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Jurnal Pilar Nusa Mandiri

pilar
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Published by STMIK Nusa Mandiri

ISSN : 19781946 EISSN : 25276514 DOI : -

Core Subject : Science,

Computer Science & IT

Jurnal Pilar merupakan jurnal ilmiah yang diterbitkan oleh program studi sistem informasi STMIK Nusa Mandiri. Jurnal ini berisi tentang karya ilmiah yang bertemakan: Rekayasa Perangkat Lunak, Sistem Pakar, Sistem Penunjang, Keputusan, Perancangan Sistem Informasi, Data Mining, Pengolahan Citra.

Arjuna Subject : -

Articles 418 Documents

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Design and Implementation of IoT Based Smart Lecture Attendance System at Mataram University of Technology Akbar, Ardiyallah; Zaenudin, Zaenudin; Yani, Ahmad; Muslim, Rudi
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4608

Student attendance is one of the reporting activities that exist in educational institutions. The problem that occurs in educational institutions is that when entering the lecture, many students are late and often absent, which can cause discipline where students often do absenteeism, so lecturers cannot know the number of students who attend accurately. From these problems, a solution is needed to help lecturers recapitulate attendance data. This system uses ESP32 as a data manager, RFID for data reading, and ESP32 to validate student attendance by taking pictures of faces. The data is stored on the web server using ESP32CAM to cover the shortcomings of RFID, which is still card-based, so that it can emphasize the flaws. To simplify the attendance in this study, utilizing the website as an interface to facilitate lecturers in knowing the number of students who are present, late, or absent more efficiently and accurately

Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based Muslim, Rudi; Zaeniah, Zaeniah; Akbar, Ardiyallah; Imran, Bahtiar; Zaenudin, Zaenudin
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4669

The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.

IMPLEMENTATION OF PROFILE MATCHING METHOD FOR THE BEST EMPLOYEE SELECTION SYSTEM PT. JENDELA DIGITAL INDONESIA Prabowo, Donni
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.2464

The implementation of profile matching method in selecting the best employees at PT Jendela Digital Indonesia aims to assist managers in making decisions with the right calculations and criteria. Employees are one of the factors that play an important role in advancing the company. Employee performance affects the company in obtaining profits. To spur employee performance, the company selects the best employees every period by giving appreciation and bonuses to selected employees. This selection system using three criteria, namely aspects of cooperation, work performance, and personality. These criteria will be used for calculations using the Profile Matching method. There are five employees who will be submitted to the selection of the best employees in this company. All criteria are given a GAP value and then will provide a ranking. The largest final score will be at the top of the ranking, followed by a smaller final score. The results of this research show that this method can provide results that assist managers in making decisions about the best employees according to the criteria desired by the company.

DEVELOPMENT OF KNOWLEDGE MANAGEMENT SYSTEM FOR JAIPONG DANCE Feta, Neneng Rachmalia
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.2930

Knowledge for culture is a concept of using information and communication technology to increase usability in the field of culture, especially in managing, documenting, disseminating information and knowledge of cultural arts, especially Sundanese cultural arts, namely the art of jaipong dance. For the art of jaipong dance to be maintained, it is necessary to manage information and knowledge that utilizes the sophistication of information and communication technology towards the noble values of jaipong dance. In that way, the art of jaipong dance can be passed on to each generation to maintain culture as self-identity and show the existence of Sundanese culture in the eyes of the national and even the world. Knowledge Management Systems (KMS) is a solution that can be used to preserve the art of jaipong dance in Indonesia by managing existing knowledge about various things about the art of jaipong dance. The research method uses an integrated knowledge system management cycle. There are three main stages: knowledge capture and/or creation, knowledge sharing and dissemination, and knowledge acquisition and application. Meanwhile, for the formation of knowledge used in this study, the SECI Nonaka model was used. KMS itself can benefit experts, organizations, and the general public. It becomes learning material for every generation. The process of transferring information and knowledge about the movements in the Jaipong dance what musical instruments are used in performances, fashion, and make-up can run. properly and can preserve the art of jaipong dance, which is one of the characteristics of dance in Indonesia.

PERFORMANCE EVALUATION OF WIRELESS SENSOR NETWORK ROUTING PROTOCOL FOR VOLCANO ACTIVITY MONITORIN Mukti, Fransiska Sisilia; Lorenzo, Juan Enrico
Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i1.3270

As a country with the most volcanoes in the world, the Indonesian government must provide accurate and up-to-date information on the activity of active volcanoes. Until 2021, only 59% of mountains were directly monitored. Monitoring volcanic activity is not an easy thing to do. Visual observation alone is not enough, and instrumental comment is needed. Wireless Sensor Network (WSN) is a new opportunity to conduct a real-time and low-cost monitoring system for volcanic activity. However, the placement of independent WSN sensors in locations that are difficult to access creates new reliability and energy consumption problems. Therefore, we need a reliable communication line design for data transmission and path determination that does not drain sensor energy. This study specifically evaluates the performance of several routing protocols on WSN (proactive, reactive, and hybrid) to provide recommendations for the best routing design for volcanic activity monitoring needs. The simulation results of 6 WSN routing protocols using the NS-2 simulator show that the proactive protocol provides the smallest delay value, and the reactive protocol shows the highest data transmission success ratio but with the best residual energy. In contrast, the hybrid protocol could maintain a stable throughput value during data transmission.

PARALLEL NUMERICAL COMPUTATION: A COMPARATIVE STUDY ON CPU-GPU PERFORMANCE IN PI DIGITS COMPUTATION Tjandra, Yozef; Lawalat, Sanga
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3291

As the usage of GPU (Graphical Processing Unit) for non-graphical computation is rising, one important area is to study how the device helps improve numerical calculations. In this work, we present a time performance comparison between purely CPU (serial) and GPU-assisted (parallel) programs in numerical computation. Specifically, we design and implement the calculation of the hexadecimal -digit of the irrational number Pi in two ways: serial and parallel. Both programs are based upon the BBP formula for Pi in the form of infinite series identity. We then provide a detailed time performance analysis of both programs based on the magnitude. Our result shows that the GPU-assisted parallel algorithm ran a hundred times faster than the serial algorithm. To be more precise, we offer that as the value grows, the ratio between the execution time of the serial and parallel algorithms also increases. Moreover, when it is large enough, that is This GPU efficiency ratio converges to a constant, showing the GPU's maximally utilized capacity. On the other hand, for sufficiently small enough, the serial algorithm performed solely on the CPU works faster since the GPU's small usage of parallelism does not help much compared to the arithmetic complexity.

CLASSIFICATION OF CORONAVIRUS DISEASE (COVID-19) THROUGH CHEST X-RAY IMAGES BASED ON DEEP LEARNING Farhandy, Erzha Anges; Wardhana, Sukma
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3293

CoV-2 virus this disease is spreading rapidly throughout the world. Various studies were carried out to control the spread of Covid-19. One way to detect Covid-19 is to study chest X-ray images of patients with Covid-19 symptoms. However, to detect Covid-19 through x-ray images, there are currently few radiology specialists needed. This study researched to detection of Covid-19 disease through chest x-ray images with a deep learning approach based on a convolutional neural network (CNN). Before training the model, data preprocessing is carried out, such as labeling and resizing. This study uses a CNN model with three layers of convolution and max-pooling layers and a fully-connected layer for the output. The results of the training using the CNN method produced a pretty good performance, with the best training accuracy (acc) value obtained in the 31st epoch with a value of 0.9593, training loss (loss) 0.1306, validation accuracy (val_acc) 0.9604, and loss validation (val_loss). 0.1399.

DISEASE DETECTION EXPERT SYSTEM IN WATERMELON PLANTS USING CERTAINTY FACTOR METHOD BASED ON MOBILE Juhartini, Juhartini; Arwidiyarti, Dwinita; Subki, Ahmad Subki
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3326

Watermelon is one of the fruit commodities currently being developed in West Nusa Tenggara by making Central Lombok Regency as its production center. The problems that are often faced by farmers in watermelon farming, apart from pests, watermelon plants are also often affected by diseases that cause the plants to wilt and then dry, white spots on the flesh, the growth of leaves and small fruit so that it has an impact on decreasing productivity caused by lack of knowledge farmers in recognizing and dealing with diseases in watermelon plants so that they are often mishandled. This research was conducted with the aim of producing an expert system that can be used to detect diseases in watermelon plants and provide solutions for handling them so that watermelon farmers can make prevention and treatment efforts quickly before the disease is transmitted to many other watermelon plants. This expert system was built with the Mobile-based Certainty Factor Method with the Android operating system so that it can be downloaded in the Playstore by watermelon farmers or anyone who needs it so that it is expected to help farmers detect diseases in watermelon plants and provide appropriate handling steps based on related theories.

IMPLEMENTATION OF SUPPORT VECTOR REGRESSION IN THE PREDICTION OF THE NUMBER OF TOURIST VISITS TO THE PROVINCE WEST NUSA TENGGARA (NTB) Zaeniah, Zaeniah
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3454

Abstract — Indonesia has a variety of interesting tourist destinations to visit in each region. One area that is used as a favorite tourist destination is the Province of West Nusa Tenggara (NTB). Data The number of tourists visiting the NTB province from 2014 to 2020 tends to change based on data obtained from the Website of the NTB Provincial Tourism Office. The data on the number of visitors will continue to change, even if there is a possibility that it will increase. This can lead to the unpreparedness of the government and other tourism actors in providing the facilities and infrastructure needed by visitors when there is an increase in the number of tourist visits coming to NTB. Therefore, it is necessary to predict the number of tourist visits to NTB with accurate results. In this study, predictions of the number of tourist visits to the Province of NTB were made using the support vector regression method. This research resulted in an application to predict the number of tourist visits to NTB based on Event, Month, and Year. so that it can provide predictive results that are close to the actual value under normal conditions. The data used in this study is data on the number of tourist visits in 2017-2021 and events held in 2017-2021.

DECISION SUPPORT SYSTEM FOR HYDROPONIC VEGETABLE SEED SELECTION USING EXPONENTIAL COMPARISON METHOD Nuraini, Rini
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3471

The issue of food security is not only the task of farmers, therefore starting to participate in growing vegetables at home through hydroponic techniques can be a solution. Hydroponics is one solution for urban farming or farming activities independently in urban areas in Indonesia. In order to get an abundant harvest on limited land, it requires quality seeds. Good vegetable seeds hold the key to determining a good harvest too. So, you should not carelessly buy vegetable seeds to grow vegetables hydroponically. But the problem is, with so many vegetable seed products for hydroponics on the market, it makes someone confused to choose the right product that suits their needs. The main purpose of this research is to develop a decision support system for selecting hydroponic vegetable seeds with a website-based Exponential Comparison Method to facilitate decision makers in determining the right seeds and according to their needs. The Exponential Comparison method can perform a priority order of decision alternatives on existing criteria and can distinguish the value of each alternative in contrast. The decision support system developed produces calculations using the valid Exponential Comparison Method because the results are in accordance with manual calculations. In addition, based on the black-box testing technique, it shows that the system built has been running well.

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