SINTECH (Science and Information Technology) Journal
SINTECH (Science and Information Technology) Journal merupakan jurnal yang dikelola dan diterbitkan oleh Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK STIKOM Indonesia, dengan e-ISSN 2598-9642 dan p-ISSN: 2598-7305. SINTECH Journal diterbitkan pertama kali pada bulan April 2018 dan memiliki periode penerbitan sebanyak dua kali dalam setahun, yaitu pada bulan April dan Oktober.
Bidang keilmuan dari SINTECH Journal mencakup bidang ilmu : Data analysis, Natural Language Processing, Artificial Intelligence, Neural Networks, Pattern Recognition, Image Processing, Genetic Algorithm, Bioinformatics/Biomedical Applications, Biometrical Application, Content-Based Multimedia Retrievals, Augmented Reality, Virtual Reality, Information System, Game Mobile, dan IT Bussiness Incubation.
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166 Documents
<![CDATA[PENGEMBANGAN SISTEM INFORMASI PENANGANAN PENDERITA GANGGUAN JIWA DENGAN PENDEKATAN ENTEPRISE SYSTEMS ]]>
<![CDATA[Cokorda Pramartha]]>;
<![CDATA[I Made Widhi Wirawan]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 1 (2022): SINTECH Journal Edition April 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i1.1070
<![CDATA[The health information system is a digital system that is quite complex so that in the development and evaluation stages, it is necessary to consider cultural, political, social and organizational structure factors. In Bali, the number of people with mental disorders is considered high and the need for treatment is in a fairly long time, that is the reason a management information system is needed to organize data and information about the patients. This study aims to develop a centralized management information system that can be utilised specifically for health workers to manage information regarding people with mental disorders. The development of the management information system in this study involves doctors, volunteers, and admin or organizational staff in every critical process. The prototyping method was chosen as a system development method because it considered more adaptive in capturing the needs of prospective system users. At the evaluation stage, functional evaluation through the Black-box method was used to test the functional capabilities of the system. In addition, Technology Acceptance Model (TAM) and Task Load Index (TLX) were used to evaluate the non-functionality of the system where all users agree that the developed management information system is useful, easy to use, and requires a small cognitive workload.]]>
<![CDATA[PENGUKURAN USER EXPERIENCE (UX) DESAIN APLIKASI TROUBLE TICKET MENGGUNAKAN METODE SUPERGOLDEN RATIO ]]>
<![CDATA[Gede Ardi Herdiana]]>;
<![CDATA[Ida Bagus Alit Swarmardika]]>;
<![CDATA[Rukmi Sari Hartati]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 1 (2022): SINTECH Journal Edition April 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i1.1093
<![CDATA[The Bali Provincial Information and Statistics Communication Service requires a Trouble Ticket application to report and document internet problems that exist in the offices of the Bali Provincial government. In addition to having functions as needed, the Trouble Ticket application must also pay attention to UI and UX. The method that has been widely used to create UI designs is the Golden Ratio. In addition to the Golden Ratio, there is a Ratio that has not been developed in the field of UI design, the Ratio is the Supergolden Ratio. Supergolden Ratio is the ratio obtained from the Ratio Limit Narayana Sequence which is worth 1.4656. In this study, a Trouble Ticket application design will be designed using the Supergolden Ratio method. The Trouble Ticket and UEQ application designs are given to users to determine the user experience (UX) when using the Trouble Ticket application design. The UEQ results show that the user experience of the application design that is designed using the Supergolden Ratio method produces positive results. Aspects of attractiveness, clarity, efficiency, accuracy, and simulation showed excellent results, while the novelty aspect showed good results.]]>
<![CDATA[ANALISIS PERILAKU KONSUMSI PADA MARKETPLACE (SHOPEE & TOKOPEDIA) MENGGUNAKAN MODEL UTAUT ]]>
<![CDATA[Ni Putu Suci Meinarni]]>;
<![CDATA[Ni Putu Ratih Pradnya Dewi]]>;
<![CDATA[Wayan Gede Suka Parwita]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 1 (2022): SINTECH Journal Edition April 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i1.1112
<![CDATA[Using a marketplace platform for trade transactions in the digital era is currently in great demand than conventional shopping. However, marketplace users cannot just give away their personal data just like that. Customer data security is the most important thing that must be considered in order to avoid computer network crimes or commonly known as cybercrime. This article aims to find out what influences the security of customer data in the marketplace on consumer shopping behavior by implementing the UTAUT (Unified Theory of Acceptance & Use of Technology) research model. The research method in this article uses descriptive quantitative methods by taking population samples of active marketplace users in Denpasar City and using samples from several respondents. In this article it can be concluded that interest in using the marketplace has a partial influence on user behavior and the facilitating condition variables moderated by experience have a simultaneous effect on user behavior. These factors are proven to both have important aspects in influencing the security of customer data on the marketplace.]]>
<![CDATA[EVALUASI SISTEM INFORMASI MANAJEMEN DAERAH – BARANG MILIK DAERAH MENGGUNAKAN FRAMEWORK ITIL PADA AREA SERVICE OPERATION DENGAN PENDEKATAN FRAMEWORK CMMI-SVC ]]>
<![CDATA[I Gede Satya Mulyawan]]>;
<![CDATA[I Made Candiasa]]>;
<![CDATA[Dewa Gede Hendra Divayana]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 1 (2022): SINTECH Journal Edition April 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i1.1092
<![CDATA[In recent years , the Sistem Informasi Manajemen Daerah (SIMDA) – Barang Milik Daerah (BMD) server in Denpasar City had been down several times. These conditions can increase the risk of incidents in service delivery that have not been detected. In this study, an evaluation of SIMDA-BMD was carried out using the Information Technology Infrastructure Library (ITIL) framework version 3 especially the service operation area combined with the Capability Maturity Model Integration for Services (CMMI-SVC) framework with the goal approach. Users and administrators of SIMDA-BMD participated in the assessment as respondents. The questionnaire used a likert scale, then tested for validity and reliability. Technical data were obtained by interviewing the SIMDA-BMD managers. The data was assessed using the capability level of the CMMI-SVC framework. The results is one process area already meeting the target and one process area has one gap level. Recommendations according to the CMMI-SVC framework are prepared based on the requirements for meeting the target capability level. Recommendations in the ITIL framework are carried out through mapping critical success factors and key performance indicators. The resulted is one recommendation from CMMI-SVC framework and six recommendations from ITIL framework that validated using Focus group discussion.]]>
<![CDATA[SPK Penerima Bantuan Sosial Menggunakan Metode BWM-SAW dengan Metodologi Team Data Science Process (TDSP) ]]>
<![CDATA[Mahendra, Gede Surya]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.983
<![CDATA[This study aims to be able to perform manual calculations using the BWM-SAW method in determining social assistance recipients. The economic crisis triggered by COVID-19 creates a need to improve the social assistance system that has been implemented so far. The socialization process, data verification and other problems often create problems in determining the recipients of social assistance. To solve this problem, DSS can be one of the solutions in determining the recipients of social assistance. This study uses 3 criteria with 10 sub-criteria with 5 alternatives. This study uses the TDSP model which is the development of the CRISP-DM model. This study succeeded in performing manual calculations well. The weighting of the criteria is very important to give a good preference value. The grouping of sub-criteria helps decision makers to more easily provide comparisons between criteria. Alternative-1 is the best candidate in receiving social assistance with a score of 0.9519]]>
<![CDATA[Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto ]]>
<![CDATA[Roshinta, Trisna Ari]]>;
<![CDATA[Masbahah, Masbahah]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.1114
<![CDATA[In self-isolation of Covid-19 patients, it is very important to carry out regular condition checks. Currently, the examination of severity of patien’s condition can be carried out by the patient himself online with the tools as measurement provided by public health center, and the data can be monitored by medic team. Several applications for monitoring the daily condition of Covid-19 patients have been developed but the parameters used in the monitoring application are not standardized and the accuracy of the application is unknown. This study aims to develop a Covid-19 patient monitoring application using more complete and accurate parameters. The input parameters used are body temperature, O2 saturation, pulse rate, and respiratory rate. The output is the level of the Covid-19 patient's condition which is divided into mild, moderate, and severe, as well as information on the actions that must be taken. This research uses the Fuzzy Inference System-Tsukamoto method. The test results between the system output and expert testing related to the condition of Covid-19 patients show that this self-checking application for monitoring has an accuracy of 95%.]]>
<![CDATA[Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means ]]>
<![CDATA[Sari, Wahyuni Eka]]>;
<![CDATA[Muslimin, Muslimin]]>;
<![CDATA[Franz, Annafi]]>;
<![CDATA[Sugiartawan, Putu]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.1146
<![CDATA[Oil extraction rate (OER) of fresh fruit bunches (FFB) of palm oil is depend on the stage of ripeness. The process of detecting the ripeness of oil palm FFB has difficult by manually. Farmers find it difficult to reach the fruit to detect ripeness with the eye, when the palm tree is tall. So farmers need a system that is able to detect the maturity level of oil palm FFB based on color. The K-Means method is capable of clustering based on the closest mean value to the centroid from a number of objects to cluster k. Data obtained from 2 oil palm plantations in East and North Kalimantan. In this study, the clustering of fresh fruit bunches of oil palm has four levels of maturity based on the calculation of the elbow method. The training data used in this study is 80 data. The test image data used in this study is 40 data. There are 36 appropriate data based on the classification method so the accuracy obtained in grouping using the k-means clustering segmentation method is 90%.]]>
<![CDATA[Kesiapan Teknologi dan Penerimaan Pengguna Sistem Informasi Sumber Daya Terintegrasi (SISTER) Menggunakan TRAM ]]>
<![CDATA[Ismiati, Maria Bellaniar]]>;
<![CDATA[Andayani, Sri]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.1155
<![CDATA[SISTER is an application that is used so that educators, namely lecturers, can manage and manage and integrate all educator data services. This makes it easier because SISTER is integrated with the DIKTI database so that the tridharma activities of higher education carried out by lecturers are well documented. Musi Charitas Catholic University (UKMC) which has urged all its lecturers to use SISTER since 2020. In using this new application (SISTER), it will definitely cause problems for some lecturers. These constraints such as several points in SISTER that are different from the BKD guide, when saving data will appear: something went wrong, if there is a revision by the assessor, the lecturer cannot see it and only the college admin can see it. The constraints above show how users are prepared and behave when facing new technology. This study will use the TRAM method to see Technology Readiness and User Acceptance of SISTER. The result is that all independent variables affect the dependent variable except for the discomfort and insecurity variables. And the respondents agree with all the existing hypotheses based on the evidence in the discussion section because they are in accordance with what they feel while using SISTER. For the level of readiness of information technology, SISTER is included on a scale of 9 because SISTER is no longer a prototype but has also become a complete system]]>
<![CDATA[Prediksi Jumlah Pasien Covid-19 Dengan Menggunakan Klasifikasi Algoritma Machine Learning ]]>
<![CDATA[Aidia, Aidia Khoiriyah Firdausy]]>;
<![CDATA[Amelia, Putri Juli]]>;
<![CDATA[Setyaning Nastiti, Vina Rahmayanti]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.1163
<![CDATA[Corona virus or servere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a disease that results in the occurrence of mild to moderate respiratory tract infections. Positive cases of Covid-19 in Indonesia were first detected on March 2, 2020 and continue until 2022. The additional number of deaths caused by COVID-19 has also increased. Therefore, the author is interested in making a predictive model of the cumulative number of COVID-19 patients who died in Indonesia. Therefore, in this study is how to predict the number of patients who die from COVID-19 in Indonesia by creating an appropriate accuracy model to help estimate the number of deaths associated with COVID-19 in Indonesia and assist the government in dealing with cases of new variants of COVID-19. In this study, the authors used the Decision Tree model using entropy criteria as well as Information Gain and Random Forest which resulted in accuracy rates of 91.83% (Decission Tree) and 73.80% (Random Forest). The results, explain that the model used is good. The more the R-squared error value is close to 1, the better the model used will be]]>
<![CDATA[Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network ]]>
<![CDATA[Kholil, Moch.]]>;
<![CDATA[Waspada , Heri Priya]]>;
<![CDATA[Akhsani , Rafika]]>
<![CDATA[ SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022 ]]>
Publisher : <![CDATA[Prahasta Publisher ]]>
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DOI: 10.31598/sintechjournal.v5i2.1179
<![CDATA[Convolutional Neural Network (CNN) is one of the Deep Learning methods that is able to carry out an independent learning process that is popular and appropriate in classifying. The development of technology in the field of Deep Learning, this study aims to assist farmers in identifying the types of infectious diseases that attack chickens based on faecal images using Convolutional Neural Network (CNN) so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning using Keras/TensorFlow, 95.40% of chicken feces images are predicted to be infected with coccidiosis, 94.97% chicken feces images are predicted to be healthy, 90.21% chicken feces images are predicted to be infected with tetelo disease, and 96.50% chicken feces images are predicted to be infected with pullorum disease]]>
