has more influence than variables X2 and X3 on user satisfaction.]]>
