Jurnal Techno Nusa Mandiri https://ejournal.nusamandiri.ac.id/index.php/techno <p>TECHNO Nusa Mandiri: Journal of Computing and Information Technology is a journal published by LPPM Universitas Nusa Mandiri. The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology was originally intended to accommodate scientific papers made by Informatics Engineering lecturers. TECHNO Nusa Mandiri Journal has ISSN: <a title="Print Media" href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1180425415&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>1978-2136</strong></a> (Print Media) and <a title="Online Media" href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1452590549&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>2527-676X</strong></a> (Online Media). The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology have the accredited National Journal status is accredited by the Indonesian Ministry of Research and Higher Education at the Sinta S4 level, in accordance with Decree on Strengthening SK Research and Development Number 21 / E / KPT / 2018 which has been in effect since July 9, 2018, for 5 years. Source: <a title="Salinan Surat Keputusan Peringkat Akreditasi Elektronik Periode I 2018" href="http://risbang.ristekdikti.go.id/wp-content/uploads/2018/07/Salinan-Surat-Keputusan-Peringkat-Akreditasi-Elektronik-Periode-I-2018.pdf" target="_blank" rel="noopener">Risbang Ristekdikti.go.id</a>. This journal is&nbsp;Rank 4 Accreditation Certificate (S4), Accreditation is valid for 5 years. Starting from Vol. 13, No. 1 the Year 2016 to Vol. 17, No. 1 the Year 2020.&nbsp;<span class="tlid-translation translation"><span title="">Journal of TECHNO Nusa Mandiri, re-accreditation remains at Rank 4 (SINTA 4), starting Vol. 16 No. 2 of 2019 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency Number 85/M/ KPT/2020, April 1, 2020</span></span></p> Lembaga Penelitian dan Pengabdian Pada Masyarakat en-US Jurnal Techno Nusa Mandiri 1978-2136 <p>The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.</p> <ol> <li class="show">The copyright in each article belongs to the author.</li> <li class="show">Authors retain all their rights to published works, not limited to the rights set out on this page.</li> <li class="show">The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).</li> <li class="show">Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);</li> <li class="show">The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.</li> <li class="show">If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.</li> </ol> DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4330 <p><em>The prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta.</em></p> Marcheilla Trecya Anindita Yoga Pristyanto Heri Sismoro Atik Nurmasani Anggit Ferdita Nugraha ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 63 70 10.33480/techno.v20i2.4330 ANALYSIS OF USABILITY USING HEURISTIC EVALUATION METHOD AND MEASUREMENT OF SUS ON PRICILIA APPLICATION https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4582 <p><em>The Presence Digital Application (PRICILIA) is a presence application owned by PT. BGR Logistics Indonesia. However, until now, there has never been an evaluation of usability testing. Complaints from users regarding the PRICILIA application include menu displays that are less interactive, long loading times, and the unavailability of other alternatives besides GPS. Of course, this affects the level of user satisfaction with the application. Therefore, usability testing is needed to be able to measure the level of user comfort, application feasibility, and the application interface. In this study, the system evaluation method used is Heuristic Evaluation with measurement using the System Usability Scale (SUS). The results of this study indicate that aspects that need to be improved with high priority are Error Prevention (H5) and Recognition rather than Recall (H6) because they have a seriousness rating on a scale of 3, while the average score of the final seriousness rating obtained from a total of 10 heuristic aspects is 1, 72 which is then rounded off to a scale of 2. The SUS test results obtained an average final SUS score of 55.13. The results of the calculation of the SUS method are that the Acceptance Ranges have low marginal status, the Grade Scale is on a D scale, and the Adjective Twigs are at the OK level. This shows that the PRICILIA application still needs improvement. Therefore, 30 recommendations for improvement are proposed for future application development.</em></p> Sri Putri Nur Aini Siti Nur Khasanah ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-29 2023-09-29 20 2 71 79 10.33480/techno.v20i2.4582 PRE-ECLAMPSIA DIAGNOSIS EXPERT SYSTEM USING FUZZY INFERENCE SYSTEM MAMDANI https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4622 <p><em>Various institutions utilize computer information systems to analyze and process data. An expert system is an information system that is used to help analyze and determine decisions on a problem based on rules determined by experts. This research focuses on creating a prototype expert system for diagnosing pre-eclampsia or pregnancy poisoning in pregnant women based on measuring blood pressure and checking proteinuria. The existing data is then analyzed using the Mamdani system's fuzzy inference method. Supporting theory regarding the fuzzy inference system of Mamdani, pre-eclampsia and its examination indicators will be used as a basis for creating this expert system prototype. The data used were secondary data on preeclampsia patients in the form of medical records of blood pressure measurements, proteinuria examinations and doctor diagnoses of preeclampsia patients at two Regional General Hospitals (RSUD), namely Atambua and Kefamenanu, totaling 20 samples. The interface or user interface of this prototype system is made as simple as possible so that it can be operated by all ordinary people. The programming language used is Visual Basic (VB) with the Visual Studio 2010 developer application. The initial prototype of this system will continue to be developed until it can become a Information systems or real applications used in hospitals. The results of this research are that the expert system for diagnosing preclampsia can be used well and easily by hospital staff and show congruence between the system diagnosis results and the diagnosis results from obstetricians or experts in the 20 processed data.</em></p> Siprianus Septian Manek Grandianus Seda Mada Yoseph P.K. Kelen ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 80 88 10.33480/techno.v20i2.4622 CLASSIFICATION OF POTATO LEAF DISEASES USING CONVOLUTIONAL NEURAL NETWORK https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4655 <p><em>Potatoes are an agricultural product that has the fourth highest content of wheat flour after corn, wheat, and rice. Although potatoes play a critical role in agriculture, this crop is susceptible to various diseases and pests. There are several potato leaf diseases that are not yet known to farmers. Dry spot potato leaf disease (late blight) and late blight. If not treated, this disease on potato leaves will spread to the stem and reduce crop yields, causing crop failure. By using technology in the form of digital image processing, this problem can be overcome. This research proposes an appropriate method for detecting disease in potato leaves. Classification will be carried out in three classes, namel, Early Blight, Healthy and Late Blight using the Deep Learning method of Convolutional Neural Network (CNN). The data used comes from an online dataset via the kaggle.com page with the file name Potato Disease Leaf Dataset (PLD) totaling 3251 training datasets which are then divided into training, testing, and validation. The processes carried out are image pre-processing, image augmentation, then image processing using a Convolutional Neural Network (CNN). In the classification process using the CNN method with RMSprop optimizer, the accuracy was 97.53% with a loss value of 0.1096.</em></p> Elly Firasari F. Lia Dwi Cahyanti ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 89 94 10.33480/techno.v20i2.4655 ELECTRICITY MANAGEMENT SYSTEM WITH TECHNOLOGY INTERNET OF THINGS https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4520 <p><em>The increasing global demand for electricity has put a strain on energy resources and raised concerns about environmental sustainability. To address these challenges, the integration of modern technologies is crucial. This research presents a study on the implementation of an Electrical Energy Management System (EMS) using Internet of Things (IoT) technology. The proposed system aims to optimize electrical energy usage, enhance efficiency, and reduce the environmental impact. The EMS employs IoT devices and sensors to monitor and collect real-time data on electricity consumption in office buildings. Through data, the system can identify patterns and anomalies in energy consumption, allowing for informed decision-making and proactive energy management strategies.</em></p> Rano Suherman Purnawarman Kahfi Nataraja Andhika Pratama Ahmad Hafidzul Kahfi ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 95 101 10.33480/techno.v20i2.4520 CLASSIFICATION OF RICE TEXTURE BASED ON RICE IMAGE USED THE CONVOLUTIONAL NEURAL NETWORK METHOD https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4666 <p><em>There are several types of rice that are commonly sold in rice stores. Many people, especially millennials, are not familiar with the different types of rice such as IR42 rice, Pera rice, sticky rice, and Pandan Wangi rice. Therefore, digital image processing techniques are needed to help analyze the types of rice to help people know what kind of rice they are going to buy at the market. The method commonly used in image processing for image classification is the convolutional neural network (CNN) method. Currently, CNN has shown the most significant results in image classification. This research used a dataset of 1560 rice images. The data was divided into two sets (training data and validation data) with an 80:20 ratio. The accuracy obtained by the CNN model using InceptionV3 for the rice data was 95.7% with a loss of 0.123. The Android application developed in this research achieved an accuracy of 83,4% based on the testing results calculated using the confusion matrix.</em></p> Gesang Budiono Rio Wirawan ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 102 107 10.33480/techno.v20i2.4666 REAL TIME DETECTION OF CHICKEN EGG QUANTITY https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4735 <p><em>A common problem currently being faced in the chicken egg production home industry is difficulty in counting the number of eggs. Currently, calculating the number of eggs is still done manually, which is less than optimal and prone to errors, so many entrepreneurs often experience losses. The manual system currently used also has the potential for this to happen. The use of technology on an MSME scale among laying hen breeders has not been widely adopted, this is due to limited access and understanding of technology. One alternative solution to deal with this problem is to build a real-time computerized system. The system that will currently be built in this research uses GLCM feature extraction and the SVM classification method. This system will detect egg production via CCTV cameras and will be stored in a database to be displayed on the website. The advantage of this system is that egg entrepreneurs can monitor chicken egg yields in real time. The results of trials that have been carried out using GLCM feature extraction and the SVM classification method in calculating the number of eggs using the SVM method with a polynomial kernel are highly recommended for use in this research because it can achieve 95% accuracy.</em></p> Cut Lika Mestika Sandy Asmaul Husna Reyhan Achmad Rizal Muhathir Muhathir ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 108 114 10.33480/techno.v20i2.4735 COMPARISON OF KNN, NAIVE BAYES, DECISION TREE, ENSEMBLE, REGRESSION METHODS FOR INCOME PREDICTION https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/4613 <p><em>Using the income classification dataset, we performed data analysis with the help of data mining to gather interesting information from the available data. Currently, data processing can be done using many tools. One of the tools that we use for data processing is the orange application. By using the dataset we looked at the welfare level ranging from marital status, school, gender, and from all fields related to income ranging from sales, to daily life to find out the income earned by employees or workers from several countries such as the United States, Cambodia, United Kingdom, Puerto-Rico, Canada, Germany, Outer US (Guam-USVI-etc). The purpose of this analysis is to determine the hourly income in one week that can affect the income classification. The classification technique uses various classification models, namely the K-Nearest Neighbor (KNN) algorithm model, Naïve Bayes, Decision Tree, Esemble Method and Linear Regression algorithm. The results of the analysis based on the test results of various algorithm models can be concluded that the best algorithm model for measuring workers' income is to use the Naive Bayes Decision. Analysis of variables based on Hours-per-Week and Capital-Gain affects Income Classification which determines whether the income earned is more than 50 thousand/50 K and the analysis results in a prediction of a person's income level.</em></p> Eri Mardiani Nur Rahmansyah Andy Setiawan Zakila Cahya Ronika Dini Fatihatul Hidayah Atira Syakira ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 20 2 115 121 10.33480/techno.v20i2.4613