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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 889 Documents
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Sistem Pakar Untuk Diagnosa Penyakit Sapi Menggunakan Metode Bayes Putri Eka Wardani; Yessica Siagian; MHD Ihsan
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2197

Abstract

Cow disease can attack animals, both young and adult, in this case it is necessary to do a quick diagnosis to provide basic knowledge about cow disease. Advances in expert systems can overcome this problem, namely by designing a web-based computer system that is integrated with databases and programming languages ​​such as PHP-MySQL so that it can help farmers to diagnose cattle disease. The purpose of this research is to build an expert system for diagnosing cow disease based on web. The application of the expert system in this decision making uses the Bayes method, in probability theory and statistics, the Bayes theorem is a theorem with two different interpretations. In Bayes' interpretation, this theorem states that one of the decision-making methods, this method was developed to solve decision-making problems by determining the probability value of the event and the value of evidence obtained from the facts about the object under study. What kind of cow did he experience in order to get a solution with treatment. From the results of testing and implementation of this expert system, it has been able to produce Brucellosis (Transmitted Keluron) disease with a weight = 2 which is higher than the weight of other cattle diseases
Penerapan Metode Certainty Factor Pada Sistem Pakar Diagnosa Penyakit Mata Putri Masliana; Yessica Siagian; Sri Rezki Maulina Azmi
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2198

Abstract

Eyes are one part of the body that has an important role in human life. Eyes are often also affected by a disease that causes patients to go blind. Sometimes patients often experience early symptoms of unknown disease as if these symptoms are normal. This is due to lack of information about eye diseases so that patients do not understand the initial symptoms they experience are symptoms of one of the eye diseases that often occur if left untreated for a long time, eye disease will get worse. The diagnostic process requires an expert and experienced expert in order to produce the right diagnosis. However, the limited time that an expert has sometimes becomes an obstacle for patients who will consult to solve a problem to get the best solution. So for that patients need information about the disease, the researchers created a web-based system and contained expert knowledge so that they could answer what eye diseases they experienced. make a diagnosis in order to easily treat the disease. The application designed is a web-based computer system that is integrated with databases and programming languages ​​such as PHP-MySQL so that it can help sufferers to diagnose the symptoms and types of eye diseases. The application of an expert system in making this decision by analyzing data using the Certainty Factor method to generate true and false values ​​on the new and old knowledge bases and comparing them with the weight values ​​in each frame so that the percentage of the disease type is obtained. From the results of the diagnostic process using this application, the system provides a choice of symptoms that must be answered by the patient based on the symptoms experienced by the patient, namely blurred vision such as foggy, seeing circles around light, and often changing the size of the glasses which results in cataract disease by 100% with a total value cf (Certainty) =26
Peramalan Penjualan Produk Sepatu dengan Menggunakan Metode Double Moving Average (DMA) Dandi Irwansyah; Jeperson Hutahaean; Suparmadi Suparmadi
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2199

Abstract

Halim Jaya Kisaran shop is one of the trading businesses that sells people's basic needs such as shoes and bags. This store requires forecasting in an effort to process data and generate accurate reports. This is done because the Halim Jaya Kisaran Store to determine the demand for shoes is unpredictable, so it often experiences a lack of shoes to be purchased in order to serve the demand for shoes needed by customers. In designing this system, the author uses the Double Moving Average (DMA) forecasting method for the decision support process in determining the number of shoe stocks to be sold for the next month, using the calculation method. This forecasting application was created using the Visual Basic Net 2010 programming language with Microsoft Access as the database. From making the system an application can be produced that can control optimal and economical demand and with a high level of accuracy and can predict the number of shoe requests at the Halim Jaya Kisaran Store so that the forecast results can help the store to avoid running out of stock of shoe requests at the Halim Jaya Kisaran Store
Sistem Pakar Untuk Mendiagnosa Penyakit Infeksi Emerging Dengan Metode Forward Chaining Yusuf Gunawan; Neni Mulyani; Andi Sapta
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2200

Abstract

Emerging Infectious Diseases (PIE) that appear and attack a population for the first time or have existed before but are increasing very rapidly, both in the number of new cases in a population. Its varied structure allows PIE to perform many functions and experience common problems such as the disease in PIE which can be fatal if the symptoms are not immediately recognized. In diagnosing PIE in humans, doctors still use a simple method by observing the symptoms of a patient's PIE so that it takes time to ensure that a patient suffers from PIE so that the patient is always queuing for a consultation. In this case, a media that acts as an expert is needed to help doctors, given the limited knowledge about PIE disease and the lack of counseling staff to conduct socialization of PIE treatment and prevention. The inference model used in making this expert system is forward chaining, while the search technique uses depth first search. Determination in diagnosing expert systems is done through a consultation process between the system and the user. The answer is adjusted to the rules in the system, if the answer is entered according to the applicable rules, the system will provide diagnostic results in the form of information on the PIE disease suffered. With the existence of an expert system with the Forward Chaining method, it can produce a decision on the PIE disease suffered by going through the symptom tracking process with IF-Then logic so that it produces the disease suffered and the solution for its handling
Sistem Pakar Menggunakan Teorema Bayes Dalam Rekomendasi Penentuan Jenis Anestesi Pada Pasien Siti Julianita Siregar; Kartika Sari
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2226

Abstract

This study discusses the problem, namely the process of determining the type of anesthesia for patients before carrying out surgery. In determining the appropriate type of anesthesia based on the conditions experienced by the patient, generally anesthesiologists or anesthesiologists still use the common method, namely by conducting interviews related to the symptoms experienced by patients before anesthesia is carried out on patients who will be operated on. Then the anesthesiologist will write down the results of the interview in the form of a written report and will adjust the results of the interview related to the symptoms experienced with the existing anesthesia guidelines. And this will certainly take more time in adjusting the results of the patient's symptoms to the type of anesthesia that will be given. Along with the rapid development of technology, determining the type of anesthesia that will be given to the patient before it is carried out can be overcome by building an information system that is able to adopt the process and way of thinking of humans, namely Artificial Intelligence or artificial intelligence which is often called the Expert System. In this case, a smart application in determining the type of anesthesia in android-based patients is designed using the Bayes Theorem calculation method, and it is possible for an anesthesiologist and anesthesiologist to administer anesthesia to a patient before a patient steps into the operation stage. Thus, it can also cause work productivity to increase and the time used to complete the work is getting shorter
Sistem Pintar Penyiram Tanaman Menggunakan Teknologi IoT dan Fuzzy Inference System dalam Rangka Mewujudkan Green Campus di UIN Syarif Hidayatullah Jakarta Nenny Anggraini; Kahfi Del Vieri; Luh Kesuma Wardhani; Ariq Cahya Wardhana; Deny Saputra
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2227

Abstract

In recent years, there has been a change in climate which has had quite a negative impact on various sectors of life. The existence of extreme climate change has become a widely discussed issue, including at the university level. The academic community as agents of change should be an example of sustainable development in the campus environment. One of the efforts to realize the concept of sustainable development is to apply the Green Campus concept. Several indicators in realizing a green campus are campus management policies that are oriented towards environmental management, including activities to save water, paper, and electricity, procurement of Green Open Space (RTH), and waste management by applying the 3R principles (reduce, reuse, recycle). UIN Jakarta currently focuses on the green environment, which focuses on activities related to the conservation and maintenance of environmental quality in all energy and natural resource consumption activities. However, the frequent changes in weather, shifts in the seasons, and the size of the land that must be maintained have made watering plants unable to rely on human labor alone. Moreover, the pandemic conditions in Indonesia have caused almost all employees to work from home, so plant maintenance will be hampered for plant watering activities. In this regard, this research proposes the creation of an automated plant watering system using a Fuzzy Inference System. The experiment was carried out using input from three scenarios including soil moisture sensors, temperature sensors, and rain sensors on the output valve. From the experiments carried out, the expected valve output is in accordance with that produced by the raspberry pi. Such as when the ground is dry, the weather is not raining, and the temperature is moderate, the valve remains on according to the fuzzy rules that have been made. The results of the study noted that this tool was in accordance with what was needed and could operate properly
Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM) Rizki Mugi Setya Adi; Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2229

Abstract

Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%
Implementasi Analytical Hierarchy Process-Topsis Dalam Penentuan Marketplace Terbaik Di Indonesia Rice Novita; Akhas Rahmadeyan; Vina Vamilina
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2232

Abstract

The marketplace industry in Indonesia is starting to grow rapidly over time. Behind the convenience, some users still feel dissatisfied when making transactions. Problems that are often encountered are product incompatibility, delivery problems, types of payment methods, unsatisfactory service, to difficult return policies. In addition, many considerations such as prices, discounts, and promos make users confused about choosing the right marketplace to make transactions. This study applies the AHP-TOPSIS method as decision support to determine the best marketplace in Indonesia with the criteria used, namely discounts & promos, services, features, product quality, payment methods, and availability of goods. While the alternatives used are Tokopedia, Shopee, Blibli, Lazada, and Bukalapak. The results of the implementation with the AHP-TOPSIS method resulted in Shopee being the best marketplace recommendation with a preference value of 0.9897, followed by Tokopedia with a value of 0.7289, Lazada with a value of 0.4145, Bukalapak with a value of 0.0641 and Blibli with a value of 0.0059. The results of this study are expected to be useful for the community in making decisions on choosing the marketplace that best suits their needs and circumstances for conducting transactions
Sistem Pakar Dalam Penentuan Mustahiq Zakat Menggunakan Dempster Shafer Feri Setiawan; Ahmadi Irmansyah Lubis
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2240

Abstract

Zakat is one of the obligations for a Muslim and is one of the five pillars of Islam. In the zakat system, it consists of two elements, namely Mustahiq (who has the right to receive zakat) and Muzakki (who has the right to pay zakat). Based on interviews conducted by the author with the Amil Zakat Infaq Shadaqoh Muhammadiyah Institute (LAZISMU) Sunggal District, Deli Serdang Regency, lies are often found because the system used is still manual in identifying the eligibility of zakat mustahiq, most of which are falsification of income data and data that have received assistance. This causes the distribution of zakat to be uneven. This study builds a system that can provide knowledge to the public, especially Muslims, about mustahiq zakat and its criteria. In the study, the development of an intelligent system that is able to identify mustahiq zakat which is computed into an Android-based application which will then be used globally by the community to identify whether a person is included or not included as mustahiq zakat. As for building the application in this study, the Dempster Shafer method based on the Expert System is used in order to produce an output that can be right on target in accordance with the expected target. The results of the study used 11 criteria, 8 asnaf and 12 rule based. The results obtained from the system testing carried out can be said that the system that has been built is able to assist in the process of systematically identifying zakat mustahiq with the expected system output
Pengembangan Sistem Pendeteksi Masker Sesuai Protokol Kesehatan dengan Algoritma Mobilenetv2 dan Raspberry Pi Imam M Shofi; Luh Kesuma Wardhani; Nenny Anggraini; Nashrul Hakiem; Denny Saputra; Ariq Cahya Wardhana
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2250

Abstract

A new type of human coronavirus was discovered in December 2019 in Wuhan, China. In humans, coronaviruses usually cause respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory (MERS) and Severe Acute Respiratory Syndrome (SARS). This new type of coronavirus was later named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2) and caused Coronavirus Disease-2019 (COVID-19). COVID-19 can cause mild to severe symptoms. So, wearing a mask and keeping a distance is very important to stop the spread of COVID-19. In previous research, a deep learning model has been developed to identify whether the person is wearing a mask or not. In previous studies, the classification was limited to whether humans wore masks or not. There is no classification as to whether the use of masks is right or wrong and whether the masks worn are masks that are in accordance with the recommendations of the Ministry of Health. So that in this study, the detection system for the use of masks is able to detect the use of masks in accordance with the recommendations of the Indonesian Ministry of Health which refers to the interim WHO Guidelines June 5, 2020, regarding recommendations regarding the use of masks in the context of COVID-19, namely the use of cloth masks, medical masks, and masks. can ensure the cover of the mouth and nose, and adjust to the bridge of the nose. The result is a system with the SSDLite Mobilenet V2 model has the highest FPS compared to a system using a system with SSDMNV2. That is, the maximum FPS obtained is 3.57 FPS and the minimum FPS is 3.45 FPS

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